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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.