Health GIS Conference Proceedings 2020

Page |B 7th International Conference on HealthGIS Association of Geoinformation Technology (AgIT) in association with co-organizers had organised the 7th International Health GIS Virtual Conference on “Geo intelligence for Smart Health Care” during 25th – 26th February 2021 in Bangkok, Thailand. Delegates from renowned health organizations such us CDC-USA, University of Missouri-USA, Apollo Hopital, Ministry of Public Health-Thailand, University of Mississippi-Medical Center, USA, Universiti Sains Malaysia, Mahasarakharm University, Asian Instiute of Technology-Thailand, Anzer IT Healthcare, Gangneung-Wonju National University-South Korea, Sam Houston State University-USA, University of Salzburg- Austria, Valaya Alongkorn Rajabhat University-Thailand. A total of 200 participants representing 11 countries attended the 2-day event. Where 70 are paper presenters in the 9 Themes of the Conference. The 6th Int. Conf. on HealthGIS was held in Mysore, India in 2015. The 5th Int. Conf. on HealthGIS was held in Bangkok, Thailand in 2013. It was a resounding success attracting 350+ delegates from more than 25 countries. This year due to Covid-19 it is decided to have a virtual conference in online mode. HealthGIS 2021 has become more relevant to organise due to the threat to humanity by the pandemic caused by Coronavirus. The 7th International HealthGIS Virtual Conference 2021 on “Geo Intelligence for Smart Health Care” had showcased the geospatial technologies supporting the applications in disease surveillance, reporting, mapping, analysing and decision making. Information technology and communications systems such as Digital Big Data, GIS, Remote sensing, sensors, Web mapping and analysis of disease-related incidences of human and animals can be vital to survival and quality of life. The number of patients is rising and the number of doctoral is insufficient. Even hospitals and clinics are in short supply. In these circumstances, telemedicine and remote patient care have become of huge importance. The immunity of the population is important as it can prevent many diseases and can make a healthy society. Nutrient deficiency is the main cause of poor health and mortality of mother and child. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |C Health GIS 2021 aims to stimulate the same enchanting experience as the earlier ones by providing an international forum to all stakeholders to come together amidst the limitations of movements due to Coronavirus Pandemic and ponder over the issues and Solutions related to human health. This conference had brought together Ministries of Health, Medical colleges, Hospitals, doctors, scientists, geographers, data scientists, policymakers, healthcare service providers and all healthcare and disease control stakeholders to interact, share, learn from each other and synergize in the future. We realised the issues of mobility due to Coronavirus so we designed the conference in a virtual format using online technologies. This initiative was highly appreciated by our partners and the participants as they can attend remotely but talk and see each other without any limitations. All the technical and keynote sessions were recorded, and videos were shared with the registered participants. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |D CONFERENCE SCOPE HGIS2021 topics included, but not limited to the research areas/fields in Vector Norn Disease, Tropical Diseases, Traditional Medicine, Big Data and Analytics in Health and Machine Learning, Infectious Diseases, Lifestyle and Diseases, Covid-19, Environment and Health and Nutrition and Human Health. The conference invited experts as Keynote, Plenary and Technical Session speakers in accordance with conference scope to discuss the challenges, opportunities and problems of application of GIS in the field of health. WELCOME AND OPENING CERMONY The first day of the conference started with a Welcome and Opening Ceremony and was attended by the representatives from the Ministry of Public Health, Thai Space Agency GISTDA, knowledge partners and and international participants. …………………………………………. Dr. Opart Karnkawinpong Director General Department of Disease Control Ministry of Public Health Thailand Prof. Iyyanki V Murali Krishna, Chief Advisor, Smart Village Movement India International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |E Prof. Nitin Kumar Tripathi Professor- Asian Institute of Technology, Thailand Organizing Chairperson, 7th Int Conf on Health GIS Editor-in-Chief, International Journal of Geoinformatics Dr. Pakorn Apaphant Executive Director GISTDA Thailand KEYNOTE AND PLEANARY SPEAKERS Polio Eradication: How Technologies are helping us Travel the Last Mile Mr. Derek Ehrhardt US Centers for Disease Control and Prevention (CDC) USA Abstract The polio has been eradicated almost throughout the world. As of January 2021, only Afghanistan and Pakistan remain endemic with poliovirus. Technology helps in eradicating the epidemic completely. Solutions such as electronic temperature monitors such as LogTag for transporting the vaccine shipment in cold chain and vaccine vial monitors (VVM) stating the vaccine status. Development of new novel Oral Polio Vaccine 2 (nOPV2) which is less prone to revision to neurovirulence. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |F Telehealth in 2020 and beyond Prof. Ganapathy Krishnan Abstract Telehealth is the future of medical practice. The major elements required for the telemedicine practice is trust along with 7 other paraments. The technology should be properly used. Apollo telehealth conducted 30 million telehealth consultations till data in India. AIIMS taps robots and telemedicine to cut contact risk. The opportunities with the telehealth are significantly bright. Telehealth reduces the care delivery gap between the requirement and services. National Digital Health Mission (NDHM) , the program by govt of India, which leads to more efficient tele health services. Post-COVID digital health care has a high growth of 700% - 3000% . Telehealth has a major role in covid vaccination which will be in a large scale. The concept of group therapy using Telehealth is being developed. Geospatially-Enabled Deep Analytics for Precision Rural Health – Case Studies on Telemedicine and Disaster Resilience Prof. Chi-Ren Shyu University of Missouri USA Abstract Geospatial Big Data ecosystem (GeoARK) to provide multi-resolution geo-located intelligence focused on factors relevant to health risk analyses. Deep analytics methods to tailor rural geospatial subgroups for decision making. Disparity studies for catchments of telemedicine services. I will also briefly present our future geospatial informatics research directions in rural one health issues in zoonotic disease and environmental health matters. Smart Healthcare: AIpowered EHR Solutions for Clinical Decisions Mr. Frank Cosley Anzer, IT Healthcare Asia Myanmar Abstract ANZER IT Healthcare was founded 40 years ago in Ontario Canada and provides world-class EHR/EMR software to the hospitals and clinics throughout Canada and SEA. Anzer EHR is an all-in-one, fully International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |G integrated seamless solution that provides all the necessary modules required by both hospitals and clinics. Anzer works closely with its Healthcare partners to improve their systems, thus enabling Healthcare teams to provide the highest levels of patient care and safety, whilst optimizing hospital processes. In addition to its comprehensive suite of Healthcare modules Anzer is now focusing on developing the most advanced and powerful Artificial Intelligence (AI) powered EHR/EMR solutions. Anzer believes that AI is the future of Healthcare and that AI will dramatically increase patient safety as well as maximizing the efficiency and effectiveness of Healthcare teams and clinicians. Using clinical intelligence, and AI algorithms, clinical decision making and overall patient care will be dramatically improved. Statistical Prediction of Hourly Particulate Matter Concentrations (PM10, PM2.5 and PM1) and their Relationship with Meteorological Parameters at a Coastal City, Korea Before, During and After The Yellow Dust Period Dr. Hyo Choi Gangneung-Wonju National University South Korea Abstract Multi-linear regression model for prediction of hourly particulate matter concentrations of PM10, PM2,5 and PM1 at Gangneung of Korea Before, during and after the Yellow Dust period influenced by large amounts of dust particles transported from Gobi Desert from March 18, 2015, to March 27. The resultant correlation coefficients among PM10, PM2.5 and PM1 at Gangneung city were very good in the range of \\0.935 to 1.00. COMS satellite images and HYSPLIT backward trajectory model of air were also used for further analysis of dust transportation to Korea. Innovation in Telemedicine Dr. Cherdchai Nopmaneejumruslers Siriraj Hospital Mahidol University Bangkok, Thailand Abstract The impact of technology and its uses that simplify the hazel in the process of meeting the doctor. The useful applications of technology in the field of medicine such as “siriraj connect”for doctor appointment, “drive thru services” for lab tests, “LABMOVE” for Home International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |H Blood Collection. The 5G cloud AI solutions in the Hospitals. Applications of Digital Technology for Thai Traditional Medicine Development Dr. Amporn Benjaponpitak Director-General, Department of Thai Traditional & Alternative Medicine, Ministry of Public Health Thailand Abstract The keynote is devoted to illustrating how Thailand (DTAM-T&CM) uses digital technology to develop Thai traditional and alternative medicine services and to increase the health literacy of Thai people on the use of Thai traditional medicine and herbal medicines for the primary health care. Thai traditional medicines and Thai herbal products are important for Thai people’s well-being and the country’s economy. in accordance with the Ministry of Public Health’s policy, Thai traditional medicine and herbal products are directly related to two priorities, Health Economics and Herb, Cannabis or Ganja, and Hemp. The presentation in the forum will focus on four applications that implemented GIS and AI technology to facilitate Thai traditional and alternative medicine services and promote the use of Thai traditional medicine and herbal products for primary health care. Pandemic of Paradoxes Dr. Mark Leipnik Sam Houston State University USA Abstract This presentation will examine numerous unexpected health ramifications of COVID; including the non-appearance of influenza epidemics, also changes in exercise and preventive health care due to pandemic precautions. Other indirect health-related impacts include the impact on mental health, suicide, alcohol consumption and childbearing. Beyond health, impacts on air quality, traffic safety, work-life balance, living arrangements and even crime patterns can be observed. A case can be made that these indirect effects of COVID may be greater and more long-lasting than the direct health impacts of the virus itself. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |I Utilizing Geospatial Data and Tools to Estimate the Outdoor Abundance of Clinically Significant Mold Spores Prof. Dr. Fazlay S. Faruque University of Mississippi, Medical Center USA Abstract In this presentation, The Speaker targeted selected HAFS, widely accepted as clinically significant mold, to estimate their spatiotemporal distribution for a study area in Mississippi, USA. For estimating the spatiotemporal abundance of these selected mold spores, the speaker utilized ground monitored meteorological data, NOAA/NASA meteorological and ground condition data, dispersion model, and also actual mold spore counts for validation purpose. For this study, the outdoor spores collected for validation include Deuteromycetes Alternaria, Cladosporium, Aspergillus, Penicillium and Fusarium The Development of Telepresence Robot for Medical Application Based Full Study on Practical Scenario in Clinical Study Dr. Jackrit Suthakorn Dean, Faculty of Engineering Mahidol University Thailand Abstract The keynote speaker is devoted to implementation of medical robotics in the field of health. The speaker shared the implementation of robotics in several field of medical surgeries. The robotics were implemented in the hospital services. The BART LAB Telepresence Robot and the BART LAB Rescue robot were developed with a multiple touch screen and dual obstacle detection along with other sensor. The application of the robotics plays a major role in the field of medical sciences and In the current world scenarios. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |J IT Enabled Applications for Improved Healthcare Prof. Vinod Menon Center for Disaster Management Yashada India Abstract Application of Health in GIS helps the administrators to respond and recover from epidemic outbreak. SAFER, ALOHA, MARPLOT, CAMEO area also available for emergency response at the period of dister management. Pandemic modelling tools, vaccine administration dashboards, PHEW, French sentinel network, IDSP from india has helps in public health early warning systems and for handling the health crises. The predict analytics using BigData , Open Data,IoT,AI algorithm, Machine Learning etc. Embracing Reproducibility and Replicability in Spatial Data Analysis Dr. Kamarul Imran Musa School of Medical Sciences Universiti Sains Malaysia Malaysia Abstract To ensure reproducibility and replicability are effective in geography and spatial data analysis, necessary steps include making data and codes available for sharing. Established and robust software such as Python and R with GIS functionality is available for researchers to help them with reproducible data analysis. The R software provides researchers with tools such as R markdown to facilitate reproducibility. The CRAN website which houses more than 16000 packages provides more than 100 packages that can perform spatial data analysis. The challenges for reproducibility include the complicated analysis of big data, the ecology fallacy, the drive to analyse the entire population, and the resistance to open tools, statistical libraries, and codes. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |K Geospatial Dashboards: Data Synthesis for Decision Support Dr. Josef Strobl Department of Geoinformatics - Z_GIS University of Salzburg Austria Abstract Integrating and linking multidimensional information and bringing together spatial, temporal and domain characteristics through dynamic links. By monitoring multiple states, indicators and trends, processes can be managed efficiently and safely by timely interventions through control variables. These ‘operations dashboards’ were focused on the control of well-understood processes and workflows. Simplification of a vast and complex data can be performed using the geospatial Dashboards. Modern dashboards are designed for interaction, reaching far beyond simple presentation interfaces. Geriatric Care through Ayurveda, Yoga and Naturopathy Dr. Alka Gupta Alka Gupta Academy of Total Health Thailand Abstract Geriatrics or Jarachikitsa or Rasayana in Ayurveda is a unique therapeutic methodology to control/slow down the ageing process in the human being during the degenerative phase in one’s life. Ayurveda has the techniques to fight against the aging process. Yogic practices can cope with Neurological Disorders like stress, depression, Alzheimer, Parkinson, Fatigue, Pain etc. Bio purificatory (Panchkarma) procedures and regular diet helps to prevent diseases, promote health and to delay the ageing. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |L AI and Machine Learning in Healthcare Prof. T. Kishore Kumar Department of Electronics & Communication Engineering National Institute of Technology India Abstract In healthcare, Machine Learning achieved great heights from diagnosis diseases to assisting in healthcare management systems. In the recent COVID Pandemic situation, ML was used to predict and understand the protein structures of the virus which largely helped in developing the COVID-vaccines. Also, ML can be used for providing management systems so that doctors appointments and treatments can be managed efficiently in crowded situations. emotion recognition systems are very useful for analysis of human emotions , which can be helpful to diagnosing the mental illness and the depression levels of the patient. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |M Acknowledgement The Organising Chair of the 7th International Health GIS Conference 2021 - Prof. Nitin Kumar Tripathi expresses his deep gratitude on behalf of Organizing and Steering Committees to the Invited Guests, Knowledge and Industry Partners, participants and delegates. Special thanks are extended to the staff of the International Journal of Geoinformatics (IJG) who worked together and extremely hard to prepare an outstanding conference. Tender thanks are expressed to students and Volunteers who did impressive and dedicated work. Closing Ceremony The Health-GIS 2021 Closing Ceremony was held online. Words of gratitude was expressed by Prof. Nitin. The 7th International Health GIS Conference on \"Geo Intelligense for Smart health Care HGIST2021\" is now drawing to a close. I would like to take this opportunity to extend our sincere gratitude and appreciation to all our Knowledge Partners, Industry Partners, colleagues and friends for their valuable contribution all throughout the conference and making this event a great success. We hope the past two days have been fruitful and that you will be able to make the most from the sessions you attended. Through a wide range of keynote speeches, Plenary, Technical session, and discussions, we have been presented with new ways to deal with some of the challenges in Spatial Epidemology and Health Care. We hope that you shared your experiences and expertise with other participants from near and far, and that a cordial relationship established among all of you during the HGIS 2021 Conference will further strengthen the joint projects and International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |N research opportunities. But, of course, the real measure of this conference success lies in how it will affect you - our participants, or more precisely, how it will affect the actions you will take after this is over and you activate the contacts made and knowledge gained. We look forward to further building partnerships with you and your organizations. I hope that all of us will continue what we have started here these days. Before I conclude, I would like to invite all of you to the next 8th Health GIS 2022 International Conference at Jaipur Manipal University, date will be announced later. Thank you for you participation and we look forward to a possible collaboration for our upcoming events and projects. Let us keep in touch Prof. Nitin Kumar Tripathi Organizing Chairperson, Health GIS 2021, Editor-in-Chief, International Journal of Geoinformatics International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |O TECHNICAL SESSIONS AGENDA A total of 211 delegates from 15 countries attended and have benefited in many ways from this conference. More than 70+ scientific papers were presented in 9 Technical Sessions. Time (Hrs) Event 08:30-09:30 Opening Ceremony, Bangkok, Thailand Welcome – Prof. Iyyanki V Murali Krishna, Chairman Opening Ceremony Introducing Conference - Prof. Nitin Kumar Tripathi – Conference Chair Address - Dr. Opart Karnkawinpong, Director General, Department of Disease Control Ministry of Public Health Address - Dr. Pakorn Apaphant- Executive Director GISTDA Address - Dr. Siriwan S, Vice-Rector Thammasat University Address – Prof. G K Prabhu – President, Manipal University, Jaipur, India (09:30-11:00) Key Note Session1: Chair- Prof. Iyyanki V Murali Krishna, Prof. Roheet Bhatnagar Dr. Derek Erhhardt – US Centers for Disease Control and Prevention (CDC), USA “Polio Eradication: How Technologies are helping us Travel the Last Mile” Dr. Ganapathy Krishnan - Director, Apollo Telemedicine Networking Foundation, India “Telehealth in 2020 and Beyond” 11:00-11:30 Dr. Chi Ren Shyu – Director IDSCI, University of Missouri, Columbia, USA 11:30-13:00 “Geospatially-Enabled Deep Analytics for Precision Rural Health – Case Studies on Telemedicine and Disaster Resilience” Tea and Poster