A Bayesian spatio-temporal study of meteorological factors affecting the spread of COVID-19

The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as the flu it's suggested that COVID-19 may become seasonal as immunity grows. Yet the effects of meteorological conditions on the spread of COVID-19 are poorly understood with previous studies producing contrasting results, due at least in part to limited and inconsistent study designs. This study investigates the effect of meteorological conditions on COVID-19 infections in England using a spatio-temporal model applied to case counts during the initial England lockdown. By modelling spatial and temporal effects to account for the nature of a human transmissible virus the model isolates meteorological effects. Inference based on 95% highest posterior density intervals shows humidity is negatively associated with COVID-19 spread. The lack of evidence for other weather factors affecting COVID-19 transmission shows care should be taken with respect to seasonality when designing COVID-19 policies and public communications.Comment: 23 pages, 13 figures (inclusive of references and appendix

A Bayesian spatio-temporal study of the association between meteorological factors and the spread of COVID-19

BACKGROUND: The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population, transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as influenza some authors have suggested COVID-19 may become seasonal as immunity grows. Despite this, the effects of meteorological conditions on the spread of COVID-19 are poorly understood. Previous studies have produced contrasting results, due in part to limited and inconsistent study designs. METHODS: This study investigates the effects of meteorological conditions on COVID-19 infections in England using a Bayesian conditional auto-regressive spatio-temporal model. Our data consists of daily case counts from local authorities in England during the first lockdown from March-May 2020. During this period, legal restrictions limiting human interaction remained consistent, minimising the impact of changes in human interaction. We introduce a lag from weather conditions to daily cases to accommodate an incubation period and delays in obtaining test results. By modelling spatio-temporal random effects we account for the nature of a human transmissible virus, allowing the model to isolate meteorological effects. RESULTS: Our analysis considers cases across England's 312 local authorities for a 55-day period. We find relative humidity is negatively associated with COVID-19 cases, with a 1% increase in relative humidity corresponding to a reduction in relative risk of 0.2% [95% highest posterior density (HPD): 0.1-0.3%]. However, we find no evidence for temperature, wind speed, precipitation or solar radiation being associated with COVID-19 spread. The inclusion of weekdays highlights systematic under reporting of cases on weekends with between 27.2-43.7% fewer cases reported on Saturdays and 26.3-44.8% fewer cases on Sundays respectively (based on 95% HPDs). CONCLUSION: By applying a Bayesian conditional auto-regressive model to COVID-19 case data we capture the underlying spatio-temporal trends present in the data. This enables us to isolate the main meteorological effects and make robust claims about the association of weather variables to COVID-19 incidence. Overall, we find no strong association between meteorological factors and COVID-19 transmission