学术文献库

Human mobility contributes to the fast spatio-temporal propagation of infectious diseases. During an outbreak, monitoring the infection situation on either side of an international border is very crucial as there is always a higher risk of disease importation associated with cross-border migration. Mechanistic models are effective tools to investigate the consequences of cross-border mobility on disease dynamics and help in designing effective control strategies. However, in practice, due to the unavailability of cross-border mobility data, it becomes difficult to propose reliable, model-based strategies. In this study, we propose a method for estimating cross-border mobility flux between any pair of regions that share an international border from the observed difference in the timing of the infection peak in each region. Assuming the underlying disease dynamics is governed by a Susceptible-Infected-Recovered (SIR) model, we employ stochastic simulations to obtain the maximum likelihood cross-border mobility estimate for any pair of regions where the difference in peak time can be measured. We then investigate how the estimate of cross-border mobility flux varies depending on the disease transmission rate, which is a key epidemiological parameter. We further show that the uncertainty in mobility flux estimates decreases for higher disease transmission rates and larger observed differences in peak timing. Finally, as a case study, we apply the method to some selected regions along the Poland-Germany border which are directly connected through multiple modes of transportation and quantify the cross-border fluxes from the COVID-19 cases data during the period $20^{\rm th}$ February $2021$ to $20^{\rm th}$ June $2021$.