Session: Display of Poster Industry Key Note Session: Chair - Prof. Rosline Hassan, Mr. Ranadheer Mandadi Mr. Frank Cosley – Director, ANZER IT Healthcare Canada and Asia “Smart Healthcare: AI-powered EHR Solutions for Clinical Decisions” 13:00-14:00 Mr. Saksin Chongolnee, CCM Thailand 14:00 -15:30 Lunch Technical Session 1: Vector-Borne Disease Chair Session: 1. Dr. Sucheera Amornmahaphun - Mahasarakham University Thailand 2. Dr. Ei Ei Phyo Mint - Mahasarakham University Thailand Geographical Information of Malaria infections in Balochistan Province of Pakistan; A Trend Analysis of Malaria Sarmad Saeed Khan, Chairat Uthaipibull, Ghani Baloch and Choosak Nithikathkul Mapping and Intervention of Malaria Situation in Sisaket Province Sutthisak Noradee, Chairat Uthaipibull and Choosak Nithikathkul Trend and Spatial Analysis of Dengue Cases in Northeast Malaysia Afiqah Syamimi Masrani Nik Rosmawati Nik Husain, Kamarul Imran Musa and Ahmad Syaarani Yasin International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |P Spatial Density of Aedes Distribution in Urban Areas: A Case Study of Breteau Index in Melaka, Malaysia Mohd Hazrin Hashim, Farihan Yatim, Mohd Amierul Fikri Mahmud, Faizul Akmal Bin Abdul Rahim, Mohd Hatta bin Abdul Mutalip Spatio-Temporal Pattern Analysis of Typhoid and Diarrhea Cases and Associated Factors in Draught Districts of Andhra Pradesh, India 2013-2017 S V S Aditya Bharadwaz Ganni Exploring Geospatial Analysis to Map Malaria Endemicity and Applied Interventions in a Rural District of Chhattisgarh, India Hitakshi Sharma and Sunil Vilasrao Gitte Spatial Mapping and Temporal Correlation Analysis between Mosquito-borne Diseases and Meteorological Factors in Mannar, Sri Lanka K.K.S. Amandika. Withanage, N.K. Tripathi, Chitrini Mozumder, R.D.J. Harishchandra, Prasad Ranaweera, A.Y.K. Perera, K.G.S. Kalansooriya, Priyadarshan Emmanuel, M.A.S.T Fernando and Mihirini Hewavitharane Factors of Knowledge and Attitude about Dengue Haemorrhagic Fever(DHF) that Affect the Behavior of DHF Prevention Using Geographic Information Systems (GIS) in Agriculturist in Taket Sub-District Uthumphon Phisai District Sisaket Province Klarnarong Wongpituk, Tammasak Saykaew, Sureewan Siladlao, Anuvat Roongpisuthipong, Warangkana Chankong and Pranee Sriboonrean Gender Roles in Preventing Malaria among The Indigenous People Siti Nur Afiqah Zahari and Abdul Rashid Retrospective Spatial Risk Assessment of Dengue Fever in Selected Province of Laos Sumaira Zafar, Oleg Shipin and Hans Overgaard 14:00 -15:30 Technical Session 2: Tropical Disease Chair Session: 1. Dr. Choosak Nithikathkul - Mahasarakham University Thailand 2. Dr. Phaisarn Jeefoo - University of Phayao, Thailand Surveillance Model for Soil-transmitted Helminthiasis in Mekong Basin: A Spatial Analysis Using Technology Information Yongyuth Puriboriboon and Choosak Nithikathkul Long Term Care for Helminthiasis in Seniors: Si Sa Ket Integration Models Sukhontip Norasan, Pacharamon Soncharoen and Choosak Nithikathkul Spatial Analysis and Intervention Program of Helminthiasis and Opisthorchiaisis in Sisaket Province: Review and Situation Thailand Pacharamon Sorncharoen and Choosak Nithikathkul Melioidosis in the Northeastern State of Malaysia: Spatial Analysis of Cases and Their Sequence Types Ahmad Filza Ismail, Siti Munirah Mohd Adib, Azian Harun and Aziah Ismail 15:30 - 16:00 Tea and Poster Session: Display of Poster International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |Q 16:00 - 17:30 Key Note Session 2: 1) Chair- Prof. Nitin Kumar Tripathi - AIT, Thailand 2) Chair – Dr. Kamal Uddin – SIAM University, Thailand Dr. Cherdchai Nopmaneejumruslers - Mahidol University, Bangkok, Thailand “Innovation in Telemedicine” Prof. Hyo Choi – Gangneung Wonju National University, South Korea \"Statistical Prediction of Hourly Particulate Matter Concentrations (PM10, PM2.5 and PM1) and their Relationship with Meteorological Parameters at a Coastal City, Korea Before, During and After The Yellow Dust Period\" Dr. Amporn Benjaponpitak - Director-General, Department of Thai Traditional Medicine, Thailand “Applications of Digital Technology for Thai Traditional Medicine Development” 17:30 – 19:00 Technical Session 3: Big data Analytics in Health, Machine Learning and Artificial Intelligence Chair Session: 1. Prof T. Kishore Kumar - NIT Warangal, India “Plenary Speaker- AI and Machine Learning in Healthcare” 2. Dr. Muhammad Shahzad Sarfraz - FAST, Pakistan Comparative Analysis of Climatic Factors on Outbreak of Malaria Using Machine Learning Pallavi Mohapatra Survey of Health Information Perception on Smartphone for Health Promotion Among Elderly People Kantapong Prabsangob and Luckwirun Chotisiri Address Geocoding Tools for Disease Mapping Using Free and Open Online Services: An Exploratory Comparative Case Study in Malaysia Noramira Monir and Abdul Rauf Abdul Rasam Social Network Analysis for Leader Identification of Social Learning among Patients with Type 2 Diabetes Mellitus and Hypertension Napa Rachata The Development of Effective Khaokho District Agriculture Tourism Route Recommendation on Semantic Web by Integration Ontology and Analytic Hierarchy Process Nakarin Chaikaew, Linlalee Siriwilailerdanun, Luethaipat Pimonsree and Nakharet Chaikaew Real-Time Detection and Diagnosis of Skin Diseases using Deep Learning Techniques Amit Prasad Nayak and Mongkol Ekpanyapong Mobile GIS Application for Nearest Health Service in Karari Locality, Khartoum, Sudan Mohammed Mahmoud Ibrahim Musa, Eltayeb Ibrahim Ahmed Wadi and Ebtisam Ali Mohammed Safe Mapping Platform: A GIS Mobile Crowd Sensing Platform for COVID-19 Self-Tracking and Self- Risk Managing Sakda Homhuan, Chanida Suwanprasit, Chakkaphong Namwong, Wipawinee Khamnoi, Raweewan Boonma, Tippawan Mate and Nathalika Wanginkhom Effect of Movement Control Order (MCO) in Determining Predictors of New Malaysian COVID-19 Cases Based on Artificial Neural Network (ANN) Model using Publicly-Available Data International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |R 17:30 – 19:00 M. A. Edre, Z. A. Muhammad Adil and A. R. Jamalludin Technical Session 4: Traditional Medicine Chair Session: 1. Dr. Alka Gupta - Alka Gupta Academy of Total Health, Thailand “Plenary Speaker- Geriatric Care Through Ayurveda, Yoga and Naturopathy\" 2. Dr. Chitrini Mozumder - Asian Institute of Technology, Thailand Anti-Malaria Traditional Herbs: Local Wisdom Suratsawadee Sinwat, Panee Sirisa-Ard and Choosak Nithikathkul Herbal using behaviours among Diabetes in Ban Chu Chi Health Promoting Hospital, Bang Chakreng subdistrict, Mueang Samut Songkhram, Samut Songkhram Province Tammasak Saykaew and Klarnarong Wongpituk Antioxidant Activity and Development of Skin Care Lotion Containing Hedychium Coronarium Essential Oil Rattana Panriansaen, Nustha Kitprathaung and Chopaka Chandham Effectiveness of the Self-Care Sandbag Exercise Program for the Osteoarthritis of Knee Patients in the Secondary Care of Thailand: A Quasi-Experimental Study Phannathat Tanthanapanyakorn, Aree Sanguanchue and Salila Cetthakrikul Antioxidant Activity, Total Phenolic Content and Cytotoxic Activity of Etlingera Pavieana Rhizome Extract Combined with Terminalia Catappa Leaves Extract Papawee Sookdee, Kingkan Iamnet, Supakaneewan Khanunthong and Rathapon Asasutjarit 26 February 2021 Time (Hrs) Event 08:30 - 10:00 Key Note Session 3: Chair – Prof. Nitin Kumar Tripathi - Asian Institute of Technology, Thailand Chair – Dr. Wutjanun Muttitanon- Mahidol University, Thailand Prof. Mark Leipnik- Sam Houston State University, USA \"Pandemic of Paradoxes” Prof. Fazlay S. Faruque - Director of GIS&RS, University of Mississippi Medical Center, USA “Utilizing Geospatial Data and Tools to Estimate the Outdoor Abundance of Clinically Significant Mold Spores” Dr. Jackrit Suthakorn - Dean of Engineering, Mahidol University, Thailand “The Development of Telepresence Robot for Medical Application Based Full Study on Practical Scenario in Clinical Study” 10:00 -10:30 Tea and Poster Session: Display of Posters 10:30 -12:00 Key Note Session 4: Chair – Prof. Deepak Lal – SHUATS, India Chair – Asst. Prof Arlene Gonzales – MMSU Philippines Prof. Vinod Menon - Center for Disaster Management Yashada, India Prof. Kamarul Imran Musa - Epidemiology & Biostatistics, USM, Malaysia “Embracing Reproducibility and Replicability in Spatial Data Analysis” International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |S Dr. Josef Strobl - University of Salzburg, Austria “Geospatial Dashboards: Data Synthesis for Decision Support” 12:00 – 13:00 Lunch 13:00-14:30 Technical Session 5: Lifestyle and Diseases Chair Session: 1. Dr. Wan Mohd Zahiruddin – USM, Malaysia 2. Dr. Haoran Zhang – SCGI, Thailand Spatial Patterns in Breast Cancer Incidence in Kelantan, North East of Malaysia Wan Mohammad Ismail Ad-Deen Wan Azman, Tengku Ahmad Damitri Al Astani Tengku Din, Ahmad Filza Ismail, Wan Faiziah Wan Abdul Rahman, Maya Mazuwin Yahya and Rosline Hassan Long Term Care: Mahasarakham Model Thailand Weerasak Aneksak, Sukhontip Norasan, Thammanoon Raveepong, Pramote Thongkrajai and Choosak Nithikathkul Alternative Trends Base on Integrated Medicine for Office Syndrome Pakawat Chaiyachit, Yingsak Jittakoat, Panich Chantachon and Choosak Nithikathkul Geospatial Distribution Patterns of Liver Cancer in Phayao Province, Thailand Phaisarn Jeefoo Understanding Lifestyle Risk Status of Tuberculosis in a Small Urban Community Using Geospatial- Epidemic Data Analysis Abdul Rauf Abdul Rasam and Nur Aimie Ridzuan Development and Application of Bal Ex Quick Balance Skeleton Tracking For Balance Rehabilitation Zuraida Zainun, Nur Intan Raihana Ruhaiyem, Ahmad Sufril Azlan Mohamed, Muhammad Umar Abd Aziz and Azliehanis Ab Hadi The Participation of Volunteers in Promoting Public Health, Elderly Health, Bang Khonthi District, Samutsongkhram Sureewan Siladlao, Tammasak Saikaew, Klarnarong Wongpituk, Wanwimon Mekwimon and Thanya Promsorn Analysis of Health Promotion Program using Dharma Way and Thai Way to Prevent Complications from Non-Communicable Diseases among Elderly Patients, (Chiang Rak Noi Sub-district, Bang Pa- in District, Phra Nakhon Si Ayutthaya Province) Aree Sanguanchue, Thassaporn Chusak, Phannathat Tanthanapanyakorn and Klarnarong Wongpituk 13:00-14:30 Technical Session 6: Infectious Disease Chair Session: 1. Dr. Chairat Utaipibull - NSTDA, Thailand 2. Dr. Sarmad Saeed Khan - Mahasarakham University, Thailand Leptospirosis in Si Sa Ket Province, Thailand; Trends and Control Thawatchai Toemjai, Supaporn Wannapinyosheep and Choosak Nithikathkul Discovery of Opisthorchiasis in Chaiyaphum Province, Thailand Prasit Kachaiyaphum, P. Chaleephom, Supaporn Wannapinyosheep, Bangon Changsap and International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |T Choosak Nithikathku Prevention and Control for Melioidosis in Si Sa Ket Province Phachara Kanjaras, Sauwanan Bumrerraj, Sutthisak Noradee and Choosak Nithikathkul Hand Foot and Mouth Disease: Prevalence and it's Spatial Relationship with Vaccine Refusal Cases in Terengganu, Malaysia Mohamad Zarudin Mat Said, Kamarul Imran Musa, Xin Wee Chen, Wira Alfatah Abdul Aziz and Azmani Wahab Incidence Rates and Geographical Distribution of Typhoid Cases in Kelantan, Malaysia from 2012 to 2015. Wira Alfatah Ab Ayah Ab Aziz, Kamarul Imran Musa and Fauziah Mohd Fish Borne Parasitic Zoonosis Surveillance in Northeastern Thailand Ei Ei Phyo Myint, Amornpun Sereemaspun, Anuwat Phalee and Choosak Nithikathkul Geospatial Analyses of Thyroid Cancer Incidence in Kelantan Population Nur Fatihah Mohd Zuhdi, Wan Faiziah Wan Abdul Rahman, Tengku Ahmad Damitri Al Astani Tengku Din, Ahmad Filza Ismail and Rosline Hassan Temporal and Spatial Comparison of Online Searches and Confirmed Cases of Listeriosis Outbreak: An Exploratory Study of Google Trends in the US Hung Nguyen Ngoc and Wantanee Kriengsinyos 14:30-15:00 Tea and Poster Session: Display of Posters 15:00-16:30 Technical Session 7: Environment and Health Chair Session: 1. Dr. Oleg Shipin - Asian Institute of Technology, Thailand 2. Dr. Indrajeet Pal - Asian Institute of Technology, Thailand Air Quality and Noise Impact Assessment of Dumpsite Biomining for Occupational Health & Safety of Workers Pawan Kumar Srikanth The Role of Remote Sensing and GIS for the Study of Environmental Aspects of Dengue and Chikungunya Epidemic Transmission in Dakshina Kannada District, Karnataka state, India Naveen Chandra Bellare, Usha Naveen, Francis Andrade and Shannon Meryl Pinto Tuberculosis Spread by Air pollution: A Case of Selected Districts of India Vijay Kumar and Tulika Tripathi Green 3R Biotechnology Based On Eco-Engineering: Starting Up a Novel Freshwater Mangrove Ecosystem for Urban Health Arlene Gonzales and Dr Oleg Shipin 15:00-16:30 Technical Session 8: COVID-19 Chair Session: 1. Prof. Iyyanki V Murali Krishna, Chief Advisor, Smart Village Movement, India 2. Prof, Jianhua Gong - Chinese Academy of Sciences, China Analysis of COVID-19 Spreading in Wenzhou, China Wenning Li, Jieping Zhou, Jianhua Gong and Lihui Zhang, Nasir Farid and Adnan Arshad COVID-19 LockDown Affecting Mental Health; Review And Situation International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |U 15:00-16:30 Ladda Pholputta, Mayuree Glubvong, Sucheera Amornmahaphun, Jariya Jiranukul, Pramote Thongkajai and Choosak Nithikathkul Visualization Analysis of COVID-19 to Respond to Infectious Disease Outbreaks Using Geoinformatics Techniques in Thailand: Opportunities and Challenges Haoran Zhang, Tanita Suepa, Lay Hong, Mot Ly and Phorn Nayelin Spatial Distribution and Time Series Analysis of COVID-19 Pandemic in Italy: A Geospatial Perspective Muhammad Farhan Ul Moazzam, Tamkeen Urooj Paracha, Ghani Rahman, Byung Gul Lee, Nasir Farid and Adnan Arshad Determine Heart Rate as a Screening Technique for Covid-19 Using Data Mining for University Student Admission Waidah Ismail, Rosline Hassan, Rabihah Md Sum, Anvar Narzullaev and Azuan Ahmad Candidate and Alternative Treatment for COVID-19 by using Thai Traditional Herbs Korakot Chaimongkhon, Choosak Nithikathkul and Pramote Thongkrajai Hospital Safety Index (HSI) for Covid-19 Pandemic Mitigation: A New GIS-Aided Framework Fahmi Charish Mustofa and Mufidatul Laily Impact of Behavior, Healthcare and Socioeconomic Factors on Covid-19 Situation of Asean Countries Htet Yamin Ko Ko and Nitin Kumar Tripathi Technical Session 9: Nutrition and Human Health Chair Session: 1. Prof. Nik Rosmawati Nik Husain – USM, Malaysia 2. Asst. Prof. Dr Arisara Charoenpanyanet- Changmai University, Thailand Predictive Models for Obesity in Non-Pregnant Women in Low- and Middle-Income Countries Xiyuan Gao, Lincoln R. Sheets and Henok G. Woldu Effects of Telemedicine in Obese Patients with Non-Alcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis Surasak Saokaew, Chayanis Kositamongkol, Kanyanat Chaiyo, Thirada Jirapisut, Narakorn Aomsin, Pit Leewongsakorn, Nathorn Chaiyakunapruk and Pochamana Phisalprapa Statistical and Spatial Analysis of Obesity Prevalence in the United States of America Ranadheer Mandadi and Nitin Kumar Tripathi Factors Related to Food Consumption Behavior for Freshmen of the College of Allied Health Sciences, Suan Sunandha Rajabhat University Phannee Rojanabenjakun, Jatuporn Ounprasertsuk, Tipvarin Benjanirat, Supaphorn Oasana, Pongsak Jaroenngarmsamer, Sasipen Krutchangthong, Sunatcha Choawai and Jirawat Sudsawad Prevalence and Associated Factors of Insomnia among Thai Adolescents in Samut Songkhram Rasornradee Pakpakorn, Chanokporn Panjinda, Papawee Sukdee and Wannee Promdao Factors Associated to Dietary Supplement Consumption Behavior among Undergraduates Students in the Central Part of Thailand; A Cross-sectional Analytical Study Phannathat Tanthanapanyakorn Naphatsaruan Roekruangrit and Tassanapun Wechasat International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |V 16:30– 17:30 Closing Ceremony – Panel Discussion and Announcement of Award Mr. Randheer Reddy – Present conference Participation Details Dr. Choosak Nithikathkul - Conference Key Outcome Message Prof. Venkatesh Raghavan- Announcement of Award in Every Theme Prof. Roheet Bhatnagar- Announcement on 8th HealthGIS Conf. 2022, India Prof. Nitin Kumar Tripathi- Closing Remarks International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |W AWARDEES In recognition of their excellent work and presentation skills during the conference, special awards were awarded to selected participants in each of the themes. Best Papers in the Conference Visualization Analysis of COVID-19 to Respond to Infectious Disease Outbreaks Using Geoinformatics Techniques in Thailand: Opportunities and Challenges Haoran Zhang, Tanita Suepa, Lay Hong, Mot Ly and Phorn Nayelin Hand Foot and Mouth Disease: Prevalence and it's Spatial Relationship with Vaccine Refusal Cases in Terengganu, Malaysia Mohamad Zarudin Mat Said, Kamarul Imran Musa, Xin Wee Chen, Wira Alfatah Abdul Aziz and Azmani Wahab Green 3R Biotechnology Based On Eco-Engineering: Starting Up a Novel Freshwater Mangrove Ecosystem for Urban Health Arlene Gonzales and Oleg Shipin Best Papers in Each Session Best Paper in Session 1 (Vector-Borne Disease) Sutthisak Noradee, Chairat Uthaipibull and Choosak Nithikathkul Geographical Information of Malaria Infections in Balochistan Province of Pakistan: A Trend Analysis of Malaria Organisation of the first author: Tropical and Parasitic Diseases Research Unit, Faculty of Medicine, Mahasarakham University, Thailand Best Paper in Session 2 (Tropical Disease) Yongyuth Puriboriboon and Choosak Nithikathkul Surveillance Model for Soil-transmitted Helminthiasis in Mekong Basin: A Spatial Analysis Using Technology Information Organisation of the first author: Tropical and Parasitic Diseases Research Unit, Faculty of Medicine, Mahasarakham University, Thailand Best Paper in Session 4 (Traditional Medicine) Rattana Panriansaen, Nustha Kitprathaung, and Chopaka Chandham Antioxidant Activity and Development pf Skin Care Lotion Containing Hedychium Coronarium Essential Oil Organisation of the first author: College of Allied Health Sciences, Suan Sunandha Rajabhat International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |X University, Thailand Best Paper in Session 5 (Lifestyle and Diseases) Pakawat Chaiyachit, Yingsak Jittakoat, Panich Chantachon and Choosak Nithikathkul Alternative Trends Base on Integrated Medicince for Office Syndrome Organisation of the first author: Tropical and Parasitic Diseases Research Unit, Faculty of Medicine, Mahasarakham University, Thailand Best Paper in Session 6 (Infectious Disease) Mohamad Zarudin Mat Said, Kamarul Imran Musa, Xin Wee Chen, Wira Alfatah Abdul Aziz and Azmani Wahab Hand Foot and Mouth Disease: Prevalence and its Spatial Relationship with Vaccine Refusal Cases in Terengganu, Malaysia Organisation of the first author: Department of Community Medicine, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Health Campus, 16150 Kubang Kerian, Kelantan, Malaysia Best Paper in Session 7 (Environmental Health) Arlene Lu-Gonzales & Oleg Shipin Green 3R Biotechnology Based on Eco-engineering: Starting Up a Novel Freshwater Mangrove Ecosystem for Urban Health Organisation of the first author: Asian Institute of Technology, Thailand Best Paper in Session 8 (Covid-19) Haoran Zhang, Tanita Suepa, Lay Hong, Mot Ly and Phorn Nayelin Visualization Analysis of Covid-19 to Respond to Infectious Disease Outbreaks Using Geinformatics Techniques in Thailand: Opportunities and Challenges Organisation of the first author: Geo-Informatics and Space Technology Development Agency (GISTDA), Thailand Best Paper in Session 9 (Nutrition and Human Health) Ranadheer Mandadi and Nitin Kumar Tripathi Statistical and Spatial Analysis of Obesity Prevalence in the United States of America Organisation of the first author: Asian Institute of technology, Thailand International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |Y Organising Team Chairman Dr. Nitin Kumar Tripathi Asian Institute of Technology Thailand Program Coordinator Oraganising Secretary (Local) Mr. Ranadheer Mandadi Dr. Choosak Nithikathkul Asian Institute of Technology Mahasarakham University Thailand Thailand International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |Z Oraganising Secretary (Initernational) Office Coordinator Dr. Roheet Bhatnagar Ms. Nitiporn Saardmoung Manipal University Geoinformatics International Co., Ltd India Thailand International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

