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  • ViralLoadModelling_WSC_21_final

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Comparing Data Collection Strategies via Input Uncertainty When Simulating Testing Policies Using Viral Load Profiles

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published
Publication date28/02/2022
Host publicationWSC '21: Proceedings of the Winter Simulation Conference
PublisherIEEE Press
Number of pages12
ISBN (electronic)9781665433112
<mark>Original language</mark>English
EventWinter Simulation Conference 2021 - Online
Duration: 13/12/2021 → 16/12/2021

Conference

ConferenceWinter Simulation Conference 2021
Abbreviated titleWSC '21
Period13/12/21 → 16/12/21

Conference

ConferenceWinter Simulation Conference 2021
Abbreviated titleWSC '21
Period13/12/21 → 16/12/21

Abstract

Temporal profiles of viral load have individual variability and are used to determine whether individuals are infected based on some limit of detection. Modelling and simulating viral load profiles allows for the performance of testing policies to be estimated, however viral load behaviour can be very uncertain. We describe an approach for studying the input uncertainty passed to simulated policy performance when viral load profiles are estimated from different data collection strategies. Our example shows that comparing the strategies solely based on input uncertainty is inappropriate due to the differences in confidence interval coverage caused by negatively biased simulation outputs.

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©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.