2
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Detecting space–time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect “active” and “emerging” space–time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space–time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25–June 7, 2020, and February 25–July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 “active” clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.

          Related collections

          Most cited references47

          • Record: found
          • Abstract: not found
          • Article: not found

          Covid-19: risk factors for severe disease and death

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Predictors of Mortality for Patients with COVID-19 Pneumonia Caused by SARS-CoV-2: A Prospective Cohort Study

            To identify factors associated with the death for patients with COVID-19 pneumonia caused by a novel coronavirus SARS-CoV-2. All clinical and laboratory parameters were collected prospectively from a cohort of patients with COVID-19 pneumonia who were hospitalised to Wuhan Pulmonary Hospital, Wuhan City, Hubei Province, China, between December 25, 2019 and February 7, 2020. Univariate and multivariate logistic regression was performed to investigate the relationship between each variable and the risk for death of COVID-19 pneumonia patients. A total of 179 patients with COVID-19 pneumonia (97 male and 82 female) were included in the present prospective study, of whom 21 died. Univariate and multivariate logistic regression analysis revealed that age ≥65 years (odd ratio, 3.765; 95% confidence interval, 1.146‒17.394; p=0.023), preexisting concurrent cardiovascular or cerebrovascular diseases (2.464; 0.755‒8.044; p=0.007), CD3+CD8+ T cells ≤75 cell·μL−1 (3.982; 1.132‒14.006; p<0.001), and cardiac troponin I≥0.05 ng·mL−1 (4.077; 1.166‒14.253; p<0.001) were associated with an increase in risk of mortality of COVID-19 pneumonia. In the sex‒, age‒, and comorbid illness-matched case study, CD3+CD8+ T cells ≤75 cell·μL−1 and cardiac troponin I≥0.05 ng·mL−1 remained to be the predictors for high mortality of COVID-19 pneumonia. We identified four risk factors, age ≥65 years, preexisting concurrent cardiovascular or cerebrovascular diseases, CD3+CD8+ T cells ≤75 cell·μL−1, and cardiac troponin I≥0.05 ng·mL−1, especially the latter two factors, were predictors for mortality of COVID-19 pneumonia patients.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Another Decade, Another Coronavirus

              For the third time in as many decades, a zoonotic coronavirus has crossed species to infect human populations. This virus, provisionally called 2019-nCoV, was first identified in Wuhan, China, in persons exposed to a seafood or wet market. The rapid response of the Chinese public health, clinical, and scientific communities facilitated recognition of the clinical disease and initial understanding of the epidemiology of the infection. First reports indicated that human-to-human transmission was limited or nonexistent, but we now know that such transmission occurs, although to what extent remains unknown. Like outbreaks caused by two other pathogenic human respiratory coronaviruses (severe acute respiratory syndrome coronavirus [SARS-CoV] and Middle East respiratory syndrome coronavirus [MERS-CoV]), 2019-nCoV causes respiratory disease that is often severe. 1 As of January 24, 2020, there were more than 800 reported cases, with a mortality rate of 3% (https://promedmail.org/). As now reported in the Journal, Zhu et al. 2 have identified and characterized 2019-nCoV. The viral genome has been sequenced, and these results in conjunction with other reports show that it is 75 to 80% identical to the SARS-CoV and even more closely related to several bat coronaviruses. 3 It can be propagated in the same cells that are useful for growing SARS-CoV and MERS-CoV, but notably, 2019-nCoV grows better in primary human airway epithelial cells than in standard tissue-culture cells, unlike SARS-CoV or MERS-CoV. Identification of the virus will allow the development of reagents to address key unknowns about this new coronavirus infection and guide the development of antiviral therapies. First, knowing the sequence of the genome facilitates the development of sensitive quantitative reverse-transcriptase–polymerase-chain-reaction assays to rapidly detect the virus. Second, the development of serologic assays will allow assessment of the prevalence of the infection in humans and in potential zoonotic sources of the virus in wet markets and other settings. These reagents will also be useful for assessing whether the human infection is more widespread than originally thought, since wet markets are present throughout China. Third, having the virus in hand will spur efforts to develop antiviral therapies and vaccines, as well as experimental animal models. Much still needs to be learned about this infection. Most important, the extent of interhuman transmission and the spectrum of clinical disease need to be determined. Transmission of SARS-CoV and MERS-CoV occurred to a large extent by means of superspreading events. 4,5 Superspreading events have been implicated in 2019-nCoV transmission, but their relative importance is unknown. Both SARS-CoV and MERS-CoV infect intrapulmonary epithelial cells more than cells of the upper airways. 4,6 Consequently, transmission occurs primarily from patients with recognized illness and not from patients with mild, nonspecific signs. It appears that 2019-nCoV uses the same cellular receptor as SARS-CoV (human angiotensin-converting enzyme 2 [hACE2]), 3 so transmission is expected only after signs of lower respiratory tract disease develop. SARS-CoV mutated over the 2002–2004 epidemic to better bind to its cellular receptor and to optimize replication in human cells, enhancing virulence. 7 Adaptation readily occurs because coronaviruses have error-prone RNA-dependent RNA polymerases, making mutations and recombination events frequent. By contrast, MERS-CoV has not mutated substantially to enhance human infectivity since it was detected in 2012. 8 It is likely that 2019-nCoV will behave more like SARS-CoV and further adapt to the human host, with enhanced binding to hACE2. Consequently, it will be important to obtain as many temporally and geographically unrelated clinical isolates as possible to assess the degree to which the virus is mutating and to assess whether these mutations indicate adaptation to the human host. Furthermore, if 2019-nCoV is similar to SARS-CoV, the virus will spread systemically. 9 Obtaining patient samples at autopsy will help elucidate the pathogenesis of the infection and modify therapeutic interventions rationally. It will also help validate results obtained from experimental infections of laboratory animals. A second key question is identification of the zoonotic origin of the virus. Given its close similarity to bat coronaviruses, it is likely that bats are the primary reservoir for the virus. SARS-CoV was transmitted to humans from exotic animals in wet markets, whereas MERS-CoV is transmitted from camels to humans. 10 In both cases, the ancestral hosts were probably bats. Whether 2019-nCoV is transmitted directly from bats or by means of intermediate hosts is important to understand and will help define zoonotic transmission patterns. A striking feature of the SARS epidemic was that fear played a major role in the economic and social consequences. Although specific anticoronaviral therapies are still in development, we now know much more about how to control such infections in the community and hospitals, which should alleviate some of this fear. Transmission of 2019-nCoV probably occurs by means of large droplets and contact and less so by means of aerosols and fomites, on the basis of our experience with SARS-CoV and MERS-CoV. 4,5 Public health measures, including quarantining in the community as well as timely diagnosis and strict adherence to universal precautions in health care settings, were critical in controlling SARS and MERS. Institution of similar measures will be important and, it is hoped, successful in reducing the transmission of 2019-nCoV.
                Bookmark