P a g e | AA 7th International Health GIS Virtual Conference on “Geo intelligence for Smart Health Care” during 25th – 26th February 2021, Thailand Special Issue Announcement: - The special issue on 7th International Conference on HealthGIS will be published in peer review journal “International Journal of Geoinformatics, Vol. 17, No. 5, October, 2021”. - Delegates can submit papers to [email protected] by 30 August 2021. - Several papers are reviewed and few are under review and finalisations - After acceptance the discounted Article Processing Charge is to be paid by 25 September 2021 - The contributor of the paper in the Special Issue will get special incentive of “Open Access” without any charge and also the DOI number Guest Editors: Prof. Mark Leipnik - Sam Houston State University, USA Prof. Fazlay S. Faruque - University of Mississippi Medical Center, USA Dr. Kamarul Imran Musa - Universiti Sains Malaysia, Malasyia Prof. Nitin Kumar Tripathi – Asian Institute of Technology, Thailand Prof. Rosline Hassan - Universiti Sains Malaysia, Malasyia Dr. Choosak Nithikathkul - Mahasarakham University, Thailand International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Content a Conference Highlight Technical Session: Infectious Disease- Lifestyle Disease 4 12 HAND FOOT AND MOUTH DISEASE: PREVALENCE AND ITS SPATIAL RELATIONSHIP 20 WITH VACCINE REFUSAL CASES IN TERENGGANU, MALAYSIA 27 Zarudin Mat Said, Xin Wee Chen, Wira Alfattah, Azmani Wahab and Kamarul 34 Imran Musa 40 EXPLORING LIFESTYLE RISK OF TUBERCULOSIS IN A SMALL URBAN COMMUNITY 44 USING GEOSPATIAL-EPIDEMIC ANALYSIS Abdul Rasam, A. R., Ridzuan, N. R., Isa, M.M., Shafie, A. F. and Tripathi, N. K. SPATIAL PATTERNS IN BREAST CANCER INCIDENCE IN KELANTAN, MALAYSIA Tengku Ahmad Damitri Al Astani Tengku Din, Nur Fatihah Mohd Zuhdi, Wan Mohammad Ismail Ad-Deen Wan Azman, Wan Faiziah Wan Abdul Rahman, Ahmad Filza Ismail, Maya Mazuwin Yahya and Rosline Hassan GEOGRAPHIC DISTRIBUTION OF THYROID CANCER INCIDENCE: A TERTIARY CENTRE EXPERIENCE Nur Fatihah Mohd Zuhdi, Wan Faiziah Wan Abdul Rahman, Tengku Ahmad Damitri Al Astani Tengku Din, Ahmad Filza Ismail and Rosline Hassan TEMPORAL AND SPATIAL COMPARISON OF ONLINE SEARCHES AND CONFIRMED CASES OF LISTERIOSIS OUTBREAK: AN EXPLORATORY STUDY OF GOOGLE TRENDS IN THE US Hung Nguyen Ngoc and Wantanee Kriengsinyos EFFECTS OF THE APPLICATION OF HEALTH PROMOTION MODEL WITH DHARMA WAY AND THAI WAY FOR PREVENTION OF COMPLICATIONS IN THE ELDERLY WITH CHRONIC NON-CONTAGIOUS DISEASES IN CHIANG RAK NOI SUB-DISTRICT, BANG PA-IN DISTRICT, PHRA NAKHON SI AYUTTHAYA PROVINCE Aree Sanguanchue, Thassaporn Chusak, Phannathat Tanthanapanyakorn, Jiaranai Pathomrotsakun and Klarnarong Wongpituk THE PARTICIPATION OF VOLUNTEERS IN PROMOTING PUBLIC HEALTH, ELDERLY HEALTH, BANG KHONTHI DISTRICT, SAMUTSONGKHRAM PROVINCE Sureewan Siladlao, Thanya Promsorn, Tammasak Saykaew, Wanwimon Mekwimon and Klarnarong Wongpituk

