Causal graph analysis of COVID-19 observational data in German districts reveals effects of determining factors on reported case numbers

Abstract

Several determinants are suspected to be causal drivers for new cases of COVID-19 infection. Correcting for possible confounders, we estimated the effects of the most prominent determining factors on reported case numbers. To this end, we used a directed acyclic graph (DAG) as a graphical representation of the hypothesized causal effects of the determinants on new reported cases of COVID-19. Based on this, we computed valid adjustment sets of the possible confounding factors. We collected data for Germany from publicly available sources (e.g. Robert Koch Institute, Germany’s National Meteorological Service, Google) for 401 German districts over the period of 15 February to 8 July 2020, and estimated total causal effects based on our DAG analysis by negative binomial regression. Our analysis revealed favorable effects of increasing temperature, increased public mobility for essential shopping (grocery and pharmacy) or within residential areas, and awareness measured by COVID-19 burden, all of them reducing the outcome of newly reported COVID-19 cases. Conversely, we saw adverse effects leading to an increase in new COVID-19 cases for public mobility in retail and recreational areas or workplaces, awareness measured by searches for “corona” in Google, higher rainfall, and some socio-demographic factors. Non-pharmaceutical interventions were found to be effective in reducing case numbers. This comprehensive causal graph analysis of a variety of determinants affecting COVID-19 progression gives strong evidence for the driving forces of mobility, public awareness, and temperature, whose implications need to be taken into account for future decisions regarding pandemic management.

Balancing timeliness of reporting with increasing testing probability for epidemic data

“…Therefore, increases in false-positives can covary with actual disease risk when there is a higher abundance of true-positives, although this does not mean that false-positives are trivial ( Surkova et al., 2020 ). Survey-based or internet trend sampling (e.g., Rubin et al., 2010 ; Steiger et al., 2021 ) could be combined with epidemic data to provide real-time measures of the dynamics modeled here. Our model, however, can represent a diversity of real-world systems, as socio-cultural dynamics may impede or enhance awareness, reassurance, and social norms.…”

“…Case counts contribute to the reporting of epidemiological information, which can generate concern regarding disease spread among the population being reported on and encourage the use of protective behaviors. Disease surveillance and subsequent reporting can thereby indirectly mitigate outbreaks by generating public awareness of disease transmission ( Rubin et al., 2010 ; Steiger et al., 2021 ). Such concern can influence wider decision-making within communities, since people aware of increased risk may be more likely to adopt or adhere to protective behaviors that reduce their likelihood of acquiring or transmitting infection ( Wise et al., 2020 ).…”

The Effects of Climate and Bioclimate on COVID-19 Cases in Poland

“…The opposite relation has also been reported [26,27], as well as there being no relations at all [28][29][30][31]. Steiger [21] showed that weather, especially temperature (which has a reducing effect on case numbers) and rainfall (which increases case numbers) affects the reported number of SARS-CoV-2 infections. This study aimed to investigate the spatial patterns of COVID-19 cases (SARS-CoV-2 infected, fatal, and recovered) and the effects of climate and bioclimate on observed changes in infected cases at the state and county levels in Poland.…”

“…The opposite relation has also been reported [26,27], as well as there being no relations at all [28][29][30][31]. Steiger [21] showed that weather, especially temperature (which has a reducing effect on case numbers) and rainfall (which increases case numbers) affects the reported number of SARS-CoV-2 infections.…”

“…The seasonality similarity of COVID-19 to other human coronaviruses and influenza let to assume hypothesis with a peak during winter months because of lower temperature, lower absolute humidity, and lower indoor relative humidity [18]. However, mixed evidence is available regarding the effect of changes in temperature on the number of cases of SARS-CoV-2 infections [21]. Several papers reported an association between an increase in temperature and a decrease in newly infected COVID-19 cases [10,12,18,[22][23][24][25][26].…”