Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada.

Modelling the spatiotemporal spread of a highly transmissible disease is challenging. We developed a novel spatiotemporal spread model, and the neighbourhood-level data of COVID-19 in Toronto was fitted into the model to visualize the spread of the disease in the study area within two weeks of the onset of first outbreaks from index neighbourhood to its first-order neighbourhoods (called dispersed neighbourhoods). We also model the data to classify hotspots based on the overall incidence rate and persistence of the cases during the study period.

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada.

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken.

Spatial variations of COVID-19 risk by age in Toronto, Canada.

The risk of coronavirus disease 2019 (COVID-19) may vary by age, biological, socioeconomic, behavioural and logistical reasons may be attributed to these variations. In Toronto, Canada, the aging population has been severely impacted, accounting for 92% of all COVID-19 deaths. Four age groups: 60-69 years, 70-79 years, 80-89 years and ≥90 years in Toronto neighbourhoods were investigated for clustering tendencies using space-time statistics.

Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling.

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto.

Spatial and population drivers of persistent cholera transmission in rural Bangladesh: Implications for vaccine and intervention targeting.

We identify high risk clusters and measure their persistence in time and analyze spatial and population drivers of small area incidence over time. The geographically linked population and cholera surveillance data in Matlab, Bangladesh for a 10-year period were used. Individual level data were aggregated by local 250 × 250 m communities.