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This study examines how temporary lockdown measures affect disease extinction in heterogeneous populations using the susceptible-infected-susceptible (SIS) model on assortative networks where nodes with similar connectivity patterns tend to connect. Using semiclassical approximation and numerical simulations, the researchers demonstrate that the risk of disease extinction depends on three key factors: the duration of the lockdown, its intensity (magnitude of transmission rate reduction), and the structural properties of the social network including degree-degree correlations between connected individuals.
Why it matters
The findings provide quantitative insights into optimizing lockdown strategies for disease control by identifying how network structure and intervention parameters influence the probability of complete disease clearance. This research could inform public health policy decisions about when temporary restrictions are most likely to eliminate infectious diseases rather than merely suppress them.
arXiv:2512.18652v2 Announce Type: replace-cross
Abstract: Changing environmental conditions can significantly affect the dynamics of disease spread. These changes may arise naturally or result from human interventions; in the latter case, lockdown measures that lead to abrupt but temporary reductions in transmission rates are used to combat disease spread. Yet, the impact of these measures on rare events in heterogeneous populations remains understudied. Here, we analyze the susceptible-infected-susceptible (SIS) model in a stochastic setting where disease extinction — a sudden clearance of the infection — occurs via a rare, large fluctuation. We use a semiclassical approximation and numerical simulations on heterogeneous assortative networks, with degree-degree correlations between neighboring nodes, to show how the extinction risk of the disease depends on the lockdown’s duration and magnitude, and on the network topology.
Source: Impact of temporary lockdown on disease extinction in assortative networks