https://doi.org/10.4081/gh.2022.1145
Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy
Submitted: 16 August 2022
Accepted: 10 November 2022
Published: 1 December 2022
Accepted: 10 November 2022
Abstract Views: 1231
PDF: 730
Appendix: 73
HTML: 65
Appendix: 73
HTML: 65
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21–65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures.
Ali K, Partridge MD, Olfert MR, 2007. Can geographically weighted regressions improve regional analysis and policy making? Int Reg Sci Rev 30:300-31.
DOI: https://doi.org/10.1177/0160017607301609
Armocida B, Formenti B, Ussai S, Palestra F, Missoni E, 2020. The Italian health system and the COVID-19 challenge. Lancet Public Health 5:e253.
DOI: https://doi.org/10.1016/S2468-2667(20)30074-8
Benson T, Chamberlin J, Rhinehart I, 2005. An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi. International Food Policy Research Institute. Washington DC, USA. Available from: http://gisweb.ciat.cgiar.org/povertymapping/download/Malawi.pdf
DOI: https://doi.org/10.1016/j.foodpol.2005.09.004
Braun JW, Rousson V, 2000. An autocorrelation criterion for bandwidth selection in nonparametric regression. J Statist Computat Simulat 68:89-101.
DOI: https://doi.org/10.1080/00949650008812057
Brunsdon C, Fotheringham AS, Charlton ME 1996. Geographically weighted regression: a method for exploring spatial non-stationarity. Geogr Anal 28:281–98.
DOI: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Bui LV, Mor Z, Chemtob D, Ha ST, Levine H, 2018. Use of Geographically Weighted Poisson Regression to examine the effect of distance on Tuberculosis incidence: A case study in Nam Dinh, Vietnam. PLoS One 13:e0207068.
DOI: https://doi.org/10.1371/journal.pone.0207068
CDC COVID-19 Response Team, 2020. Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) - United States, February 12-March 16, 2020. MMWR Morb Mortal Wkly Rep 69:343–6.
DOI: https://doi.org/10.15585/mmwr.mm6912e2
CDC, 2020. Coronavirus COVID-19: symptoms of coronavirus. Centers for Disease Control and Prevention. Accessed April 18, 2020. Available from: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
Chen J, Liu L, Zhou S, Xiao L, Jiang C, 2017. Spatial variation relationship between floating population and residential burglary: a case study from ZG, China. ISPRS Int J Geo-Inf 6:246.
DOI: https://doi.org/10.3390/ijgi6080246
Chen M, Chen Y, Wilson JP, Tan H, Chu T, 2022. Using an eigenvector spatial filtering-based spatially varying coefficient model to analyze the spatial heterogeneity of COVID-19 and its influencing factors in mainland China. ISPRS Int J Geo-Inf 11:67.
DOI: https://doi.org/10.3390/ijgi11010067
Chen VY-J, Wu P-C, Yang T-C, Su H-J, 2010. Examining non-stationary effects of social determinants on cardiovascular mortality after cold surges in Taiwan. Sci Total Environ 408:2042–9.
DOI: https://doi.org/10.1016/j.scitotenv.2009.11.044
Chen YY, Assefa Y, 2021. The heterogeneity of the COVID-19 pandemic and national responses: an explanatory mixed-methods study. BMC Public Health 21:835.
DOI: https://doi.org/10.1186/s12889-021-10885-8
Choi M, Lee M, Lee MJ, Jung D, 2017. Physical activity, quality of life and successful aging among community-dwelling older adults. Int Nurs Rev 64:396–404.
DOI: https://doi.org/10.1111/inr.12397
Choi WS, Kang CI, Kim Y, Choi JP, Joh JS, Shin HS, Kim G, Peck KR, Chung DR, Kim HO, Song SH, Kim YR, Sohn KM, Jung Y, Bang JH, Kim NJ, Lee KS, Jeong HW, Rhee JY, Kim ES, Woo H, Oh WS, Huh K, Lee YH, Song JY, Lee J, Lee CS, Kim BN, Choi YH, Jeong SJ, Lee JS, Yoon JH, Wi YM, Joung MK, Park SY, Lee SH, Jung SI, Kim SW, Lee JH, Lee H, Ki HK, Kim YS; Korean Society of Infectious Diseases, 2016. Clinical presentation and outcomes of Middle East respiratory syndrome in the Republic of Korea. Infect Chemother 48:118–26.
