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Compartmental epidemic models increasingly capture demographic and contact heterogeneities, yet behavioral responses are typically treated as uniform: as cases or deaths rise, an average person perceives greater risk and increases compliance with non-pharmaceutical interventions (e.g., masking), reducing transmission. But when is treating societal behavior as a single average feedback loop a safe simplification? We develop a two-group compartmental behavioral epidemic model in which groups differ in combinations of infection fatality ratio, susceptibility, and contact rates, which leads to distinct behavioral responses to the same epidemic signals. We show increased variation in mortality risk (through different infection fatality ratio) alters dynamics: homogeneous assumptions can lead to underestimated prevalence and overestimated fatality. In contrast, heterogeneity in susceptibility alone reduces cumulative cases and deaths compared to homogeneous assumptions. Furthermore, differences in mixing patterns specifically amplify the effects of mortality risk heterogeneity. Overall, a counterintuitive result emerges: a lower-risk group which leads to a weaker response reaches herd immunity early, indirectly shielding a more cautious (i.e., responsive), higher-risk group. We further extend the model to nine age groups reflecting COVID-19 risk variation, examining how behavioral heterogeneity shapes outcomes at a finer scale. Together, these findings clarify when explicitly representing heterogeneous behavioral responses is essential for reliable epidemic modeling. Highlights • Most behavioral epidemic models assume homogeneous behavioral response. • We study when behavioral variation can be simplified as average behavior. • Ignoring behavioral heterogeneity can bias epidemic burden estimates. • Lower-risk groups reaching herd immunity early can shield higher-risk groups. • Mixing patterns can amplify the effects of behavioral heterogeneity. 

During the recent pandemic, a rise in COVID-19 cases was followed by a decline in influenza. In the absence of cross-immunity, a potential explanation for the observed pattern is behavioural: non-pharmaceutical interventions (NPIs) designed and promoted for one disease also reduce the spread of others. We study short-term and long-term dynamics of two pathogens where NPIs targeting one pathogen indirectly influence the spread of another – a phenomenon we term behavioural spillover. We examine how perceived risk of and response to one disease substantially alter the spread of other pathogens, revealing how waves of different pathogens emerge over time as a result of behavioural interdependencies and human response. Our analysis identifies the parameter space where two diseases simultaneously co-exist, and where shifts in prevalence occur. Our findings are consistent with observations from the COVID-19 pandemic, where NPIs contributed to significant declines in infections such as influenza, pneumonia, and Lyme disease. 

The risk and intensity of mosquito-borne disease outbreaks are tightly linked to the frequency at which mosquitoes feed on blood, also known as the biting rate. However, standard models of mosquito-borne disease transmission inherently assume that mosquitoes bite only once per reproductive cycle—an assumption commonly violated in nature. Drivers of multiple biting, such as host defensive behaviors or climate factors, also affect the mosquito gonotrophic cycle duration (GCD), a quantity customarily used to estimate the biting rate. Here, we present a novel framework for incorporating more complex mosquito biting behaviors into transmission models. This framework can account for heterogeneity in and linkages between mosquito biting rates and multiple biting. We provide general formulas for the basic offspring number,$$\mathcal {N}_0$$ $N_0$ , and basic reproduction number,$$\mathcal {R}_0$$ $R_0$ , threshold measures for mosquito population and pathogen transmission persistence, respectively. To exhibit its flexibility, we expand on specific models derived from the framework that arise from empirical, phenomenological, or mechanistic modeling perspectives. Using the gonotrophic cycle duration as a standard quantity to make comparisons among the models, we show that assumptions about the biting process strongly affect the relationship between GCD and$$\mathcal {R}_0$$ $R_0$ . While under the standard assumption of one bite per reproductive cycle,$$\mathcal {R}_0$$ $R_0$ is an increasing linear function of the inverse of the GCD, alternative models of the biting process can exhibit saturating or concave relationships. Critically, from a mechanistic perspective, decreases in the GCD can lead to substantial decreases in$$\mathcal {R}_0$$ $R_0$ . Through sensitivity analysis of the mechanistic model, we determine that parameters related to probing and ingesting success are the most important targets for disease control. This work highlights the importance of incorporating the behavioral dynamics of mosquitoes into transmission models and provides a method for evaluating how individual-level interventions against mosquito biting scale up to determine population-level mosquito-borne disease risk. 

Abstract Pathogenic organisms are typically thought to be constrained by a tradeoff between the rate and duration of transmission, an assumption that underpins a considerable body of evolutionary theory. Here we test for a transmission-duration tradeoff using detailed historical malaria infection data from an era prior to widespread use of antibiotics when humans were deliberately infected with malaria parasites as treatment for neurosyphilis (malariatherapy). These time series follow individual human infections until recovery or treatment with antimalarial drugs due to acute need (a proxy for virulence), and include data on the abundance of specialized transmission stages that can be used to project parasite fitness. We fit a model to estimate initial parasite multiplication rates (PMRs) and find that faster within-host multiplication extends infection duration (time until recovery) and enhances parasite fitness without a discernible cost, such as increased virulence. Initial PMRs exhibit strain-specific differences, a feature required for evolution by natural selection, but our results contradict the idea that the evolution of human malaria parasites is constrained by a transmission-duration tradeoff. Significance statementPathogenic organisms are usually assumed to face a tradeoff such that aggressively exploiting host resources enables more efficient transmission but at the cost of shorter infections. If such a classic transmission-duration tradeoff is not general, then it is not clear what prevents pathogenic organisms from evolving to exploit their hosts ever more aggressively. We use historical data from human malaria infections to show a remarkable lack of evidence for a transmission-duration tradeoff, since faster parasite multiplication tends to prolong infections and enable more efficient transmission. Therefore, classic predictions regarding the evolution of infection-induced harm to hosts may not apply to human malaria parasites, and efforts to locate general evolutionary constraints on pathogenic organisms should look beyond a classic tradeoff. FundingThis work was supported by the Cornell University College of Agriculture & Life Sciences (M.A.G.). L.M.C. was partially supported by the National Science Foundation Grant # 2144680. Data availabilityAll supporting data and code are provided as supplemental files. NoteFor ease of reviewing, this MS includes all elements (including figures) embedded in the text. If accepted, we would be happy to provide elements as separate files. 