                Author and article information

                Contributors
                Eric.Delmelle@uncc.edu
                Journal
                J Geogr Syst
                J Geogr Syst
                Journal of Geographical Systems
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1435-5930
                1435-5949
                8 March 2021
                8 March 2021
                : 1-30
                Affiliations
                [1 ]GRID grid.411247.5, ISNI 0000 0001 2163 588X, Department of Geography, Tourism and Humanities, Research Group: Center for Studies in Landscape Ecology and Conservation, , Federal University of São Carlos, ; Sorocaba, SP Brazil
                [2 ]GRID grid.411281.f, ISNI 0000 0004 0643 8003, Department of Geography, Research Group: Center for Studies in Landscape Ecology and Conservation, , Federal University of Triângulo Mineiro, ; Uberaba Campus, State of Minas Gerais Brazil
                [3 ]GRID grid.411247.5, ISNI 0000 0001 2163 588X, Department of Environmental Sciences, Research Group: Center for Studies in Landscape Ecology and Conservation, , Federal University of São Carlos, ; Sorocaba, SP Brazil
                [4 ]GRID grid.8430.f, ISNI 0000 0001 2181 4888, Faculty of Law, , State University of Minas Gerais, ; Ituiutaba Campus, Brazil
                [5 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Epidemiology, , Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, ; Baltimore, MD 21205 USA
                [6 ]GRID grid.266859.6, ISNI 0000 0000 8598 2218, Department of Geography and Earth Sciences, , Center for Applied Geographic Information Science, University of North Carolina at Charlotte, ; Charlotte, NC 28223 USA
                [7 ]GRID grid.9668.1, ISNI 0000 0001 0726 2490, Department of Geographical and Historical Studies, , University of Eastern Finland, ; 80101 Joensuu, Finland
                Author information
                http://orcid.org/0000-0002-7464-2431
                http://orcid.org/0000-0002-1637-2840
                http://orcid.org/0000-0001-6106-1200
                http://orcid.org/0000-0002-5117-2238
                Article
                344
                10.1007/s10109-020-00344-0
                7938278
                33716567
                051202c9-2ec2-4b4f-b4ba-4afd7026cc8b
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 June 2020
                : 15 December 2020
                Funding
                Funded by: University of Eastern Finland (UEF) including Kuopio University Hospital
                Categories
                Original Article

                disease surveillance,covid-19,geographic information systems,relative risk,space–time statistics,spatial models,c18,c31,i100,c020

                Comments

                Comment on this article