Researchers developed an approach for integrating whole-genomic viral sequences and epidemiological information for serial interval (SI) estimation.
By Pooja Toshniwal PahariaAug 16 2023Reviewed by Sophia Coveney In a recent study published in Nature Communications, researchers developed an approach for integrating genetic information, presented as whole-genomic viral sequences, and epidemiological information, for serial interval estimation, especially in cases of inadequate contact tracing data.
The team concentrated on the cluster-specific prediction of SI, a fundamental parameter reflecting infectious disease propagation, described as the period between symptom onset in primary and secondary cases. For inferring the SI distribution in incompletely-sampled case clusters, virus sequences were employed instead of direct data on infection pairings.
The team investigated the effect employing cluster-specific serial interval estimations had on downstream estimates of the time-dependent reproduction number . Validation was performed using simulated influenza-like epidemic data with a known SI distribution. The results are more ambiguous than many previously published estimates, although most estimates were based on small-sized populations with documented contact pairings not accounting for probable underreporting.
The technique performed well in estimating the mean SI but with an increase in uncertainty with a decrease in the percentage of instances. The results for the SI standard deviation were identical.
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