Technical Session: Vector-Borne Disease-Tropical Disease 48 57 MELIOIDOSIS IN NORTHEASTERN STATE OF MALAYSIA: SPATIAL ANALYSIS OF CASES AND THEIR SEQUENCE TYPES 64 Siti Munirah Mohd Adib, Azian Harun, Ahmad Filza Ismail and Aziah Ismail 69 SPATIAL MAPPING AND TEMPORAL CORRELATION ANALYSIS BETWEEN MOSQUITO-BORNE DISEASES AND METEOROLOGICAL FACTORS IN MANNAR, SRI LANKA Withanage, K.K.S.A.,Tripathi, N. K., Chitrini Mozumder, RDJ Harishchandra, Prasad Ranaweera, AYK Perera, KGS Kalansooriya, Priyadarshan Emmanuel, MAST Fernando and Mihirini Hewavitharane SURVEILLANCE MODEL FOR SOIL-TRANSMITTED HELMINTHIASIS IN MEKONG BASIN: A SPATIAL ANALYSIS USING TECHNOLOGY INFORMATION Yongyuth Puriboriboon, Sutthisak Noradee and Choosak Nithikathkul FACTORS OF KNOWLEDGE AND ATTITUDE ABOUT DENGUE HAEMORRHAGIC FEVER (DHF) THAT AFFECT THE BEHAVIOR OF DHF PREVENTION USING GEOGRAPHIC INFORMATION SYSTEMS (GIS) IN AGRICULTURIST IN TAKET SUB- DISTRICT UTHUMPHON PHISAI DISTRICT SISAKET PROVINCE Klarnarong Wongpituk, Aree Sanguanchue, Nirobon Ma-oon, Sutthida kaewmoongkun, Nonlapan Khantikulanon, Naphatsaran Roekruangrit, Sasiwimol Chanmalee, Tammasak Saykaew, Warangkana Chankong and Tiwakron Prachaiboon Technical Session: Bigdata and Analytics in Health and 75 Machince Leaning-Traditional Medicine 82 86 DISEASE MAPPING FOR PUBLIC HEALTH USING FREE / OPEN ONLINE GEOCODING 90 SERVICES: A COMPARATIVE ANALYSIS IN MALAYSIA Noramira Monir, Abdul Rauf Abdul Rasam, Nor Aizam Adnan and Hendy Fitrian 95 Suhandri LEADER SELECTION MODEL OF SOCIAL LEARNING Napa Rachata SURVEY OF HEALTH INFORMATION PERCEPTION ON SMARTPHONE FOR HEALTH PROMOTION AMONG ELDERLY PEOPLE Kantapong Prabsangob and Luckwirun Chotisiri ANTIOXIDANT ACTIVITY, TOTAL PHENOLIC CONTENT AND CYTOTOXIC ACTIVITY OF ETLINGERA PAVIEANA RHIZOME EXTRACT COMBINED WITH TERMINALIA CATAPPA LEAVES EXTRACT Papawee Sookdee, Kingkan Iamnet, Supakaneewan Khanunthong and Rathapon Asasutjarit ANTIOXIDANT ACTIVITY AND DEVELOPMENT OF SKIN CARE LOTION CONTAINING HEDYCHIUM CORONARIUM ESSENTIAL OIL Rattana Panriansaen, Nustha Kitprathaung and Chopaka Janthum

Technical Session: Environment and Health/ COVID-19/ 99 Nutrition and Human Health 106 113 NOVEL URBAN ECOSYSTEM BASED ON FRESHWATER MANGROVES FOR URBAN HEALTH 122 Arlene Lu-Gonzales and Oleg Shipin 128 135 TUBERCULOSIS SPREAD AND AIR QUALITY: A CASE OF SELECTED DISTRICTS OF 143 INDIA Vijay Kumar and Tulika Tripathi 148 153 VISUALIZATION ANALYSIS OF COVID-19 TO RESPOND TO INFECTIOUS DISEASE 163 OUTBREAKS USING GEOINFORMATICS TECHNIQUES IN THAILAND: OPPORTUNITIES AND CHALLENGES Haoran Zhang, Tanita Suepa, Lay Hong, Phorn Nayelin, Ly Mot and Anurak Chakpor COVID-19 SCREENING TECHNIQUE FRAMEWORK FOR UNIVERSITY STUDENTS’ADMISSION Waidah Ismail, Rosline Hassan, Rabihah Md Sum, Anvar Narzullaev, Azuan Ahmad, Hani Ajrina Zulkeflee, Razan Hayati Zulkeflee and Rimuljo Hendradi IMPACT OF BEHAVIOR, HEALTHCARE AND SOCIOECONOMIC FACTORS ON COVID-19 SITUATION OF ASEAN COUNTRIES Htet Yamin Ko Ko, Nitin Kumar Tripathi and Ranadheer Mandadi STATISTICAL AND SPATIAL ANALYSIS ON OBESITY PREVALENCE IN UNITED STATES Ranadheer Mandadi and Nitin Tripathi FACTORS RELATED TO FOOD CONSUMPTION BEHAVIOR FOR FRESHMEN OF THE COLLEGE OF ALLIED HEALTH SCIENCES, SUAN SUNANDHA RAJABHAT UNIVERSITY Phannee Rojanabenjakun, Jatuporn Ounprasertsuk, Tipvarin Benjanirat, Supaphorn Oasana, Pongsak Jareanngamsamear, Sasipen Krutchangthong, Sunatcha Choawai, Jirawat Sudsawad and Panupan Sripan PREVALENCE AND ASSOCIATED FACTORS OF INSOMNIA AMONG THAI ADOLESCENTS IN SAMUT SONGKHRAM Rasornradee Pakpakorn, Chanokporn Panjinda, Papawee Sookdee and Wannee Promdao Development Analysis of Waste 4.0 Assessment Tool: Transforming Urban MSWM under Industrial Revolution 4.0 focusing Circular Economy Abhishek Kanojia AIR QUALITY AND NOISE IMPACT ASSESSMENT OF DUMPSITE MINNG FOR OCCUPATIONAL SAFETY OF WORKERS Pawan Kumar Srikanth Abstract 167 Authors Index 213

Page |4 HAND FOOT AND MOUTH DISEASE: PREVALENCE AND ITS SPATIAL RELATIONSHIP WITH VACCINE REFUSAL CASES IN TERENGGANU, MALAYSIA Zarudin Mat Said,1 Xin Wee Chen,2 Wira Alfattah,3 Azmani Wahab4 and Kamarul Imran Musa1 1Department of Community Medicine, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Health Campus, 16150 Kubang Kerian, Kelantan, Malaysia, E-mail: [email protected], E-mail: [email protected] 2Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Jalan Hospital, 47000 Sungai Buloh, Selangor, Malaysia, E-mail: [email protected] 3Kota Bharu District Health Office, Jalan Doktor, Bandar Kota Bharu, 15000 Kota Bharu, Kelantan, Malaysia E-mail: [email protected] 4Communicable Disease Control and Prevention Unit, Terengganu State Health Department, 20920 Kuala Terengganu, Terengganu, Malaysia, E-mail: [email protected] ABSTRACT Hand, foot, and mouth disease (HFMD) is a global public health problem with pandemic potential. The progressive increment of HFMD cases in Malaysia needs further investigation to identify the pattern of disease spread including its proximity to vaccine refusal. We sought to estimate the prevalence of HFMD in Terengganu and identify the spatial relationship between HFMD and vaccine refusal cases. This study employed data from the national electronic communicable disease notification system and vaccine refusal database maintained by the Communicable Disease Control (CDC) Unit and Maternal and Child Health Care (MCH) Unit, respectively. Data from all cases recorded in the year 2016 were provided by the Terengganu State Health Department, Malaysia. The number of HFMD cases for each district was estimated using the points-in-polygons function in R software. The spatial relationship between HFMD cases and vaccine refusal cases was tested using the cross K-function test. A total of 811 HFMD cases was notified in 2016 with the overall prevalence at 80.2 cases per 100,000 population. Among all districts in Terengganu, the prevalence of HFMD ranged from 19.2 to 230.9 cases per 100,000 population, with the cases highly concentrated in three districts: Kuala Terengganu, Marang, and Dungun. There was evidence of a spatial cluster of HFMD cases based on the Nearest Neighbour Index, r = 0.27 (p < 0.01). Moreover, the locations of HFMD cases were statistically and closely related to the locations of vaccine refusal cases (cross K test, p < 0.010). The prevalence of HFMD from year to year was high. HFMD cases and vaccine refusal cases formed clusters in the districts with a high-density population. The proximity of HFMD cases and vaccine refusal cases in Terengganu warrantsfurther investigation. KEYWORDS: HFMD, Vaccine Refusal, Spatial, Prevalence, Cluster 1. INTRODUCTION Pumpaibool, 2013; Aswathyraj et al., 2016). The Hand, foot, and mouth disease (HFMD) is a average incidence rate of HFMD in Malaysia for the common childhood illness caused by various year 2000 to 2008 was 25.0 ranged from 1.5 to 60.6 enteroviruses (Xing et al., 2014). A person is most per 100,000 population (Chan et al., 2011). contagious during the first week of the illness, and Furthermore, local statistics reported an increment most of the outbreaks often found in nurseries, of 347.33% in the numbers of HFMD cases, from playgroups, schools, and households where young 7,002 to 31,322 cases in 2011 and 2014, children have lots of close contacts with one another respectively; as well as highlighting the occurrence (Chang et al., 2011). of this rapid increment in most of the states in Malaysia (Ministry of Health Malaysia, 2016). Outbreaks of HFMD have been reported in However, from our extensive search, no study has countries of the Western Pacific Region and South- yielded the latest prevalence of HFMD in Malaysia. East Asia, inclusive of Malaysia; and the cases are increasing every year (Charoenchokpanit and International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

