Evidence map›Paper›PMID 38086402›Full record

ArticleJournal of the Royal Society, Interface2023

Exact calculation of end-of-outbreak probabilities using contact tracing data.

N V Bradbury, W S Hart, F A Lovell-Read, J A Polonsky, R N Thompson

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

N V BradburyMathematics Institute, University of Warwick, Coventry CV4 7AL, UK.
W S HartMathematical Institute, University of Oxford, Oxford OX2 6GG, UK.ORCID 0000-0002-2504-6860
F A Lovell-ReadMathematical Institute, University of Oxford, Oxford OX2 6GG, UK.ORCID 0000-0002-3039-1159
J A PolonskyGeneva Centre of Humanitarian Studies, University of Geneva, Geneva 1205, Switzerland.
R N ThompsonMathematical Institute, University of Oxford, Oxford OX2 6GG, UK.ORCID 0000-0001-8545-5212

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A key challenge for public health policymakers is determining when an infectious disease outbreak has finished. Following a period without cases, an estimate of the probability that no further cases will occur in future (the end-of-outbreak probability) can be used to inform whether or not to declare an outbreak over. An existing quantitative approach (the Nishiura method), based on a branching process transmission model, allows the end-of-outbreak probability to be approximated from disease incidence time series, the offspring distribution and the serial interval distribution. Here, we show how the end-of-outbreak probability under the same transmission model can be calculated exactly if data describing who-infected-whom (the transmission tree) are also available (e.g. from contact tracing studies). In that scenario, our novel approach (the traced transmission method) is straightforward to use. We demonstrate this by applying the method to data from previous outbreaks of Ebola virus disease and Nipah virus infection. For both outbreaks, the traced transmission method would have determined that the outbreak was over earlier than the Nishiura method. This highlights that collection of contact tracing data and application of the traced transmission method may allow stringent control interventions to be relaxed quickly at the end of an outbreak, with only a limited risk of outbreak resurgence.

Indexed as

Contact TracingHemorrhagic Fever, EbolaDisease OutbreaksHumansProbabilityPublic Healthend-of-outbreak declarationinfectious disease epidemiologylocal extinctionmathematical modellingpublic health measuresresurgence

Identifiers

PMID38086402
PMCPMC10715912

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.