A novel coronavirus disease (COVID-19) was reported in Wuhan, China late December 2019. The disease has as of the end of March 2020, affected over 35 countries (with over 570,000 cases and 26,000 deaths) worldwide. This includes the U.S., where cases are increasing by the thousands every day (100,000 cases with 1500 deaths as of April 2020). We set out to investigate new or increased stressful life events (SLEs) as a result of this pandemic in the U.S. In this exploratory qualitative study, we examined new or heightened SLEs during an active phase of this outbreak. We used a list of SLEs acquired from the first phase of our study, whereby we conducted open-ended surveys and performed an in-depth focus group. We applied Lazarus and Folkman’s transactional model of stress and coping to understand diverse focus-group participants’ appraisal of events. We coded survey data and applied sentiment analysis. Participants varied in perceived threat and challenge appraisals of COVID-19, indicating both calm and fear. From 267 coded and sentiment analyzed events from survey text, 95% were predominantly negative; 112 (42%) very negative and 142 (53%) moderately negative. Social capital was unanimously emphasized upon as monumental for example: family, friends or technology mediated. We additionally identified seven major themes of SLEs due to the pandemic. Our sample profile is not inclusive of all subsets of the population. Participants mostly shared similar frustrations and a variety of SLEs such as fear of the unknown and concern for loved ones as a result of COVID-19.

Background/Objectives: Geographic Information Systems (GIS) offer essential capabilities for identifying spatial concentrations of vulnerability and strengthening context-aware prevention strategies. This manuscript describes a geospatial architecture designed to generate anticipatory, place-based risk identification applicable across diverse community and institutional environments. Interpersonal Violence (IPV), one of several preventable harms that benefit from this spatially informed analysis, remains a critical public health challenge shaped by structural, ecological, and situational factors. Methods: The conceptual framework presented integrates de-identified surveillance data, ecological indicators, environmental and temporal dynamics into a unified spatial epidemiological model. Multilevel data layers are geocoded, spatially matched, and analyzed using clustering (e.g., Getis-Ord Gi*), spatial dependence metrics (e.g., Moran’s I), and contextual modeling to support anticipatory identification of elevated vulnerability. Framework Outputs: The model is designed to identify spatial clustering, mobility-linked risk patterns, and emerging escalation zones using neighborhood disadvantage, built-environment factors, and situational markers. Outputs are intended to support both clinical decision-making (e.g., geocoded trauma screening, and context-aware discharge planning), and community-level prevention (e.g., targeted environmental interventions and cross-sector resource coordination). Conclusions: This framework synthesizes behavioral theory, spatial epidemiology, and prevention science into an integrative architecture for coordinated public health response. As a conceptual foundation for future empirical research, it advances the development of more dynamic, spatially informed, and equity-focused prevention systems.

Background/Objectives: Geographic Information Systems (GIS) offer essential capabilities for identifying spatial concentrations of vulnerability and strengthening context-aware prevention efforts across healthcare and community systems. Interpersonal Violence (IPV) remains a critical public health challenge shaped by structural, ecological, and situational factors, but it represents only one of several preventable harms that benefit 24 from this spatially informed analysis. This manuscript presents a geospatial public health architecture designed to generate anticipatory, place-based risk forecasting applicable across diverse community and institutional environments. Methods: The framework integrates de-identified surveillance data, ecological indicators, environmental exposures, and temporal dynamics into a unified spatial epidemiology model. Multilevel data layers are geocoded, spatially matched, and analyzed using clustering, spatial dependence metrics, and contextual modeling to identify micro-environments of elevated vulnerability. Results: The model identifies spatial clustering, mobility-linked risk patterns, and emerging escalation zones using neighborhood disadvantage, built-environment factors, and situational markers. Spatial outputs support clinical applications such as geocoded trauma screening, risk-stratified decision support, and context-aware discharge planning. Community applications include targeted environmental interventions, cross-sector resource coordination, and equity-focused prevention planning. Conclusions: By merging behavioral theory, spatial epidemiology, and prevention science, this methodological model enhances anticipatory detection, strengthens trauma-informed practice, and supports coordinated public health response. It establishes a con- 41 ceptual foundation for future empirical research using high-resolution restricted datasets and advances spatially informed prevention across diverse settings, consequently promoting the continued development of dynamic, integrated equity-focused prevention systems.