Infection spreading and recovery in a square lattice

Impact of diffusion on synchronization pattern of epidemics in non-identical meta-population networks

In epidemic networks, it has been demonstrated that implementing any intervention strategy on nodes with specific characteristics (such as a high degree or node betweenness) substantially diminishes the outbreak size. We extend this finding with a disease-spreading meta-population model using testkits to explore the influence of migration on infection dynamics within the distinct communities of the network. Notably, we observe that nodes equipped with testkits and no testkits tend to segregate into two separate clusters when migration is low, but above a critical migration rate, they coalesce into one single cluster. Based on this clustering phenomenon, we develop a reduced model and validate the emergent clustering behavior through comprehensive simulations. We observe this property in both homogeneous and heterogeneous networks.

Optimal test-kit-based intervention strategy of epidemic spreading in heterogeneous complex networks

We propose a deterministic compartmental model of infectious disease that considers the test kits as an important ingredient for the suppression and mitigation of epidemics. A rigorous simulation (with an analytical argument) is provided to reveal the effective reduction of the final outbreak size and the peak of infection as a function of basic reproduction number in a single patch. Furthermore, to study the impact of long and short-distance human migration among the patches, we consider heterogeneous networks where the linear diffusive connectivity is determined by the network link structure. We numerically confirm that implementation of test kits in a fraction of nodes (patches) having larger degrees or betweenness centralities can reduce the peak of infection (as well as the final outbreak size) significantly. A next-generation matrix-based analytical treatment is provided to find out the critical transmission probability in the entire network for the onset of epidemics. Finally, the optimal intervention strategy is validated in two real networks: the global airport network and the transportation network of Kolkata, India.