https://doi.org/10.4081/gh.2022.1066
Spatial association between COVID-19 incidence rate and nighttime light index
Submitted: 26 December 2021
Accepted: 16 February 2022
Published: 18 March 2022
Accepted: 16 February 2022
Abstract Views: 1448
PDF: 390
HTML: 77
HTML: 77
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= ˆ’0.0078 and ˆ’0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= ˆ’0.0086 and ˆ’0.0083 with P=0.067 and 0.056, respectively). All of the obtained results confirmed the negative association between the COVID-19 pandemic and socioeconomic activity revealing the future extensive applications of satellite imagery as an alternative data source for the timely monitoring of the multidimensional impacts of the pandemic.
Abulibdeh A, 2021. Modeling electricity consumption patterns during the COVID-19 pandemic across six socioeconomic sectors in the State of Qatar. Energy Strategy Rev 38:1-17.
DOI: https://doi.org/10.1016/j.esr.2021.100733
Alcântara E, Mantovani J, Rotta L, Park E, Rodrigues T, 2020. Investigating spatiotemporal patterns of the COVID-19 in São Paulo State, Brazil. Geospat Health 15:201-9.
DOI: https://doi.org/10.4081/gh.2020.925
Al-Kindi KM, Alkharusi A, Alshukaili D, 2020. Spatiotemporal assessment of COVID-19 spread over Oman using GIS techniques. Earth Syst Environ 4:797-811.
DOI: https://doi.org/10.1007/s41748-020-00194-2
Ahasan R, Hossain MM, 2021. Leveraging GIS and spatial analysis for informed decision-making in COVID-19 pandemic. HPT 10:7-9.
DOI: https://doi.org/10.1016/j.hlpt.2020.11.009
Anselin L, 1995. Local indicators of spatial association (LISA). Geogr Anal 27:93-115.
DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin L, 2003. An introduction to spatial autocorrelation analysis with GeoDa. Spatial Analysis Laboratory, University of Illinois, IL, USA. Available from: https://personal.utdallas.edu/~briggs/poec6382/geoda_spauto.pdf
Anselin L, Syabri I, Kho Y, 2006. GeoDa: an introduction to spatial data analysis. Geogr Anal 38:5-22.
DOI: https://doi.org/10.1111/j.0016-7363.2005.00671.x
Anselin L, Arribas-Bel D, 2013. Spatial fixed effects and spatial dependence in a single cross-section. Pap Reg Sci 92:3-17.
DOI: https://doi.org/10.1111/j.1435-5957.2012.00480.x
Aral N, Bakir H, 2022. Spatiotemporal analysis of Covid-19 in Turkey. Sustain Cities Soc 76:103421.
DOI: https://doi.org/10.1016/j.scs.2021.103421
Asian Development Bank (ADB), 2021. Pandemic preparedness and response strategies: COVID-19 lessons from the Republic of Korea, Thailand, and Viet Nam. Available from: https://www.adb.org/sites/default/files/publication/743746/pandemic-preparedness-covid-19-lessons.pdf
Bergquist R, Rinaldi L, 2020. Covid-19: Pandemonium in our time. Geospat Health 15:1-5.
DOI: https://doi.org/10.4081/gh.2020.880
BeyerRC, Franco-BedoyaS, Galdo V, 2021. Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity. World Dev 140:1-33.
DOI: https://doi.org/10.1016/j.worlddev.2020.105287
Bustamante-Calabria M, Sánchez de Miguel A, MartÃn-Ruiz S, Ortiz J L, VÃlchez JM, 2021. Effects of the COVID-19 lockdown on urban light emissions: ground and satellite comparison. Remote Sens 13:1-21.
DOI: https://doi.org/10.3390/rs13020258
Cao C, Chang C, Xu M, Zhao J, Gao M, et al., 2010. Epidemic risk analysis after the Wenchuan Earthquake using remote sensing. Int J Remote Sens 31:3631-42.
DOI: https://doi.org/10.1080/01431161003727713
Demirgüç-Kunt A, Lokshin M, Torre I, 2020. The sooner, the better: The early economic impact of non-pharmaceutical interventions during the COVID-19 pandemic. World Bank Policy Res Work Pap 9257:1-93.
DOI: https://doi.org/10.1596/1813-9450-9257
Department of Disease Control (DDC), 2021. Thailand Situation (COVID-19). Department of Disease Control, Nonthaburi, Thailand. Available from: https://covid19.ddc.moph.go.th/
El Deeb O, 2021. Spatial autocorrelation and the dynamics of the mean centre of COVID-19 infections in Lebanon. Front Appl Math Stat 6:620064.
DOI: https://doi.org/10.3389/fams.2020.620064
Elvidge CD, Ghosh T, Hsu FC, Zhizhin M, Bazilian M, 2020. The dimming of lights in China during the COVID-19 pandemic. Remote Sens 12:1-16.
