Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria

Abstract

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

Supporting COVID-19 policy-making with a predictive epidemiological multi-model warning system

“…The onset of the third wave, however, coincided with the takeover of the Alpha variant in Austria, which was successfully anticipated by the models. Taken together, these findings suggest that mechanistic epidemiological model can foresee certain types of turning points (e.g., due to NPIs or the emergence of more transmissible variants), while further research is needed to integrate other classes of drivers, such as changes in mobility 22 or meteorological factors 33 .…”

“…Seasonal forcing is modelled by the cosine function, with a maximum positive impact on transmission rates in January. The magnitude of seasonal effects are based on literature estimates 33 . The number of imported cases is based on Austrian contact-tracing data 34 .…”

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of Tropical Andean South America: a spatiotemporally disaggregated time series analysis

“…85 A small, inverse effect of wind speed above a threshold high in the distribution was identified, consistent with that identified by Clouston and colleagues. 86 While several other studies have also found inverse effects 61,63 , and still others have reported direct 37,64,67 , or negligible 65 effects of wind speed, it seems highly plausible that faster wind speeds suppress transmission of SARS-CoV-2 in outdoor environments by increasing air circulation and dispersing infective aerosols away from susceptible individuals, much as ventilation does in indoor environments. 80,86 The modeled effects of several non-hydrometeorological variables were consistent with the a priori hypotheses justifying their inclusion.…”

“…12,20,22,59 More recently, as attempts to track the pandemic have coalesced into a wide variety of open datasets and online interfaces 15,16,60 , researchers have begun to address these knowledge gaps. Numerous recent studies have assessed effects on COVID-19 outcomes adjusting for multiple hydrometeorological variables 35,61 and other covariates including population density 44 , age structure 62 , NPI compliance 33,63 and government interventions 64 , while others have focused on single countries in equatorial regions 65,66 or multiple countries and locations spanning wide latitudes and both hemispheres. 19,67,68 This study is the first to bring together all these elements and at a high temporal resolution, multiple, cross-cutting spatial scales and for three neighboring countries that, despite including diverse populations and ecologies, share important commonalities in their pandemic experiences.…”

Association between non-pharmaceutical interventions and the spread of infectious disease during the Covid-19 outbreak in Germany: Application of a hierarchical Bayesian Approach

“…We found similar substantial reductions in the reproduction number for restaurants permitted only with tests and restaurant closure. Ledebur et al [26] reported restrictions in gastronomy reduced transmissions by about 17%. Although we should keep in mind that their analysis focused on less disruptive measures that did not consist of full closures, but rather of restrictions such as mandatory registration of visitors, limits for the opening hours, or the number of people seated at a table.…”