DOI: https://doi.org/10.3947/ic.2016.48.2.118
Cox D, 1972. Regression models and life tables (with discussion). J Roy Stat Soc 34:187–202.
DOI: https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
Davies TM, Lawson AB, 2019. An evaluation of likelihood-based bandwidth selectors for spatial and spatiotemporal kernel estimates. J Statist Computat Simulat 89:1131-52.
DOI: https://doi.org/10.1080/00949655.2019.1575066
Dowd JB, Andriano L, Brazel DM, Rotondi V, Block P, Ding X, Liu Y, Mills MC, 2020. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proc Natl Acad Sci U S A 117:9696-9698.
DOI: https://doi.org/10.1073/pnas.2004911117
Fotheringham AS, Brunsdon C, Charlton M. 2003. Geographically weighted regression: The analysis of spatially varying relationships John Wiley & Sons.
Fotheringham AS, Kelly MH, Charlton M, 2013. The demographic impacts of the Irish famine: Towards a greater geographical understanding. Transact Inst Bri Geograph 38:221-237.
DOI: https://doi.org/10.1111/j.1475-5661.2012.00517.x
Fresán U, Guevara M, Trobajo-Sanmartín C, Burgui C, Ezpeleta C, Castilla J, 2021. Hypertension and related comorbidities as potential risk factors for COVID-19 hospitalization and severity: a prospective population-based cohort study. J Clin Med 10:1194.
DOI: https://doi.org/10.3390/jcm10061194
Gatto M, Bertuzzo E, Mari L, Miccoli S, Carraro L, Casagrandi R, Rinaldo A, 2020. Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proc Natl Acad Sci U S A 117:10484-10491.
DOI: https://doi.org/10.1073/pnas.2004978117
Gaudart J, Landier J, Huiart L, Legendre E, Lehot L, Bendiane MK, Chiche L, Petitjean A, Mosnier E, Kirakoya-Samadoulougou F, Demongeot J, Piarroux R, Rebaudet S, 2021. Factors associated with the spatial heterogeneity of the first wave of COVID19 in France: a nationwide geo-epidemiological study. Lancet Public Health 6:e222-e231.
DOI: https://doi.org/10.1016/S2468-2667(21)00006-2
Goovaerts, P. 2005. Analysis and detection of health disparities using geostatistics and a space-time information system: The case of prostate cancer mortality in the United States, 1970-1994. Proceedings of GIS Planet 2005. (May 30-June 2, 2005, Estoril, Portugal).
Hadayeghi A, Amer S. Shalaby, Bhagwant N. Persaud. 2009. Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. Accid Anal Prev 42:676-88.
DOI: https://doi.org/10.1016/j.aap.2009.10.016
Haque U, Scott LM, Hashizume M, Fisher E, Haque R, Yamamoto T, Glass GE, 2012. Modelling malaria treatment practices in Bangladesh using spatial statistics. Malar J 201211:63.
DOI: https://doi.org/10.1186/1475-2875-11-63
Hou X, Gao S, Li Q, Kang Y, Chen N, Chen K, Rao J, Ellenberg JS, Patz JA, 2021. Intra county modelling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age, and race. Proc Natl Acad Sci U S A 118:e2020524118.
DOI: https://doi.org/10.1073/pnas.2020524118
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B, 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395:497-506.
DOI: https://doi.org/10.1016/S0140-6736(20)30183-5
Jayaswal SK, Singh S, Malik PS, Venigalla SK, Gupta P, Samaga SN, Hota RN, Bhatia SS, Gupta I, 2021. Detrimental effect of diabetes and hypertension on the severity and mortality of COVID-19 infection: A multi-center case-control study from India. Diabetes Metab Syndr 15:102248.
DOI: https://doi.org/10.1016/j.dsx.2021.102248
Klekotka RB, Mizgała E, Król W. 2015. The ethology of lower respiratory tract infections in people with diabetes. Pneumonol Alergol Pol 2015;83:401-8.
DOI: https://doi.org/10.5603/PiAP.2015.0065
Landstra CP, de Koning EJP, 2021. COVID-19 and Diabetes: Understanding the Interrelationship and Risks for a Severe Course. Front Endocrinol (Lausanne) 12:649525.
DOI: https://doi.org/10.3389/fendo.2021.649525
Li Z, Wang W, Liu P, Bigham JM, Ragland DR, 2013. Using Geographically Weighted Poisson Regression for county-level crash modelling in California, Safety Sci 58:89-97.
