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

Abstract

This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

Geospatial Analysis of COVID-19: A Scoping Review

“…A study in Rio De Janeiro, Brazil by Santos et al [ 44 ] used ArcGIS to determine that city neighborhoods with higher average household density, high tuberculosis incidence, and large older populations (>60 years) were more vulnerable to COVID-19 infections. Gomes et al [ 40 ] used a combination of various spatial statistical techniques in a set of diverse software packages (Joint Point Regression Program for time trend analysis, SaTScan, TerraView, and QGIS) to investigate spatiotemporal clusters of risk transmission of COVID-19 in Brazil. Their results showed higher COVID-19 risk in the northeastern metropolitan areas, as compared to the more rural parts of Brazil.…”

“…Many authors reported that the interpretation of their findings was limited by ecological fallacy [ 61 ]. Some limitations that were pointed out by researchers included bias of data because of low testing rate and asymptomatic population [ 43 ], underreporting of COVID-19 cases and deaths [ 39 ], and lack of locational data [ 40 ]. Because of such limitations, most of the studies did not explore causal relationships among COVID-19 variables [ 48 ].…”

On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data

“…A tracking system of the nighttime population using mobile phone location data would be helpful for policy decision makers to monitor these dynamics in a real-time manner. Space-time dispersions in transmission of COVID-19 have occurred from metropolitan areas towards the countryside [ 31 ]. Thus, an automated information system to support strategic policy decision making is of particular importance in metropolitan areas with high population density and mobility, where there is an elevated risk of COVID-19 transmission.…”

COVID-19 Dynamics: A Heterogeneous Model

“…During the time this manuscript was under review, the COVID-19 epidemic continued to develop, and the predictions of our model were confirmed. A spatial spread of the epidemic was observed in Asia ( 55 , 56 ), Europe ( 57 , 58 ), Africa ( 59 , 60 ), South America ( 61 ), and North America ( 62 – 65 ). Moreover, as predicted by the model, lifting anti-epidemic measures resulted in a second wave of the epidemic across the world, which we are currently witnessing ( 66 – 69 ).…”