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

Author:

Kummer Allisandra G.1,Zhang Juanjuan23,Jiang Chenyan4,Litvinova Maria1,Ventura Paulo C.1,Garcia Marc A.5,Vespignani Alessandro6,Wu Huanyu4,Yu Hongjie23,Ajelli Marco1ORCID

Affiliation:

1. Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics Indiana University School of Public Health Bloomington Indiana USA

2. Shanghai Institute of Infectious Disease and Biosecurity, Department of Epidemiology, School of Public Health Fudan University Shanghai China

3. Department of Epidemiology, School of Public Health Fudan University, Key Laboratory of Public Health Safety, Ministry of Education Shanghai China

4. Shanghai Municipal Center for Disease Control and Prevention Shanghai China

5. Lerner Center for Public Health Promotion, Aging Studies Institute, Department of Sociology, and Maxwell School of Citizenship & Public Affairs Syracuse University Syracuse New York USA

6. Laboratory for the Modeling of Biological and Socio‐technical Systems Northeastern University Boston Massachusetts USA

Abstract

ABSTRACTBackgroundHuman 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.

Funder

Council of State and Territorial Epidemiologists

Publisher

Wiley

Reference39 articles.

1. CDC “How Flu Spreads ” (2018) Available from:https://www.cdc.gov/flu/about/disease/spread.htm.

2. CDC “Respiratory Syncytial Virus Infection (RSV) ” (2020) Centers for Disease Control and Prevention.

3. Cyclical patterns and predictability in infection

4. Seasonal infectious disease epidemiology

5. A Nice Day for an Infection? Weather Conditions and Social Contact Patterns Relevant to Influenza Transmission

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