DOI: https://doi.org/10.1016/j.ssci.2013.04.005
Loubert L, 3.27 - Using GIS to Understand Schools and Neighborhoods. Comprehensive Geographic Information Systems. Elsevier, 2018, pp. 422-439.
DOI: https://doi.org/10.1016/B978-0-12-409548-9.09684-6
Magnavita N, Sacco A, Chirico F, 2020. Covid-19 pandemic in Italy: pros and cons. Zdrowie Publicznei Zarządzanie 18:32-35.
DOI: https://doi.org/10.4467/20842627OZ.20.002.12656
Matthews SA, Yang TC. Mapping the results of local statistics: Using geographically weighted regression. Demogr Res 2012 26:151-166.
DOI: https://doi.org/10.4054/DemRes.2012.26.6
Mennis JL, Jordan LM, 2005. The distribution of environmental equity: exploring spatial non-stationarity in multivariate models of air toxic releases. Ann Ass Am Geographers doi:10.1111/j.1467-8306.2005.00459.x.
DOI: https://doi.org/10.1111/j.1467-8306.2005.00459.x
Mollalo A, Alimohammadi A, Shirzadi MR, Malek MR, 2015. Geographic information system-based analysis of the spatial and spatio-temporal distribution of zoonotic cutaneous leishmaniasis in Golestan Province, north-east of Iran. Zoonoses Public Health 62:18-28.
DOI: https://doi.org/10.1111/zph.12109
Mollalo A, Khodabandehloo E 2016. Zoonotic cutaneous leishmaniasis in northeastern Iran: a GIS-based spatio-temporal multi-criteria decision-making approach. Epidemiol Infect 144:2217-29.
DOI: https://doi.org/10.1017/S0950268816000224
Nakaya T, Fotheringham AS, Brunsdon C, Charlton M, 2005. Geographically weighted Poisson regression for disease association mapping. Stat Med 24:2695-717.
DOI: https://doi.org/10.1002/sim.2129
Nakaya T. 2014. GWR4 User Manual: Windows application for geographically weighted regression modelling [Internet]. Available from: https://gwr.maynoothuniversity.ie/wp-content/uploads/2013/04/GWR4_Manual.pdf
Nassar M, Daoud A, Nso N, Medina L, Ghernautan V, Bhangoo H, Nyein A, Mohamed M, Alqassieh A, Soliman K, Alfishawy M, Sachmechi I, Misra A, 2021. Diabetes Mellitus and COVID-19: Review Article. Diabetes Metab Syndr 15:102268.
DOI: https://doi.org/10.1016/j.dsx.2021.102268
Oshan TM, Li Z, Kang W, Wolf LJ, Fotheringham AS, 2019. mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS Int J Geo-Inf 8:269.
DOI: https://doi.org/10.3390/ijgi8060269
Pedro SA, Ndjomatchoua FT, Jentsch P, Tchuenche JM, Anand M and Bauch CT. 2020 Conditions for a Second Wave of COVID-19 Due to Interactions Between Disease Dynamics and Social Processes. Front Phys doi: 10.3389/fphy.2020.574514.
DOI: https://doi.org/10.1101/2020.05.22.20110502
Poliart A, Kirakoya-Samadoulougou F, Ouédraogo M, Collart P, Dubourg D, Samadoulougou S, 2021. Using geographically weighted Poisson regression to examine the association between socio-economic factors and hysterectomy incidence in Wallonia, Belgium. BMC Womens Health 21:373.
DOI: https://doi.org/10.1186/s12905-021-01514-y
Qiu Y, Chen X, Shi W, 2020. Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China. J Popul Econ 33:1127-1172.
DOI: https://doi.org/10.1007/s00148-020-00778-2
Shawky M., Abdullah K, Alkhattab S, Adham S, Peter A. 2021. Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustain Cities Soc 65:102627.
DOI: https://doi.org/10.1016/j.scs.2020.102627
Slocum, TA .1999. Thematic Cartography and Visualization. Prentice-Hall.
Specchia ML, Di Pilla A, Sapienza M, Riccardi MT, Cicchetti A, Damiani G, Group IR, 2021. Dealing with COVID-19 Epidemic in Italy: Responses from Regional Organizational Models during the First Phase of the Epidemic. Int J Environ Res Public Health 18:5008.
DOI: https://doi.org/10.3390/ijerph18095008
Srivastava A, Chowell G. 2020. Understanding Spatial Heterogeneity of COVID-19 Pandemic Using Shape Analysis of Growth Rate Curves. medRxiv [Preprint]. doi: 10.1101/2020.05.25.20112433.
