# data[].attributes.title
Coping with feast and famine: Integrated behavioral and metabolically flexible responses of wild orangutans to ecologically driven dietary variation

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The species coalescent indicates possible bat and pangolin origins of the COVID-19 pandemic

# data[].attributes.publications[].title
The species coalescent indicates possible bat and pangolin origins of the COVID-19 pandemic

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Dataset for 'Mucosal immunization with ChAd-SARS-CoV-2-S prevents sequential transmission of SARS-CoV-2 to unvaccinated hamsters'

# data[].attributes.title
Data from: Global patterns in coronavirus diversity

# data[].attributes.publications[].title
Global patterns in coronavirus diversity

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PI3Kg inhibition circumvents inflammation and mortality in SARS-CoV-2 and other infections

# data[].attributes.title
A patient centered approach to tailoring human papilloma virus self-sampling for cervical cancer screening (PATH)

# data[].attributes.title
Dataset for 'Immunogenicity and efficacy of XBB.1.5 rS vaccine against EG.5.1 variant of SARS-CoV-2 in Syrian hamsters'

# data[].attributes.title
Human presence shifts the landscape of fear for a free-living mammal

# data[].attributes.title
Lessons from movement ecology for the return to work: Modeling contacts and the spread of COVID-19

# data[].attributes.publications[].title
Lessons from movement ecology for the return to work: Modeling contacts and the spread of COVID-19

# data[].attributes.title
Survey of risk perception, trust, and behavioral intention during the COVID-19 pandemic

# data[].attributes.grants[].title
Center for Modeling Complex Interactions

# data[].attributes.grants[].abstract
Modeling efforts for COVID-19 within the US have focused primarily on helping urban centers cope with the consequent health care crisis. The impact of the pandemic on rural communities is still emerging, and these areas have not received the same degree of modeling attention. At the same time, rural communities are different from urban centers in ways that affect the disease and its dynamics: they have lower densities, are more isolated, have smaller social networks, tend to be poorer and older, and have scant health care infrastructure. Rural communities are also the primary source of food production and natural resource extraction in this country. As the pandemic unfolds across the coming months, rural communities will be faced with highly variable circumstances: some will have no infections and be focused on early detection; some will have active cases and be attempting to stop their spread; some will have eliminated active cases and be attempting to reopen economic and community activities while guarding against resurgence. Treating all communities as the same would be foolish. At the local level, decision makers need tools tailored to real communities: tools that emulate the way people come and go and interact there, tools to consider the most relevant interventions, and tools that account for real variation in how able and willing people will be to comply with possible interventions. At the larger health-district and state level, officials need forecasts of how local decisions, health care infrastructure, and the virus itself will interact to drive the epidemic. The purpose of the current proposal is to provide these tools by building a model of COVID-19 for largely rural states that links the dynamics within communities together into a statewide network. This will be achieved in three specific aims. In Aim 1, we develop a predictive epidemiological model of COVID-19 spread and intensity for rural states. This will be done with a spatial, age-structured metapopulation model that relies on differential equations and their stochastic extensions. In Aim 2, we evaluate how potential interventions in individual communities affect outbreak risk, transmission, access to health care, and intervention efficacy and adoption. Here we combine surveys—of both rural and urban communities in Idaho and several broader regions of the US—to estimate patterns of compliance and the motivations behind them. Using these results, we will then use agent-based models of synthetic communities to simulate interventions. Net effects will be relayed up to the statewide model. In Aim 3, we provide support for decision making to state public health officials and local policy makers in rural communities. This will be done by developing two online graphical interfaces for visualizing forecasts and exploring interventions—one high-level application for non-specialists and a second, more sophisticated version, for public health professionals. Education, empowerment, and appreciation of uncertainty will be emphasized. Finally, the models and tools we develop here will be implemented in Idaho, but will be designed for easy export to states with significant rural populations.

# data[].attributes.grants[].keywords
['Address', 'Adoption', 'Affect', 'Age', 'Air', 'Area', 'Attention', 'Award', 'Biomedical Research', 'COVID-19', 'Centers of Research Excellence', 'Cities', 'Commerce', 'Communication', 'Communities', 'Complex', 'Consequentialism', 'Country', 'Data', 'Decision Making', 'Differential Equation', 'Disease', 'Disease Outbreaks', 'Disease model', 'Early Diagnosis', 'Economics', 'Education', 'Effectiveness of Interventions', 'Emotional', 'Ensure', 'Epidemic', 'Epidemiology', 'Faculty', 'Food Chain', 'Food production', 'Growth', 'Health', 'Health Personnel', 'Health Professional', 'Healthcare', 'Hospitals', 'Human Resources', 'Hybrids', 'Idaho', 'Immunology', 'Individual', 'Infection', 'Infrastructure', 'Intervention', 'Link', 'Measures', 'Modeling', 'Motivation', 'Natural Resources', 'Outcome', 'Output', 'Pattern', 'Phase', 'Play', 'Policy Maker', 'Positioning Attribute', 'Public Health', 'Recurrence', 'Research', 'Risk', 'Risk Factors', 'Role', 'Rural', 'Rural Community', 'Rural Population', 'Scientist', 'Shelter facility', 'Social Distance', 'Social Network', 'Source', 'Structure', 'Surveys', 'Time', 'Training', 'Travel', 'Treatment Efficacy', 'Uncertainty', 'Universities', 'Variant', 'Virus', 'Work', 'base', 'care burden', 'cost', 'dashboard', 'density', 'design', 'empowerment', 'epidemiological model', 'food resource', 'graphical user interface', 'health care availability', 'infection burden', 'insight', 'model design', 'next generation', 'pandemic disease', 'professor', 'recruit', 'tool', 'transmission process', 'urban area', 'virology', 'willingness']

# data[].attributes.publications[].title
Effects of trust, risk perception, and health behavior on COVID-19 disease burden: Evidence from a multi-state US survey