It was reported that the population Page |5 densitysignificantly affects the risk for HFMD events. This includesthe population of the states or Population and Housing Census 2010, Department districts and the population density among the of Statistics Malaysia (Brinkhoff, 2018). household (Huang et al., 2014). The household Terengganu has eight administrative districts setting, as well as schools and communities, play including Besut, Dungun, Hulu Terengganu, essential roles in the transmission of HFMD (Chang Kemaman, Kuala Nerus, Kuala Terengganu, et al., 2004). Apart from the climate factors and the Marang and Setiu. The ethnic composition of child population density, the population immunity, Terengganu shows that 97% of the population are and environmental hygiene contribute to the Malay and Bumiputera, 2.6% are Chinese, 0.2% are occurrence, transmission, and spread of HFMD (Hu Indian, and 0.1% represented the others. The age et al., 2012). distribution shows that population in the 0 – 14 years-old group comprises of 32.3% of the total Furthermore, a recent nationwide study population. This is followed by the 15 – 64 years- highlighted that the overall immunisation coverage old group (62.8%) and the elderly (aged 65 years of 86.4% in Malaysia was unsatisfactory (Lim et al., and above) comprises of 4.9% of the total 2017) and vaccine refusal was one of the population (Brinkhoff, 2018). determinants of low immunisation coverage in Malaysia (Ahmad et al., 2017). Vaccine refusal 2.2 Data Sources and Case Ascertainment refers to the individuals who had refused some or all In this study, we employed two data sources, 1) the vaccines scheduled in the Malaysia National notified HFMD cases made available from the Immunization Programme (Lim et al., 2016). There Malaysia Ministry of Health through the national is an increasing number of vaccine refusal, and electronic communicable disease notification system hesitancy worldwide, including Malaysia, wherein (Communicable Diseases Control Information the number of parents who refused to get their System, CDCIS E-Notification Version 2011.1); children vaccinated has almost increased three-fold, and 2) the vaccine refusal registry from the Maternal from 637 cases in 2013 to 1054 cases in the middle and Child Health (MCH) Unit database governed by of the year 2015 (Ahmed et al., 2018). Terengganu the Child Division of Terengganu State Health reported the second-highest number of Department. Both types of data were notified and unimmunized children (233 cases) in Malaysia registered by the Terengganu State Health (Ministry of Health Malaysia, 2018). The utilisation Department from January to December 2016. We of a geographic information system (GIS) has not excluded cases with missing coordinates and been extensively used in the public health research imported HFMD cases (i.e., cases who lived outside (including that of HFMD) in Malaysia. We found Terengganu). The CDCIS system contains HFMD- only a single study on HFMD using GIS in 2012, in related data and the coordinates (referring to the which they gathered data from 11 districts of residence of the cases) which were geocoded during Sarawak. Their findings on the spatial distribution active case investigation. Besides that, the vaccine and analyses were used to facilitate public health refusal cases were documented once case was team responses upon HFMD outbreaks (Sham et al., reported from the health clinic or district health 2014). office by MCH Unit. Their residential addresses were geocoded during the routine immunisation Therefore, we aimed to use the data from the schedule or home visit. Both the coordinates of the Malaysia Ministry of Health national surveillance notified HFMD cases and vaccine refusal cases system to estimate the overall, sex-specific, age- were converted into the format of the projected specific, and district-specific prevalence of HFMD coordinate system; the Kertau Rectified Skewed cases in Terengganu. We also sought to estimate the Orthomorphic (RSO) Malaya (EPSG:3168). density of HFMD and vaccine refusal case distribution, subsequently determine their spatial Apart from that, we also obtained the census relationship. tract derived from the latest nationwide census and contained the aggregated demographic data for the 2. METHODS state and districts of Terengganu (The Population and Housing Census 2010, DOSM). 2.1 Study Venue Terengganu, a state of Malaysia, is located in the 2.3 Statistical Analysis East of Peninsular Malaysia and bounded to We estimated the overall prevalence of HFMD Kelantan in the north and northwest; the South cases in Terengganu 2016 by dividing the total China Sea in the east; and Pahang in the south and number of confirmed HFMD cases by the total southwest. The state has a total area of 12,959 km2 population size of the Terengganu. Then we with a total population of 1,011,363 based on the calculated the sex-specific, age-specific, and International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |6 district-specific prevalence. Subsequently, we confidence limit for random distribution created performed a spatial distribution analysis. First, data using Monte Carlo simulations. The upper and of HFMD cases in the Microsoft Excel format were lower bounds of these permutations were plotted converted into the ESRI shapefile (.shp) format. together with the observed difference function. Any Using Quantum Geographic Information System deviation above the envelope formed by the upper (QGIS) Version 2.16.3 software (QGIS and lower bounds indicates significant clustering of Development Team, 2018), the spatial distribution cases, relative to non-cases/controls (referring to of both cases were analysed for heat map analysis – vaccine refusal cases) (Pfeiffer et al., 2008). a graphical representation of point data showing different colours to represent areas with different The study was approved by both the Malaysia concentrations of points and trends (Yeap et al., Research and Ethics Committee, Ministry of Health 2014). Next, by using the point pattern analysis, we Malaysia, and the Human Ethics Committee of examined the spatial spread (clustered, dispersed, or Universiti Sains Malaysia (USM/JEPeM/17120685). random) based on the Nearest Neighbour Index (NNI) analysis. 3. RESULTS We obtained records from 811 HFMD cases The NNI measures the spatial distribution from zero (notified to the CDCIS, Terengganu) in 2016. The (clustered pattern) to one (randomly dispersed mean (standard deviation, SD) age of HFMD was pattern) to 2.15 (regularly dispersed /uniform 2.48 (3.36) years. Table 1 shows the characteristics pattern) (Lai et al., 2009). The spatial relationship of the study subjects, the distributions of HFMD between the HFMD cases and vaccine refusal cases cases based on sex, age categories, and district, with was assessed using the Ripley’s K-function (a.k.a the corresponding prevalence rate per 100,000 cross K-function) analysis from ‘spastat’ package in populations. Almost all cases were from the Malay R Software Version 3.5.1 (R Core Team, 2018). The population with 97.6% (792/811), while 2.4% K-function was calculated in a bivariate form (19/811) were from Chinese, and (1.6%, 13/811) between both cases and counted the number of from India ethnic groups. In Figure 1, we show the neighbouring HFMD cases found within a given age distribution of HFMD cases, wherein the distance of each vaccine refusal case. Then, the highest age group contributing was one-years significant level of the function was compared to a (34.2%, 277/811) and the percentage decreases with the increment of age up until the age of 15 years. Table 1: The overall, age-specific, sex-specific, and district-specific prevalence of HFMD cases in Terengganu in 2016 (n=811) Variables n (%) Prevalence* Total Population Overall 811 80.2 1,011,363 Sex 460 (56.7) 89.2 515,579 Male 351 (43.3) 70.8 495,784 Female 724 (89.3) 760.6 95,189 Age-groups 73 (9.0) 63.7 114,528 14 (1.7) 12.0 116,803 0 – 4 years 5 – 9 years 72 (8.9) 52.7 136,563 10 – 14 years 117 (14.4) 78.1 149,851 59 (7.3) 83.3 70,800 Districts Besut 32 (3.9) 19.2 166,750 Dungun Hulu 276 (34.0) 81.8 337,553 Terengganu Kemaman 129 (15.9) 135.4 95,283 Kuala 126 (15.5) 230.9 54,563 Terengganu /K. Nerus Marang Setiu International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |7 Figure 1: Distributions of HFMD Cases in Terengganu in 2016 by age (n=811) Figure 4: Geographical Density of HFMD Cases in Figure 5: Geographical Density of Vaccine Refusal Terengganu Cases in Terengganu The cases from aged less than one-year were 8.8% cases were randomly distributed over the district (71/811) of the total notified cases in 2016. The region. There were 281 vaccine refusal cases majority of HFMD cases, 89.6% (727/811), were registered in Terengganu for the year 2016. The classified as single/sporadic case and only 10.4% highest prevalence of vaccine refusal was in Kuala (84/811) were classified as cluster or outbreak in Terengganu (26.7%, 75/281), followed by Dungun Terengganu. A similar trend was found for both (17.8%, 50/281), Kemaman (16%, 45/281), Marang single and cluster cases across the epidemiological (13.2%, 37/281), and Kuala Nerus (12.8%, 36/281). week in 2016. Other districts contributing of less than 10% of the total vaccine refusal cases – Hulu Terengganu 3.1 Geographical Distribution of HFMD Cases district (7.5%, 21/281), Setiu district (3.9%, 11/281) and Besut district (2.1%, 6/281). Figure 3 maps the and Vaccine Refusal Cases in Terengganu pattern of the geographical distribution of the Based on the distribution pattern (see Figure 2), it vaccine refusal cases, wherein the cases were was shown that HFMD cases were located in the primarily concentrated over the high-density central region or urban area of every district, population areas such as in Kuala Terengganu, including Setiu, Kuala Terengganu, Marang, Hulu Marang, Dungun, and Kemaman. The cases Terengganu, and Dungun. However, in Setiu, Hulu appeared to concentrate in the central region or Terengganu, Dungun, and Kemaman, the HFMD International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |8 urban area. However, in Besut and Setiu, the dashed line) shown in Figure 6 demonstrates that distribution was less centralized (more randomly both the HFMD and vaccine refusal cases clustered located) all over the district region. closely together. An observed cross K-curve was plotted well above the theoretical K-curve, which 3.2 Cluster of HFMD and Vaccine Refusal Cases indicates that HFMD cases seem to cluster around vaccine refusal cases. in Terengganu The NNI value for HFMD cases at 0.27 indicates The Monte Carlo simulation of random labelling the presence of clusters among the cases (which of events shown in Figure 7 shows that the observed might be due to the large population density in those cross K-function is in a straight curve and the areas rather than the true infection dynamics). The confidence envelope is represented by the grey zone vaccine refusal cases also formed clusters among area. The cross K-function (between HFMD and the cases with NNI value stands at 0.23 (The smaller vaccine refusal cases) revealed that there was an the value of NNI, the more significant the clusters interaction between both cases as the observed would be). Table 2 showed that both results were curve is above the Poisson curve (what would be significant with a p-value < 0.01. expected under randomness). It means that the HFMD cases stayed closely to vaccine refusal cases 3.3 Density of HFMD and Vaccine Refusal Cases under the complete spatial randomness. Hence, the hypothesis of complete spatial randomness of in Terengganu HFMD and vaccine refusal cases was rejected. Data The heat map analysis of HFMD and vaccine refusal also revealed significant clustering evidence of cases are shown in Figure 4 and Figure 5, vaccine refusal cases around the HFMD cases. respectively. Dark red and brown areas in Figure 4 and Figure 5 show the highest density and hot spot 4. DISCUSSION areas of cases per three-kilometre radius, followed The overall prevalence of HFMD in Terengganu by lighter colours. There were three prominent hot from our data showed a 17.3% increment in the spots of HFMD cases in Terengganu, specifically, in number of cases as compared to the previous year Kuala Terengganu and Marang. (2015), which recorded 68.4 per 100,000 population. However, our estimate was lower than the 2016 3.4 Spatial Relationship Between of HFMD Cases national estimate at 148.5 cases per 100,000 population (Ministry of Health Malaysia, 2018). and Vaccine Refusal Cases The observed cross Ripley’s K-function (straight line) and the theoretical Poisson K-function (red Table 2: Nearest Neighbouring Index (NNI) for HFMD and Vaccine Refusal Cases in Terengganu Variable n Observed Expected NNI z score p-value* Mean Distance HFMD 811 691.23 2695.04 0.27 -40.51 < 0.01 Vaccine Refusal 281 979.26 4237.35 0.23 -24.66 < 0.01 Figure 6: Cross K-Function of HFMD and Vaccine Figure 7: Envelope Cross K-Function of HFMD and Refusal Cases Vaccine Refusal Cases (using Monte Carlo simulation) International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

Page |9 Furthermore, the sex-specific prevalence in our variance of HFMD incidence compared to climate study is similar with findings reported by prior factors which explained about 52% (Hu et al., 2012). studies (Ang et al., 2009; Wang et al., 2013). It is Therefore, for HFMD disease prevention and unclear whether the susceptibility attributes to the control, everyone including children, parents, health sex-related difference in HFMD prevalence at the care workers, and others shallbe particular about host genetic level (Chang et al., 2012), the role of hand hygiene and sanitation (Chang et al., 2011). sex hormones in the regulation of the immune Our data showed the distribution of the vaccine system (Falagas et al., 2007), or the socio-cultural refusal cases in Terengganu in 2016 was akin to that value where parents paying more attention to sons of the HFMD cases; both were concentrated in the compared to daughters (Wang et al., 2013). We high-density population areas and that parents who showed that HFMD cases predominantly infected refused vaccine were majority were from Malay children aged below five years, with cases from the ethnic group, they had secondary-level education, one-year-old age group has the highest age-specific they came from the low socio-economic group and prevalence and that HFMD prevalence decreased they did not trust vaccine as being protective (Lim with increasing age. et al., 2016 and Ahmad et al., 2017). A study in Michigan showed that clustering of non-medical These findings are consistent to those reporting exemption to immunisation was associated with that children less than five years had the highest risk high population density, the proportion of for HFMD infection (Hooi et al., 2002; Ang et al., racial/ethnic minorities in a census tract, the 2009; NikNadia et al., 2016; Huang et al., 2018). proportion of children aged less than five years, and This could be due toseveral factors; for example, (i) mean family size (Omer et al., 2008). A study done as the child grows up, the maternally derived in Romania suggested that the cause for vaccination antibodies declines, leading to lack of protective refusal depends on multiple factors including immunity; (ii) secondly, there has been an cultural, emotional, religious, and social issues increasing number of childcare centres in Malaysia, (Barbacariu, 2014). Present findings highlighted the as well as in Terengganu (evident by that threat that comes from inadequate immunisation documented in the Malaysian Early Childhood Care coverage in Malaysia (Lim et al., 2017) and and Education Policy Review 2008). It was reported reinforced the need for national electronic database that the number of preschool centres increased from that records childhood immunisation to facilitate 17% to 29% for each year from 2003 to 2009 vaccination tracking (Ahmad et al., 2017). The (Curriculum Development Center Ministry of strength of the present study included; a) the use of Education of Education Malaysia, 2008). As a result, state-wide data and b) the application of extensive more children congregate inside limited spaces, spatial analysis on HFMD and vaccine refusal cases. providing environments with higher risk of rapid Collectively, our results appear consistent with circulation of the virus along with higher risk of abroad studies. Moreover, our findings suggested transmission to their family members and the rest of that the clusters of HFMD cases were statistically the population (Deng et al., 2011). significantly and spatially related to the vaccine refusal clusters that were rising in Terengganu. Our study shows that HFMD cases mostly concentrated in the central region (urban areas) of However, the extent to which vaccine refusal each district in Terengganu, except for Besut and contributing to HFMD infections is unknown. It is Kemaman districts. This finding is in line with the important to highlight the fact that vaccine refusal previous study conducted in Sarawak, wherein the events were proven not only to cause the vaccine- most HFMD cases stayed in more urban divisions preventable disease cluster but also expose the (Kuching, Sibu and Miri) as compared to the rural young children at increased risk for illness and death divisions (Noraishah and Krishnarajah, 2016). The related to infectious disease (Omer et al., 2009). apparent high density of HFMD cases in towns Spatial analysis and GIS (in investigating seems to be attributed to the nature of disease communicable diseases) provide additional insight transmission, in which HFMD spread mainly for the public health physicians to formulate through the faecal-oral route and respiratory effective strategies in the prevention and control of droplets; body excreta including saliva, sputum, HFMD. They can do that by considering these nasal discharge, and faeces (Chang et al., 2004). The parameters: 1) density of population, 2) transmission rate increases in crowded and high identification of a disease cluster, 3) the number of concentration populations, especially involving child care centres, in particular those overcrowded infants and children in school and childcare centres ones, and 4) presence of vaccine refusal events in (Hu et al., 2012). that area (Safian et al., 2008; Samphutthanon et al., 2013). A study conducted in China reported that child- population density alone could explain 56% of the International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

