Geostatistical COVID-19 infection risk maps for Portugal

Abstract

The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality’s population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time.

Spatiotemporal analysis and hotspots detection of COVID-19 using geographic information system (March and April, 2020)

“…Specially, it goes beyond just clustering analysis to map the spatiotemporal variation based on CIR and CMR values by the end of March and April. The areas with hot and cold spots can help public health authorities to take preventive measures as suggested earlier [ 33 , 34 ]. The output of current research and other studies which would be conducted in the future might lead to the construction of a modeling system to predict the prevalence of COVID-19 among high risk countries and to take preventive strategies as previously done regarding various infectious diseases [ 35 , 36 ].…”

Spatiotemporal Modelling of COVID-19 Infection Risk in Portugal

“…It is important to note that with the stochastic simulation, we intend to generate realizations of FPC factors and use them to reconstruct the evolution curves of infection ratios. At each point in space and time, the average of infection ratios is, by definition, the average risk of infection [2].…”

“…One of the crucial factors that have most conditioned approaches to characterizing the risk of infection by the SARS-CoV-2 virus is the lack of knowledge about the behaviour history of the virus and uncertainty about the way it spreads in predictions of new waves, and an inability to discriminate it, over space and time, from other viruses that present similar symptoms (e.g., seasonal influenza) [1]. Azevedo et al (2020) [2] proposed a method for mapping the risk of infection and uncertainty based on Poisson kriging [3] and a geostatistical model of stochastic simulation [4], which considers the daily infection rates by municipality and the uncertainty associated with the number of inhabitants. This work resulted in the creation of daily update maps of infection risk and associated uncertainty for Portugal: a useful tool for the management of the pandemic risk in the country.…”

“…When adding six months' of daily infection rate data from a large set of municipalities, there is a need to reduce the dimension of the problem so the local time series can be better understood in order to respond to those challenges. Here we build upon the work by Azevedo et al (2020) [2] and propose adding the temporal dimension to the characterization of infection risk maps (i.e. the simultaneous spatiotemporal modelling of space and time), combining Functional Data Analysis [5] and geostatistical stochastic models [6].…”

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis

“…There are several methods that have evolved and used in epidemiological studies in the recent past. These techniques can be categorized as linear and non-linear regression-based analysis, (non)spatial explicit models ( Franch-Pardo et al, 2020 ; Mollalo et al, 2020 ; Chakraborti et al, 2018 ; Chakraborti et al, 2019 ), spatio-temporal cluster analysis ( Cordes and Castro, 2020 ; Kang et al, 2020 ), agent based modelling ( Xu et al, 2019 ), block-sequential analysis ( Azevedo et al, 2020 ), Bayesian approach ( Gayawan et al, 2020 ), neural network analysis ( Kapoor et al, 2020 ), to name a few. Spatial explicit models such as Geographically Weighted Regression and Multiscale Geographically Weighted Regression, have been dominantly used in previous studies ( Cordes and Castro, 2020 ; Kang et al, 2020 ; Mollalo et al, 2020 ; Sun et al, 2020 ).…”