Abstract

The concurrent circulation of SARS-CoV-2 and influenza presents a complex immunological landscape. While biological evidence suggests that prior or current infection with one virus can alter susceptibility to the other, conventional epidemiological models often obscure these effects by aggregating co-infected populations into a single compartment. This structural simplification limits our ability to quantify how infection history shapes population-level transmission dynamics. We developed a stratified, deterministic co-infection model that explicitly distinguishes between single, concurrent, and sequential infections by accounting for sequence-dependent heterogeneity in susceptibility and transmissibility. Our primary innovation is a transmission source–pathway decomposition framework that mathematically attributes the rate of new infections to its specific transmission source (i.e., which infectious subpopulation is generating the transmission: the singly-infected, co-infected, or sequentially-infected class) and transmission pathway (i.e., which susceptibility class is receiving new infections: fully susceptible individuals, or those with prior immunity, or those with active co-infection). This framework accounts for altered susceptibility and transmissibility dependent on infection history. Our model-based analysis reveals a profound, sequence-driven asymmetry in transmission. In a baseline co-epidemic scenario, COVID-19 is predominantly driven by a sequential source: individuals who contracted COVID-19 after recovering from influenza are estimated to account for approximately 73% of new COVID-19 cases and approximately 76% of the disease burden, as predicted by our model. Conversely, influenza transmission remains driven by singly infected individuals (approximately 96% of new influenza cases inferred using our model). This sequence-driven asymmetry was robust to changes in model structure (especially, the inclusion of an influenza latent period in a sensitivity analysis) and across scenarios of varying relative transmissibility for the two viruses. Interventions exhibit pathway-specific effects: COVID-19 vaccination, for instance, disproportionately disrupts this dominant sequential transmission engine by protecting the most immunologically vulnerable hosts. Our model-based findings suggest that infection history may be a primary driver of co-epidemic dynamics. Our framework reveals a plausible, asymmetric interaction where an initial influenza wave can fundamentally reshape the transmission landscape for COVID-19, and demonstrates how a prior COVID-19 wave may fuel subsequent influenza transmission under specific temporal conditions. These findings generate the testable hypothesis that cross-viral susceptibility is a key control point and underscore the importance of pathway-aware intervention strategies that account for infection history.

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