P a g e | 10 5. CONCLUSION Enterovirus 71 Infections in Household Contacts HFMD cases are geographically spread over the in Taiwan. Jama, Vol. 291(2), 222-227. Terengganu region and mostly concentrated in the Chang, A. C., Jacobsen, K. H., Lin, K. W. and Teng, high-density population areas such as Kuala L.-J., 2011, Enterovirus Knowledge and Terengganu, Marang, and Dungun. Similar Handwashing Practices among Nurses in a distributional pattern was seen for the vaccine Hospital in Taipei, Taiwan. Taiwan refusal cases. The locations of HFMD cases in Epidemiology Bulletin, Vol. 27(6), 81-101. Terengganu were statistically related to the location Chang, H.-L., Chio, C.-P., Su, H.-J., Liao, C.-M., of the vaccine refusal cases. Lin, C.-Y., Shau, W.-Y., Chi, Y.-C., Cheng, Y.- T., Chou, Y.-L. and Li, C.-Y., 2012, The ACKNOWLEDMENT Association Between Enterovirus 71 Infections The authors would like to acknowledge the Director and Meteorological Parameters in Taiwan. PloS General of Health Malaysia for his permission to one, Vol. 7(10), e46845. publish this paper, and also to thank everyone who Charoenchokpanit, R. and Pumpaibool, T., 2013, has contributed to this study. Knowledge Attitude and Preventive Behaviors Towards Hand Foot and Mouth Disease among REFERENCES Caregivers of Children Under Five Years old in Bangkok, Thailand. Journal of Health Research, Ahmad, N., Jahis, R., Kuay, L., Jamaluddin, R. and Vol. 27(5), 281-286. Aris, T., 2017, Primary Immunization among Deng, C., Yang, C., Wan, J., Zhu, L. and Leng, Q., Children in Malaysia: Reasons for Incomplete 2011, Irregular Poliovirus Vaccination Vaccination. Journal of Vaccines & Vaccination, Correlates to Pulmonary Edema of Hand, Foot, Vol. 8(3), 1-8. and Mouth Disease. Clinical and Vaccine Immunology, Vol. 18(9), 1589-1590. Ahmed, A., Lee, K. S., Bukhsh, A., Al-Worafi, Y. Falagas, M. E., Mourtzoukou, E. G. and Vardakas, M., Sarker, M. M. R., Ming, L. C. & Khan, T. K. Z., 2007, Sex Differences in the Incidence M. (2018). Outbreak of vaccine-preventable and Severity of Respiratory Tract Infections. diseases in Muslim majority countries. Journal Respiratory medicine, Vol. 101(9), 1845-1863. of infection and public health, 11(2), 153-155. Hooi, P., Chua, B., Lee, C., Lam, S. and Chua, K., 2002, Hand, Foot and Mouth Disease: Ang, L. W., Koh, B. K., Chan, K. P., Chua, L. T., University Malaya Medical Centre experience. James, L. and Goh, K. T., 2009, Epidemiology The Medical journal of Malaysia, Vol. 57(1), and Control Of Hand, Foot And Mouth Disease 88-91. in Singapore. Ann Acad Med Singapore, Vol. Hu, M., Li, Z., Wang, J., Jia, L., Liao, Y., Lai, S., 38(2), 106-112. Guo, Y., Zhao, D. and Yang, W., 2012, Determinants of the Incidence of Hand, Foot and Aswathyraj, S., Arunkumar, G., Alidjinou, E. K. and Mouth Disease in China Using Geographically Hober, D., 2016, Hand, Foot and Mouth Disease Weighted Regression Models. PloS one, Vol. (HFMD): Emerging Epidemiology and the Need 7(6), e38978. for a Vaccine Strategy. Medical Microbiology Huang, J., Wang, J., Bo, Y., Xu, C., Hu, M. and and Immunology, Vol. 205(5), 397-407. doi: Huang, D., 2014, Identification of Health Risks 10.1007/s00430-016-0465-y. Of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique. Barbacariu, C. L., 2014, Parents’ Refusal to Vaccinate their Children: An Increasing Social International Journal of Environmental Phenomenon Which Threatens Public Health. Research and Public Health, Vol. 11(3), 3407- Procedia-Social and Behavioral Sciences, Vol. 3423. 149, 84-91. Huang, J., Liao, Q., Ooi, M. H., Cowling, B. J., Chang, Z., Wu, P., Liu, F., Li, Y., Luo, L. and Brinkhoff, T., 2018, Terengganu. Retrieved Yu, S., 2018, Epidemiology of Recurrent Hand, from:https://www.citypopulation.de/php/malaysi Foot and Mouth Disease, China, 2008–2015. a-admin.php?adm1id=11 [Accessed November Emerging infectious diseases, Vol. 24(3), 432. 22]. Lai, P.-C., So, F.-M. and Chan, K.-W. G., 2009, Chan, Y.-F., Sam, I.-C., Wee, K.-L. and Abubakar, Spatial Epidemiological Approaches in Disease S., 2011, Enterovirus 71 in Malaysia: a Decade Mapping and Analysis (First ed. Vol. 1). United Later. Neurology Asia, Vol. 16(1), 1-15. States of America: CRC Press Taylor & Francis Group. Chang, L.-Y., Tsao, K.-C., Hsia, S.-H., Shih, S.-R., Huang, C.-G., Chan, W.-K., Hsu, K.-H., Fang, T.-Y., Huang, Y.-C. and Lin, T.-Y., 2004, Transmission and Clinical Features of International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

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P a g e | 12 EXPLORING LIFESTYLE RISK OF TUBERCULOSIS IN A SMALL URBAN COMMUNITY USING GEOSPATIAL-EPIDEMIC ANALYSIS Abdul Rasam, A. R.1* Ridzuan, N. R.1 Isa, M.M.2 Shafie, A. F.3 and Tripathi, N. K.4 1Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture, Planning and Surveying, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia, E-mail: [email protected]* 2Centre of Studies for Landscape Architecture, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia 3Centre of Environmental Health and Safety, Faculty of Health Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia 4School of Engineering and Technology, Asian Insitute of Technology (AIT), Thailand ABSTRACT Airborne diseases have widely concerned people such as the bacteria Mycobacterium that causes the tuberculosis (TB) diseases. Many risk factors might influence the TB diseases in Malaysia, in particular lifestyle factors. But, the current geospatial-based studies related to the factors are quite limited in the country. The main point is what are the factors contributing to the disease in an urban study area of Section 7 Shah Alam. The aim of the study is to map and analyse the impacts of lifestyle risk factors for TB occurrences using geographical information system (GIS) and geostatistical approaches in four stages. The first stage was a preliminary study. It tackled the problem and the issue based on the lifestyle factors of TB. The second stage was data collection, covering the community-based questionnaires and base map preparation. Expert opinions from the Selangor State Health Department were also obtained for local risk factor ranking and selection. The third stage was data processing, involving a geostatistical analyst and the last stage was the local TB risk status in a five scale (i.e. 1=no risk and 5=very risk). The feedbacks of the public community and the experts has suggested the TB risk level of lifestyle factors is a low-medium class. The experts have suggested the factors are mainly dominant by HIV, nutritional status and diabetes. It is followed by living conditions, physical exercise, smoking, socioeconomic status (SES), obesity and alcoholism. The risk map of lifestyle index has also shown that people in the Section have a medium risk level of lifestyles towards TB infection. A geospatial approach is a helpful tool to manage disease cases globally, in particular for the earlier action system and control airborne diseases. KEYWORDS: Lifestyles, Airborne Disease, Tuberculosis Epidemic, Geospatial Mapping, Urban Community 1. INTRODUCTION health should be highly considered. Therefore, Lifestyle is a way used by people, groups and according to the existing studies, it can be said that nations and is formed in specific geographical, lifestyle has a significant influence on physical and economic, political, cultural and religious text. mental health of human being, but there are Lifestyle can be assigned as the characteristics of different forms of such influences (Farhud, 2015). the inhabitants of a region in an extraordinary time and place. It includes day to day behaviours and One of the airborne diseases is TB. TB is well functions of individuals in job, activities, fun and known to be associated with poverty and multiple diet (Farhud, 2015). In recent decades, lifestyle as social factors (Narasimhan et al., 2013, Toque et al., an important factor of health is more interested by 2001 and Rasam et al., 2018). However, relatively researchers. Concurring to World Health little attention has been paid to the behavioural and Organization (WHO), 60% of related factors to lifestyle factors, apart from those associated with individual health and quality of life is correlated drug abuse, alcoholism and HIV infection lifestyle. Millions of people follow an unhealthy (Mohammed et al., 2011). In recent years, the roles lifestyle. Hence, they encounter illness, disability of other lifestyle related factors have been and even death. The relationship of lifestyle and increasingly recognised. In particular, smoking has International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

P a g e | 13 been associated with excess risks of TB infection, factors and TB using GIS technology (Abdul Rasam disease and mortality (Chang and Leung, 2008). et al., 2016, Abdul Rasam et al., 2017 and Rosli et Roughly 9.2 million new TB cases and 1.7 million al., 2018), but this study did not examine in depth TB-related deaths were reported in 2006 (Ibrahim et the lifestyle factors with TB pattern. al., 2011). Besides this, most of the previous studies only TB ordinarily spread between family applied cross-sectional or case-control studies, to individuals, companions or who are near to us. TB control all the potential confounders adequately is caused by a microorganism called (Leung et al., 2007, Lienhard et al., 2003 and Lin et Mycobacterium. TB could be an infection or al., 2013). Only a few studies relate this statistical irresistible infection. It is spread from person-to- method with advanced GIS techniques. For person. A few strains of Mycobacterium example, in a prospective study conducted in Hong tuberculosis have created medicate resistance, Kong, the annual TB notification rates were 735, which can make treatment of the infection 427, and 174 per 100 000 current smokers, ex- troublesome. In case untreated, TB can cause smokers and never-smokers respectively (Change serious impacts on wellbeing. A person is routinely and Leung, 2008). Thus, the purpose of this study is tainted by breathing in the germs. These germs are to apply spatial analysis and GIS mapping to sprayed into the air by somebody with the active examine the effects of lifestyle risk factors for TB disease WHO coughs. The related microscopic cases and to explore the capability GIS to study the organisms Mycobacterium bovis and lifestyle factors and airborne disease distribution. Mycobacterium africanum can moreover cause TB. Combination of GIS, statistical and questionnaire survey can explore how much lifestyle risk factors The body's reaction to dynamic TB contributing to TB occurrences. Kang-tsung (2019) contamination produces irritation that can harm the stated a GIS is a computer system for gathering, lungs. Zones influenced by dynamic TB steadily fill managing, analysing, and presenting geographical with scar tissue. Side effects of dynamic TB of data. It has been increasingly applied in many corporate hacking that keeps going for three or more disciplines such as spatial epidemiology, in weeks, chest torments or trouble taking full breaths, particular the association between pathological inadvertent weight misfortune, depletion or factors (e.g. TB) and their spatial environments weariness, hacking up blood, fever and chills, night (Cromley, 2003, Davis et al., 2018, Rasam et al., sweats and misfortune of craving. The lifestyle of an 2019 and Abdul Rasam et al., 2020) individual also can contribute the development of TB. 2. MATERIALS AND METHODS Methodology is the important part which covers the There are many lifestyle factors that commonly data collection, data creation, data processing and influence TB such as poverty or low SES, living data analysis as shown in Figure 1. conditions or overcrowding (Andrejs et al., 2016, Cramm et al., 2011, Kashyap et al., 2016, Lopez De 3. STUDY AREA AND TOOLS Fede et al., 2008, Muniyandi et al., 2011, Olson et The study area was conducted at Section 7 Shah al., 2012 and Yen et al., 2017), malnutrition and Alam. Shah Alam is located in the Selangor, other related disease infection (Leung et al., 2007, Malaysia. Shah Alam is one of the areas that had Lin et al., 2018, Lonnrothet al., 2010 and higher population than other areas in Selangor areas. Mohammed et al., 2011). The case-control study in In addition, Shah Alam is one of the towns which Erbil city has shown that around one quarter of the rapidly in their urban development. This may cases were smokers compared among other controls influence the lifestyle effect on TB disease became (Ibrahim et al., 2011). Based on the previous dynamic. This study applied spatial vector analysis studies, the highest percentages of tuberculosis and geostatistical analysis. disease were smokers compared to other factors (Change and Leung, 2008). According to the 4. DATA COLLECTION AND DATABASE Department of Health’s Central Health Education Unit, Hong Kong (Ngai, 2002), the best weapon is 4.1 Research Questionnaire as Nonspatial Data our own defence system by paying attention to This part elaborates how to obtain the data. The personal hygiene, adopting a healthy lifestyle and questionnaire was structured by the researchers to eating a well-balanced diet, getting adequate rest gain the lifestyle data of the local community. The and exercise, not smoking or drinking alcohol, questionnaire was randomly distributed to the keeping the environment clean and avoiding poorly people who stay in Shah Alam in several days with ventilated areas. However, risk factors associated two ways. The first one is by hardcopy which is in with TB can change over time. In Malaysia, there the printed paper way. are several local attempts to link environmental International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

