Temporal variation of spatial autocorrelation of COVID-19 cases identified in Poland during the year from the beginning of the pandemic

Stach, Alfred

doi:0016-7282 (print)

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Temporal variation of spatial autocorrelation of COVID-19 cases identified in Poland during the year from the beginning of the pandemic Stach, Alfred

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Temporal variation of spatial autocorrelation of COVID-19 cases identified in Poland during the year from the beginning of the pandemic

Geographia Polonica Vol. 94 No. 3 (2021)

Stach, Alfred : Autor

spatial autocorrelation ; COVID-19 pandemic ; COVID-19 cases ; Poisson risk semivariogram ; Poland ; counties (poviats)

The spread of the COVID-19 pandemic has a simultaneous temporal and spatial component. This pattern results from a complex combination of factors, including social ones, that lead to significant differences in the evolution of space-time distributions, both between and within countries. The aim of this study was to assess changes in the regularity of the spatial distribution of the number of diagnosed COVID-19 cases in Poland over more than a year of the pandemic. The analysis utilized daily and weekly data for 380 counties (poviats), using the local – Poisson risk semivariogram – measure of spatial autocorrelation. Despite the heterogeneity and errors in the source data, it was possible to identify clear patterns of temporal changes in the spatial distribution of COVID-19 cases, manifested by differences in the nature and extent of their autocorrelation.