DOI: https://doi.org/10.2196/preprints.21011
Sun P, Lu X, Xu C, Sun W, Pan B. 2020. Understanding of COVID‐19 based on current evidence. J Med Virol 2020;92:548-551.
DOI: https://doi.org/10.1002/jmv.25722
Takele K, Zewotir T, Ndanguza D, 2019. Understanding correlates of child stunting in Ethiopia using generalized linear mixed models. BMC Public Health 19:626.
DOI: https://doi.org/10.1186/s12889-019-6984-x
Ufficio Statistica Regionale Regione Emilia Romagna. 10 April 2019. Available from: https://sociale.regione.emilia-romagna.it/notizie/2019/aprile/la-fotografia-dei-cittadini-stranieri-a-bologna-150-le-nazionalita (in Italian).
Umpierrez GE, Isaacs SD, Bazargan N, You X, Thaler LM, Kitabchi AE.2002. Hyperglycemia: an independent marker of in-hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol Metab 87:978-82.
DOI: https://doi.org/10.1210/jcem.87.3.8341
Wang Y and Teunis P. 2020. Strongly heterogeneous transmission of COVID-19 in mainland China: Local and regional variation. Front Med 7:329.
DOI: https://doi.org/10.3389/fmed.2020.00329
Wang W, Li D, 2017. Structure identification and variable selection in geographically weighted regression models. J Statist Computat Simulat 87:2050-2068.
DOI: https://doi.org/10.1080/00949655.2017.1311896
WHO, 2020a. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Available from: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
WHO, 2020b. Coronavirus disease (COVID-19) outbreak. Available from: https://www.who.int/westernpacific/health-topics/coronavirus
Worldometers, “COVID-19 CORONAVIRUS PANDEMIC,” 2021. Accessed: 07-April-2022. Available: https://www.worldometers.info/coronavirus/
Yang T-C, Teng HW, Haran M. 2009. The impacts of social capital on infant mortality in the U.S.: A spatial investigation. Applied Spatial Analysis and Policy 2:211-227.
DOI: https://doi.org/10.1007/s12061-009-9025-9
Yang TC, Matthews SA, 2012. Understanding the non-stationary associations between distrust of the health care system, health conditions, and self-rated health in the elderly: a geographically weighted regression approach. Health Place 18:576-85.
DOI: https://doi.org/10.1016/j.healthplace.2012.01.007
Young LJ, Gotway CA. 2010.Using geostatistical methods in the analysis of public health data: the final frontier? geoENV VII - Geostatistics for Environmental Applications.
DOI: https://doi.org/10.1007/978-90-481-2322-3_8
Zeleke AJ, Moscato S, Miglio R, Chiari L, 2022. Length of stay analysis of COVID-19 hospitalizations using a count regression model and quantile regression: a study in Bologna, Italy. Int J Environ Res Public Health 19:2224.
DOI: https://doi.org/10.3390/ijerph19042224
Zhang H, Liu Y, Chen F, Mi B, Zeng L, Pei L, 2021.The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis. BMC Infect Dis 21:428.
DOI: https://doi.org/10.1186/s12879-021-06128-1
Zhao F, Park N, 2004. Using geographically weighted regression models to estimate annual average daily traffic. In: Transportation Research Record 1879. Washington, DC: Transportation Research Board, National Research Council: 99-107.
DOI: https://doi.org/10.3141/1879-12
Zheng X, Qin G, Tu D, 2017. A generalized partially linear mean-covariance regression model for longitudinal proportional data, with applications to the analysis of quality of life data from cancer clinical trials. Stat Med 36:1884-1894.
DOI: https://doi.org/10.1002/sim.7240
Zhou YB, Wang QX, Liang S, Gong YH, Yang MX, Chen Y, Nie SJ, Nan L, Yang AH, Liao Q, Yang Y, Song XX, Jiang QW, 2015. Geographical variations in risk factors associated with HIV infection among drug users in a prefecture in Southwest China. Infect Dis Poverty 4:38.
DOI: https://doi.org/10.1186/s40249-015-0073-x
How to Cite
Zeleke, A. J., Miglio, R., Palumbo, P., Tubertini, P., Chiari, L., & and the National Institute for Nuclear Physics (INFN), B. M. S. G. of the U. of B. (2022). Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy. Geospatial Health, 17(2). https://doi.org/10.4081/gh.2022.1145
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
