Phylogenetic modeling of viral transmission dynamics at the human-wildlife interface in Uganda

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: 5R01TW012704-02

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Key facts

  • Disease

    COVID-19, Disease X
  • Start & end year

    2023
    2028
  • Known Financial Commitments (USD)

    $667,311
  • Funder

    National Institutes of Health (NIH)
  • Principal Investigator

    ASSISTANT PROFESSOR Krista Milich
  • Research Location

    United States of America
  • Lead Research Institution

    WASHINGTON UNIVERSITY
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Pathogen genomics, mutations and adaptations

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Many infectious diseases that threaten humans originated among wildlife, yet we know relatively little about the real-world ecological conditions that enable spillover events. Despite its importance, identifying novel viral pathogens and characterizing their transmission dynamics remains difficult because it requires advanced genetic sequencing technologies, sampling wildlife likely to harbor pathogens of concern to humans, and sophisticated modeling techniques. We will study red colobus monkeys in Kibale National Park, Uganda, other nonhuman primates, and people who neighbor these wildlife populations to quantify transmission dynamics within and between species. Our team will collect behavioral ecology data on red colobus monkeys living in areas of the forest with different degrees of anthropogenic disturbance and conduct interviews with people living along the boundary of the park with varying exposure risks for zoonotic diseases. We will conduct repeat sampling of people and individually identifiable red colobus monkeys to analyze the gut virome, assess infection with gastrointestinal parasites known to infect both red colobus and people, discover previously undocumented viral diversity, detect the presence of novel pathogens of concern to humans, red colobus monkeys, and other primates (e.g. SARS-CoV-2), and track the evolutionary spread of detected pathogens. To model how red colobus-associated viruses spread, we will develop new phylodynamic models that allow longitudinal ecological and biogeographical data to structure time-heterogenous epidemiological event rates. We will also create, test, and distribute new software for simulation, Bayesian inference, and deep learning-based inference to model how infectious diseases spread in a wide variety of ecosystem-level transmission scenarios. Our proposed project will benefit public health and wildlife conservation and expand STEM training in the USA and Uganda. Working with Ugandan communities, we will co-create solutions to address risks for zoonotic disease transmission and test mitigation strategies to reduce transmission pathways.