gesjGEOGRAPHY, ENVIRONMENT, SUSTAINABILITYGEOGRAPHY, ENVIRONMENT, SUSTAINABILITY2071-93882542-1565Russian Geographical Society10.24057/2071-9388-2021-090gesj-2191Research ArticleSpecial Issue "Geography of the COVID-19 pandemic: public health, economic and environmental consequences"Using Moran’s I For Detection And Monitoring Of The Covid-19 Spreading Stage In Thailand During The Third Wave Of The PandemicWetchayontParichat

Department of Geography, Faculty of Social Sciences

114 Sukhumvit 23, Khlong Toei Nuea, Wattana District, Bangkok 10110

parichatw@g.swu.ac.thWaiyasusriKatawut

Geography and Geo-Informatics Program, Faculty of Humanities and Social Sciences

1 U-Thong Nok Road, Dusit, Bangkok 10300

Srinakharinwirot UniversityThailandSuan Sunandha Rajabhat UniversityThailand202129122021144155167Copyright © Wetchayont P., Waiyasusri K., 20212021Wetchayont P., Waiyasusri K.Wetchayont P., Waiyasusri K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ges.rgo.ru/jour/article/view/2191

Spatial distribution and spreading patterns of COVID-19 in Thailand were investigated in this study for the 1 April – 23 July 2021 period by analyzing COVID-19 incidence’s spatial autocorrelation and clustering patterns in connection to population density, adult population, mean income, hospital beds, doctors and nurses. Clustering analysis indicated that Bangkok is a significant hotspot for incidence rates, whereas other cities across the region have been less affected. Bivariate Moran’s I showed a low relationship between COVID-19 incidences and the number of adults (Moran’s I = 0.1023- 0.1985), whereas a strong positive relationship was found between COVID-19 incidences and population density (Moran’s I = 0.2776-0.6022). Moreover, the difference Moran’s I value in each parameter demonstrated the transmission level of infectious COVID-19, particularly in the Early (first phase) and Spreading stages (second and third phases). Spatial association in the early stage of the COVID-19 outbreak in Thailand was measured in this study, which is described as a spatio-temporal pattern. The results showed that all of the models indicate a significant positive spatial association of COVID-19 infections from around 10 April 2021. To avoid an exponential spread over Thailand, it was important to detect the spatial spread in the early stages. Finally, these findings could be used to create monitoring tools and policy prevention planning in future.

