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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">gesj</journal-id><journal-title-group><journal-title xml:lang="en">GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY</journal-title><trans-title-group xml:lang="ru"><trans-title>GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2071-9388</issn><issn pub-type="epub">2542-1565</issn><publisher><publisher-name>Russian Geographical Society</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.24057/2071-9388-2021-054</article-id><article-id custom-type="elpub" pub-id-type="custom">gesj-2187</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>Special Issue "Geography of the COVID-19 pandemic: public health, economic and environmental consequences"</subject></subj-group></article-categories><title-group><article-title>Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic</article-title><trans-title-group xml:lang="ru"><trans-title></trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="western" xml:lang="en"><surname>Kieu</surname><given-names>Quoc-lap</given-names></name></name-alternatives><bio xml:lang="en"><p>Faculty of Natural Resources and Environment</p><p>Tan Thinh Ward, Thainguyen, 250000</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="western" xml:lang="en"><surname>Nguyen</surname><given-names>Tien-thanh</given-names></name></name-alternatives><bio xml:lang="en"><p>Faculty of Surveying, Mapping and Geographic Information</p><p>No. 41A, Phu Dien Road, North-Tu Liem District, Hanoi,100000</p></bio><email xlink:type="simple">tdgis_ntthanh@163.com</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="western" xml:lang="en"><surname>Hoang</surname><given-names>Anh-huy</given-names></name></name-alternatives><bio xml:lang="en"><p>Faculty of Environment</p><p>No. 41A, Phu Dien Road, North-Tu Liem District, Hanoi,100000</p></bio><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff xml:lang="en" id="aff-1"><institution>Thainguyen University of Sciences</institution><country>Viet Nam</country></aff><aff xml:lang="en" id="aff-2"><institution>Hanoi University of Natural Resources and Environment</institution><country>Viet Nam</country></aff><pub-date pub-type="collection"><year>2021</year></pub-date><pub-date pub-type="epub"><day>29</day><month>12</month><year>2021</year></pub-date><volume>14</volume><issue>4</issue><fpage>117</fpage><lpage>124</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Kieu Q., Nguyen T., Hoang A., 2021</copyright-statement><copyright-year>2021</copyright-year><copyright-holder xml:lang="ru">Kieu Q., Nguyen T., Hoang A.</copyright-holder><copyright-holder xml:lang="en">Kieu Q., Nguyen T., Hoang A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://ges.rgo.ru/jour/article/view/2187">https://ges.rgo.ru/jour/article/view/2187</self-uri><abstract><p>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.</p><p> </p></abstract><kwd-group xml:lang="en"><kwd>remote sensing</kwd><kwd>applications</kwd><kwd>COVID-19</kwd><kwd>viral infection</kwd><kwd>impacts</kwd><kwd>environment</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">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 &amp; Practice, 12, 1063-1103.</mixed-citation><mixed-citation xml:lang="en">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 &amp; Practice, 12, 1063-1103.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Bhunia G.S., Roy S, Shit PK (2021). Spatio-temporal analysis of COVID-19 in India–a geostatistical approach Spatial Information Research, 1-12.</mixed-citation><mixed-citation xml:lang="en">Bhunia G.S., Roy S, Shit PK (2021). Spatio-temporal analysis of COVID-19 in India–a geostatistical approach Spatial Information Research, 1-12.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Castro M.C. et al. (2021). Spatiotemporal pattern of COVID-19 spread in Brazil Science.</mixed-citation><mixed-citation xml:lang="en">Castro M.C. et al. (2021). Spatiotemporal pattern of COVID-19 spread in Brazil Science.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Chen Z-L et al. (2020). Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China Chinese medical journal.</mixed-citation><mixed-citation xml:lang="en">Chen Z-L et al. (2020). Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China Chinese medical journal.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">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 &amp; Infection 149.</mixed-citation><mixed-citation xml:lang="en">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 &amp; Infection 149.</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Gomes D. et al. (2020). Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling Epidemiology &amp; Infection 148.</mixed-citation><mixed-citation xml:lang="en">Gomes D. et al. (2020). Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling Epidemiology &amp; Infection 148.</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Gross B. et al. (2020). Spatio-temporal propagation of COVID-19 pandemics EPL (Europhysics Letters), 131, 8003.