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

TL;DR

This study uses a data-driven model to analyze how meteorological factors and non-pharmaceutical interventions influence regional SARS-CoV-2 spread in Austria, explaining over 60% of the variation.

Contribution

It introduces an age-structured compartmental model that quantifies the impact of weather and interventions on local transmission differences.

Findings

Unfavorable weather conditions significantly increase transmission rates.

Lack of mitigation measures for large events correlates with higher spread.

Over 60% of regional variation explained by combined factors.

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.