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This paper introduces a novel multi-scale immuno-epidemiological model where individual behavior modification in response to influenza-like illness is directly driven by personal symptom severity, not just population case counts. By linking within-host immune dynamics to systemic symptom scores (muscle ache, fatigue, headache, feverishness), the model captures how individual illness perception influences transmission. The key finding is that stronger illness-driven behavior change effectively delays and lowers epidemic peaks, while maintaining a qualitatively similar epidemic trajectory to standard SEIR models without behavior change, unlike population-level feedback models.
Individual symptom severity, not just population case counts, significantly alters epidemic trajectories, delaying and lowering peaks without qualitatively diverging from standard SEIR models.
For many infectious diseases, behavior change is not a population-level reaction to rising case counts, but a personal one, triggered by how sick an individual feels. Yet most models fail to capture how illness severity drives this response. Multi-scale models that link the immunological scale to the epidemiological scale seem well-suited for capturing this behavior. Most multi-scale models link within-host viral load to population-level transmission rates through linking functions that are a function of viral load. However, viral load is often misaligned with symptom severity and thus possibly ill-suited for the purpose of informing behavior at a population level. We present a novel multi-scale model in which symptomatic individuals dynamically modify their transmission based on systemic symptom scores (a composite measure of muscle ache, fatigue, headache, and feverishness informed by within-host immune dynamics) while asymptomatic individuals do not change behavior. We find that stronger illness-driven behavior change delays and lowers the epidemic peak. Moreover, the resulting epidemic trajectory remains qualitatively similar to that of a standard SEIR model without behavior change, across several distinct choices of linking functions connecting the two scales. This stands in contrast to models where behavior change is driven by population-level feedback (e.g., reported cases or deaths), which can produce qualitatively distinct signatures such as plateaus, shoulders, or an elongated epidemic decline. In the case when an exponential function of symptom score is used as a linking function, we show that the standard SEIR model can closely reproduce the total number of infectious individuals at a given point in time generated through the multi-scale level.