| Autor: |
Stockdale, Jessica E., Susvitasari, Kurnia, Tupper, Paul, Sobkowiak, Benjamin, Mulberry, Nicola, Gonçalves da Silva, Anders, Watt, Anne E., Sherry, Norelle L., Minko, Corinna, Howden, Benjamin P., Lane, Courtney R., Colijn, Caroline |
| Předmět: |
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| Zdroj: |
Nature Communications; 8/10/2023, Vol. 14 Issue 1, p1-16, 16p |
| Abstrakt: |
Serial intervals – the time between symptom onset in infector and infectee – are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals' exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2–3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities. The serial interval (time between symptom onset in an infector and infectee) is usually estimated from contact tracing data, but this is not always available. Here, the authors develop a method for estimation of serial intervals using whole genome sequencing data and apply it data from clusters of SARS-CoV-2 in Victoria, Australia. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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