Evidence map›Paper›PMID 33436609›Full record

ArticleNature communications2021

Inferring high-resolution human mixing patterns for disease modeling.

Dina Mistry, Maria Litvinova, Ana Pastore Y Piontti, Matteo Chinazzi, Laura Fumanelli, Marcelo F C Gomes, Syed A Haque, Quan-Hui Liu, Kunpeng Mu, Xinyue Xiong and 5 more

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
149citing papers in PubMed, 2 pooled it
–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

149 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  16. The health and economic repercussions of declining MMR coverage in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  17. VIBES: A multiscale modeling approach integrating within-host and between-hosts dynamics in epidemics.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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89 more citing papers are in PubMed but not listed here.

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

15 authors.

Dina MistryInstitute for Disease Modeling, Global Health Division, Bill and Melinda Gates Foundation, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-1598-1388
Maria LitvinovaLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.ORCID http://orcid.org/0000-0001-6393-1943
Ana Pastore Y PionttiLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
Matteo ChinazziLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
Laura FumanelliBruno Kessler Foundation, Trento, Italy.
Marcelo F C GomesFiocruz, Scientific Computing Program, Grupo de Métodos Analíticos em Vigilância Epidemiológica, Rio de Janeiro, Brazil.ORCID http://orcid.org/0000-0003-4693-5402
Syed A HaqueLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.ORCID http://orcid.org/0000-0002-8371-2366
Quan-Hui LiuCollege of Computer Science, Sichuan University, Chengdu, Sichuan, China.
Kunpeng MuLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
Xinyue XiongLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
M Elizabeth HalloranFred Hutchinson Cancer Research Center, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-3127-1757
Ira M LonginiDepartment of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Stefano MerlerBruno Kessler Foundation, Trento, Italy.
Marco AjelliLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA. marco.ajelli@gmail.com.ORCID http://orcid.org/0000-0003-1753-4749
Alessandro VespignaniLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA. a.vespignani@northeastern.edu.ORCID http://orcid.org/0000-0003-3419-4205

Funding

Understanding Transmission with Integrated Genetic and Epidemiologic InferenceU54GM111274 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI VESPIGNANI, ALESSANDRO · 2014 to 2018
$12.5M
NIGMS NIH HHS U54 GM111274
6 · The paper itself

Abstract

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

Indexed as

Models, StatisticalAge FactorsAustraliaBasic Reproduction NumberChinaCluster AnalysisCommunicable DiseasesHumansInfluenza, HumanSurveys and Questionnaires

Identifiers

PMID33436609
PMCPMC7803761

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.