ORCID Profile
0000-0001-8142-9930
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Publisher: Cold Spring Harbor Laboratory
Date: 03-03-2022
DOI: 10.1101/2022.02.23.22271355
Abstract: Estimating key aspects of transmission is crucial in infectious disease control. Serial intervals – the time between symptom onset in an infector and infectee – are fundamental, and help to define rates of transmission, estimates of reproductive numbers, and vaccination levels needed to prevent transmission. However, estimating the serial interval requires knowledge of in iduals’ contacts and exposures (who infected whom), which is typically obtained through resource-intensive contact tracing efforts. We develop an alternate framework that uses virus sequences to inform who infected whom and thereby estimate serial intervals. The advantages are many-fold: virus sequences are often routinely collected to support epidemiological investigations and to monitor viral evolution. The genomic approach offers high resolution and cluster-specific estimates of the serial interval that are comparable with those obtained from contact tracing data. Our approach does not require contact tracing data, and can be used in large populations and over a range of time periods. We apply our techniques to SARS-CoV-2 sequence data from the first two waves of COVID-19 in Victoria, Australia. We find that serial interval estimates vary between clusters, supporting the need to monitor this key parameter and use updated estimates in onward applications. Compared to an early published serial interval estimate, using cluster-specific serial intervals can cause estimates of the effective reproduction number R t to vary by a factor of up to 2–3. We also find that serial intervals estimated in settings such as schools and meat processing acking plants tend to be shorter than those estimated in healthcare facilities.
Publisher: Springer Science and Business Media LLC
Date: 10-08-2023
DOI: 10.1038/S41467-023-40544-Y
Abstract: 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 in iduals’ 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-s led 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 acking plants are shorter than those in healthcare facilities.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Kurnia Susvitasari.