Azevedo, L., Pereira, M.J., Ribeiro, M.C., Soares, A. (2020). Geostatistical COVID-19 infection risk maps for Portugal. International Journal of Health Geographics, 19(25). https://doi.org/10.1186/s12942-020-00221-5
Bhunia, G.S., Roy, S., Shit, P.K. (2021). Spatio-temporal analysis of COVID-19 in India - a geostatistical approach. Spatial Information Research, 1-12. https://doi.org/10.1007/s41324-020-00376-0
Bochenek, B., Jankowski, M., Gruszczynska, M., Nykiel, G., Gruszczynski, M., Jaczewski, A., Ziemianski, M., Pyrc, R., Figurski, M., Pinkas, J. (2021). Impact of meteorological conditions on the dynamics of the COVID-19 pandemic in Poland. International Journal of Environmental Research and Public Health, 18, 3951. https://doi.org/10.3390/ijerph18083951
Castro, R,R., Santos, R.S.C., Sousa, G.J.B., Pinheiro, Y.T., Martins, R.R.I.M., Pereira, M.L.D., Silva, R.A.R. (2021). Spatial dynamics of the COVID-19 pandemic in Brazil. Epidemiology and Infection, 149, e60, 1-9. https://doi.org/10.1017/S0950268821000479
Danon, L., Brooks-Pollock, E., Bailey, M., Keeling, M. (2020). A spatial model of COVID-19 transmission in England and Wales: early spread and peak timing. MedRxiv, 2020.02.12.20022566. https://doi.org/10.1101/2020.02.12.20022566
De Oliveira, V. (2014). Poisson kriging: A closer investigation. Spatial Statistics, 7, 1-20. https://doi.org/10.1016/j.spasta.2013.11.001
Fatima, M., O'Keefe, K.J., Wei, W., Arshad, S., Gruebner, O. (2021). Geospatial analysis of COVID-19: A scoping review. International Journal of Environmental Research and Public Health, 18(5), 2336. https://doi.org/10.3390/ijerph18052336
Feng Y, Li Q, Tong X, Wang R, Zhai S, Gao C, et al. (2020). Spatiotemporal spread pattern of the COVID-19 cases in China. PLoS ONE, 15(12), e0244351. https://doi.org/10.1371/journal.pone.0244351
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. https://doi.org/10.1016/j.scitotenv.2020.140033
Gaudart, J., Landier, J., Huiart, L., Legendre, E., Lehot, L., Bendiane, M.K., Chiche, L., Petitjean, A., Mosnier, E., Kirakoya-Samadoulougou, F., Demongeot, J., Piarroux, R., Rebaudet, S. (2021). Factors associated with the spatial heterogeneity of COVID-19 in France: a nationwide geo-epidemiological study. Lancet Public Health, 6(4), E222-E231. https://doi.org/10.1016/S2468-2667(21)00006-2
Geostatistics Poland. (2020). https://geo.stat.gov.pl/start/-/asset_publisher/jNfJiIujcyRp/content/id/36734
Getis, A. (2010). B.3 Spatial Autocorrelation. In M.M. Fischer, A. Getis (Eds.), Handbook of applied spatial analysis: Software tools, methods and applications (pp. 255-278). Berlin-Heidelberg: Springer-Verlag. https://doi.org/10.1007/978-3-642-03647-7_14
Gomes, D.S., Andrade, L.A., Ribeiro, C.J.N., Peixoto, M.V.S., Lima, S.V.M.A., Duque, A.M., Cirilo, T.M., Góes, M.A.O., Lima, A.G.C.F., Santos, A.D. (2020). Risk clusters of COVID-19 transmission in northeastern Brazil: Prospective space-time modelling. Epidemiology and Infection, 148, e188, 1-8. https://doi.org/10.1017/S0950268820001843
Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University Press.
Goovaerts, P. (2005). Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. International Journal of Health Geographics, 4(31), 1-33. https://doi.org/10.1186/1476-072X-4-31
Goovaerts, P. (2017). Geostatistical interpolation of rate data using Poisson kriging. In S. Shekhar, H. Xiong, X. Zhou (Eds.), Encyclopedia of GIS: Second Edition (pp. 767-774). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-17885-1
Gupta, D., Biswas, D., Kabiraj, P. (2021). COVID-19 outbreak and Urban dynamics: Regional variations in India. GeoJournal. https://doi.org/10.1007/s10708-021-10394-6
Hass, F.S., Arsanjani, J. (2021). The geography of the COVID-19 pandemic: A data-driven approach to exploring geographical driving forces. International Journal of Environmental Research and Public Health, 18, 2803. https://doi.org/10.3390/ijerph18062803
Hernández-Flores, M. de la L., Escobar-Sánchez, J., Paredes-Zarco, J.E., Franyuti Kelly, G.A., CarranzaRamírez, L. (2020). Prediction and potential spatially explicit spread of COVID-19 in Mexico's megacity North Periphery. Healthcare, 8(4), 453. https://doi.org/10.3390/healthcare8040453
Hohl, A., Delmelle, E.M., Desjardins, M.R., Lan, Y. (2020). Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States. Spatial and Spatio-temporal Epidemiology, 34, 100354. https://doi.org/10.1016/j.sste.2020.100354
