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- Title
Surveillance strategies for the detection of new pathogen variants across epidemiological contexts.
- Authors
Oliveira Roster, Kirstin I.; Kissler, Stephen M.; Omoregie, Enoma; Wang, Jade C.; Amin, Helly; Di Lonardo, Steve; Hughes, Scott; Grad, Yonatan H.
- Abstract
Surveillance systems that monitor pathogen genome sequences are critical for rapidly detecting the introduction and emergence of pathogen variants. To evaluate how interactions between surveillance capacity, variant properties, and the epidemiological context influence the timeliness of pathogen variant detection, we developed a geographically explicit stochastic compartmental model to simulate the transmission of a novel SARS-CoV-2 variant in New York City. We measured the impact of (1) testing and sequencing volume, (2) geographic targeting of testing, (3) the timing and location of variant emergence, and (4) the relative variant transmissibility on detection speed and on the undetected disease burden. Improvements in detection times and reduction of undetected infections were driven primarily by increases in the number of sequenced samples. The relative transmissibility of the new variant and the epidemic context of variant emergence also influenced detection times, showing that individual surveillance strategies can result in a wide range of detection outcomes, depending on the underlying dynamics of the circulating variants. These findings help contextualize the design, interpretation, and trade-offs of genomic surveillance strategies of pandemic respiratory pathogens. Author summary: To prevent the spread of infections that are more transmissible, evade immunity, or cause more serious illness, public health agencies must quickly detect changes in pathogens such as the virus responsible for COVID-19, which is done by testing the population to identify infections and then sequencing the positive cases to determine which virus variants caused the infections. However, it is unclear how different factors, such as the volume of testing and sequencing, the timing in the outbreak, or the transmissibility of the new variants affect our ability to quickly detect new variants of concern. In our study, we used mathematical simulations of disease spread in New York City to better understand how these factors influence the time it takes to detect a new variant and how many people have been infected by the time it is detected. In our simulations, the greatest improvement in detection speed was achieved by increasing the number of positive cases that are sampled for sequencing. However, factors beyond policymakers' control also influenced the time it took to detect the new variant, meaning that a wide range of detection outcomes was possible even under an ideal public health strategy. These findings help guide decision making for future outbreaks.
- Subjects
INFECTIOUS disease transmission; STOCHASTIC models; DECISION making; COVID-19; SARS-CoV-2; PUBLIC health
- Publication
PLoS Computational Biology, 2024, Vol 20, Issue 9, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1012416