Comparison of theoretical approaches for epidemic processes with waning immunity in complex networks

Author(s)

Silva, Jose Carlos M. ; Silva, Diogo H. ; Rodrigues, Francisco A. ; Ferreira, Silvio C.

• Document type: Journal article
• Source: PHYSICAL REVIEW E; v. 106, n. 3, p. 9-pg., 2022-09-22.

Abstract

The role of waning immunity in basic epidemic models on networks has been undervalued while being noticeably fundamental for real epidemic outbreaks. One central question is which mean-field approach is more accurate in describing the epidemic dynamics. We tackled this problem considering the susceptible-infected-recovered-susceptible (SIRS) epidemic model on networks. Two pairwise mean-field theories, one based on recurrent dynamical message-passing (rDMP) theory and the other on the pair quenched mean-field (PQMF) theory, are compared with extensive stochastic simulations on large networks of different levels of heterogeneity. For waning immunity times longer than or comparable with the recovering time, rDMP outperforms PQMF theory on power-law networks with degree distribution P(k) similar to k(-gamma). In particular, for gamma > 3, the epidemic threshold observed in simulations is finite, in qualitative agreement with rDMP, while PQMF leads to an asymptotically null threshold. The critical epidemic prevalence for gamma > 3 is localized in a finite set of vertices in the case of the PQMF theory. In contrast, the localization happens in a subextensive fraction of the network in rDMP theory. Simulations, however, indicate that localization patterns of the actual epidemic lay between the two mean-field theories, and improved theoretical approaches are necessary to understanding the SIRS dynamics. (AU)

• FAPESP's process: 21/00369-0 - Localization in spreading processes on complex networks
• Grantee: Diogo Henrique da Silva
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/23293-0 - Prediction and inference in complex systems
• Grantee: Francisco Aparecido Rodrigues
• Support Opportunities: Regular Research Grants