The role of inter-regional mobility in forecasting SARS-CoV-2 transmission

TL;DR

This study evaluates how incorporating inter-regional mobility data into SEIR models improves the accuracy of SARS-CoV-2 transmission forecasts at the municipality level, especially during different policy phases.

Contribution

It introduces a mobility-enhanced SEIR model using commuter data and maximum likelihood estimation, demonstrating when mobility data improves forecast accuracy.

Findings

Mobility data generally improves forecast accuracy.

Policy measures reduce the impact of mobility on transmission.

Forecast improvements are minimal during strict contact-reduction policies.

Abstract

In this paper, we present a method to forecast the spread of SARS-CoV-2 across regions with a focus on the role of mobility. Mobility has previously been shown to play a significant role in the spread of the virus, particularly between regions. Here, we investigate under which epidemiological circumstances incorporating mobility into transmission models yields improvements in the accuracy of forecasting, where we take the situation in the Netherlands during and after the first wave of transmission in 2020 as a case study. We assess the quality of forecasting on the detailed level of municipalities, instead of on a nation-wide level. To model transmissions, we use a simple mobility-enhanced SEIR compartmental model with subpopulations corresponding to the Dutch municipalities. We use commuter information to quantify mobility, and develop a method based on maximum likelihood estimation to determine the other relevant parameters. We show that taking inter-regional mobility into account generally leads to an improvement in forecast quality. However, at times when policies are in place that aim to reduce contacts or travel, this improvement is very small.