COVID-19Spatio-temporalDetectionMoran’s ISocioeconomicHealth careReferencesAnselin L. (1995). Local Indicators of Spatial Association LISA, Geographical Analysis, 27, DOI: 10.1111/j.1538-4632.1995.tb00338.x.Anselin L. (1995). Local Indicators of Spatial Association LISA, Geographical Analysis, 27, DOI: 10.1111/j.1538-4632.1995.tb00338.x.Anselin L. (2004). GeoDa 0.95i Release Notes. Urbana-Champaign, IL: Spatial Analysis Laboratory (SAL), Department of Agricultural and Consumer Economics, University of Illinois.Anselin L. (2004). GeoDa 0.95i Release Notes. Urbana-Champaign, IL: Spatial Analysis Laboratory (SAL), Department of Agricultural and Consumer Economics, University of Illinois.Anselin L. (2019). A local indicator of multivariate spatial association: extending Geary’s C. Geogr Anal, 51, 133-50, DOI: 10.1111/gean.12164.Anselin L. (2019). A local indicator of multivariate spatial association: extending Geary’s C. Geogr Anal, 51, 133-50, DOI: 10.1111/gean.12164.Anselin L., and Bao S. (1997). Exploratory Spatial Data Analysis Linking Space Stat and ArcView, Chapter 3 in Recent Developments in Spatial Analysis, Spatial Statistics, Behavioral Modeling and Computational Intelligence, M.M. Fischer and A. Getis (Eds.). Berlin: SpringerVerlag, 35-59.Anselin L., and Bao S. (1997). Exploratory Spatial Data Analysis Linking Space Stat and ArcView, Chapter 3 in Recent Developments in Spatial Analysis, Spatial Statistics, Behavioral Modeling and Computational Intelligence, M.M. Fischer and A. Getis (Eds.). Berlin: SpringerVerlag, 35-59.DDC (2021). The Digital Government Development Agency, https://data.go.th/dataset/covid-19-daily, (Аccessed 23 July 2021).DDC (2021). The Digital Government Development Agency, https://data.go.th/dataset/covid-19-daily, (Аccessed 23 July 2021).Fang L.Q., Vlas S.J., Feng D., Liang S., Xu Y.F., and Zhou J.P. (2009). Geographical spread of SARS in mainland China, Trop Med Int Health, 14, DOI: 10.1111/j.1365-3156.2008.02189.x.Fang L.Q., Vlas S.J., Feng D., Liang S., Xu Y.F., and Zhou J.P. (2009). Geographical spread of SARS in mainland China, Trop Med Int Health, 14, DOI: 10.1111/j.1365-3156.2008.02189.x.Green M.S., Nitzan D., Schwartz N., Niv Y., Peer V. (2021). Sex differences in the case-fatality rates for COVID-19 — A comparison of the age-related differences and consistency over seven countries, PLoS ONE, 16, 4, DOI: 10.1371/journal.pone.0250523.Green M.S., Nitzan D., Schwartz N., Niv Y., Peer V. (2021). Sex differences in the case-fatality rates for COVID-19 — A comparison of the age-related differences and consistency over seven countries, PLoS ONE, 16, 4, DOI: 10.1371/journal.pone.0250523.Kang D., Choi H., Kim J.H., and Choi J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China, Int J Infect Dis, 94, DOI: 10.1016/j.ijid.2020.03.076.Kang D., Choi H., Kim J.H., and Choi J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China, Int J Infect Dis, 94, DOI: 10.1016/j.ijid.2020.03.076.Laohasiriwong W., Puttanapong N., and Luenam A. (2018). A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand, F1000Research, 6,1819, DOI: 10.12688/f1000research.12128.2.Laohasiriwong W., Puttanapong N., and Luenam A. (2018). A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand, F1000Research, 6,1819, DOI: 10.12688/f1000research.12128.2.Lee S.I. (2001). Developing a bivariate spatial association measure: An integration of Pearson’s r and Moran’s I, J. Geograph Syst, 3, DOI: 10.1007/s101090100064.Lee S.I. (2001). Developing a bivariate spatial association measure: An integration of Pearson’s r and Moran’s I, J. Geograph Syst, 3, DOI: 10.1007/s101090100064.Li B., Deng A., Li K., Hu Y., Li Z., Xiong Q., Liu ,Z., Guo Q., Zou L., Zhang H., Zhang M., Ouyang F., Su J., Su W., Xu J., Lin H., Sun J., Peng J., Jiang H., Zhou H., Zhen H., Xiao J., Liu T., Che R., Zeng H., Zheng Z., Huang Y., Yu J., Yi L., Wu J., Chen J., Zhong H., Deng X., Kang M., Pybus O., Hall M., Lythgoe K., Li Y., Yuan J., He J., and Lu J. (2021). Viral infection and transmission in a large well-traced outbreak caused by the Delta SARS-CoV-2 variant, medRxiv, Published online: July 12, 2021:2021.07.07.21260122, DOI:10.1101/2021.07.07.21260122.Li B., Deng A., Li K., Hu Y., Li Z., Xiong Q., Liu ,Z., Guo Q., Zou L., Zhang H., Zhang