Huang, X., Zhou, H., Yang, X., Zhou, W., Huang, J., Yuan, Y. (2021). Spatial characteristics of Coronavirus disease 2019 and their possible relationship with environmental and meteorological factors in Hubei Province, China. GeoHealth, 5, e2020GH000358. https://doi.org/10.1029/2020GH000358
Jarynowski, A., Wójta-Kempa, M., Płatek, D., Krzowski, Ł., Belik, V. (2020). Spatial diversity of COVID-19 cases in Poland explained by mobility patterns - Preliminary results (June 6, 2020). https://doi.org/10.2139/ssrn.3621152
Jarynowski, A., Wójta-Kempa, M., Krzowski, Ł. (2020). An attempt to optimize human resources allocation based on spatial diversity of COVID-19 cases in Poland. medRxiv, 2020.10.14.20090985. https://doi.org/10.1101/2020.10.14.20090985
Kim, S., Marcia, C. 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. https://doi.org/10.1016/j.ijid.2020.07.004
Kowalski, P.A., Szwagrzyk, M., Kiełpinska, J., Konior, A., Kusy, M. (2021). Numerical analysis of factors, pace and intensity of the corona virus (COVID-19) epidemic in Poland. Ecological informatics, 63, 101284. https://doi.org/10.1016/j.ecoinf.2021.101284
Krivoruchko, K., Gribov, A., Krause, E. (2011). Multivariate areal interpolation for continuous and count data. Procedia Environmental Sciences, 3, 14-19. https://doi.org/10.1016/j.proenv.2011.02.004
Krzysztofik, R., Kantor-Pietraga, I., Spórna, T. (2020). Spatial and functional dimensions of the COVID-19 epidemic in Poland. Eurasian Geography and Economics, 619(4-5), 573-586, https://doi.org/10.1080/15387216.2020.1783337
Lai, P.-C., So, F.-M., Chan, K.-W. (2008). Spatial epidemiological approaches in disease mapping and analysis. CRC Press. https://doi.org/10.1201/9781420045536
Lawson, A.B. (2006). Statistical methods in spatial epidemiology, Second Edition. John Wiley & Sons. https://doi.org/10.1002/9780470035771
Li, S. (2020). The relationship between weekly periodicity and COVID-19 progression. medRxiv, preprint 2020.11.24. https://doi.org/10.1101/2020.11.24.20238295
Lipsitt, J., Chan-Golston, A.M., Liu, J., Su, J., Zhu, Y., Jerrett, M. (2021). Spatial analysis of COVID-19 and traffic-related air pollution in Los Angeles. Environment International, 153. https://doi.org/10.1016/j.envint.2021.106531
Liu, Y., He, Z., Zhou, X. (2020). Space-time variation and spatial differentiation of COVID-19 confirmed cases in Hubei Province based on extended GWR. ISPRS International Journal of Geo-Information, 9(9), 536. https://doi.org/10.3390/ijgi9090536
Medonet. (2020). https://www.medonet.pl/koronawirus/koronawirus-w-polsce,testy-na-covid-19-w-polsce---aktualizacja-,artykul,58274591.html
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, https://doi.org/10.1016/j.scitotenv.2020.138884
Monestiez, P., Dubroca, L., Bonnin, E., Durbec, J.-P., Guinet, C. (2006). Geostatistical modelling of spatial distribution of Balaenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogeneous observation efforts. Ecological Modelling, 193(3-4), 615-628. https://doi.org/10.1016/j.ecolmodel.2005.08.042
Mościcka, A., Araszkiewicz, A., Wabiński, J., Kuźma, M., Kiliszek, D. (2021). Modeling of various spatial patterns of SARS-CoV-2: The case of Germany. Journal of Clinical Medicine, 10(7), 1409. https://doi.org/10.3390/jcm10071409
Mounir Amdaoud, M., Arcuri, G., Levratto, N., Succurro, M., Costanzo, D. (2020). Geography of COVID-19 outbreak and first policy answers in European regions and cities. https://halshs.archives-ouvertes.fr/halshs-03046489
Niu B, Liang R, Zhang S, Zhang, H., Qu, X., Su, Q., Zheng, L., Chen, Q. (2020). Epidemic analysis of COVID-19 in Italy based on spatiotemporal geographic information and Google Trends. Transboundary and Emerging Diseases. https://doi.org/10.1111/tbed.13902
Oliver, M.A. (2010). B.6 The Variogram and Kriging. In M.M. Fischer, A. Getis (Eds.), Handbook of applied spatial analysis: Software tools, methods and applications (pp. 319-352). Berlin Heidelberg: Springer-Verlag. https://doi.org/10.1007/978-3-642-03647-7_17
Oliver, M.A., Muir, K.R., Webster, R., Parkes, S.E., Cameron, A.H., Stevens, M.C., Mann, J.R. (1992). A geostatistical approach to the analysis of pattern in rare disease. Journal of Public Health, 14(3), 280-289. https://doi.org/10.1093/oxfordjournals.pubmed.a042744
