The paradox of neglecting changes in behavior: How standard epidemic models misestimate both transmissibility and final epidemic size
4 affiliations
- 1Department of Mathematics and Statistics, Mississippi State University, Jackson, Mississippi, United States of America
- 2Machine Intelligence Group for the Betterment of Health and the Environment, Northeastern University, Boston, Massachusetts, United States of America
- 3Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- 4Network Science Institute, Northeastern University London, London, United Kingdom
Abstract
During epidemic outbreaks, populations adapt their behavior in response to disease burden, fundamentally altering transmission dynamics. Despite this, most compartmental models assume constant contact rates throughout outbreaks. To quantify biases from this assumption, we fitted a baseline SEIRD model with constant transmission and three behavioral variants—incorporating mortality-driven transmission reduction via exponential, rational, and mixed functional forms—to COVID-19 mortality data from 20 selected US locations during the first pandemic wave (March–July 2020). All three behavioral models achieved a lower median normalized sum of squared error in at least 18 of 20 locations, and Bayesian model selection favored them in at least 18 of 20 locations. More importantly, we identified systematic biases when behavioral responses are ignored: the baseline model consistently underestimated the basic reproduction number (ℛ0) while paradoxically overestimating the final epidemic size. Median ℛ0 estimates from the behavioral models exceeded the baseline estimates across all 20 locations, yet baseline models predicted larger cumulative infection burdens. Controlled synthetic experiments—where mortality trajectories were generated from behavioral models with known parameters—confirmed these biases result from model misspecification rather than data quality or stochastic variation. We prove analytically that for any fixed ℛ0, the baseline model overestimates cumulative infections compared to behavioral models where mortality reduces transmission, regardless of functional form. This dual bias has potential implications for pandemic response: standard models may simultaneously underestimate pathogen contagiousness, which could contribute to delayed or insufficient early interventions while overestimating infection burden, which could bias planning for later epidemic phases. Our findings across 20 geographically diverse locations demonstrate that incorporating behavioral change substantially improves both model fit and estimation of epidemiological parameters relevant for public health policy. Author summary: When diseases spread, people change their behavior—avoiding crowds, wearing masks, washing hands more frequently. Yet many mathematical models used to predict epidemics assume people behave the same way throughout an outbreak. We asked: What happens when models ignore these behavioral changes? Using COVID-19 data from 20 selected US locations, we compared a traditional model, which assumes constant transmission, against models where transmission decreases as deaths increase. We found a troubling paradox: models ignoring behavior consistently suggest diseases are less contagious than estimates from behavioral models, yet simultaneously predict that more people will ultimately become infected. This mismatch matters for decision-making: underestimating how easily a disease spreads could contribute to delayed or insufficient early interventions, while overestimating total infections could bias planning for later epidemic phases. We confirmed this paradox through computer simulations where we knew the true answer, and showed analytically that behavioral adaptation reduces final epidemic size when the basic reproduction number is held constant. Our work shows that incorporating human behavioral responses is not just a modeling refinement—it is important for epidemic predictions that inform public health decisions.
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BibTeX
@article{pant2026paradox,
title = {{The paradox of neglecting changes in behavior: How standard epidemic models misestimate both transmissibility and final epidemic size}},
author = {Binod Pant and Marko Lalovic and István Z. Kiss and Mauricio Santillana},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014746},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014746},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The paradox of neglecting changes in behavior: How standard epidemic models misestimate both transmissibility and final epidemic size
AU - Binod Pant
AU - Marko Lalovic
AU - István Z. Kiss
AU - Mauricio Santillana
PY - 2026
DA - 2026/09/03
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014746
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014746
AB - During epidemic outbreaks, populations adapt their behavior in response to disease burden, fundamentally altering transmission dynamics. Despite this, most compartmental models assume constant contact rates throughout outbreaks. To quantify biases from this assumption, we fitted a baseline SEIRD model with constant transmission and three behavioral variants—incorporating mortality-driven transmission reduction via exponential, rational, and mixed functional forms—to COVID-19 mortality data from 20 selected US locations during the first pandemic wave (March–July 2020). All three behavioral models achieved a lower median normalized sum of squared error in at least 18 of 20 locations, and Bayesian model selection favored them in at least 18 of 20 locations. More importantly, we identified systematic biases when behavioral responses are ignored: the baseline model consistently underestimated the basic reproduction number (ℛ0) while paradoxically overestimating the final epidemic size. Median ℛ0 estimates from the behavioral models exceeded the baseline estimates across all 20 locations, yet baseline models predicted larger cumulative infection burdens. Controlled synthetic experiments—where mortality trajectories were generated from behavioral models with known parameters—confirmed these biases result from model misspecification rather than data quality or stochastic variation. We prove analytically that for any fixed ℛ0, the baseline model overestimates cumulative infections compared to behavioral models where mortality reduces transmission, regardless of functional form. This dual bias has potential implications for pandemic response: standard models may simultaneously underestimate pathogen contagiousness, which could contribute to delayed or insufficient early interventions while overestimating infection burden, which could bias planning for later epidemic phases. Our findings across 20 geographically diverse locations demonstrate that incorporating behavioral change substantially improves both model fit and estimation of epidemiological parameters relevant for public health policy. Author summary: When diseases spread, people change their behavior—avoiding crowds, wearing masks, washing hands more frequently. Yet many mathematical models used to predict epidemics assume people behave the same way throughout an outbreak. We asked: What happens when models ignore these behavioral changes? Using COVID-19 data from 20 selected US locations, we compared a traditional model, which assumes constant transmission, against models where transmission decreases as deaths increase. We found a troubling paradox: models ignoring behavior consistently suggest diseases are less contagious than estimates from behavioral models, yet simultaneously predict that more people will ultimately become infected. This mismatch matters for decision-making: underestimating how easily a disease spreads could contribute to delayed or insufficient early interventions, while overestimating total infections could bias planning for later epidemic phases. We confirmed this paradox through computer simulations where we knew the true answer, and showed analytically that behavioral adaptation reduces final epidemic size when the basic reproduction number is held constant. Our work shows that incorporating human behavioral responses is not just a modeling refinement—it is important for epidemic predictions that inform public health decisions.
SN - 1553-7358
ER -