Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic
https://doi.org/10.24057/2071-9388-2021-054
Abstract
The spread of the 2019 novel coronavirus disease (COVID-19) has engulfed the world with a rapid, unexpected, and far-reaching global crisis. In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have played an important role in many aspects, especially in the fight against COVID-19. This review summarises 102 scientific papers on applications of GIS and RS on studies of the COVID-19 pandemic. In this study, two themes of GIS and RS-related applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGISbased mapping, the correlation between the COVID-19 and natural, socio-economic factors, and the environmental impacts. The findings of this study provide insight into how to apply new techniques (GIS and RS) to better understand, better manage the evolution of the COVID-19 pandemic and effectively assess its impacts.
About the Authors
Quoc-lap KieuViet Nam
Faculty of Natural Resources and Environment
Tan Thinh Ward, Thainguyen, 250000
Tien-thanh Nguyen
Viet Nam
Faculty of Surveying, Mapping and Geographic Information
No. 41A, Phu Dien Road, North-Tu Liem District, Hanoi,100000
Anh-huy Hoang
Viet Nam
Faculty of Environment
No. 41A, Phu Dien Road, North-Tu Liem District, Hanoi,100000
References
1. Acharya P et al. (2021). Revisiting the levels of Aerosol Optical Depth in South-Southeast Asia, Europe and USA amid the COVID-19 pandemic using satellite observations Environmental research,193(110514).
2. Adwibowo A (2020). Does social distancing have an effect on water quality?: An evidence from Chlorophyll-a level in the water of populated Southeast Asian coasts.
3. Ali G, Abbas S, Qamer F.M, Wong M.S, Rasul G., Irteza S.M, Shahzad N. (2021a). Environmental impacts of shifts in energy, emissions, and urban heat island during the COVID-19 lockdown across Pakistan Journal of Cleaner Production, 291(125806).
4. Ali T., Mortula M., Sadiq R. (2021b). GIS-based vulnerability analysis of the United States to COVID-19 occurrence Journal of Risk Research, 1-16.
5. Alqasemi A.S., Hereher M.E., Kaplan G., Al-Quraishi A.M.F., Saibi H. (2021). Impact of COVID-19 lockdown upon the air quality and surface urban heat island intensity over the United Arab Emirates Science of The Total Environment, 767(144330).
6. Amdaoud M., Arcuri G., Levratto N. (2021). Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe The European Journal of Health Economics, 1-14.
7. Arif M., Kumar R., Parveen S., Verma N. (2020). Reduction in water pollution in Yamuna river due to lockdown under COVID-19 pandemic ChemRxiv Preprint.
8. Avtar R., Kumar P., Supe H., Jie D., Sahu N., Mishra B.K., Yunus A.P. (2020). Did the COVID-19 lockdown-induced hydrological residence time intensify the primary productivity in lakes? Observational results based on satellite remote sensing Water, 12(2573).
9. Bachilo E., Barylnik J., Shuldyakov A., Efremov A., Novikov D. (2020). Mental health of medical workers during the COVID-19 pandemic in Russia: Results of a cross-sectional study medRxiv.
10. Badillo-Rivera E., Fow-Esteves A., Alata-López F., Virú-Vásquez P., Medina-Acuña M. (2020). Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2). using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru medRxiv.
11. Bag R., Ghosh M., Biswas B., Chatterjee M. (2020). Understanding the spatio–temporal pattern of COVID–19 outbreak in India using GIS and India’s response in managing the pandemic Regional Science Policy & Practice, 12, 1063-1103.
12. Bhunia G.S., Roy S, Shit PK (2021). Spatio-temporal analysis of COVID-19 in India–a geostatistical approach Spatial Information Research, 1-12.
13. Bisanzio D., Kraemer M.U., Bogoch II, Brewer T., Brownstein J.S., Reithinger R. (2020). Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale Geospatial health 15.
14. Boulos M.N.K., Geraghty E.M. (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. BioMed Central.
15. Brito P.L., Kuffer M., Koeva M., Pedrassoli J.C., Wang J., Costa F., Freitas A.Dd. (2020). The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil ISPRS International Journal of Geo-Information, 9(557).
16. Castro M.C. et al. (2021). Spatiotemporal pattern of COVID-19 spread in Brazil Science.
17. Chen J., Gao M., Huang S., Hou W. (2021). Application of remote sensing satellite data for carbon emissions reduction Journal of Chinese Economic and Business Studies, 1-9.
