Impact of air temperature and containment measures on mitigating the intrahousehold transmission of SARS-CoV-2: a data-based modelling analysis.

Abstract

Air temperature has been considered a modifiable and contributable variable in COVID-19 transmission. Implementation of non-pharmaceutical interventions (NPIs) has also made an impact on COVID-19 transmission, changing the transmission pattern to intrahousehold transmission under stringent containment measures. Therefore, it is necessary to re-estimate the influence of air temperature on COVID-19 transmission while excluding the influence of NPIs. This study is a data-based comprehensive modelling analysis. A stochastic epidemiological model, the ScEIQR model (contactable susceptible-exposed-infected-quarantined-removed), was established to evaluate the influence of air temperature and containment measures on the intrahousehold spread of COVID-19. Epidemic data on COVID-19, including daily confirmed cases, number of close contacts, etc, were collected from the National Health Commission of China. The model was fitted using the Metropolis-Hastings algorithm with a cost function based on the least squares method. The LOESS (locally weighted scatterplot smoothing) regression function was used to assess the relationship between air temperature and rate of COVID-19 transmission within the ScEIQR model. The ScEIQR model indicated that the optimal temperature for spread of COVID-19 peaked at 10℃ (50℉), ranging from 5℃ to 14℃ (41℉-57.2℉). In the fitted model, the fitted intrahousehold transmission rate (β') of COVID-19 was 10.22 (IQR 8.47-12.35) across mainland China. The association between air temperature and β' of COVID-19 suggests that COVID-19 might be seasonal. Our model also validated the effectiveness of NPIs, demonstrating that diminishing contactable susceptibility (Sc) and avoiding delay in diagnosis and hospitalisation (η) were more effective than contact tracing (κ and ρ). We constructed a novel epidemic model to estimate the effect of air temperature on COVID-19 transmission beyond implementation of NPIs, which can inform public health strategy and predict the transmission of COVID-19.