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Understanding how humans respond to an ongoing pandemic and interven-tions is crucial to monitoring and forecasting the dynamics of viral trans-mission. Heterogeneous response over time and geographical regions may depend on the individual beliefs and information consumption patterns of populations. To address the need for more precise and accurate epidemio-logical models we are researching Psychologically Valid Agent models of human responses to epidemic information and non-pharmaceutical inter-ventions during the COVID-19 global pandemic with input drivers induced from sources including online media that provide indicators of pandemic awareness, beliefs, and attitudes. 

Strategic response options to the COVID-19 pandemic have been greatly influenced by predictive epidemiological models. Effects of non-pharmaceutical interventions (NPIs; such as mask wearing) unfortunately are based on an abundance of very large uncertainties around the extent to which the population adopts risk reducing behaviors. The effects of NPIs appear to have large heterogeneity across regions, subgroups, and individual mindsets and capabilities. We hypothesize that these uncertainties can be improved with higher-fidelity computational modeling of the social-psychological reactions of individuals, groups, and populations. We build up the ACT-R theory and Instance-Based Learning Theory to formulate psychologically valid agents and develop a framework that integrates multi-level cognitive and social simulation with information networks analysis, and epidemiological predictions. We present initial results from analyses of beliefs and sentiments about COVID-19 NPIs induced from online social media that can provide inputs to seed and validate cognitive agents. We present illustrations of cognitive model hypotheses about the dynamics of behavior change in response to intentions, attitudes, messaging, and source credibility. We present an example of social networks propagating attitude change in response to NPIs. 

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.