Oliver, M.A., Webster, R., Lajaunie, C., Muir, K.R., Parkes, S.E., Cameron, A.H., Stevens, M.C.G., Mann, J.R. (1998). Binomial cokriging for estimating and mapping the risk of childhood cancer. Mathematical Medicine and Biology: A Journal of the IMA, 15(3), 279-297, https://doi.org/10.1093/imammb/15.3.279
Pardo-Iguzquiza, E. (1999). VARFIT: A Fortran-77 program for fitting variogram models by weighted least squares. Computers and Geosciences, 25, 251-261. https://doi.org/10.1016/S0098-3004(98)00128-9
Parvin, F., Ali, S.A., Hashmi, S.N.I. Ateeque, A. (2021). Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique. Spatial Information Research. https://doi.org/10.1007/s41324-020-00375-1
Pfeiffer, D., Robinson, T., Stevenson, M., Stevens, K., Rogers, D., Clements, A. (2008). Spatial analysis in epidemiology. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198509882.001.0001
Pozzer, A., Dominici, F., Haines, A., Witt, C., Münzel, T., Lelieveld, J. (2020). Regional and global contributions of air pollution to risk of death from COVID-19. Cardiovascular Research, 116(14), 2247-2253. https://doi.org/10.1093/cvr/cvaa288
Ramírez-Aldana, R., Gomez-Verjan, J.C., Bello-Chavolla, O.Y. (2020). Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level. PLoS Neglected Tropical Diseases, 14(11), e0008875. https://doi.org/10.1371/journal.pntd.0008875
Rogalski, M. (2020). Internetowa baza danych o zakażeniach COVID według województw i powiatów, aktualizowana codziennie. https://docs.google.com/spreadsheets/d/1ierEhD6gcq51HAm433knjnVwey4ZE5DCnu1bW7PRG3E/edit?usp=sharing
Rosińska, M., Sadkowska-Todys, M., Stępień, M., Kitowska, W., Milczarek, M., Juszczyk, G. (2020). COVID-19 epidemic in Poland in spring and summer 2020. In B. Wojtyniak, P. Goryński (Eds.), Health status of Polish population and its determinants 2020 (pp. 333-350). Warsaw: National Institute of Public Health, National Institute of Hygiene. https://www.pzh.gov.pl/download/21915/
Rosińska, M., Sadkowska-Todys, M., Stępień, M., Kitowska, W., Milczarek, M., Juszczyk, G. (2020). Badanie seroprewalencji w populacji ogólnej i w grupie pracowników medycznych. Suplement do Rozdziału 7. Epidemia COVID-19 w Polsce na wiosnę i w lecie 2020. In B. Wojtyniak, P. Goryński (Eds.), Health status of Polish population and its determinants 2020 (pp. 1-9). Warsaw: National Institute of Public Health, National Institute of Hygiene. https://www.pzh.gov.pl/wp-content/uploads/2021/02/Suplement-do-Rozdzialu-7-seroprewalencja.pdf
Rynek Zdrowia. 2020. https://www.rynekzdrowia.pl/Polityka-zdrowotna/Koronawirus-w-Polsce2-036-700-potwierdzonych-zakazen-zmarlo-49-159-osob,204119,14.html
Sannigrahi, S., Pilla, F., Basu, B., Basu, A.S., Molter, A. (2020). Examining the association between sociodemographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable Cities and Society, 62, 102418. https://doi.org/10.1016/j.scs.2020.102418
Shadi Nazari, S., Norouzi, S., Asghari Jafar-abadi, M. (2020). How is Coronavirus distributed in the world? A Spatial-Temporal Assessment Using Geographic Information System Approach. Jorjani Biomedicine Journal, 8(1): P 24-33. https://doi.org/10.29252/jorjanibiomedj.8.1.24
Stach, A., Wysocka, P. (2014). Zastosowanie metody krigingu Poissona w badaniach rozkładu przestrzennego problemów społecznych na przykładzie Poznania. Acta Universitatis Lodziensis, Folia Geographica Socio-Oeconomica, 16, 169-188. http://dspace.uni.lodz.pl:8080/xmlui/bitstream/handle/11089/10674/Strony%20od%20FOLIA_16-10-STACH_WYSOCKApdf.pdf?sequence=1&isAllowed=y
Statistics Poland. 2020. https://stat.gov.pl/en/regional-statistics/classification-of-territorial-units/administrative-division-of-poland/
Śleszyński, P. (2020). Prawidłowości przebiegu dyfuzji przestrzennej rejestrowanych zakażeń koronawirusem SARS-CoV-2 w Polsce w pierwszych 100 dniach epidemii. Czasopismo Geograficzne, 91(1-2), 5-18. http://czasgeo.ptgeo.org.pl/ojs31/index.php/geo/issue/view/25/0045-9453%202020%20%281-2%29
Vaz, E. (2021). COVID-19 in Toronto: A Spatial exploratory analysis. Sustainability, 13, 498. https://doi.org/10.3390/su13020498
Waller, L.A., Gotway, C.A. (2004). Applied spatial statistics for public health data. New Jersey: John Wiley & Sons, Inc. https://doi.org/10.1002/0471662682
Weiss, D.J., Bertozzi-Villa, A., Rumisha, S.F., et al. (2020). Indirect effects of the COVID-19 pandemic on malaria intervention coverage, morbidity, and mortality in Africa: A geospatial modelling analysis. The Lancet Infectious Diseases, 21(1), 59-69. https://doi.org/10.1016/S1473-3099(20)30700-3