18. Chen Z-L et al. (2020). Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China Chinese medical journal.
19. Cicalò E., Valentino M. (2019). Mapping and visualisation on of health data. The contribution on of the graphic sciences to medical research from New York yellow fever to China Coronavirus Disegnarecon, 12:12-11-12.19.
20. Das R.D., Bandopadhyay S., Das M., Chowdhury M. (2020). Global Air Quality Change Detection During Covid-19 Pandemic Using SpaceBorne Remote Sensing and Global Atmospheric Reanalysis. In: 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), IEEE, 158-161.
21. Davies T.M., Lawson A.B. (2019). An evaluation of likelihood-based bandwidth selectors for spatial and spatiotemporal kernel estimates Journal of Statistical Computation and Simulation, 89(1131-1152).
22. Dong E., Du H., Gardner L. (2020). An interactive web-based dashboard to track COVID-19 in real time The Lancet infectious diseases, 20(533-534).
23. DuClos C et al. (2021). Peer Reviewed: Mapping Chronic Disease Risk Factors With ArcGIS Online in Support of COVID-19 Response in Florida Preventing Chronic Disease 18.
24. Elson R., Davies T.M., Lake I.R., Vivancos R., Blomquist P.B., Charlett A., Dabrera G. (2021). The spatio-temporal distribution of COVID-19 infection in England between January and June 2020 Epidemiology & Infection 149.
25. Fan C., Li Y., Guang J., Li Z., Elnashar A., Allam M., de Leeuw G. (2020). The impact of the control measures during the COVID-19 outbreak on air pollution in China Remote Sensing, 12(1613).
26. Feng Z., Xiao C., Li P., You Z., Yin X., Zheng F. (2020). Comparison of spatio-temporal transmission characteristics of COVID-19 and its mitigation strategies in China and the US Journal of Geographical Sciences, 30(1963-1984).
27. Filippini T., Rothman K.J., Goffi A., Ferrari F., Maffeis G., Orsini N., Vinceti M. (2020). Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy Science of The Total Environment, 739(140278).
28. Filonchyk M., Hurynovich V., Yan H., Gusev A., Shpilevskaya N. (2020). Impact assessment of COVID-19 on variations of SO2, NO2, CO and AOD over East China Aerosol and Air Quality Research, 20, 1530-1540.
29. Firozjaei M.K., Fathololomi S., Kiavarz M., Arsanjani J.J., Homaee M., Alavipanah S.K. (2021). Modeling the impact of the COVID-19 lockdowns on urban surface ecological status: A case study of Milan and Wuhan cities Journal of environmental management, 286(112236).
30. Franch-Pardo I., Napoletano B.M., Rosete-Verges F., Billa L. (2020). Spatial analysis and GIS in the study of COVID-19. A review Science of The Total Environment, 739 (140033).
31. Gao S., Rao J., Kang Y., Liang Y., Kruse J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19 SIGSpatial Special, 12, 16-26.
32. Gelfand M.J. et al. (2021). The relationship between cultural tightness–looseness and COVID-19 cases and deaths: a global analysis The Lancet Planetary Health, 5, e135-e144.
33. Giuliani D., Dickson M.M., Espa G., Santi F. (2020). Modelling and predicting the spatio-temporal spread of coronavirus disease 2019 (COVID-19) in Italy Available at SSRN 3559569.
34. Gomes D. et al. (2020). Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling Epidemiology & Infection 148.
35. Graves B.A. (2012). A model for assessment of potential geographical accessibility: a case for GIS Online Journal of Rural Nursing and Health Care, 9(6-55).
36. Gross B. et al. (2020). Spatio-temporal propagation of COVID-19 pandemics EPL (Europhysics Letters), 131, 8003.
37. Guan W-j et al. (2020). Clinical characteristics of coronavirus disease 2019 in China New England journal of medicine, 382, 1708-1720.
38. He J. et al. (2020). Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran Science of the Total Environment 747:141447.
39. Ionov D.V. et al. (2021). The CO 2 integral emission by the megacity of St. Petersburg as quantified from ground-based FTIR measurements combined with dispersion modelling Atmospheric Chemistry and Physics Discussions:1-29.
