Surveillance of the first cases of COVID-19 in Sergipe using a prospective spatiotemporal analysis: the spatial dispersion and its public health implications

Abstract

Introduction: Coronavirus disease 2019 (COVID-19) has become a global public health emergency with lethality ranging from 1% to 5%. This study aimed to identify active high-risk transmission clusters of COVID-19 in Sergipe. Methods: We performed a prospective space-time analysis using confirmed cases of COVID-19 during the first 7 weeks of the outbreak in Sergipe. Results: The prospective space-time statistic detected "active" and emerging spatio-temporal clusters comprising six municipalities in the south-central region of the state. Conclusions: The Geographic Information System (GIS) associated with spatio-temporal scan statistics can provide timely support for surveillance and assist in decision-making.

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

“…There were more than 17 million cases and over 650 000 deaths by COVID-19 confirmed worldwide [1]. Notably, the pandemic has been challenging for health systems and governments, as the extent of the social and economic impacts of the pandemic is still uncertain [3].…”

“…Following the disease's dynamics and the exponential growth of the number of cases, several studies have been reported [2][3][4][5]. Particularly, those that perform temporal and spatial analyses of COVID-19 have demonstrated the impact of morbidity, mortality and global geographical dissemination of the disease in the world.…”

“…Particularly, those that perform temporal and spatial analyses of COVID-19 have demonstrated the impact of morbidity, mortality and global geographical dissemination of the disease in the world. The use of aggregate spatial data allows to map the patterns of the rapid progression of the disease and to support decision-making in the allocation of resources for the prevention and control of COVID-19 in priority areas [3][4][5][6].…”

Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

“…The contribution of cluster detections and analysis in COVID-19 pandemic is becoming useful nowadays as it detects active and emerging clusters of COVID-19 and notify epidemiologist, decision makers, and public health care officials, which can help in eradicating infections from affected sites, and improving interventions, quarantine, and isolation measures. The significant applications of clustering with respect to infectious diseases modelling are demonstrated across the world (Zarikas et al 2020 ), for example, India (Bhosale and Shinde 2020 ), USA (Desjardins et al 2020 ; Hohl et al 2020 ), Brazil (Martines et al 2020 ), Italy (Cereda et al 2020 ), China (Ji et al 2020 ; Liu et al 2020a , b ; Qiu et al 2020 ; Zhang et al 2020 ), Singapore (Bhosale and Shinde 2020 ; Pung et al 2020 ), South Korea (Shim et al 2020 ), French Alps (Danis et al 2020 ), Germany (Pfefferle et al 2020 ), Sergipe (Andrade et al 2020 ), etc.…”

Is it time to talk about the end of social distancing? A joinpoint analysis of COVID-19 time series in Brazilian capitals

“…The study of spatio-temporal patterns can help us understand the mechanisms of disease spread in a population, allowing the detection of behaviors in time and spatial clusters that represent characteristics related to dissemination 10 . Specifically, the use of segmented regression analysis enabled us to identify points in time when the trends changed significantly, generating quantitative hypotheses about changes in the behavior of the pandemic among Brazilian capitals.…”