Evidence map›Paper›PMID 38733199›Full record

ArticleInfluenza and other respiratory viruses2024

Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases.

Allisandra G Kummer, Juanjuan Zhang, Chenyan Jiang, Maria Litvinova, Paulo C Ventura, Marc A Garcia, Alessandro Vespignani, Huanyu Wu, Hongjie Yu, Marco Ajelli

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Influenza and other respiratory viruses, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
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

10 authors.

Allisandra G KummerLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, USA.
Juanjuan ZhangShanghai Institute of Infectious Disease and Biosecurity, Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Chenyan JiangShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Maria LitvinovaLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, USA.
Paulo C VenturaLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, USA.
Marc A GarciaLerner Center for Public Health Promotion, Aging Studies Institute, Department of Sociology, and Maxwell School of Citizenship & Public Affairs, Syracuse University, Syracuse, New York, USA.
Alessandro VespignaniLaboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, USA.
Huanyu WuShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Hongjie YuShanghai Institute of Infectious Disease and Biosecurity, Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.
Marco AjelliLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, USA.ORCID 0000-0003-1753-4749

Funding

CDC HHS NU38OT000297
6 · The paper itself

Abstract

backgroundHuman contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified.

methodsWe investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period.

resultsWe identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%).

conclusionsOur findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Indexed as

Influenza, HumanSeasonsAdolescentAdultChildChinaCross-Sectional StudiesFemaleHumansIncidenceInfluenza A Virus, H1N1 SubtypeMaleMiddle AgedRespiratory Tract InfectionsTemperatureYoung Adulthuman contact patternsmathematical modelingrespiratory pathogensseasonality

Identifiers

PMID38733199
PMCPMC11087848

What Socratic 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 Socratic graph.