DOI: https://doi.org/10.3390/rs12193194
Fatima M, O’Keefe KJ, Wei W, Arshad S, Gruebner O, 2021. Geospatial Analysis of COVID-19: A Scoping Review. Int J Environ Res Public Health 18:2-14.
DOI: https://doi.org/10.3390/ijerph18052336
Grainger R, Machado PM, Robinson PC, 2021. Novel coronavirus disease-2019 (COVID-19) in people with rheumatic disease: epidemiology and outcomes. Best Pract Res Clin Rheumatol 101657:1-11.
DOI: https://doi.org/10.1016/j.berh.2020.101657
Guo A, Yang J, Xiao X, Xia J, Jin C, 2020. Influences of urban spatial form on urban heat island effects at the community level in China. Sustain. Cities Soc 53:101972.
DOI: https://doi.org/10.1016/j.scs.2019.101972
Henderson JV, Storeygard A, Weil DN, 2012. Measuring economic growth from outer space. Am Econ Rev 102:994-1028.
DOI: https://doi.org/10.1257/aer.102.2.994
Iftimie S, López-Azcona AF, Vallverdú I, Hernández-Flix S, De Febrer G, 2021. First and second waves of coronavirus disease-19: A comparative study in hospitalised patients in Reus, Spain. PLoS One 16:1-13.
DOI: https://doi.org/10.1371/journal.pone.0248029
Islam A, Sayeed MA, Rahman MK, Ferdous J, Islam S, Hassan MM, 2021. Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh. Transbound Emerg Dis 68:3643-57.
DOI: https://doi.org/10.1111/tbed.13973
Jechow A, Hölker F, 2020. Evidence that reduced air and road traffic decreased artificial night-time skyglow during COVID-19 lockdown in Berlin, Germany. Remote Sens 12:1-23.
DOI: https://doi.org/10.3390/rs12203412
Khan A, Haleem, Javaid M, 2020. Analysing COVID-19 pandemic through cases, deaths, and recoveries. JOBCR10:450-69.
DOI: https://doi.org/10.1016/j.jobcr.2020.08.003
Kolak M, Li X, Lin Q, Wang R, Menghaney M, et al., 2021. The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic. Trans GIS 25:1741-65.
DOI: https://doi.org/10.1111/tgis.12786
Kraemer MU, Yang CH, Gutierrez B, Wu CH, Klein B, et al., 2020. The effect of human mobility and control measures on the COVID-19 epidemic in China. Sci 368:493-97.
DOI: https://doi.org/10.1126/science.abb4218
Kunno J, Supawattanabodee B, Sumanasrethakul C, Wiriyasivaj B, Kuratong S, 2021. Comparison of different waves during the COVID-19 pandemic: retrospective descriptive study in Thailand. Adv Prev Med 2021:1-8.
DOI: https://doi.org/10.1155/2021/5807056
Lan T, Shao G, Tang L, Xu Z, Zhu W, 2021. Quantifying spatiotemporal changes in human activities induced by COVID-19 pandemic using daily nighttime light data. IEEE J Sel Top Appl Earth Obs Remote Sens 14:2740-53.
DOI: https://doi.org/10.1109/JSTARS.2021.3060038
Liu Q, Li Y, Yu M, Chiu LS, Hao X, 2019. Daytime rainy cloud detection and convective precipitation delineation based on a deep neural network method using GOES-16 ABI images. Remote Sens 11:1-18.
DOI: https://doi.org/10.3390/rs11212555
Liu Q, Sha D, Liu W, Houser P, Zhang L, 2020. Spatiotemporal patterns of COVID-19 impact on human activities and environment in mainland China using nighttime light and air quality data. Remote Sens 12:1-14.
DOI: https://doi.org/10.3390/rs12101576
Li D, Zhao X, Li X, 2016. Remote sensing of human beings–a perspective from nighttime light. Geo Spat Inf Sci 19:69-79.
DOI: https://doi.org/10.1080/10095020.2016.1159389
Li K, Liang Y, Li J, Liu M, Feng Y, 2020. Internet search data could Be used as novel indicator for assessing COVID-19 epidemic. Infect Dis Model 5:848-54.
DOI: https://doi.org/10.1016/j.idm.2020.10.001
Luenam A, Puttanapong N, 2020. Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015. Geospat Health 15:217-24.
DOI: https://doi.org/10.4081/gh.2020.856
Maloney WF, Taskin T, 2020. Determinants of social distancing and economic activity during COVID-19: A global view. World Bank Policy Res Work Pap 9242:1-21.
DOI: https://doi.org/10.1596/1813-9450-9242
Mollalo A, Vahedi B, Rivera KM, 2020. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Sci Total Environ 728:1-8.
DOI: https://doi.org/10.1016/j.scitotenv.2020.138884
Moran PAP, 1950. Notes on continuous stochastic phenomena. Biometrika 37:17-23.
