Pritchard, Alexander J;
Silk, Matthew J;
Carrignon, Simon;
Bentley, R Alexander;
Fefferman, Nina H;
(2022)
Balancing timeliness of reporting with increasing testing probability for epidemic data.
Infectious Disease Modelling
, 7
(2)
pp. 106-116.
10.1016/j.idm.2022.04.001.
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Abstract
Reporting of epidemiological data requires coordinated action by numerous agencies, across a multitude of logistical steps. Using collated and reported information to inform direct interventions can be challenging due to associated delays. Mitigation can, however, occur indirectly through the public generation of concern, which facilitates adherence to protective behaviors. We utilized a coupled-dynamic multiplex network model with a communication- and disease-layer to examine how variation in reporting delay and testing probability are likely to impact adherence to protective behaviors, such as reducing physical contact. Individual concern mediated adherence and was informed by new- or active-case reporting, at the population- or community-level. Individuals received information from the communication layer: direct connections that were sick or adherent to protective behaviors increased their concern, but absence of illness eroded concern. Models revealed that the relative benefit of timely reporting and a high probability of testing was contingent on how much information was already obtained. With low rates of testing, increasing testing probability was of greater mitigating value. With high rates of testing, maximizing timeliness was of greater value. Population-level reporting provided advanced warning of disease risk from nearby communities; but we explore the relative costs and benefits of delays due to scale against the assumption that people may prioritize community-level information. Our findings emphasize the interaction of testing accuracy and reporting timeliness for the indirect mitigation of disease in a complex social system.
| Type: | Article |
|---|---|
| Title: | Balancing timeliness of reporting with increasing testing probability for epidemic data |
| Location: | China |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.idm.2022.04.001 |
| Publisher version: | http://dx.doi.org/10.1016/j.idm.2022.04.001 |
| Language: | English |
| Additional information: | © 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Mathematical & Computational Biology, Infectious Diseases, Behavioral science, Communicable diseases, Disease outbreaks, Disease reporting, Surveillance, RISK, IMPACT, REASSURANCE, SARS-COV-2, INFECTION, RESPONSES, COVERAGE, TRUST |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10198571 |
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