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      Spatial dynamics of the COVID-19 pandemic in Brazil

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          Abstract

          The objective of this study was to analyse the dynamics of spatial dispersion of the coronavirus disease 2019 (COVID-19) in Brazil by correlating them to socioeconomic indicators. This is an ecological study of COVID-19 cases and deaths between 26 February and 31 July 2020. All Brazilian counties were used as units of analysis. The incidence, mortality, Bayesian incidence and mortality rates, global and local Moran indices were calculated. A geographic weighted regression analysis was conducted to assess the relationship between incidence and mortality due to COVID-19 and socioeconomic indicators (independent variables). There were confirmed 2 662 485 cases of COVID-19 reported in Brazil from February to July 2020 with higher rates of incidence in the north and northeast. The Moran global index of incidence rate (0.50, P = 0.01) and mortality (0.45 with P = 0.01) indicate a positive spatial autocorrelation with high standards in the north, northeast and in the largest urban centres between cities in the southeast region. In the same period, there were 92 475 deaths from COVID-19, with higher mortality rates in the northern states of Brazil, mainly Amazonas, Pará and Amapá. The results show that there is a geospatial correlation of COVID-19 in large urban centres and regions with the lowest human development index in the country. In the geographic weighted regression, it was possible to identify that the percentage of people living in residences with density higher than 2 per dormitory, the municipality human development index (MHDI) and the social vulnerability index were the indicators that most contributed to explaining incidence, social development index and the municipality human development index contributed the most to the mortality model. We hope that the findings will contribute to reorienting public health responses to combat COVID-19 in Brazil, the new epicentre of the disease in South America, as well as in other countries that have similar epidemiological and health characteristics to those in Brazil.

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          Most cited references32

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

            During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
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              Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China

              This study models local and cross-city transmissions of the novel coronavirus in China between January 19 and February 29, 2020. We examine the role of various socioeconomic mediating factors, including public health measures that encourage social distancing in local communities. Weather characteristics 2 weeks prior are used as instrumental variables for causal inference. Stringent quarantines, city lockdowns, and local public health measures imposed in late January significantly decreased the virus transmission rate. The virus spread was contained by the middle of February. Population outflow from the outbreak source region posed a higher risk to the destination regions than other factors, including geographic proximity and similarity in economic conditions. We quantify the effects of different public health measures in reducing the number of infections through counterfactual analyses. Over 1.4 million infections and 56,000 deaths may have been avoided as a result of the national and provincial public health measures imposed in late January in China.
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                Author and article information

                Journal
                Epidemiol Infect
                Epidemiol Infect
                HYG
                Epidemiology and Infection
                Cambridge University Press (Cambridge, UK )
                0950-2688
                1469-4409
                2021
                25 February 2021
                : 149
                : e60
                Affiliations
                [1 ]Postgraduate program in Clinical Nursing Care and Health, Universidade Estadual do Ceará , Fortaleza, Ceará, Brasil
                [2 ]Postgraduate program in Nursing, Faculdade Metropolitana de Ciências e Tecnologia, Parnamirim, Rio Grande do Norte, Brasil
                [3 ]Faculdade Maurício de Nassau , João Pessoa, Paraíba, Brasil
                [4 ]Faculdade Santo Antônio de Caçapava , São Paulo, Brasil
                [5 ]Postgraduate program in Clinical Care in Nursing and Health, Universidade Estadual do Ceará , Fortaleza, Ceará, Brasil
                [6 ]Postgraduate program in Nursing, Universidade Federal do Rio Grande do Norte , Natal, Rio Grande do Norte, Brasil
                [7 ]Postgraduate program in Collective Health, Universidade Federal do Rio Grande do Norte , Natal, Rio Grande do Norte, Brasil
                Author notes
                Author for correspondence: R. R. Castro, E-mail: revia_ribeiro@ 123456hotmail.com
                Author information
                https://orcid.org/0000-0002-9260-4148
                https://orcid.org/0000-0003-0291-6613
                Article
                S0950268821000479
                10.1017/S0950268821000479
                7985898
                33629938
                2954e0c1-6c9b-4a8c-b67b-ce93e38f79b9
                © The Author(s) 2021

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 August 2020
                : 16 February 2021
                : 18 February 2021
                Page count
                Figures: 3, Tables: 2, References: 36, Pages: 9
                Categories
                Special Collection Question
                Post-Covid 19
                Original Paper

                Public health
                brazil,covid-19,pandemic,spatial analysis
                Public health
                brazil, covid-19, pandemic, spatial analysis

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