Risk mapping and estimation of COVID-19 transmission in South Sulawesi, Indonesia by a self-identification survey

Submitted: 4 July 2021
Accepted: 15 February 2022
Published: 18 March 2022
Abstract Views: 1575
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The rapid transmission rate of coronavirus disease 2019 (COVID-19) is multi-factorial but primarily due to population mobility and aggregation. This research aimed at estimating the rate based on risk mapping and investigation of geospatial distribution. It was divided into different phases that included data collection through a self-identification form available online; data validation of the data collected; application of spatial statistics; comparison with official numbers of positive COVID-19; and mapping of the results. The results show that self-identification based on procurement of independent personal data online had an accuracy of 89%.

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Citations

Gangwar HS, Champati PK, 2021. Geographic information system-based analysis of COVID-19 cases in India during pre-lockdown, lockdown, and unlock phases. Int J Infct Dis105:424-35. DOI: https://doi.org/10.1016/j.ijid.2021.02.070
KariaR, Gupta I, Khandait H, Yadav A, Yadav A, 2020. COVID-19 and its modes of transmission. SN Compr Clin Med 1:1-4. DOI: https://doi.org/10.1007/s42399-020-00498-4
Langran E, DeWitt J, 2020. Mapping for a better world - navigating place-based learning: mapping for a better world. Springer International Publishing AG, Cham, Switzerland, pp.141-154. DOI: https://doi.org/10.1007/978-3-030-55673-0_6
Liu H, Ye C, Wang Y, Zhu W, Shen Y, Xue C, Zhang H, Zhang Y, Li S, Zhao B, Xu H, Hao L, Zhou Y, 2021.The effectiveness of active surveillance measures for COVID-19 cases in Pudong New Area Shanghai, China, 2020. J Med Virol 93:2918-24. DOI: https://doi.org/10.1002/jmv.26805
McCall MK, 2021. Participatory mapping and PGIS: secerning facts and values, representation and representativity. IJEPlR 10:105-23. DOI: https://doi.org/10.4018/IJEPR.20210701.oa7
Ministry of Health of Indonesia, 2020. Indonesia COVID-19 map. Available from: https://COVID19.go.id/peta-sebaran
Pelucchi M, Sali J, Gerber V, Cameron A, Mickiewicz C, Pedroni PM, 2020. Development of a WebGIS tool for rapid screening and visualization of biodiversity and sensitive areas in the Gulf of Mexico. Conference paper. Available from: https://www.researchgate.net/publication/339704989 Accessed: 10 Feb 2022. DOI: https://doi.org/10.2118/199415-MS
Seaman DE, Powell RA, 1996. An evaluation of the accuracy of kernel density estimators for home range analysis. Ecol 77:2075-85. DOI: https://doi.org/10.2307/2265701
Setiati S, Azwar MK, 2020. COVID-19 and Indonesia. Acta Med Indones 52:84-9.
Setiawan E, Murfi H, Satria Y. 2016. Analysis of the use of kernel density estimation method in loss distribution approach for operational risk. J Integr Math 12:11-1. DOI: https://doi.org/10.24198/jmi.v12.n1.10248.11-18
Soma, AS, Kubota, T, 2018. Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-Loe Watershed in South Sulawesi. For Soc 2:79. DOI: https://doi.org/10.24259/fs.v2i1.3594
WHO, 2020. Modes of transmission of virus causing COVID-19: implications for IPC precaution recommendations. Available from: https://reliefweb.int/report/world/modes-transmission-virus-causing-COVID-19-implications-ipc-precaution-recommendations?gclid=EAIaIQobChMI_YzYoMb39QIVQQWiAx3bugfDEAAYAiAAEgJQh_D_BwE Accessed: 10 Feb 2022.

How to Cite

Soma, A. S. ., Zainuddin, A. A., Riskiyani, S., Nurdin, N. ., Kasim, M. F., Hendarto, J., & Masriadi. (2022). Risk mapping and estimation of COVID-19 transmission in South Sulawesi, Indonesia by a self-identification survey. Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1034