Abstract Many pathogenic organisms, including malaria parasites, produce specialized life stages for within-host multiplication (asexual) versus onward transmission (sexual reproduction). Restrained investment into transmission stage production—by allowing faster multiplication—is predicted to curtail the lifespan of infection via faster host recovery or mortality, a classic tradeoff between the rate and duration of transmission. In contrast, under a reproduction–survival tradeoff, restraining investment into reproduction should extend survival (for parasites, infection duration). To distinguish between these predictions, we develop a within-host mathematical model incorporating immunity to track dynamics across infection age (time since start of blood stage infection) for human malaria infections. When transmission investment is constant across infection age, increased investment reduces infection duration and parasite fitness. Optimal transmission investment occurs at a lower value (around $$5\%$$) than predicted by models lacking feedback between transmission investment and immunity. When strategies vary with infection age, our model shows that malaria parasites benefit from delaying transmission investment to allow for faster within-host multiplication. We show that adaptive immunity can impose a survival–reproduction tradeoff, an emergent property of the model. Our theoretical framework provides a basis for understanding the timing and duration of infectiousness, with implications for parasite evolution in response to control efforts. 

The multiplication rates of pathogenic organisms influence disease progression, efficacy of immunity and therapeutics, and potential for within-host evolution. Thus, accurate estimates of multiplication rates are essential for biological understanding. We recently showed that common methods for inferring multiplication rates from malaria infection data substantially overestimate true values (i.e. under simulated scenarios), providing context for extraordinarily large estimates in human malaria parasites. A key unknown is whether this bias arises specifically from malaria parasite biology or represents a broader concern. Here, we identify the potential for biased multiplication rate estimates across pathogenic organisms with different developmental biology by generalizing a within-host malaria model. We find that diverse patterns of developmental sampling bias—the change in detectability over developmental age—reliably generate overestimates of the fold change in abundance, obscuring not just true growth rates but potentially even whether populations are expanding or declining. This pattern emerges whenever synchrony—the degree to which development is synchronized across the population of pathogenic organisms comprising an infection—decays with time. Only with simulated increases in synchrony do we find noticeable underestimates of multiplication rates. Obtaining robust estimates of multiplication rates may require accounting for diverse patterns of synchrony in pathogenic organisms. This article is part of the Theo Murphy meeting issue ‘Circadian rhythms in infection and immunity’. 

The recent pandemic emphasized the need to consider the role of human behavior in shaping epidemic dynamics. In particular, it is necessary to extend beyond the classical epidemiological structures to fully capture the interplay between the spread of disease and how people respond. Here, we focus on the challenge of incorporating change in human behavior in the form of “risk response” into compartmental epidemiological models, where humans adapt their actions in response to their perceived risk of becoming infected. The review examines 37 papers containing over 40 compartmental models, categorizing them into two fundamentally distinct classes: exogenous and endogenous approaches to modeling risk response. While in exogenous approaches, human behavior is often included using different fixed parameter values for certain time periods, endogenous approaches seek for a mechanism internal to the model to explain changes in human behavior as a function of the state of disease. We further discuss two different formulations within endogenous models as implicit versus explicit representation of information diffusion. This analysis provides insights for modelers in selecting an appropriate framework for epidemic modeling. 

COVID-19 highlighted the importance of considering human behavior change when modeling disease dynamics. This led to developing various models that incorporate human behavior. Our objective is to contribute to an in-depth, mathematical examination of such models. Here, we consider a simple deterministic compartmental model with endogenous incorporation of human behavior (i.e., behavioral feedback) through transmission in a classic Susceptible–Exposed–Infectious–Recovered (SEIR) structure. Despite its simplicity, the SEIR structure with behavior (SEIRb) was shown to perform well in forecasting, especially compared to more complicated models. We contrast this model with an SEIR model that excludes endogenous incorporation of behavior. Both models assume permanent immunity to COVID-19, so we also consider a modification of the models which include waning immunity (SEIRS and SEIRSb). We perform equilibria, sensitivity, and identifiability analyses on all models and examine the fidelity of the models to replicate COVID-19 data across the United States. Endogenous incorporation of behavior significantly improves a model’s ability to produce realistic outbreaks. While the two endogenous models are similar with respect to identifiability and sensitivity, the SEIRSb model, with the more accurate assumption of the waning immunity, strengthens the initial SEIRb model by allowing for the existence of an endemic equilibrium, a realistic feature of COVID-19 dynamics. When fitting the model to data, we further consider the addition of simple seasonality affecting disease transmission to highlight the explanatory power of the models.