# Ebola modeling: behavior, asymptomatic infection, and contacts

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $336,787

## Abstract

Project Summary
!
 The impact of unrecognized Ebola virus (EBOV) infection (asymptomatic and symptomatic) on
transmission dynamics during the 2013–2016 West Africa Ebola outbreak is poorly understood. Individuals
who had asymptomatic EBOV infection or unrecognized symptomatic Ebola virus disease (EVD) represent
two groups who may have had different levels of exposure and rates of EBOV transmission. Increasingly
protective behaviors to avoid contact with EVD cases may have resulted in lower levels of exposure, and
these exposures may be associated with asymptomatic EBOV infection. On the other hand, individuals who
had symptomatic EVD but were never diagnosed may be disproportionately important to transmission
dynamics because some of these individuals were part of transmission chains leading to Ebola outbreaks in
previously unaffected communities.
 Our research question focuses on understanding the drivers of EBOV transmission leading to
epidemic decline. Competing hypotheses were centered around issues of preventive behaviors, health-
seeking behaviors, saturation of transmission among contacts, and asymptomatic EBOV infection. Newly
available, detailed serologic, social network, behavioral, ethnographic, and vaccination data from research
collaborations in Liberia, Sierra Leone, and Democratic Republic of Congo will allow us to test competing
hypotheses in the following aims: 1) Dynamical effects of unrecognized EBOV infection in social network
structure, 2) Unrecognized symptomatic EVD cases, barriers to care, and preventive behaviors, and 3)
Causes of asymptomatic EBOV infection. These findings have the potential to quantify what ended the Ebola
pandemic and improve mathematical models. Mathematical modeling applications will improve forecasting
during new outbreaks and inform ways to deliver vaccines to contacts, by ring vaccination or novel social
network algorithms.
 As Ebola outbreaks continue to occur, two in 2018, this R01 proposal will provide lessons learned
that are immediately applicable to future outbreaks of EBOV, other viral hemorrhagic fevers, and emerging
infectious diseases.
!

## Key facts

- **NIH application ID:** 10242840
- **Project number:** 5R01GM130900-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Travis Christian Porco
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $336,787
- **Award type:** 5
- **Project period:** 2019-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10242840

## Citation

> US National Institutes of Health, RePORTER application 10242840, Ebola modeling: behavior, asymptomatic infection, and contacts (5R01GM130900-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10242840. Licensed CC0.

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