Evidence map›Paper›PMID 26150502›Full record

Trial reportProceedings of the National Academy of Sciences of the United States of America2015

Inferring influenza dynamics and control in households.

Max S Y Lau, Benjamin J Cowling, Alex R Cook, Steven Riley

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

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  3. medRxiv : the preprint server for health sciences · 2026
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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

4 authors.

Max S Y LauDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540; Department of Statistics and Actuarial Science, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom; Bio-mathematics and Statistics, Edinburgh EH9 3FD, United Kingdom; msylau@princeton.edu.
Benjamin J CowlingSchool of Public Health, University of Hong Kong, Hong Kong;
Alex R CookSaw Swee Hock School of Public Health, National University of Singapore, Singapore 117549; Yale-NUS College, Singapore 138614;
Steven RileyMRC Centre for Outbreak Analysis and Modelling, Department Infectious Disease Epidemiology, School of Public Health, Imperial College, London SW7 2AZ, United Kingdom.

Funding

Models for synthesising molecular, clinical and epidemiological data, and translaU01GM110721 · NIGMS · IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE · PI CAUCHEMEZ, SIMON, FERGUSON, NEIL MORRIS · 2014 to 2018
$1.9M
Medical Research Council MR/J008761/1Medical Research Council MR/K010174/1NIGMS NIH HHS U01 GM110721
6 · The paper itself

Abstract

Household-based interventions are the mainstay of public health policy against epidemic respiratory pathogens when vaccination is not available. Although the efficacy of these interventions has traditionally been measured by their ability to reduce the proportion of household contacts who exhibit symptoms [household secondary attack rate (hSAR)], this metric is difficult to interpret and makes only partial use of data collected by modern field studies. Here, we use Bayesian transmission model inference to analyze jointly both symptom reporting and viral shedding data from a three-armed study of influenza interventions. The reduction in hazard of infection in the increased hand hygiene intervention arm was 37.0% [8.3%, 57.8%], whereas the equivalent reduction in the other intervention arm was 27.2% [-0.46%, 52.3%] (increased hand hygiene and face masks). By imputing the presence and timing of unobserved infection, we estimated that only 61.7% [43.1%, 76.9%] of infections met the case criteria and were thus detected by the study design. An assessment of interventions using inferred infections produced more intuitively consistent attack rates when households were stratified by the speed of intervention, compared with the crude hSAR. Compared with adults, children were 2.29 [1.66, 3.23] times as infectious and 3.36 [2.31, 4.82] times as susceptible. The mean generation time was 3.39 d [3.06, 3.70]. Laboratory confirmation of infections by RT-PCR was only able to detect 79.6% [76.5%, 83.0%] of symptomatic infections, even at the peak of shedding. Our results highlight the potential use of robust inference with well-designed mechanistic transmission models to improve the design of intervention studies.

Indexed as

Family CharacteristicsAdultChildComputer SimulationHong KongHumansInfluenza A virusInfluenza, HumanModels, BiologicalReal-Time Polymerase Chain ReactionReproducibility of ResultsTime FactorsBayesian inferencehousehold transmissioninfluenza dynamicsMarkov chain Monte Carlononpharmaceutical interventions

Identifiers

PMID26150502
PMCPMC4517268

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.