Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil (original) (raw)
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2020
We present the first geographic study that uses space-time statistics to monitor COVID-19 in Brazil. The first cases of COVID-19 were confirmed in December 2019 in Wuhan, China, caused by the contamination of the SARS-CoV-2 virus, and quickly turned into a pandemic. In Brazil, the first case occurred on January 23rd, 2020 but was officially reported by the Brazilian Ministry of Health on February 25th. Since then, the number of deaths and people infected by COVID-19 in Brazil have been steadily increasing. Despite the underreporting of coronavirus cases by government agencies across the country, the State of São Paulo has the highest rate among all Brazilian States. Thus, it is essential to detect which areas contain the highest concentration of COVID-19 to implement public policies, to mitigate the spread of the epidemic. To identify these critical areas, we utilized daily confirmed case data from the Brasil.IO website between February 25th, 2020 to May 5th, 2020; which were aggreg...
2020
The first case of COVID-19 in South America occurred in Brazil on February 25th, 2020. By June 7th, 2020, there were 691,758 confirmed cases, 36,455 confirmed deaths, and a mortality rate of 5.3%. To assist with the establishment of measures for the strategic planning to combat the COVID-19 pandemic in Brazil, we present the first Brazilian geographic study with the aims to examine “active” hand “emerging” space-time clusters of COVID-19. We examine the associations between clusters and mortality rate, vulnerability, and social inequality. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk from February 25th - June 7th, 2020 in 5,570 Brazilian municipalities. We apply a Spearman’s statistic to measure correlation between the relative risk of each cluster and mortality rate, GINI index, and social inequality. We detected 11 emerging space-time clusters of COVID-19 occurring in all Brazilian regions, with seven of them wit...
Space-time analysis of the first year of COVID-19 pandemic in the city of Rio de Janeiro, Brazil
Revista Brasileira de Epidemiologia, 2021
ABSTRACT: Objective: To describe the space-time evolution of cases and deaths due to COVID-19 in the Rio de Janeiro municipality, Brazil, during the first year of the pandemic. Methods: An ecological study was carried out. The units of analysis were the neighborhoods of the city of Rio de Janeiro. Incidence and mortality rates, excess risk, Global Moran's Index (Moran's I), local indicator for spatial association, standardized incidence ratio, and standardized mortality ratio were estimated for neighborhoods in the municipality of Rio de Janeiro. Results: Over the first year of the pandemic, registries in the city of Rio de Janeiro included 204,888 cases and 19,017 deaths due to COVID-19. During the first three months of the pandemic, higher incidence rates were verified in the municipality compared with the state of Rio de Janeiro and Brazil, in addition to higher mortality rates compared with the state of Rio de Janeiro and Brazil from May 2020 to February 2021. Bonsucesso...
2021
Objective: to analyze the spatial-temporal distribution of COVID-19 in the state of Piauí. Method: ecological, retrospective study, with data available from the COVID-19 Epidemiological Panel Piauí. A time series of cases and deaths accumulated monthly was constructed and incidence, mortality and lethality rates were calculated and choropleth maps were constructed using Quantum GIS, version 2.18.6. Results: in March 2020, three cases were recorded, without death, reaching September 2020 with 90,370 cases and 2,037 deaths, with a slight reduction in the growth of rates from August. Teresina presented the second lowest incidence coefficient of the state, the second highest mortality coefficient and the highest lethality. Conclusion: there was a wide growth of the pandemic in the state, especially until August 2020, with lethality within the expected, and the spatial distribution of cases and deaths concentrated in the capital and surroundings, evidencing the need for strong preventive...
International Journal of Population Data Science
ObjectivesCovid-19 databases have detailed information about each affected person in Brazil, but it has flaws in counting the number of cases, which are underreported. We aimed to construct and correct the cases dataset by linking different sources of data observations to study the pandemic evolution in Brazilian municipalities. ApproachUsing the electronic Unified Health System (e-SUS), a public and governmental database, we calculated the pandemic curves of COVID-19 cases. We applied the following approaches to investigate data anomalies a) to perform a descriptive analysis and compare these results with a non-governmental database using Dynamic Time Warping distance; b) to verify and correct municipalities data anomalies linking to other public governmental database namely National Council of Health Secretaries (CONASS) with e-SUS. c) To apply a K-means DTW Barycenter Averaging in clustering analysis to describe the general behaviors of pandemic in Brazilian Municipalities. Resu...