40. Kanga S., Meraj G., Farooq M., Nathawat M., Singh S.K. (2021). Analyzing the Risk to COVID–19 Infection using Remote Sensing and GIS Risk Analysis.
41. Kim S., Castro M.C. (2020). Spatiotemporal pattern of COVID-19 and government response in South Korea (as of May 31, 2020) International Journal of Infectious Diseases, 98, 328-333.
42. Kodge B. (2021). A review on current status of COVID19 cases in Maharashtra state of India using GIS: a case study Spatial Information Research, 29, 223-229.
43. Kuchler T., Russel D., Stroebel J. (2020). The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook. National Bureau of Economic Research.
44. Kuznetsov I., Panidi E., Kolesnikov A., Kikin P., Korovka V., Galkin V. (2020a). Gis-based infectious disease data management on a city scale, case study of St. Petersburg, Russia The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 1463-1467.
45. Kuznetsov I., Panidi E., Korovka V., Galkin V., Voronov D. (2020b). Web-based representation and management of infectious disease data on a city scale, case study of St. Petersburg, Russia The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 44, 87-91.
46. Lee W. et al. (2020). COVID-19 in South Korea: epidemiological and spatiotemporal patterns of the spread and the role of aggressive diagnostic tests in the early phase International journal of epidemiology, 49, 1106-1116.
47. Li W., Thomas R., El-Askary H., Piechota T., Struppa D., Ghaffar K.A.A. (2020). Investigating the significance of aerosols in determining the coronavirus fatality rate among three European Countries Earth Systems and Environment, 4, 513-522.
48. Liu D., Yang H., Thompson J.R., Li J., Loiselle S., Duan H. (2022). COVID-19 lockdown improved river water quality in China Science of The Total Environment, 802(149585).
49. Liu Q. et al. (2020). Spatiotemporal Patterns of COVID-19 Impact on Human Activities and Environment in China Using Nighttime Light and Air Quality Data arXiv preprint arXiv:200502808.
50. Maithani S., Nautiyal G., Sharma A. (2020). Investigating the effect of lockdown during COVID-19 on land surface temperature: study of Dehradun city, India Journal of the Indian Society of Remote Sensing, 48, 1297-1311.
51. Martines M.R., Ferreira R.V., Toppa R.H., Assunção L., Desjardins M.R., Delmelle E.M. (2021). Detecting space–time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities Journal of Geographical Systems, 23, 7-36.
52. Masrur A., Yu M., Luo W., Dewan A. (2020). Space-time patterns, change, and propagation of COVID-19 risk relative to the intervention scenarios in Bangladesh International journal of environmental research and public health, 17(5911).
53. Mazhar U., Jin S., Bilal M., Ali M.A., Khan R. (2021). Reduction of surface radiative forcing observed from remote sensing data during global COVID-19 lockdown Atmospheric Research, 105729.
54. Meng T. (2021). Clusters in the Spread of the COVID-19 Pandemic: Evidence From the G20 Countries Frontiers in Public Health, 8(948).
55. Metya A., Dagupta P., Halder S., Chakraborty S., Tiwari Y.K. (2020). COVID-19 lockdowns improve air quality in the South-East Asian regions, as seen by the remote sensing satellites Aerosol and Air Quality Research, 20, 1772-1782.
56. Mollalo A., Vahedi B., Rivera K.M. (2020). GIS-based spatial modeling of COVID-19 incidence rate in the continental United States Science of the total environment, 728(138884).
57. Momynaliev K., Khoroshun D., Akimkin V. (2021). Web-queries as an evaluation marker for epidemiological situation of SARS-COV-2 infection in Russia Antimicrobial Resistance and Infection Control.
58. Mooney P., Juhász L (2020). Mapping COVID-19: How web-based maps contribute to the infodemic Dialogues in Human Geography, 10,265-270.
59. Najah A. et al. (2021). Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia International Journal of Environmental Science and Technology, 18, 1009-1018.
60. Nakada L.Y.K., Urban R.C. (2020). COVID-19 pandemic: Impacts on the air quality during the partial lockdown in São Paulo state, Brazil Science of the Total Environment, 730, 139087.
61. Naqvi H.R., Datta M., Mutreja G., Siddiqui M.A., Naqvi D.F., Naqvi A.R. (2021). Improved air quality and associated mortalities in India under COVID-19 lockdown Environmental Pollution, 268, 115691.