</mixed-citation><mixed-citation xml:lang="en">Gross B. et al. (2020). Spatio-temporal propagation of COVID-19 pandemics EPL (Europhysics Letters), 131, 8003.</mixed-citation></citation-alternatives></ref><ref id="cit37"><label>37</label><citation-alternatives><mixed-citation xml:lang="ru">Guan W-j et al. (2020). Clinical characteristics of coronavirus disease 2019 in China New England journal of medicine, 382, 1708-1720.</mixed-citation><mixed-citation xml:lang="en">Guan W-j et al. (2020). Clinical characteristics of coronavirus disease 2019 in China New England journal of medicine, 382, 1708-1720.</mixed-citation></citation-alternatives></ref><ref id="cit38"><label>38</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit39"><label>39</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit40"><label>40</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit41"><label>41</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit42"><label>42</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit43"><label>43</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit44"><label>44</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit45"><label>45</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit46"><label>46</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit47"><label>47</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit48"><label>48</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit49"><label>49</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit50"><label>50</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit51"><label>51</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit52"><label>52</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit53"><label>53</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit54"><label>54</label><citation-alternatives><mixed-citation xml:lang="ru">Meng T. (2021). Clusters in the Spread of the COVID-19 Pandemic: Evidence From the G20 Countries Frontiers in Public Health, 8(948).</mixed-citation><mixed-citation xml:lang="en">Meng T. (2021). Clusters in the Spread of the COVID-19 Pandemic: Evidence From the G20 Countries Frontiers in Public Health, 8(948).</mixed-citation></citation-alternatives></ref><ref id="cit55"><label>55</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit56"><label>56</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit57"><label>57</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit58"><label>58</label><citation-alternatives><mixed-citation xml:lang="ru">Mooney P., Juhász L (2020). Mapping COVID-19: How web-based maps contribute to the infodemic Dialogues in Human Geography, 10,265-270.</mixed-citation><mixed-citation xml:lang="en">Mooney P., Juhász L (2020). Mapping COVID-19: How web-based maps contribute to the infodemic Dialogues in Human Geography, 10,265-270.</mixed-citation></citation-alternatives></ref><ref id="cit59"><label>59</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit60"><label>60</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit61"><label>61</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit62"><label>62</label><citation-alternatives><mixed-citation xml:lang="ru">NASA (2020). NASA, ESA, JAXA Release Global View of COVID-19 Impacts. https://svs.gsfc.nasa.gov/13647. [Accessed May 8, 2021].</mixed-citation><mixed-citation xml:lang="en">NASA (2020). NASA, ESA, JAXA Release Global View of COVID-19 Impacts. https://svs.gsfc.nasa.gov/13647. [Accessed May 8, 2021].</mixed-citation></citation-alternatives></ref><ref id="cit63"><label>63</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit64"><label>64</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit65"><label>65</label><citation-alternatives><mixed-citation xml:lang="ru">Nichol J.E., Bilal M., Ali M., Qiu Z. (2020). Air pollution scenario over China during COVID-19 Remote Sensing, 12, 2100.</mixed-citation><mixed-citation xml:lang="en">Nichol J.E., Bilal M., Ali M., Qiu Z. (2020). Air pollution scenario over China during COVID-19 Remote Sensing, 12, 2100.</mixed-citation></citation-alternatives></ref><ref id="cit66"><label>66</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit67"><label>67</label><citation-alternatives><mixed-citation xml:lang="ru">Onafeso O.D. et al. (2021). Geographical trend analysis of COVID-19 pandemic onset in Africa Social Sciences &amp; Humanities Open, 4, 100137.</mixed-citation><mixed-citation xml:lang="en">Onafeso O.D. et al. (2021). Geographical trend analysis of COVID-19 pandemic onset in Africa Social Sciences &amp; Humanities Open, 4, 100137.</mixed-citation></citation-alternatives></ref><ref id="cit68"><label>68</label><citation-alternatives><mixed-citation xml:lang="ru">Oto-Peralías D. (2020). Regional correlations of COVID-19 in Spain.</mixed-citation><mixed-citation xml:lang="en">Oto-Peralías D. (2020). Regional correlations of COVID-19 in Spain.