The benefits of remoteness – digital mobility data, regional road infrastructure, and COVID-19 infections

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Abstract

We investigate the regional distribution of the COVID-19 outbreak in Germany. We use a novel digital mobility dataset, that traces the undertaken trips on Easter Sunday 2020 and instrument them with regional accessibility as measured by the regional road infrastructure of Germany’s 401 NUTS III regions. We identify a robust negative association between the number of infected cases per capita and average travel time on roads to the next major urban center. What has been a hinderance for economic performance in good economic times, appears to be a benevolent factor in the COVID-19 pandemic: bad road infrastructure. Using road infrastructure as an instrument for mobility reductions we assess the causal effect of mobility reductions on infections. The study shows that keeping mobility of people low is a main factor to reduce infections. Aggregating over all regions, our results suggest that there would have been about 55,600 infections less on May 5th, 2020, if mobility at the onset of the disease were 10 percent lower.

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How Accessibility Shapes Land Use

Walter G. Hansen (1959)

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Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

Jonas Dehning, Johannes Zierenberg, F. Spitzner … (2020)

As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.