62. NASA (2020). NASA, ESA, JAXA Release Global View of COVID-19 Impacts. https://svs.gsfc.nasa.gov/13647. [Accessed May 8, 2021].
63. Nath B., Majumder S., Sen J., Rahman M.M. (2021). Risk Analysis of COVID–19 Infections in Kolkata Metropolitan City: A GIS–Based Study and Policy Implications GeoHealth, 5, e2020GH000368.
64. Nekliudov N.A. et al. (2020). Excessive media consumption about COVID-19 is associated with increased state anxiety: outcomes of a large online survey in Russia Journal of medical Internet research, 22, e20955.
65. Nichol J.E., Bilal M., Ali M., Qiu Z. (2020). Air pollution scenario over China during COVID-19 Remote Sensing, 12, 2100.
66. Niroumand-Jadidi M., Bovolo F., Bruzzone L., Gege P. (2020). Physics-based bathymetry and water quality retrieval using planetscope imagery: Impacts of 2020 Covid-19 lockdown and 2019 extreme flood in the Venice Lagoon Remote Sensing, 12, 2381.
67. Onafeso O.D. et al. (2021). Geographical trend analysis of COVID-19 pandemic onset in Africa Social Sciences & Humanities Open, 4, 100137.
68. Oto-Peralías D. (2020). Regional correlations of COVID-19 in Spain.
69. Parra Boronat M. (2020). Analysis of the evolution of sea water quality in the Spanish coast from satellite images before and during the quarantine caused by COVID-19.
70. Pramanik M., Udmale P., Bisht P., Chowdhury K., Szabo S., Pal I. (2020). Climatic factors influence the spread of COVID-19 in Russia International journal of environmental health research, 1-15.
71. Rahman M.H., Zafri N.M., Ashik F.R., Waliullah M., Khan A. (2021a). Identification of risk factors contributing to COVID-19 incidence rates in Bangladesh: A GIS-based spatial modeling approach Heliyon, 7, e06260.
72. Rahman M.S., Azad M.A.K., Hasanuzzaman M., Salam R., Islam A.R.M.T., Rahman M.M., Hoque M.M.M. (2021b). How air quality and COVID-19 transmission change under different lockdown scenarios? A case from Dhaka city, Bangladesh Science of The Total Environment, 762(143161).
73. Rose-Redwood R. et al. (2020). Geographies of the COVID-19 pandemic Dialogues in Human Geography, 10(97-106).
74. Rossman H. et al. (2020). A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys Nature Medicine, 26,634-638.
75. Rui R., Tian M., Tang M.-L., Ho GT-S., Wu C-H. (2021). Analysis of the spread of COVID-19 in the USA with a spatio-temporal multivariate time series model International Journal of Environmental Research and Public Health, 18(774).
76. Saeed U., Sherdil K., Ashraf U., Younas I., Butt H., Ahmad S. (2021). Identification of potential lockdown areas during COVID-19 transmission in Punjab, Pakistan Public health, 190, 42-51.
77. Sannino A., D’Emilio M., Castellano P., Amoruso S., Boselli A. (2020). Analysis of Air Quality during the COVID-19 Pandemic Lockdown in Naples (Italy) Aerosol and Air Quality Research, 20.
78. Sartorius B., Lawson A., Pullan R. (2021). Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England Scientific reports, 11, 1-11.
79. Sathe Y., Gupta P., Bawase M., Lamsal L., Patadia F., Thipse S. (2021). Surface and satellite observations of air pollution in India during COVID-19 lockdown: Implication to air quality Sustainable cities and society, 66(102688).
80. Shankar K., Gnanachandrasamy G., Mahalakshmi M., Devaraj N., Prasanna M., Chidambaram S., Thilagavathi R. (2021). Meteorological parameters and COVID-19 spread-Russia a case study. In: Environmental Resilience and Transformation in Times of COVID-19. Elsevier, 179- 190.
81. Shepherd M. (2020). Why Geography is a key-part of fighting the COVID-19 Coronavirus outbreak Forbes.
82. Singh R.K. et al. (2020). Prediction of the COVID-19 pandemic for the top 15 affected countries: advanced autoregressive integrated moving average (ARIMA) model JMIR public health and surveillance, 6(e19115).