Spatial and spatiotemporal clustering of the COVID-19 pandemic in Ecuador
Revista de la Facultad de Medicina
Introduction: In Ecuador, the first COVID-19 case, the disease caused by the SARS-CoV-2 virus, was officially reported on February 29, 2020. As of April 2, the officially confirmed numbers of COVID-19 cases and deaths from it were 3 163 and 120, respectively, that is, a mortality rate of 3.8%. Objective: To identify spatial and spatiotemporal clusters of COVID-19 cases officially confirmed in Ecuador. Materials and methods: Case series study. An analysis of all COVID-19 cases officially confirmed in Ecuador from March 13, 2020 to April 2, 2020 was performed. Relative Risk (RR) of COVID-19 contagion was determined using the discrete Poisson distribution model in the SaTScan software. Clusters were generated using purely spatial and spatiotemporal scan statistics. Significance of each cluster was obtained through 999 iterations using the Monte Carlo simulation, obtaining the most probable random model. Results: As of April 2, spatiotemporal clustering allowed identifying two clusters ...
Incidence and Lethality of COVID-19 Clusters in Brazil via Circular Scan Method
REVISTA BRASILEIRA DE BIOMETRIA
The COVID-19 pandemic has spread rapidly around the world in a frightening way. In Brazil, the third country with the highest number of infected and deaths from the disease, it is important for government health authorities to identify the federation units that stand out in cases and deaths from this disease to target resources. The circular scan statistic proposed by Martin Kulldorff allows to identify with some statistical significance the units of the federation that stand out in relation to the number of cases and deaths of COVID-19 in Brazil. Such units of federation are known as clusters. Once these clusters were identified, we used the coefficients of incidence and lethality to better describe the behavior of these clusters during three phases of the pandemic: the initial phase, the peak phase, and also the stability and fall phase. We observed changes in the location of the clusters identified in these three phases and used the R software and also the SaTScan software to obt...
Covid-19 Space-time Cluster Detection Using Retrospective Analysis
2022
Background: As of the 31st of January 2021, there had been 102,399,513 con rmed cases of COVID-19 worldwide, with 2,217,005 deaths reported to WHO The goal of this study is to uncover the spatiotemporal patterns of COVID 19 in Ethiopia, which will aid in the planning and implementation of essential preventative measures. Methods We obtained data on COVID 19 cases reported in Ethiopia from November 23 to December 29, 2021, from an Ethiopian health data website that is open to the public. Kulldorff's retrospective space-time scan statistics were utilized to detect the temporal, geographical, and spatiotemporal clusters of COVID 19 at the county level in Ethiopia, using the discrete Poisson probability model.
Temporal and spatial characteristics of the spread of COVID-19 in Rio de Janeiro state and city
medRxiv, 2020
From the first cases detected in Wuhan, China, of infections by the disease of the new coronavirus, COVID-19, until the present moment of this pandemic, millions of people have already been infected and hundreds of thousands have died worldwide. The way in which the virus has been dispersed in Brazil, and more specifically in Rio de Janeiro, is the motivation of the present work. Our studies consist of analyzing temporal and spatial characteristics of the spread of COVID-19 in the municipalities of the state of Rio de Janeiro and in the neighborhoods of the state capital, based on open data published by the Health Departments of Governments of the State of RJ and the Municipality of Rio de Janeiro, covering the period from February 27, 2020 to April 27, 2020. For that, we use analysis of time evolution graphs and mappings of spatial distributions and statistics analysis of spatial correlation. Our results suggest that the initial stages of spreading the virus across the state occur ...
COVID-19 in medium-sized municipalities in the 14 health macro-regions of Minas Gerais, Brazil
Brazilian Journal of Medical and Biological Research, 2021
The present study focused on the scenario of confirmed cases of SARS-CoV-2 infection in the state of Minas Gerais (MG), Brazil, from March 2020 to March 2021. We evaluated the evolution of COVID-19 prevalence and death in one municipality from each of the 14 health macro-regions of MG state. Socio-demographic characteristics and variables related to the municipalities were analyzed. The raw dataset used in this study was freely sourced from the website Brasil.io. From the raw dataset, two time series were extracted: the cumulative confirmed cases of COVID-19 and cumulative death counts, and they were compared to the state data using a nowcasting approach. In order to make time series comparisons possible, all data was normalized per 100,000 inhabitants. When analyzing in light of colored wave code interventions initiated in August 2020 in MG, for the majority of the municipalities, there was an absence of clear influence on prevalence and deaths. The national holidays in the first s...