Abstract
Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.
Highlights
Mathematical models are an essential tool to understand and predict epidemic spread in space and time [1]
Using time-averaged mobile phone data resulted in a good approximation to the spatio-temporal disease dynamics in Bangladesh, projected by models informed by daily mobility data
Our finding is in accordance with studies of individual mobile phone mobility trajectories, showing that human mobility is highly predictable and regular in both time and space [22,23,24]
Summary
Mathematical models are an essential tool to understand and predict epidemic spread in space and time [1]. Understanding the limitations of using time-averaged mobile phone data and model approximations to human movement is essential to guide the choice of mobility measures in models and further development in this field. The country does not have detailed census data for commuting and travel flow prediction, and synthetic models for movement patterns or mobile phone data are in demand. We conduct a data-driven simulation study to compare the spatial dissemination of influenza in Bangladesh using highly detailed mobile phone data. To this aim, we extend a fine-scaled stochastic SEIIaR metapopulation model developed in [12], and fit the model to influenza hospital case data. We document the feasibility of applying sequential Monte Carlo approximate Bayesian computation (ABC-SMC)-techniques to estimate parameters in a stochastic metapopulation model informed by scarce influenza case data
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