# Center for Modeling Complex Interactions

> **NIH NIH P20** · UNIVERSITY OF IDAHO · 2020 · $492,598

## 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 appr...

## Key facts

- **NIH application ID:** 10192030
- **Project number:** 3P20GM104420-06A1S1
- **Recipient organization:** UNIVERSITY OF IDAHO
- **Principal Investigator:** Holly A Wichman
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $492,598
- **Award type:** 3
- **Project period:** 2015-03-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10192030

## Citation

> US National Institutes of Health, RePORTER application 10192030, Center for Modeling Complex Interactions (3P20GM104420-06A1S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10192030. Licensed CC0.

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