83. Sivakumar B. (2021). COVID-19 and water. Springer.
84. Sun X., Liu J., Wang J., Tian L., Zhou Q., Li J. (2021). Integrated monitoring of lakes’ turbidity in Wuhan, China during the COVID-19 epidemic using multi-sensor satellite observations International Journal of Digital Earth, 14(443-463).
85. Talukdar S., Mahato S., Pal S., Debanshi S., Das P., Rahman A. (2020). Modelling the Global Air Quality Conditions in Perspective of COVID-19 Stimulated Lockdown Periods Using Remote Sensing Data.
86. Teufel B. et al. (2021). Impact of COVID-19-Related Traffic Slowdown on Urban Heat Characteristics Atmosphere, 12(243).
87. Tiboni M., Pezzagno M., Vetturi D., Alexander C., Botticini F. (2020). Data analysis and mapping for monitoring health risk. What has the spread of the Covid-19 pandemic in northern Italy taught us? TeMA-Journal of Land Use, Mobility and Environment, 343-360.
88. Tripathi G., Pandey A.C., Parida B.R. (2020). Spatio-Temporal Analysis of Turbidity in Ganga River in Patna, Bihar Using Sentinel-2 Satellite Data Linked with Covid-19 Pandemic. In: 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), IEEE, 29-32.
89. United Nations (2020). The Social Impact of COVID-19. https://www.un.org/development/desa/dspd/2020/04/social-impact-ofcovid-19/. [Accessed May 2021].
90. Vasilyev A. (2021). EXPERIMENTAL RESEARCH OF ENVIRONMENTAL NOISE IN URBAN CONDITIONS BEFORE AND DURING COVID-19 PERIOD ON THE EXAMPLE OF SAMARA REGION OF RUSSIAN FEDERATION Journal Akustika, 39.
91. Viana J., Santos J.V., Neiva R.M., Souza J., Duarte L., Teodoro A.C., Freitas A. (2017). Remote sensing in human health: A 10-year bibliometric analysis Remote Sensing, 9(1225).
92. Wagh P., Sojan J.M., Babu S.J., Valsala R., Bhatia S., Srivastav R. (2021). Indicative Lake Water Quality Assessment Using Remote Sensing Images-Effect of COVID-19 Lockdown Water, 13(73).
93. Wang Y., Liu Y., Struthers J., Lian M. (2021). Spatiotemporal characteristics of the COVID-19 epidemic in the United States Clinical infectious diseases, 72, 643-651.
94. Wei Z., Kondragunta S., Yang K., Zhang H., McDonald B.C. (2020). Correlating Economic Activity Indicators and Tropospheric Column Nitrogen Dioxide during COVID-19 Pandemic in the United States. In: AGU Fall Meeting Abstracts, A005-0026.
95. WHO (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). https://www.who.int/docs/defaultsource/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf. [Accessed May 2020]
96. WHO (2021). WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/. [Accessed October 2021].
97. Wyche K., Nichols M., Parfitt H., Beckett P., Gregg D., Smallbone K., Monks P. (2021). Changes in ambient air quality and atmospheric composition and reactivity in the South East of the UK as a result of the COVID-19 lockdown Science of the Total Environment, 755(142526).
98. Xu C., Zhang X., Wang Y. (2020). Mapping of health literacy and social panic via web search data during the COVID-19 public health emergency: infodemiological study Journal of Medical Internet Research, 22(e18831).
99. Xu H., Xu G., Wen X., Hu X., Wang Y. (2021). Lockdown effects on total suspended solids concentrations in the Lower Min River (China) during COVID-19 using time-series remote sensing images International Journal of Applied Earth Observation and Geoinformation, 98(102301).
100. Yunus A.P., Masago Y., Hijioka Y. (2020). COVID-19 and surface water quality: Improved lake water quality during the lockdown Science of the Total Environment, 731(139012).
101. Zemtsov S., Baburin V. (2020). COVID-19: Spatial dynamics and diffusion factors across Russian regions Regional Research of Russia, 10(273-290).
102. Zheng B. et al. (2020). Satellite-based estimates of decline and rebound in China’s CO2 emissions during COVID-19 pandemic Science Advances 6:eabd4998.
Review
For citations:
Kieu Q., Nguyen T., Hoang A. Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2021;14(4):117-124. https://doi.org/10.24057/2071-9388-2021-054