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

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 nationwide 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. By contrast, the improvement becomes larger when municipalities have a relatively large amount of incoming mobility compared with the number of inhabitants.

Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case

“…The major limitation of this compartment model is that the total population is assumed to be constant throughout the entire process, while in the real world, the total population always keeps changing, which indicates that external human mobility was not considered in the model. Some studies showed that take human mobility into consideration will improve the forecast accuracy ( 33 ), and quantifying the trade-off between mobility and infection can provide guidelines for governments to make appropriate directives ( 34 ). In addition, demographic data such as age, gender, and income were not considered in the model, as well as some digital data from social medium may provide earlier indication that help to make prediction in time ( 35 ).…”

Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations

“…We chose to use a compartmental model for disease spread within each town, as is often done when mathematically modelling COVID-19 in a homogeneous, fully-mixed population ( 5 , 27 , 28 – 30 ). Our model is based on the Susceptible-Exposed-Infected-Removed ( ) model, but the Susceptible ( ) state has been split into the four different possible vaccination states at the time of carrying this modeling out: from unvaccinated ( ) to triple vaccinated ( ).…”

“…Western Australia is unique both geographically and in how the pandemic was handled and while topical and close to home for the authors, this example was selected mostly because it captures many aspects of the challenges that can be faced when working with aggregated data. Specifically, we deal with sparse and small flows between rural towns with small populations, meaning our raw data is often incomplete, which necessitates a different approach to that of similar work done for densely populated regions ( 5 ).…”