P a g e | 14 Figure 1: Research Methodology The second is by softcopy or known as Google demographic is divided into a group such as gender, Form via social media such as WhatsApp, age, race, education, household income and also Instagram, Facebook and Twitter. In advanced, the occupation. The final total data collected was 100 data proven were evaluated by an expert opinion respondents. Four opinions of the local public health regarding the lifestyle factors of TB diseases before in the state were also obtained for risk factor distributing them to the local community. ranking and selection using rank sum technique as shown in Table 2. Earlier studies on TB factors were reviewed to identify the risk factors based on the environment, 4.2 Spatial Data demography and population and socio-economic Spatial data describe the locations and status of the residents, but in this study was only characteristics of spatial features. To locate spatial focused on the lifestyle factors on TB diseases. features on the earth’s surface, the geography or Demographic respondents were collected based on projected coordinate system is used precisely. their gender, age, race, level of education, total Collecting data on spatial from the research income, occupation and address. This research questionnaire related the lifestyle factors of TB questionnaire survey also asked the details on the diseases was also conducted. The spatial data in this lifestyle factors of TB. The lifestyle factors include research include the address of the respondent that smoking, alcohol drinking, physical exercise, can get the location of x and y on the risk map. The nutritional status, obesity, socioeconomic status base map of Selangor state was obtained from the (SES), living conditions, HIV and diabetes. Department Surveying and Mapping Malaysia (JUPEM). The thematic map was digitized from the The questionnaires were disseminated to the base map of Selangor from the scanning image respondents. Additional information and some format such as .JPEG, .TIFF an.IMG. Other than comment were recorded as well. For example, the that, the base map can also get from the ArcGIS address should be in details include the house Online. The information contains the boundary of number and section. The other non-spatial data are Shah Alam area and spatial data were carried out nine selected lifestyle factors. In addition, based on and performed using GIS software. the research questionnaire, there are a few social- demographic were asked. Socio-demographic is categorized as non-spatial data. The socio- International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

4.3 Data Processing and Data Analysis P a g e | 15 The risk of lifestyle factors was calculated by using rank sum technique. For example, respondent 1 the (low=green), Class 3 (medium=yellow) and Class 4 lifestyle of TB score in Table 1. For spatial Vector (high=orange). The final step was to produce the Analysis in ArcGIS, an opinion from the expert risk map of TB diseases. Such maps would make it were collected in order to prove the scale ranking. conceivable to arrange control measures in high-risk The data were processed by evaluating the score by ranges and enormously increment the fetched rank sum technique. ArcGIS software is one of the effectiveness of these control programs. These software that used in the GIS field. It is used for spatial data methods can be compelling apparatuses growing and the use of maps, compiling geographic in TB checking and observation contributing to fill data, inspecting mapped information, sharing and the crevices within the current understanding of discovering geographic information, using maps and illness dispersion. GIS and inaccessible detecting geographic records in a variety of applications, and methods were utilized to outline the spatial managing geographic statistics in a database. Before conveyance of TB illnesses and potential risk areas. processing the next step, make sure export the data The risk map shows the risk of TB rate in Shah into shapefile. Alam created with the ArcMap. In addition, the technique used in this study is 5. Result and Discussion geostatistical analysis, namely Inverse Distance This part details the result of the study. It also Weighting (IDW). From the IDW, it can identify the focuses on analysis and implications of the lifestyle risk of the lifestyle factors of TB at Section 7. The factors study on the TB among the society in Shah other software also used is a Microsoft Excel to Alam, a risk factor map of lifestyle effect on arrange the data and make a graph. Next, The airborne disease in Shah Alam and the other geostatistical technique was used is the Inverse objective is to determine the qualitative relationship Distance Weighting (IDW). Inverse Distance between lifestyle factors and TB occurrences. Weighting (IDW) is a speedy deterministic interpolator that is exact. There are exceptionally 5.1 The Lifestyle Risk Factors of Airborne few choices to create with respect to demonstrate parameters. It can be an incredible way to require a Diseases (TB) to start with see at an interpolated surface. There are The lifestyle risk factors are used to identify the no suspicions required of the information. For percentage that influences TB. Nine common spatial interpolation, IDW is one of the geostatistical lifestyle factors are selected since there is no a methods. It can classify the classes. 5 Classes or specific standard guideline used for global levels is chosen based on the five scales stated in preferences. Based on the research questionnaire research questionnaire. Class 1 shows the lowest or conducted, this is the percentage of each the no risk (blue), while Class 5 indicates the highest lifestyle factors of the airborne disease according to risk (red). The other classes include Class 2 public and expert opinion. Based on the public respondents, the overall average risk level of lifestyle factors is 2.00-2.50 (low-medium). Table 1: lifestyle of tuberculosis score International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

P a g e | 16 In terms of each factor is dominant by obesity There are 95.6% of them. On the other scale is in (medium), nutritional status (medium), living low percentage. Based on the WHO, TB deaths conditions (medium), physical exercise (medium), among HIV-negative people per 100 000 population SES (medium) and diabetes (low-medium). The rest per year) is falling at about 3% per year, and the of the lower level, such as smoking (low), alcohol overall reduction in the period 2000–2017 was 42%. (low) and HIV (low). The HIV level is low. Referring to smokers among at Section 7 Lastly, the percentage of history of diabetes residents, the highest percentage is 85% which had level among respondents is medium. 42.5% have never smoked. 6.2% are rarely smokers and 5.3% never had a family history of diabetes. At the scale 2 are sometimes smokers. The often smokers show and 3 are in the medium percentage. 7.1% have 3.5% over the total respondent’s result. The Section been influenced by diabetes. Referring to selected residents are not an active smoker. The overall expert’s opinion on the influences of lifestyle smoking level is only 1.57 (low). In terms of factors on the disease causes as shown in Table 2. alcoholism, 92.9% of the Section 7 residents are not Each of the risk factors is ranked by using rank sum an alcohol drinker. Most of the respondents who are technique and the selected local expert opinion of an alcohol drinker are non-Islamic communities. the Jabatan Kesihatan Negeri Selangor (JKNS). This The percentage of alcohol drinker at the scale 2 is is the simplest technique to quantify the vital of 2.7% only, meanwhile the 3.5% is quite often weights. For example, the alcoholism is the lowest drinker. The alcohol level is 1.22 (low). While for weight as 1. Then, this weight value was divided by physical exercise aspect, 61.1% are active in sport. the total values of weight (45) and multiplied by 1 to Based on the site visit around Section 7, they get the normalised weight value of the income of usually exercise such as playing badminton, running 0.20 (i.e. 1/45*1). and playing football. The percentage of rarely taking Five factors are selected as a low-moderately part in physical exercise is 22.1% and often is influential risk factor for influencing the TB 11.5%. Meanwhile the other is at low percentages. occurrences, orderly ranking by HIV status, The physical exercise level is 2.92 (medium). nutritional status, diabetes status, living conditions Based on the respondents result on the and physical exercise status. Then followed by nutritional status shows that 62.8% are taking good smoking status, socioeconomic status, obesity status diet in their meals. The second highest percentage is and alcoholism. According to the local expert, the 19.5% which is at rarely scale. One of the reasons overall risk level of lifestyle factors is low-medium that they cannot manage to take a good diet is too founded on the results by public feedbacks. The busy with their works. The percentage who always little gap feedback may be contributed by the takes good diet is 4.4% only. The nutritional status number of public respondents who answered the is medium. Regard with obesity problem among questionnaires. Shah Alam residents. The highest percentage is 26.5%, the second highest is 25.7% and followed by 5.2 Mapping the Between Lifestyle Factors and 24.8%. 7.1% of them are never faced the obesity problem. The obesity level is medium. Everyone in Airborne Diseases Figure 3 shows the risk map of lifestyle factors the residents has the socioeconomic problem. It can related to historical TB occurrences at Section 7. be in many ways. One that is a financial problem. Although the distribution of the disease is random Only 2.7% always have a socioeconomic problem. pattern, the cases are stilled clustered in certain 48.7% are at the average level. localities, for example, in Flat PKNS, Flat Nilam, In the previous studies, TB rates were highest in Jalan Platinum. Overall, the concentration of TB the quartile with low SES (Andrejs et al., 2016, occurrences is not directly correlated with the status Cramm et al 2011, Kashyap et al., 2016, Lopez De of lifestyle factors due to the minimal significant Fede et al., 2008, Muniyandi et al., 2011, Olson et correlation between these variables using al., 2012, Yen et al., 2017 and Abdul Rasam et al., geospatial-epidemic analysis. But, in -depth studies 2016) both and foreign-born populations. The need to be conducted for some of the lifestyles socioeconomic status is medium. In addition, Living factors that have the potential to affect the local TB condition affected the TB among respondents. epidemics. Most of the cases occurred in medium- 29.2% of the Section residents are staying in low risk (1-3 scale). This reveals that TB can attack medium scale of the crowded area. But 8% of them any person of having lifestyle risk, but the more are staying in high-density of population in Section risk will occur on certain risk factors such as a 7. The living condition level is medium. Most of the person suffering from two non-communicable residents have never been influenced by HIV. diseases at the same time. International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

P a g e | 17 Table 2: The risk factors derived from expert opinion Figure 3: The risk map of lifestyle factors For example, the risk map of the physical exercise to the disease occurrences. Both diet and physical status shows Shah Alam has medium to high risk activity play a critical role in maintaining a healthy level. The medium risk level is between 3.0 to 3.5. body weight, losing excess body weight, or Meanwhile, high risk level between 4 to 5. The maintaining successful weight loss. Inadequate estimated risk level is medium-high. For the risk nutrition was associated with increased map of nutritional status, it shows that shows Shah susceptibility to infection, but not active TB. Alam has low risk level. The risk level is in between Interventions addressed in improving nutrition may 2.0 to 4.0. The area in between Hospital Shah Alam reduce susceptibility to infection in settings where and Flat PKNS is 0 to 0.5 risk level. The estimated access to healthy foods is limited. By using a risk level is medium-high. However, the impact of geospatial approach such as GIS can manage the the these factors (physical exercise status and risk of an area properly by producing a risk map of nutritional status) will be effective to attack a person TB for the precaution steps. In addition, as GIS to get TB infection if they are combined together. In technology is important for monitoring and control conclusion, in a geospatial-qualitative context, there of TB from time to time, these findings suggested is minimal or no significant relationship between the that public health actions are needed to promote nine selected lifestyle factors and TB diseases. education on TB patients, about the significance of However, advanced computation techniques and nutritional support, and, further interventions in TB comprehensive data collection need to be further patients’ nutritional intakes are also required. It is carried out for quantifying the relationship. For also necessary to develop a detailed guidelines for example the living condition, poor physical exercise these factors, such as the guideline in physical status and poor nutritional status seem have related activity in with a precise onset of the rehabilitation International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

programs, adapted and individualized exercise P a g e | 18 types, as well as other measures for assessing the outcomes. Health in Africa, 143-146. DOI:10.4081- /jphia.2011.e34. Acknowledgement Cromley, E. K., 2003, GIS And Diseases. Annu. The authors gratefully acknowledge the assistance Rev. Public Health , 7-24. of the Ministry of Higher Education (MOHE) and Davis, G. S., Sevdalis, N. and Drumright, L. N., Universiti Teknologi MARA Selangor for providing 2018, Spatial and Temporal Analyses to Fundamental Research Grant Scheme (FRGS) 600- Investigate Infectious Diseases Transmission IRMI/FRGS 5/3 (093/2019). The research is within Healthcare Settings. Malaysian Journal registered in the National Medical Research of Public Health Medicine, Vol. 86(4), 227-243. Register, Malaysia (ID: NMR R -15-2499-24207). Farhud, D. D., 2015, Impact of Lifestyle on Health. The authors are also thankful to the Ministry of Iran J Public Health, 1442-1444. Health Malaysia for providing TB datasets used in Ibrahim, H. M., Badia, M. N. and Namir, G. A., this study. 2011, Association between Lifestyle Factors and Pulmonary Tuberculosis in Erbil. Zanco Journal REFERENCES of Medical Sciences, Vol. 15(3), 6-11. Kang-tsung, C., 2019, Introduction To Geographic Abdul Rasam, A. R., Shariff, N. M. and Dony, J. F., Information System, Nineth Edition. New York: McGraw-Hill. 2016, Identifying High-Risk Populations of Kashyap, R. S., Nayak, A. R. and Husain, A. A., 2016, Impact of Socioeconomic Status and Tuberculosis Using Environmental Factors and Living Condition on Latent Tuberculosis Diagnosis among the Tribal Population of GIS Based Multi-Criteria Decision Making Melghat: A Cohort Study. Lung India, 372-380. Leung, C. C., Lam, T. H. and Chan, W. M., 2007, Method. The International Archives of the Lower Risk of Tuberculosis in Obesity. Arch Intern Med, 1297-1304. Photogrammetry, Remote Sensing and Spatial Lienhardt, C., Fielding, K., Sillah, J. and Tunkara, Information Science, Vol. XLII-4/W1, 9-13. A., 2003, Risk Factors for Tuberculosis Infection DOI:10.5194/isprs-archives-XLII-4-W1-9-2016 in Sub-Saharan Africa. American Journal of Respiratory and Critical Care Medicine, Vol. Abdul Rasam, A. R., Shariff, N. M., Dony, J. F. and 168(4), 448-455. Lin, C. Y., Chen, T. C., Lu, P. L., Lai, C. C. and Punitha, M., 2017, Mapping Risk Areas of Yang, Y. H., 2013, Effects of Gender and Age on Development of Concurrent Extrapulmonary Tuberculosis Using Knownledge-Driven GIS Tuberculosis in Patients with Pulmonary Tuberculosis: A Population Based Study. PLoS Model in Shah Alam, Malaysia. Pertanika J. ONE, Vol. 8(5), e63936. doi:10.1371/journal- Soc. Sci & Hum., Vol. 25 (S), 135-144. .pone.0063936. Abdul Rasam, A. R. ., Mohd Shariff, N. and Dony, Lin, H. H., Wu, C. Y., Wang, C. H., Fu, H., Lonnroth, K., Chang, Y. C. and Huang, Y. T., J., 2020, The Invention of Geospatial Decision 2018, Association of Obesity, Diabetes, and Risk of Tuberculosis: Two Population-Based Cohorts. Support System for Malaysian Tuberculosis Clinical Infectious Diseases, Vol. 66(5), 699- 705. Surveillance Data Management. Environment- Lonnroth, K., Williams, B., Cegielski, P. and Dye, C., 2010, A Consistent Log-Linear Relationship Behaviour Proceedings Journal, Vol. 5(SI3), between Tuberculosis Incidence and Body Mass Index. International Journal of Epidemiology, 269-274. https://doi.org/10.21834/ebpj.v5i- Vol. 39(1), 149-155. Lopez De Fede, A., Stewart, J. E., Harris, M. J. and SI3.2564 Mayfi eld-Smith, K., 2008, Tuberculosis in Socio-Economically Deprived Neighborhoods: Andrejs, I., Ieva, S. K. and Ludmila, V., 2016, The Missed Opportunities for Prevention. The Impact of Socioeconomic Factors on International Journal of Tuberculosis and Lung Disease, Vol. 12(12), 1425-1430. Tuberculosis Prevalence in Latvia. Universal Journal of Public Healt, Vol. 4(5), 230-238. Brunet, L., Pai, M., Davids, V., Ling, D., Paradis, G. and Lenders, L., 2011, High Prevalence of Smoking among Patients with Suspected Tuberculosis in South Africa. European Respiratory Journals, 139-146. Chang, K. C. and Leung, C. C., 2008, Impact of Lifestyle on Tuberculosis. Respirology, Vol.13, 65-72. Cramm, J. M., Koolman, X., Moller, V. and Nieboer, A. P., 2011, Socio-economic Status And Self-Reported Tuberculosis: A Multilevel Analysis in a Low-Income Township in the Eastern Cape, South Africa. Journal of Public International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

