Urban contact structures for epidemic simulations: Correcting biases in data-driven approaches

TL;DR

This paper compares epidemic simulation models based on public transit data versus mobile phone data, revealing that transit data underestimates outbreak severity and delays peak timing due to incomplete movement information.

Contribution

It introduces a method to correct biases in epidemic models caused by reliance on public transit data by comparing with mobile phone movement data.

Findings

Transit-based models underestimate epidemic spread.

Epidemic peaks are weaker and later with transit data.

Mobile phone data provides more accurate movement patterns.

Abstract

Epidemics are emergent phenomena depending on the epidemiological characteristics of pathogens and the interaction and movement of people. Public transit systems have provided much important information about the movement of people, but there are also other means of transportation (e.g., bicycle and private car), that are invisible to public transit data. This discrepancy can induce a bias in disease models that leads to mispredictions of epidemic growth (e.g., peak prevalence and time). In our study, we aim to advance and compare the epidemic spreading dynamics using public transit trips, in contrast to more accurate estimates of population movement using mobile phones traces. In our study, we simulate epidemic outbreaks in a cohort of two million mobile phone users. We use a metapopulation model incorporating susceptible-infected-recovered dynamics to analyze and compare different effective contract matrices, constructed by the public transit systems and mobile phones respectively, on the process of epidemics. We find that epidemic outbreaks using public transit trips tend to be underestimated in terms of the epidemic spreading dynamics, reaching epidemic peaks weaker and later. This is rooted in a later introduction of new infectious people into uninfected locations.