Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation.

Background: Respiratory pathogens inflict a substantial burden on public health and the economy. Although the severity of symptoms caused by these pathogens can vary from asymptomatic to fatal, the factors that determine symptom severity are not fully understood. Correlations in symptoms between infector-infectee pairs, for which evidence is accumulating, can generate large-scale clusters of severe infections that could be devastating to those most at risk, whilst also conceivably leading to chains of mild or asymptomatic infections that generate widespread immunity with minimal cost to public health. Although this effect could be harnessed to amplify the impact of interventions that reduce symptom severity, the mechanistic representation of symptom propagation within mathematical and health economic modelling of respiratory diseases is understudied. Methods and findings: We propose a novel framework for incorporating different levels of symptom propagation into models of infectious disease transmission via a single parameter, α. Varying α tunes the model from having no symptom propagation (α = 0, as typically assumed) to one where symptoms always propagate (α = 1). For parameters corresponding to three respiratory pathogens—seasonal influenza, pandemic influenza and SARS-CoV-2—we explored how symptom propagation impacted the relative epidemiological and health-economic performance of three interventions, conceptualised as vaccines with different actions: symptom-attenuating (labelled SA), infection-blocking (IB) and infection-blocking admitting only mild breakthrough infections (IB_MB). In the absence of interventions, with fixed underlying epidemiological parameters, stronger symptom propagation increased the proportion of cases that were severe. For SA and IB_MB, interventions were more effective at reducing prevalence (all infections and severe cases) for higher strengths of symptom propagation. For IB, symptom propagation had no impact on effectiveness, and for seasonal influenza this intervention type was more effective than SA at reducing severe infections for all strengths of symptom propagation. For pandemic influenza and SARS-CoV-2, at low intervention uptake, SA was more effective than IB for all levels of symptom propagation; for high uptake, SA only became more effective under strong symptom propagation. Health economic assessments found that, for SA-type interventions, the amount one could spend on control whilst maintaining a cost-effective intervention (termed threshold unit intervention cost) was very sensitive to the strength of symptom propagation. Conclusions: Overall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis. Author summary: Symptom propagation occurs when the symptoms of an infected individual depend, at least partially, on the symptoms of the person who infected them. An example is when an individual is more likely to develop severe symptoms if infected by someone with severe symptoms themselves. Symptom propagation has important implications for infection control strategies and could be harnessed to amplify the impact of vaccines that reduce the probability of severe disease. Evidence for symptom propagation is growing, yet it is rarely included in models of infectious disease transmission. Here, we provide a new infectious disease transmission model with a single parameter representing the strength of symptom propagation and study the consequences for vaccination control strategies. We show that the strength of symptom propagation has profound effects on infectious disease outbreaks, including notably on the proportion of cases that are severe. We demonstrate that vaccines that reduce symptom severity are more effective in reducing severe and overall cases when symptom propagation is stronger. Knowing the strength of symptom propagation can help understand the effectiveness of vaccines that reduce the risk of infection relative to those that reduce symptoms, helping to shape public health strategy. [ABSTRACT FROM AUTHOR]