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P a g e | 20 SPATIAL PATTERNS IN BREAST CANCER INCIDENCE IN KELANTAN, MALAYSIA Tengku Ahmad Damitri Al Astani Tengku Din,1* Nur Fatihah Mohd Zuhdi,2 Wan Mohammad Ismail Ad-Deen Wan Azman,3 Wan Faiziah Wan Abdul Rahman,4 Ahmad Filza Ismail,5 Maya Mazuwin Yahya6 and Rosline Hassan7 1Department of Chemical Pathology, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia & Breast Cancer Awareness and Research Unit (BestARi), Hospital Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia, E-mail: [email protected] 2Department of Pathology, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia, E-mail: [email protected] 3Department of Chemical Pathology, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia, E-mail: [email protected] 4Department of Pathology, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan,Malaysia & Hospital Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia E-mail: [email protected] 5Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia. ([email protected]) 6Department of Surgery, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan & Breast Cancer Awareness and Research Unit (BestARi), Hospital Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, E-mail: [email protected] 7Department of Hematology, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan,Malaysia, E-mail: [email protected] * Corresponding author ABSTRACT The Age Standardized Rate (ASR) for breast cancer in Kelantan is increased from 15.9 to 18.1 per 100,000 populations between 2007 and 2011. Geographic information system (GIS) has emerged as a great tool in geospatial technology and becoming more prominent in healthcare implementation. In the present study, GIS was employed to map the breast cancer incidences, analyse the spatial distribution of the cases and assess their geographical accessibility to public hospitals. The data of breast cancer cases registered at Hospital Universiti Sains Malaysia, from 2018 to 2020 were retrospectively retrieved from the patients medical records Unit and histopathological reports. Informations regarding age, year of diagnosis, gender, residential addresses, histological subtypes were collected and analysed. The coordinates of residential addresses and public hospitals were obtained using Global Positioning System (GPS). Then, all data were inserted into ArcGIS 10.2 software and spatial analysis was performed. A total of 206 breast cancer cases were mapped. The commonest histological subtypes were invasive carcinoma of no special type (90%). Spatial pattern analysis resulted in clustered pattern with average nearest neighbour ratio pattern of 0.408935, Z-score: -15.99, p-value <0.01). Hot-spot analysis showed that hot-spot areas fell on the northwest and northeast part of Kelantan. Buffer analysis revealed that most of the cases (60.19%, 124 cases) were located within 10 km from public hospitals and hence, have good access to these hospitals. Whereas, the remaining cases (39.81%, 82 cases) that situated >10 km have poor access to public hospitals. The findings from this study are useful for public health authorities and health practitioners in planning strategies and interventions by directing more efforts towards population in the hot-spot areas that will minimize the breast cancer incidences in Kelantan. KEY WORDS: Spatial analysis, geographic information system, breast cancer, Kelantan 1. INTRODUCTION In Asia, breast cancer incidences have been reported Breast cancer (BC) remains the leading cause of to increase by two or three-fold in the past 20 years death among women worldwide (Zhu et al., 2021). International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

P a g e | 21 (Ghoncheh et al., 2016). Out of all cases, Asian (Santos and Melo, 2011). In addition, the mapping population recorded the highest incidence and of breast cancer geographic patterns can also help to mortality with 1,026 171 cases and 346, 009 cases design, evaluate, and implement cancer control respectively (Globocan, 2020). The incidence of programs (Hermann et al., 2015) as well as breast cancer in Malaysia shows an increasing trend developing better interventions and health policies with 21,634 cases of female breast cancer registered (Madhu et al., 2016). This study aimed to map the in National Cancer Registry between 2012-2016, breast cancer incidences in Hospital USM, much higher than the previous 2011-2017 report analyzing the pattern of spatial distribution and its Azizah et al., (2015). This cancer accounted for correlation with the access to public hospitals 34.1% of all cancer among women, making it the available in Kelantan. most common cancer in Malaysian women. The age-standardized incidence rate (ASR) recorded is 2. MATERIALS AND METHODS 34.1 per 100,000 women, which means 1 in 27 This study was conducted in Hospital Universiti women will develop breast cancer at some stage in Sains Malaysia, situated at Kelantan state. This state their lives. However, this incidence rate differs by was in the northeast of Peninsular Malaysia. ethnicity with Chinese recorded the highest, Currently, Hospital USM provide an extensive followed by Indian and Malay. Global Cancer breast services in the East Coast of Peninsular Observatory estimated that the number of breast Malaysia with the establishment of Breast Cancer cancer incidences in 2020 were the highest with Awareness & Research Unit (BestARi) as one stop 8,418 cases and ranked second in mortality with centre for patients with breast related complaints. In 3,503 cases (Globocan, 2020). this study, all cases from Kelantan which involves 10 districts were included. In addition, cases from To reduce the burden of breast cancer morbidity the neighbouring state, Terengganu that were and mortality, more breast cancer patients need to registered in Hospital USM were also covered be screened, diagnosed, and treated from developing involving 2 districts. A total of 206 cases of breast this disease in the first place. Although demographic cancer were included in this study. Data were data on factors that contribute to breast cancer are collected retrospectively by reviewing all breast available, spatial distribution patterns and associated cancer cases between 2018-2020 registered in factors are often overlooked (Rocha-Brischiliari et medical record office, Hospital Universiti Sains al., 2018). In addition, researches on access barrier Malaysia. The variables extracted for each year are limited in developing countries where delay in from the registries were age, gender, and residential treatment and debilitating diseases are common addresses. The data were carefully inspected to (Unger-Saldana, 2014). Therefore, it is important to ensure no duplicate entry in the same case. In determine geographical distribution of cancer cases addition, the histopathological report of patients was as a first step (Mehrabani et al., 2008). Many studies retrieved from Department of Pathology to obtain have reported a strong association between health informations regarding the histological subtypes of status and the living area (Yomralioglu et al., 2009 breast cancer and date of specimen taken. Global and Mohebbi et al., 2008). The attributes of living Positioning System (GPS) was used to obtain area that are known to have strong impact on cancer geographic coordinate of the addresses and all incidence and health consequence includes public hospitals in Kelantan. A total of 10 public industrial environment, climate, socioeconomic hospitals involved in this study. The radius was set status, lifestyle, and racial groups (Mohebbi et al., within and beyond 10 km from these hospitals. 2008, Yomralioglu et al., 2009 and Merletti et al., After data collection, informations were inserted 2011). into Excel datasheets. Then, the data were loaded into ArcGIS 10.2 software to perform the spatial Geographic information system (GIS) is a analysis. Spatial global pattern analysis by average computer-based system that is capable of visualizing nearest neighbour ratio was carried out to determine health data by linking statistical and thematic data the spatial distribution of breast cancer cases in on maps. Evaluation of cancer data using GIS has Hospital USM. The spatial distribution of the values been conducted in many countries (Yomralioglu et in the dataset were interpreted as al., 2009 and Guajardo et al., 2009). However, only clustered/random/dispersed. In addition, Hot-Spot few studies have been undertaken on geographic analysis was performed using Getis-Ord Gi* to variation of cancer cases by using GIS in Malaysia identify locations of statistically significant ‘Hot- (Samat et al., 2010, 2013). Incorporating spatial Spot’ or ‘Cold-Spot’ in the data. A disease Hot-Spot location in studying diseases allow the identification area was defined as an area that not only has high of causal relationship with environment, health services usage or behavioral analysis of the users International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

incidence of disease, but also with neighbouring P a g e | 22 area of high disease incidence. In contrast, ‘Cold- Spots’ are areas with not only low incidence of 3.3 Spatial Distribution of Breast Cancer Cases disease, but also with low-incidence neighbour as Spatial analysis shows that breast cancer cases in well. The result was interpreted based on Z-score Hospital USM represented a clustered pattern and P-value. The larger (or lower) the Z score, the (NNR: 0.408935, Z-score: -15.99, p-value <0.01) more intense the clustering of hot-spot/cold-spot. A (Figure 3). Z-score near zero means no spatial clustering. 3. RESULTS 3.1 Geographical Distribution of Breast Cancer Cases According yo Types Figure 1 shows the geographical location mapping of all breast cancer cases from 2018 to 2020 in HUSM (n=206). It was found that breast cancer cases with invasive carcinoma of no special type were distributed throughout the state with high concentration in the northern part of Kelantan. Figure 2: Hot-spots and cold-spots areas of breast cancer cases Figure 1: Geographical distribution of breast cancer Figure 3: Spatial distribution of breast cancer cases according to types incidences 3.2 Hot-Spot Analysis of Breast Cancer 3.4 Geographical Accessibility of Breast Cancer Incidences Cases to Public Hospitals The Hot-Spot analysis shows that the distribution of Figure 4 shows the mapping of geographical high-risk breast cancer areas was located at the location for all breast cancer cases (n=206) from all northeast and northwest areas of Kelantan (90% public hospitals in Kelantan (n=10). Majority of Confidence). Whereas, the cold-spot areas were cases were located within 10 km radius from public observed at the southwest areas (90% Confidence) hospitals and have good access to these hospitals. (Figure 2). Meanwhile, the remaining cases were located beyond 10 km radius and have poor access to the public hospitals (Figure 4). International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International

P a g e | 23 Figure 4: Geographical distribution of all breast Figure 6: Percentage of breast cancer histological cancer cases within and beyond 10 km radius from subtypes in HUSM from year 2018 to 2020 public hospitals 3.7 Breast Cancer Cases According to Districts 3.5 Number of Breast Cancer Cases According to The numbers of cases were reported according to districts. A total of 10 districts from Kelantan state Distance from Public Hospitals and 2 districts from Terengganu state are involved Most of the cases (60.19%, 124 cases) were located in this study, as shown in Table 1. within 10 km radius from public hospitals. Out of 124 cases, 57 cases were found to overlap between Table 1: Number of breast cancer cases according to two hospitals and 4 case overlapped between three districts in Kelantan and Terengganu hospitals. The remaining 82 cases (39.18%) were situated beyond 10 km radius from public hospitals, as shown in Figure 5. 80 39.81 >10 km radius 60 40 60.19 20 0 ≤ 10 km radius ≤ 10 km radius >10 km radius 4. DISCUSSION This study showed that most of the cases were Figure 5: Number of breast cancer cases according concentrated at the northern part of Kelantan, where to the distance with radius of ≤ 10 km and > 10 km the state capital city, Kota Bharu was located. from public hospitals Similarly, a local study conducted in Penang state also found that most cases were distributed at major 3.6 Histological Subtypes of Breast Cancer town centres (Samat et al., 2010 and 2013). The most common histological subtypes observed Furthermore, the hot-spot analysis revealed that the were invasive carcinoma, no special type (90%), distribution of breast cancer cases in high-risk areas followed by intraductal carcinoma (4%), invasive were at the northeast and northwest areas of lobular carcinoma (3%), mucinous carcinoma (2%) Kelantan. Higher density of cases observed in these and other types of carcinoma (1%) which comprised hot-spot areas might be due to greater availability of of secondary metastatic, metaplastic and malignant health facilities (Samat et al., 2010) and more phyllodes, as illustrated in Figure 6. people were screened to detect breast cancer among population. There were many screening facilities provided in the Kota Bharu district as compared to International Journal of Geoinformatics, Conference Proceedings for 7th HealthGIS Conference ISBN No: 978-616-90698-5-0 © Geoinformatics International