Abstract
Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran's I spatial autocorrelation, and Local Moran's I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.
Highlights
This study aimed to produce community-level geospatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making
We identified which of these postal codes (PC) streaks occurred in close proximity (Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl) based on the neighbors of each postal code from PC centroid data
Changes in the distribution of COVID-19 infections over time were evaluated through geostatistical analysis within both forward sortation area (FSA) and PCs
Summary
This study aimed to produce community-level geospatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This included the case fatality rate, at what point during the natural history of the disease was it most transmissible (before, during or after the onset of symptoms), the frequencies of asymptomatic presentation of positive cases, and the duration of infectivity.[1] Quarantining, social distancing, and isolation of infected populations were quickly recognized as effective public health measures Without implementation of these measures in Ontario, COVID-19 patients presenting with severe disease would have exceeded available hospital capacity.[2] Delayed testing and the sheer numbers of infected individuals often precluded comprehensive contact tracing.[3] COVID-19 testing was limited in many regions within Canada early in the pandemic, as was the availability of necessary resources (e.g., intensive care units, nursing and other clinical personnel, personal protective equipment).[4,5] Limited resources must be adequately allocated to those locations where the rates of transmission of COVID-19 are high and where the highest numbers of infected individuals reside.
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