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

Abstract

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. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.

Oscillating Spatiotemporal Patterns of COVID-19 in the United States

“…1 Although reporting can be incomplete and imperfect, we were able to mine this rich spatiotemporal data similar to other studies that have employed advanced computational methods. [2][3][4][5] By detecting and describing heretofore unrecognized recurring patterns we provide new evidence that the COVID-19 epidemic in the USA has recurring spatiotemporal components.…”

A Bayesian spatio-temporal model of COVID-19 spread in England

“…We used this ecological study design because the data required for an individual-level study were not available due to confidentiality reasons. We note, however, that this ecological approach has been predominant in the COVID-19 literature that focuses on exploring the distribution pattern of the pandemic and its influencing risk factors 2 , 75 , 76 . Our findings outlined above should be treated as indicative associations, rather than conclusive evidence of individual-level causation.…”

COVID-19 Pandemic Risk Assessment: Systematic Review

“…The Markov Chain Monte Carlo method with different initial values was used to fit each model The Joinpoint Regression Program, which uses the least-squares regression method, was used to find the best-fit line from the temporal (weekly) pattern [19] (Continued) instructions for open access to the data and the codes for their studies. Even if counting as open access the seven articles that offered possible accessibility to their data and codes upon request from the corresponding authors, more than half of the 62 included articles (34 articles) still did not provide this option.…”

“…The outbreak in 2019 of the novel coronavirus disease (COVID- 19), which the World Health Organization (WHO) officially declared a global pandemic on 11 March 2020, 1 is currently the most detrimental worldwide public health event of the twenty-first century. The disease's rapid transmission not only has imposed tremendous pressures on the public health systems, but it also has severely disrupted the financial markets, 2,3 our society and the global economy, 4 and our environment.…”