The Impact of Illness on Social Networks: Implications for Transmission and Control of Influenza

Overview

Journal Am J Epidemiol

Publisher Oxford University Press

Specialty Public Health

Date 2013 Oct 9

PMID 24100954

Citations 53

Authors

Kim Van Kerckhove

Niel Hens

W John Edmunds

Ken T D Eames

Affiliations

Soon will be listed here.

Abstract

We expect social networks to change as a result of illness, but social contact data are generally collected from healthy persons. Here we quantified the impact of influenza-like illness on social mixing patterns. We analyzed the contact patterns of persons from England measured when they were symptomatic with influenza-like illness during the 2009 A/H1N1pdm influenza epidemic (2009-2010) and again 2 weeks later when they had recovered. Illness was associated with a reduction in the number of social contacts, particularly in settings outside the home, reducing the reproduction number to about one-quarter of the value it would otherwise have taken. We also observed a change in the age distribution of contacts. By comparing the expected age distribution of cases resulting from transmission by (a)symptomatic persons with incidence data, we estimated the contribution of both groups to transmission. Using this, we calculated the fraction of transmission resulting from (a)symptomatic persons, assuming equal duration of infectiousness. We estimated that 66% of transmission was attributable to persons with symptomatic disease (95% confidence interval: 0.23, 1.00). This has important implications for control: Treating symptomatic persons with antiviral agents or encouraging home isolation would be expected to have a major impact on transmission, particularly since the reproduction number for this strain was low.

Citing Articles

Assessing the role of children in the COVID-19 pandemic in Belgium using perturbation analysis.

Angeli L, Caetano C, Franco N, Coletti P, Faes C, Molenberghs G Nat Commun. 2025; 16(1):2230.

PMID: 40044649 PMC: 11882900. DOI: 10.1038/s41467-025-57087-z.

Juvenile agile frogs spatially avoid ranavirus-infected conspecifics.

Herczeg D, Horvath G, Bokony V, Herczeg G, Kasler A, Holly D Sci Rep. 2024; 14(1):23945.

PMID: 39397128 PMC: 11471862. DOI: 10.1038/s41598-024-74575-2.

Chronic disease patients have fewer social contacts: A pilot survey with implications for transmission dynamics.

Vanderlocht J, Mogelmose S, Van Kerckhove K, Beutels P, Hens N Infect Dis Model. 2024; 9(3):926-930.

PMID: 38800113 PMC: 11127182. DOI: 10.1016/j.idm.2024.05.002.

Bat behavioral immune responses in social contexts: current knowledge and future directions.

Stockmaier S Front Immunol. 2023; 14:1232556.

PMID: 37662931 PMC: 10469833. DOI: 10.3389/fimmu.2023.1232556.

Death is overrated: the potential role of detection in driving virulence evolution.

Kennedy D Proc Biol Sci. 2023; 290(1995):20230117.

PMID: 36987649 PMC: 10050922. DOI: 10.1098/rspb.2023.0117.

References

Eames K, Tilston N, Brooks-Pollock E, Edmunds W . Measured dynamic social contact patterns explain the spread of H1N1v influenza. PLoS Comput Biol. 2012; 8(3):e1002425. PMC: 3297563. DOI: 10.1371/journal.pcbi.1002425. View

Miller E, Hoschler K, Hardelid P, Stanford E, Andrews N, Zambon M . Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study. Lancet. 2010; 375(9720):1100-8. DOI: 10.1016/S0140-6736(09)62126-7. View

Diekmann O, Heesterbeek J, Metz J . On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J Math Biol. 1990; 28(4):365-82. DOI: 10.1007/BF00178324. View

Baguelin M, Hoschler K, Stanford E, Waight P, Hardelid P, Andrews N . Age-specific incidence of A/H1N1 2009 influenza infection in England from sequential antibody prevalence data using likelihood-based estimation. PLoS One. 2011; 6(2):e17074. PMC: 3044152. DOI: 10.1371/journal.pone.0017074. View