M., Ouyang F., Su J., Su W., Xu J., Lin H., Sun J., Peng J., Jiang H., Zhou H., Zhen H., Xiao J., Liu T., Che R., Zeng H., Zheng Z., Huang Y., Yu J., Yi L., Wu J., Chen J., Zhong H., Deng X., Kang M., Pybus O., Hall M., Lythgoe K., Li Y., Yuan J., He J., and Lu J. (2021). Viral infection and transmission in a large well-traced outbreak caused by the Delta SARS-CoV-2 variant, medRxiv, Published online: July 12, 2021:2021.07.07.21260122, DOI:10.1101/2021.07.07.21260122.Li H., Calder C.A., and Cressie N. (2007). Beyond Moran’s I: testing for spatial dependence based on the spatial autoregressive model, Geogr Anal, 39, 4, DOI: 10.1111/j.1538-4632.2007.00708.x.Li H., Calder C.A., and Cressie N. (2007). Beyond Moran’s I: testing for spatial dependence based on the spatial autoregressive model, Geogr Anal, 39, 4, DOI: 10.1111/j.1538-4632.2007.00708.x.Li H., Li H., Ding Z., Hu Z., Chen F., Wang K., Peng Z., and Shen H. (2020). Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China, Geospatial Health, 15, 1, DOI: 10.4081/gh.2020.867.Li H., Li H., Ding Z., Hu Z., Chen F., Wang K., Peng Z., and Shen H. (2020). Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China, Geospatial Health, 15, 1, DOI: 10.4081/gh.2020.867.Meng B., Wang J., Liu J., Wu J., Zhong E. (2005). Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing. Public Health, 119, 12, DOI: 10.1016/j.puhe.2005.02.003.Meng B., Wang J., Liu J., Wu J., Zhong E. (2005). Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing. Public Health, 119, 12, DOI: 10.1016/j.puhe.2005.02.003.Mollalo A., Vahedi B., and Rivera K.M. (2020). GIS-based spatial modeling of COVID-19 incidence rate in the continental United States, Science of the Total Environment, 728, DOI: 10.1016/j.scitotenv.2020.138884.Mollalo A., Vahedi B., and Rivera K.M. (2020). GIS-based spatial modeling of COVID-19 incidence rate in the continental United States, Science of the Total Environment, 728, DOI: 10.1016/j.scitotenv.2020.138884.Moran P.A. (1950). A test for the serial independence of residuals. Biometrika, 37, DOI: 10.1093/biomet/37.1-2.178.Moran P.A. (1950). A test for the serial independence of residuals. Biometrika, 37, DOI: 10.1093/biomet/37.1-2.178.NSO (2021). The National Statistical Office of Thailand, http://statbbi.nso.go.th/staticreport-/page/sector/th/index.aspx, (Accessed 23 July 2021).NSO (2021). The National Statistical Office of Thailand, http://statbbi.nso.go.th/staticreport-/page/sector/th/index.aspx, (Accessed 23 July 2021).Pei Z., Han G., Ma X., Su H., and Gong W. (2020). Response of major air pollutants to COVID-19 lockdowns in China, Sci Total Environ, 743, DOI: 10.1016/j.scitotenv.2020.140879.Pei Z., Han G., Ma X., Su H., and Gong W. (2020). Response of major air pollutants to COVID-19 lockdowns in China, Sci Total Environ, 743, DOI: 10.1016/j.scitotenv.2020.140879.Rajatanavin N., Tuangratananon T., Suphanchaimat R., and Tangcharoensathien V. (2021). Responding to the COVID-19 second wave in Thailand by diversifying and adapting lessons from the first wave BMJ Global Health, 6, 7, DOI: 10.1136/bmjgh-2021-006178.Rajatanavin N., Tuangratananon T., Suphanchaimat R., and Tangcharoensathien V. (2021). Responding to the COVID-19 second wave in Thailand by diversifying and adapting lessons from the first wave BMJ Global Health, 6, 7, DOI: 10.1136/bmjgh-2021-006178.Ramírez-Aldana R., Gomez-Verjan J.C., and Bello-Chavolla O.Y., (2020). Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level, PLoS Negl Trop Dis, 18, 14(11), DOI: 10.1371/journal.pntd.0008875. PMID: 33206644; PMCID: PMC7710062.Ramírez-Aldana R., Gomez-Verjan J.C., and Bello-Chavolla O.Y., (2020). Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level, PLoS Negl Trop Dis, 18, 14(11), DOI: 10.1371/journal.pntd.0008875. PMID: 33206644; PMCID: PMC7710062.Raymundo C.E., Oliveira M.C., Eleuterio T. A., André S. R., da Silva M. G., Queiroz ERDS, and Medronho R.A. (2021). Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil, PLoS One, 6, 3, DOI: 10.1371/journal.pone.0247794.Raymundo C.E., Oliveira M.C., Eleuterio T. A., André S. R., da Silva M. G., Queiroz ERDS, and Medronho R.A. (2021). Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil, PLoS One, 6, 3, DOI: 10.1371/journal.pone.0247794.Rey S.J., and Anselin L. (2010). PySAL: A Python Library of Spatial Analytical Methods. In: Fischer M., Getis A. (eds) Handbook of Applied Spatial Analysis. Springer, Berlin, Heidelberg, DOI: 10.1007/978-3-642-03647-7_11.Rey S.J., and Anselin L. (2010). PySAL: A Python Library of Spatial Analytical Methods. In: Fischer M., Getis A. (eds) Handbook of Applied Spatial Analysis. Springer, Berlin, Heidelberg, DOI: 10.1007/978-3-642-03647-7_11.Rotejanaprasert C., Lawpoolsri S., Panngum W., and Maude R.J. (2020). Preliminary estimation of temporal and spatiotemporal dynamic measures of COVID-19 transmission in Thailand, PLoS ONE, 15, 9, DOI: 10.1371/journal.pone.0239645.Rotejanaprasert C., Lawpoolsri S., Panngum W., and Maude R.J. (2020). Preliminary estimation of temporal and spatiotemporal dynamic measures of COVID-19 transmission in Thailand, PLoS ONE, 15, 9, DOI: 10.1371/journal.pone.0239645.Sannigrahi S., Pilla F., Basu B., Basu A.S., and Molter A. (2020). Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach, Sustain Cities Soc, 62, DOI: 10.1016/j.scs.2020.102418.Sannigrahi S., Pilla F., Basu B., Basu A.S., and Molter A. (2020). Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach, Sustain Cities Soc, 62, DOI: 10.1016/j.scs.2020.102418.Triukose S., Nitinawarat S., Satian P., Somboonsavatdee A., Chotikarn P., Thammasanya T., Wanlapakorn N., Sudhinaraset N., Boonyamalik P., Kakhong B., and Poovorawan Y. (2021). Effects of public health interventions on the epidemiological spread during the first wave of the COVID-19 outbreak in Thailand, PLoS ONE, 16, 2, DOI: 10.1371/journal.pone.0246274.Triukose S., Nitinawarat S., Satian P., Somboonsavatdee A., Chotikarn P., Thammasanya T., Wanlapakorn N., Sudhinaraset N., Boonyamalik P., Kakhong B., and Poovorawan Y. (2021). Effects of public health interventions on the epidemiological spread during the first wave of the COVID-19 outbreak in Thailand, PLoS ONE, 16, 2, DOI: 10.1371/journal.pone.0246274.Tu J., and Xia Z.G. (2008). Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation, Sci Total Environ., 407, 358-378, DOI: 10.1016/j.scitotenv.2008.09.031.Tu J., and Xia Z.G. (2008). Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation, Sci Total Environ., 407, 358-378, DOI: 10.1016/j.scitotenv.2008.09.031.Tu, J. and Xia Z.G., (2008). Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation, Sci Total Environ., 407, 358-378, DOI: 10.1016/j.scitotenv.2008.09.031.Tu, J. and Xia Z.G., (2008). Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation, Sci Total Environ., 407, 358-378, DOI: 10.1016/j.scitotenv.2008.09.031.Wang Q., Dong W., Yang K., Ren Z., Huang D., Zhang P., and Wang J. (2021). Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors, Int J Infect Dis., 105, DOI: 10.1016/j.ijid.2021.03.014.Wang Q., Dong W., Yang K., Ren Z., Huang D., Zhang P., and Wang J. (2021). Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors, Int J Infect Dis., 105, DOI: 10.1016/j.ijid.2021.03.014.Wetchayont P. (2021). Investigation on the Impacts of COVID-19 Lockdown and Influencing Factors on Air Quality in Greater Bangkok, Thailand, Advances in Meteorology, 2021, 6697707, DOI: 10.1155/2021/6697707.Wetchayont P. (2021). Investigation on the Impacts of COVID-19 Lockdown and Influencing Factors on Air Quality in Greater Bangkok, Thailand, Advances in Meteorology, 2021, 6697707, DOI: 10.1155/2021/6697707.Wetchayont P., Hayasaka T., and Khatri P. (2021). Air Quality Improvement during COVID-19 Lockdown in Bangkok Metropolitan, Thailand: Effect of the Long-range Transport of Air Pollutants, Aerosol Air Qual. Res., 21, 200662, DOI: 10.4209/aaqr.200662.Wetchayont P., Hayasaka T., and Khatri P. (2021). Air Quality Improvement during COVID-19 Lockdown in Bangkok Metropolitan, Thailand: Effect of the Long-range Transport of Air Pollutants, Aerosol Air Qual. Res., 21, 200662, DOI: 10.4209/aaqr.200662.Zhang X., Rao H., and Wu Y. (2020). Comparison of spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China, BMC Infect Dis, 20, 805, DOI: 10.1186/s12879-020-05537-y.Zhang X., Rao H., and Wu Y. (2020). Comparison of spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China, BMC Infect Dis, 20, 805, DOI: 10.1186/s12879-020-05537-y.

The authors declare that there are no conflicts of interest present.