Speaker
Description
Reconstructing the phylogenetic trees of pathogens responsible for disease outbreaks has become a standard tool among public health agencies. But particularly when sampling is non-constant and non-uniform, it can be challenging to link sequences and corresponding phylogenetic trees to epidemiological quantities of interest such as rates of infection, reproduction numbers, or serial intervals. We present ECHO (Estimating Cryptic Hosts from Outbreak trees), a simple model that relates the total branch length of an observed timed phylogenetic tree to the total number of cases related to that tree. Using commonly known or estimated information about the epidemiology of the disease, ECHO uses the total phylogenetic time to infer the number of infections that were not sampled. This could allow public health practitioners to estimate the number of unobserved cases in an ongoing outbreak, using a timed phylogeny together with information about the duration of the latent and infectious periods and the effective reproduction number. ECHO is designed to be robust to the sampling fraction, which is often unknown and can be difficult to estimate, as well as to sampling regimes that vary through time or by clade. As a demonstration, we apply ECHO to phylogenetic trees constructed from a measles outbreak that occurred in the United States in 2021 during Operation Allies Welcome. We find that ECHO is able to accurately identify the number of missing cases and is able to combine information from multiple lineages within one outbreak into a single analysis.
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