Project Grant 2230125

RAISE: IHBEM: INTEGRATING TRADITIONAL SURVEY AND DIGITAL SOCIOBEHAVIORAL DATA INTO INFECTIOUS DISEASE MODELS FOR LONG-TERM FORECASTING -THE COVID-19 PANDEMIC CAUSED UNPRECEDENTED IMPACTS ACROSS ALL FACETS OF EVERYDAY LIFE. ALONGSIDE THE EBB AND FLOW OF THE DISEASE, SOCIETAL EMOTIONS, BEHAVIORS, AND PUBLIC HEALTH POLICIES CHANGED LEADING TO A COMPLEX FEEDBACK LOOP: PEOPLE?S BEHAVIORS SHAPED DISEASE TRANSMISSION, BUT AS DISEASE PREVALENCE CHANGED, SO TOO DID PEOPLE?S BEHAVIORS. FOR EXAMPLE, A COVID-19 SURGE MAY TRIGGER PEOPLE?S ANXIETY AND LEAD THEM TO REDUCE THEIR IN-PERSON INTERACTIONS AND PUT IN PLACE POLICIES SUCH AS STAY-AT-HOME ORDERS OR MASK MANDATES. COMBINED, THESE CHANGES TRANSIENTLY CONTROL THE SURGE UNTIL POLICIES AND BEHAVIORS RELAX AND TRANSMISSION REBOUNDS AGAIN. UNLIKE MANY OF THE PREVIOUS COVID-19 STUDIES THAT HAVE FOCUSED ONLY ON THE UNIDIRECTIONAL RELATIONSHIPS WITHIN THIS FEEDBACK LOOP, SUCH AS THE WAY POLICIES IMPACT BEHAVIOR AND THE WAY PANDEMIC TRENDS IMPACT POLICIES, THIS PROJECT WILL DEVELOP MODELS THAT CAN PROVIDE A HOLISTIC UNDERSTANDING OF THE COMPLEX INTERRELATIONS BETWEEN EMOTIONS, BEHAVIOR, POLICIES, AND INFECTION TRENDS USING THE AVAILABLE COVID-19 DATA IN THE US AND OTHER COUNTRIES. THIS EFFORT WILL HELPFUL FOR COMPREHENSIVELY UNDERSTANDING COMPLEX DISEASE TRANSMISSION DYNAMICS, WHICH WILL IMPROVE THE ABILITIES TO ANTICIPATE INFECTIOUS DISEASE TRENDS AND ENACT MEANINGFUL PUBLIC HEALTH POLICIES FOR FUTURE INFECTIOUS DISEASE THREATS. IN MORE CONCRETE TERMS, THE TEAM WILL FOCUS ON TWO UNDERSTUDIED SOCIO-BEHAVIORAL COMPONENTS THAT COMPLEMENT EACH OTHER TO FORM THE FEEDBACK LOOP OF COVID-19 DYNAMICS. THRUST 1 WILL EXAMINE THE ROLE OF PSYCHOLOGICAL PROCESSING AND DECISION-MAKING USING A COLLECTION OF AGGREGATED TIME-SERIES DATA OF SARS-COV-2 INFECTIONS, POLICIES, EMOTIONS, AND BEHAVIORS FROM 20 MAJOR METROPOLITAN REGIONS IN THE US (E.G., REDDIT CONVERSATIONAL DATA FOR TRACKING EMOTIONS). THE GOAL WILL BE TO INTEGRATE PSYCHOLOGICAL AND BEHAVIORAL PROCESSES MECHANISTICALLY INTO COVID-19 EPIDEMIOLOGICAL MODELS AND STATISTICALLY ESTIMATE THE TIME-VARYING RELATIONSHIPS ACROSS THE FIRST TWO PLUS YEARS OF THE PANDEMIC. THRUST 2 WILL EXAMINE THE ROLE OF DYNAMIC AND HETEROGENEOUS SOCIAL CONTACT PATTERNS IN INFECTIOUS DISEASE MODELING IN RELATION TO SOCIAL, BEHAVIORAL, AND POLITICAL FACTORS. EXISTING AND NEWLY OBTAINED SOCIAL MIXING SURVEYS THAT COLLECT PEOPLE?S CONTACT DATA ALONG WITH SOCIO-BEHAVIORAL VARIABLES WILL BE ANALYZED TO IDENTIFY FACTORS THAT CHARACTERIZE PEOPLE?S CONTACT PATTERNS AND RESPONSES TO POLICIES. SIMULATION STUDIES WILL ALSO BE CONDUCTED TO ASSESS THE DEGREE TO WHICH INCORPORATING THESE FACTORS IN INFECTIOUS DISEASE MODELING COULD INFLUENCE MODEL PREDICTIONS. THIS PROJECT IS JOINTLY FUNDED BY THE DIVISION OF MATHEMATICAL SCIENCES (DMS) IN THE DIRECTORATE OF MATHEMATICAL AND PHYSICAL SCIENCES (MPS) AND THE DIVISION OF SOCIAL AND ECONOMIC SCIENCES (SES) IN THE DIRECTORATE OF SOCIAL, BEHAVIORAL AND ECONOMIC SCIENCES (SBE). THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.