Computational Modeling of Regional Dynamics of Pandemic Behavior using Psychologically Valid Agents (preprint)

Regional Psychologically Valid Agents (R-PVAs) are computational models representing cognition and behavior of regional populations. R-PVAs are developed using ACT-R—a computational implementation of the Common Model of Cognition. We developed R-PVAs to model mask-wearing behavior in the U.S. over the pre-vaccination phase of COVID-19 using regionally organized demographic, psychographic, epidemiological, information diet, and behavioral data. An R-PVA using a set of five regional predictors selected by stepwise regression, a psychological self-efficacy process, and context-awareness of the effective transmission number, Rt, yields good fits to the observed proportion of the population wearing masks in 50 U.S. states [R2 = 0.92].  An R-PVA based on regional Big 5 personality traits yields strong fits [R2 = 0.83].  R-PVAs can be probed with combinations of population traits and time-varying context to predict behavior. R-PVAs are a novel technique to understand dynamical, nonlinear relations amongst context, traits, states, and behavior based on cognitive modeling.

Masking Behaviors in Epidemiological Networks with Cognitively-plausible Reinforcement Learning (preprint)

The COVID-19 pandemic highlighted the critical role of human behavior in influencing infectious disease transmission and the need for models capturing this complex dynamic. We present an agent-based model integrating an epidemiological simulation of disease spread with a cognitive architecture driving individual mask-wearing decisions. Agents decide whether to mask based on a utility function weighting factors like peer conformity, personal risk tolerance, and mask-wearing discomfort. By conducting experiments systematically varying behavioral model parameters and social network structures, we demonstrate how adaptive decision-making interacts with network connectivity patterns to impact population-level infection outcomes. The model provides a flexible computational framework for gaining insights into how behavioral interventions like mask mandates may differentially influence disease spread across communities with diverse social structures. Findings highlight the importance of integrating realistic human decision processes in epidemiological models to inform policy decisions during public health crises.

A Ten-Year Prospectus for Mathematical Epidemiology (preprint)

There is little significant work at the intersection of mathematical and computational epidemiology and detailed psychological processes, representations and mechanisms. This is true despite general agreement in the scientific community and the general public that human behavior–in its seemingly infinite variation and heterogeneity, susceptibility to bias, context and habit–is an integral if not fundamental component of what drives the dynamics of infectious disease. The COVID-19 pandemic serves as a close and poignant reminder. We offer a ten-year prospectus of kinds that centers around an unprecedented scientific approach: the integration of detailed psychological models into rigorous mathematical and computational epidemiological frameworks in a way that pushes the boundaries of both psychological science and population models of behavior.

Utility of human judgment ensembles during times of pandemic uncertainty: A case study during the COVID-19 Omicron BA.1 wave in the USA (preprint)

Responding to a rapidly evolving pandemic like COVID-19 is challenging, and involves anticipating novel variants, vaccine uptake, and behavioral adaptations. Human judgment systems can complement computational models by providing valuable real-time forecasts. We report findings from a study conducted on Metaculus, a community forecasting platform, in partnership with the Virginia Department of Health, involving six rounds of forecasting during the Omicron BA.1 wave in the United States from November 2021 to March 2022. We received 8355 probabilistic predictions from 129 unique users across 60 questions pertaining to cases, hospitalizations, vaccine uptake, and peak/trough activity. We observed that the case forecasts performed on par with national multi-model ensembles and the vaccine uptake forecasts were more robust and accurate compared to baseline models. We also identified qualitative shifts in Omicron BA.1 wave prognosis during the surge phase, demonstrating rapid adaptation of such systems. Finally, we found that community estimates of variant characteristics such as growth rate and timing of dominance were in line with the scientific consensus. The observed accuracy, timeliness, and scope of such systems demonstrates the value of incorporating them into pandemic policymaking workflows.

Evaluating the impact of international airline suspensions on COVID-19 direct importation risk (preprint)

Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information on air traffic between international airports has been demonstrated to be useful in retrospectively validating and prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as effective distance on the global air traffic data from IATA to quantify risk of emergence for different countries as a consequence of direct importation from China, and compare it against arrival times for the first 24 countries. Using this model trained on official first reports from WHO, we estimate time of arrival (ToA) for all other countries. We then incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in delaying the estimated arrival time for all other countries. Finally we use the infectious disease vulnerability indices to explain some of the estimated reporting delays.