DOI: https://doi.org/10.1093/biomet/37.1-2.17
Ministry of Public Health (MoPH), 2021. Strategy: Managing the new wave of the Covid-19 epidemic. Ministry of Public Health. Nonthaburi. Thailand. Available from: https://ddc.moph.go.th/viralpneumonia/eng/file/main/en_Thailand%20Covid-19%20plan_MOPH_2021.pdf
National Oceanic and Atmospheric Administration (NOAA), 2019. National Centers for Environmental Information. Version 1 VIIRS day/night band nighttime lights. Available from: https://www.ngdc.noaa.gov/eog/viirs/download_dnb_composites.html
Ning J, Chu Y, Liu X, Zhang D, Zhang J, et al., 2021. Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China. Environ Sci Pollut Res Int 28:48298-311.
DOI: https://doi.org/10.1007/s11356-021-14092-1
Pinkovskiy M, Sala-i-Martin X, 2016. Lights, camera…income! Illuminating the national accounts-household surveys debate. Q J Econ 131:579-631.
DOI: https://doi.org/10.1093/qje/qjw003
Puttanapong N, Martinez Jr. A, Addawe M, Bulan JAN, Durante RL, 2020. Predicting poverty using geospatial data in Thailand. Asian Development Bank Economics Working Paper Series No. 630. Available from: https://www.adb.org/publications/predicting-poverty-using-geospatial-data-thailand
DOI: https://doi.org/10.22617/WPS200434-2
Ruan G, Wu D, Zheng X, Zhong H, Kang C, 2020. A cross-domain approach to analyzing the short-run impact of COVID-19 on the US electricity sector. Joule 4:2322-37.
DOI: https://doi.org/10.1016/j.joule.2020.08.017
Sangkasem K, Puttanapong N, 2021. Analysis of spatial inequality using DMSP-OLS nighttime-light satellite imageries: a case study of Thailand. Reg Sci Policy Pract 1-22.
DOI: https://doi.org/10.1111/rsp3.12386
Shams SA, Haleem A, Javaid, M, 2020. Analyzing COVID-19 pandemic for unequal distribution of tests, identified cases, deaths, and fatality rates in the top 18 countries. Diabetes Metab Syndr Clin Res Rev 14:953-61.
DOI: https://doi.org/10.1016/j.dsx.2020.06.051
Small C, Sousa D, 2021. Spatiotemporal evolution of COVID-19 infection and detection within night light networks: comparative analysis of USA and China: Appl Netw Sci 6:10.
DOI: https://doi.org/10.1007/s41109-020-00345-4
Sun F, Matthews SA, Yang TC, Hu MH, 2020. A spatial analysis of the COVID-19 period prevalence in US counties through June 28, 2020: where geography matters?. Ann Epidemiol 52:54-9.
DOI: https://doi.org/10.1016/j.annepidem.2020.07.014
Steiniger S, Hunter AJ, 2013. The 2012 free and open source GIS software map: A guide to facilitate research, development, and adoption. Comput Environ Urban Syst 39:136-50.
DOI: https://doi.org/10.1016/j.compenvurbsys.2012.10.003
Ward MD, Gleditsch KS, 2018. Spatial regression models. Sage Publications.
DOI: https://doi.org/10.4135/9781071802588
WHO, 2020. WHO Thailand situation report - 1. Available from: https://cdn.who.int/media/docs/default-source/searo/thailand/20200206-tha-sitrep-01-ncov-final.pdf?sfvrsn=7c8cb671_0
WHO, 2021. WHO Thailand situation report - 203. Available from: https://cdn.who.int/media/docs/default-source/searo/thailand/2021_09_30_eng-sitrep-203-covid19.pdf?sfvrsn=97de5f7b_5
WHO, 2022. Coronavirus disease (COVID-19) Weekly Epidemiological Update and Weekly Operational Update. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
World Bank, 2021.A year deferred - early experiences and lessons from Covid-19 in Vietnam. Available from: http://documents.worldbank.org/curated/en/826921633442977133/A-Year-Deferred-Early-Experiences-and-Lessons-from-Covid-19-in-Vietnam
Wu Z, Chen Y, Han Y, Ke T, Liu Y, 2020. Identifying the influencing factors controlling the spatial variation of heavy metals in suburban soil using spatial regression models. Sci Total Environ 717:137212.
DOI: https://doi.org/10.1016/j.scitotenv.2020.137212
Xu H, Yu T, Gu XF, Cheng TH, Xie DH, et al., 2013. The research on remote sensing dust aerosol by using split window emissivity. Spectrosc Spect Anal 33:1189-93.
Xu G, Xiu T, Li X, Liang X, Jiao L, 2021. Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities. Int J Appl Earth Obs 102:102421.
DOI: https://doi.org/10.1016/j.jag.2021.102421
How to Cite
Luenam, A., & Puttanapong , N. . (2022). Spatial association between COVID-19 incidence rate and nighttime light index. Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1066
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Ei Sandar U, Wongsa Laohasiriwong, Kittipong Sornlorm, Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand , Geospatial Health: Vol. 18 No. 1 (2023)
You may also start an advanced similarity search for this article.