Mikolajczyk R, Akmatov M, Rastin S, Kretzschmar M . Social contacts of school children and the transmission of respiratory-spread pathogens. Epidemiol Infect. 2007; 136(6):813-22. PMC: 2870867. DOI: 10.1017/S0950268807009181. View

Cattuto C, Van den Broeck W, Barrat A, Colizza V, Pinton J, Vespignani A . Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS One. 2010; 5(7):e11596. PMC: 2904704. DOI: 10.1371/journal.pone.0011596. View

Eames K, Tilston N, Edmunds W . The impact of school holidays on the social mixing patterns of school children. Epidemics. 2011; 3(2):103-8. DOI: 10.1016/j.epidem.2011.03.003. View

Conlan A, Eames K, Gage J, von Kirchbach J, Ross J, Saenz R . Measuring social networks in British primary schools through scientific engagement. Proc Biol Sci. 2010; 278(1711):1467-75. PMC: 3081745. DOI: 10.1098/rspb.2010.1807. View

Melegaro A, Jit M, Gay N, Zagheni E, Edmunds W . What types of contacts are important for the spread of infections?: using contact survey data to explore European mixing patterns. Epidemics. 2011; 3(3-4):143-51. DOI: 10.1016/j.epidem.2011.04.001. View

10.

McCaw J, Forbes K, Nathan P, Pattison P, Robins G, Nolan T . Comparison of three methods for ascertainment of contact information relevant to respiratory pathogen transmission in encounter networks. BMC Infect Dis. 2010; 10:166. PMC: 2893181. DOI: 10.1186/1471-2334-10-166. View

11.

Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R . Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med. 2008; 5(3):e74. PMC: 2270306. DOI: 10.1371/journal.pmed.0050074. View

12.

Alizon S, Hurford A, Mideo N, van Baalen M . Virulence evolution and the trade-off hypothesis: history, current state of affairs and the future. J Evol Biol. 2009; 22(2):245-59. DOI: 10.1111/j.1420-9101.2008.01658.x. View

13.

Hens N, Goeyvaerts N, Aerts M, Shkedy Z, Van Damme P, Beutels P . Mining social mixing patterns for infectious disease models based on a two-day population survey in Belgium. BMC Infect Dis. 2009; 9:5. PMC: 2656518. DOI: 10.1186/1471-2334-9-5. View

14.

Ogunjimi B, Hens N, Goeyvaerts N, Aerts M, Van Damme P, Beutels P . Using empirical social contact data to model person to person infectious disease transmission: an illustration for varicella. Math Biosci. 2009; 218(2):80-7. DOI: 10.1016/j.mbs.2008.12.009. View

15.

Eagle N, Pentland A, Lazer D . Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci U S A. 2009; 106(36):15274-8. PMC: 2741241. DOI: 10.1073/pnas.0900282106. View

16.

Read J, Edmunds W, Riley S, Lessler J, Cummings D . Close encounters of the infectious kind: methods to measure social mixing behaviour. Epidemiol Infect. 2012; 140(12):2117-30. PMC: 4288744. DOI: 10.1017/S0950268812000842. View

17.

Eames K, Tilston N, White P, Adams E, Edmunds W . The impact of illness and the impact of school closure on social contact patterns. Health Technol Assess. 2010; 14(34):267-312. DOI: 10.3310/hta14340-04. View

18.

Edmunds W, OCallaghan C, Nokes D . Who mixes with whom? A method to determine the contact patterns of adults that may lead to the spread of airborne infections. Proc Biol Sci. 1997; 264(1384):949-57. PMC: 1688546. DOI: 10.1098/rspb.1997.0131. View

19.

Hens N, Ayele G, Goeyvaerts N, Aerts M, Mossong J, Edmunds J . Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries. BMC Infect Dis. 2009; 9:187. PMC: 2799408. DOI: 10.1186/1471-2334-9-187. View

20.

Carrat F, Vergu E, Ferguson N, Lemaitre M, Cauchemez S, Leach S . Time lines of infection and disease in human influenza: a review of volunteer challenge studies. Am J Epidemiol. 2008; 167(7):775-85. DOI: 10.1093/aje/kwm375. View