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Epidemiological inference for emerging viruses using segregating sites
Yeongseon Park1, Michael A Martin1,2, Katia Koelle3,4
1Graduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, GA, 30322, USA.
Nature Communications
|May 29, 2023
Summary
This study introduces a new method using pathogen genetic data to estimate disease spread parameters early in an outbreak. The approach accurately models viral lineage expansion and infectious disease dynamics.
Area of Science:
- Epidemiology
- Genomics
- Computational Biology
Background:
- Epidemiological models often use case and pathogen sequence data for parameter estimation and inferring disease dynamics.
- Early-stage viral lineage expansion presents unique challenges for traditional modeling approaches.
Purpose of the Study:
- To develop and validate an inference approach for fitting epidemiological models using pathogen sequence data during the early stages of viral lineage expansion.
- To assess the utility of population genetic summary statistics for inferring epidemiological parameters.
Main Methods:
- A Sequential Monte Carlo (SMC) framework was employed, utilizing a trajectory of segregating sites from pathogen sequence data.
- The approach was tested using simulated data under a single-introduction scenario.
- The method was applied to real-world SARS-CoV-2 sequence data from France.
Main Results:
- The approach accurately recovered key epidemiological quantities in simulated data.
- Inference of a basic reproduction number (R0) between 2.3-2.7 for SARS-CoV-2 in France, allowing for multiple introductions.
- Demonstrated the informativeness of population genetic summary statistics for epidemiological inference.
Conclusions:
- The presented inference approach is effective for early-stage viral lineage expansion.
- Sequence data-based inference can reconstruct infectious disease dynamics.
- Population genetic methods offer valuable insights into epidemiological parameters.
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