Using long-range mobility data to model the spread of human pathogens

DESCRIPTION
This is a hands-on tutorial aimed at people who want to learn about some specific methodologies for analyzing mobility data.

ABSTRACT
Human mobility is a key factor that influences the spread of infectious diseases. Short-distance movements can cause mixing patterns that affect local pathogen circulation and transmission rates. Conversely, long-distance mobility can cause pathogens to spread from epidemic hotspots to areas that were previously unaffected. With the availability of high-resolution mobility data across various spatial and temporal scales, it is crucial to properly analyze and integrate these data into mathematical models of epidemic spread. In this workshop, we will assume the detection of a new pathogen in a particular area and focus on assessing the risk of exporting it to other parts of the world using air traffic data. Given the limited knowledge about the new pathogen and the disease it causes, estimating the risk of importation is crucial early on in the epidemic. Therefore, the model we will use needs to be simple, relying more on mobility data than on epidemiological data since the latter may not be readily available. As such, we will develop a model that uses a few parameters and heavily relies on mobility data to estimate the risk of importation.
Inspired by these publications:
Pullano et al. (2020) Eurosurveillance
Gilbert et al. (2020) The Lancet