</mixed-citation></citation-alternatives></ref><ref id="cit69"><label>69</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit70"><label>70</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit71"><label>71</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit72"><label>72</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit73"><label>73</label><citation-alternatives><mixed-citation xml:lang="ru">Rose-Redwood R. et al. (2020). Geographies of the COVID-19 pandemic Dialogues in Human Geography, 10(97-106).</mixed-citation><mixed-citation xml:lang="en">Rose-Redwood R. et al. (2020). Geographies of the COVID-19 pandemic Dialogues in Human Geography, 10(97-106).</mixed-citation></citation-alternatives></ref><ref id="cit74"><label>74</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit75"><label>75</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit76"><label>76</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit77"><label>77</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit78"><label>78</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit79"><label>79</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit80"><label>80</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit81"><label>81</label><citation-alternatives><mixed-citation xml:lang="ru">Shepherd M. (2020). Why Geography is a key-part of fighting the COVID-19 Coronavirus outbreak Forbes.</mixed-citation><mixed-citation xml:lang="en">Shepherd M. (2020). Why Geography is a key-part of fighting the COVID-19 Coronavirus outbreak Forbes.</mixed-citation></citation-alternatives></ref><ref id="cit82"><label>82</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit83"><label>83</label><citation-alternatives><mixed-citation xml:lang="ru">Sivakumar B. (2021). COVID-19 and water. Springer.</mixed-citation><mixed-citation xml:lang="en">Sivakumar B. (2021). COVID-19 and water. Springer.</mixed-citation></citation-alternatives></ref><ref id="cit84"><label>84</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit85"><label>85</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit86"><label>86</label><citation-alternatives><mixed-citation xml:lang="ru">Teufel B. et al. (2021). Impact of COVID-19-Related Traffic Slowdown on Urban Heat Characteristics Atmosphere, 12(243).</mixed-citation><mixed-citation xml:lang="en">Teufel B. et al. (2021). Impact of COVID-19-Related Traffic Slowdown on Urban Heat Characteristics Atmosphere, 12(243).</mixed-citation></citation-alternatives></ref><ref id="cit87"><label>87</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit88"><label>88</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit89"><label>89</label><citation-alternatives><mixed-citation xml:lang="ru">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].</mixed-citation><mixed-citation xml:lang="en">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].</mixed-citation></citation-alternatives></ref><ref id="cit90"><label>90</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit91"><label>91</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit92"><label>92</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit93"><label>93</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit94"><label>94</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref><ref id="cit95"><label>95</label><citation-alternatives><mixed-citation xml:lang="ru">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]</mixed-citation><mixed-citation xml:lang="en">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]</mixed-citation></citation-alternatives></ref><ref id="cit96"><label>96</label><citation-alternatives><mixed-citation xml:lang="ru">WHO (2021). WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/. [Accessed October 2021].</mixed-citation><mixed-citation xml:lang="en">WHO (2021). WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/. [Accessed October 2021].</mixed-citation></citation-alternatives></ref><ref id="cit97"><label>97</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit98"><label>98</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit99"><label>99</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit100"><label>100</label><citation-alternatives><mixed-citation xml:lang="ru">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).</mixed-citation><mixed-citation xml:lang="en">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).</mixed-citation></citation-alternatives></ref><ref id="cit101"><label>101</label><citation-alternatives><mixed-citation xml:lang="ru">Zemtsov S., Baburin V. (2020). COVID-19: Spatial dynamics and diffusion factors across Russian regions Regional Research of Russia, 10(273-290).</mixed-citation><mixed-citation xml:lang="en">Zemtsov S., Baburin V. (2020). COVID-19: Spatial dynamics and diffusion factors across Russian regions Regional Research of Russia, 10(273-290).</mixed-citation></citation-alternatives></ref><ref id="cit102"><label>102</label><citation-alternatives><mixed-citation xml:lang="ru">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.</mixed-citation><mixed-citation xml:lang="en">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.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
