Evidence map›Paper›PMID 32060265›Full record

ArticleNature communications2020

Systematic selection between age and household structure for models aimed at emerging epidemic predictions.

Lorenzo Pellis, Simon Cauchemez, Neil M Ferguson, Christophe Fraser

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
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  14. Household crowding hampers mitigating the transmission of SARS-CoV-2.Revista da Sociedade Brasileira de Medicina Tropical · 2021
    Article
  15. Article
  16. A statistical theory of the strength of epidemics: an application to the Italian COVID-19 case.Proceedings. Mathematical, physical, and engineering sciences · 2020
    Article
  17. Article
  18. Article
  19. Article
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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.

Lorenzo PellisDepartment of Mathematics, University of Manchester, Manchester, UK. lorenzo.pellis@manchester.ac.uk.
Simon CauchemezMathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015, Paris, France.
Neil M FergusonMRC Centre for Global Infectious Disease Analysis, J-IDEA, School of Public Health, Imperial College, London, UK.ORCID http://orcid.org/0000-0002-1154-8093
Christophe FraserOxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-2399-9657

Funding

Medical Research Council G0800596Medical Research Council MR/R015600/1Wellcome TrustWellcome Trust (Wellcome) 202562/Z/16/Z
6 · The paper itself

Abstract

Numerous epidemic models have been developed to capture aspects of human contact patterns, making model selection challenging when they fit (often-scarce) early epidemic data equally well but differ in predictions. Here we consider the invasion of a novel directly transmissible infection and perform an extensive, systematic and transparent comparison of models with explicit age and/or household structure, to determine the accuracy loss in predictions in the absence of interventions when ignoring either or both social components. We conclude that, with heterogeneous and assortative contact patterns relevant to respiratory infections, the model's age stratification is crucial for accurate predictions. Conversely, the household structure is only needed if transmission is highly concentrated in households, as suggested by an empirical but robust rule of thumb based on household secondary attack rate. This work serves as a template to guide the simplicity/accuracy trade-off in designing models aimed at initial, rapid assessment of potential epidemic severity.

Indexed as

Age FactorsFamily CharacteristicsCommunicable Diseases, EmergingEpidemicsHumansModels, Statistical

Identifiers

PMID32060265
PMCPMC7021781

What OpenQuestion holds

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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.