Inferring high-resolution human mixing patterns for disease modeling

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.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 75

1. 1.

   Van Kerkhove MD, Ferguson NM. Epidemic and intervention modelling: a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic. Bull World Health Organ. 2012;90:306-310.

   Not in our library10.2471/blt.11.097949

2. 2.

   Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. 1991.

   Not in our library

3. 3.

   Metcalf CJE, Edmunds WJ, Lessler J. Six challenges in modelling for public health policy. Epidemics. 2015;10:93-96.

   Not in our library10.1016/j.epidem.2014.08.008

4. 4.

   Keeling M, Woolhouse M, May R, Davies G, Grenfell BT. Modelling vaccination strategies against foot-and-mouth disease. Nature. 2003;421:136-142.

   Not in our library10.1038/nature01343

5. 5.
   Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions · 2007

   No notice
6. 6.

   Longini IM, et al. Containing pandemic influenza at the source. Science. 2005;309:1083-1087.

   Not in our library10.1126/science.1115717

7. 7.

   Ferguson NM, et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature. 2005;437:209-214.

   Not in our library10.1038/nature04017

8. 8.

   Merler S, Ajelli M. The role of population heterogeneity and human mobility in the spread of pandemic influenza. Proc. R Soc. B. 2010;277:557-565.

   Not in our library10.1098/rspb.2009.1605

9. 9.

   Fenton KA, et al. Sexual behaviour in Britain: reported sexually transmitted infections and prevalent genital Chlamydia trachomatis infection. The Lancet. 2001;358:1851-1854.

   Not in our library10.1016/s0140-6736(01)06886-6

10. 10.
    Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosis Infection · 2015

    No notice
11. 11.
    Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China · 2020

    No notice
12. 12.
    Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK · 2020

    No notice
13. 13.

    Wallinga J, Teunis P, Kretzschmar M. Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. Am. J. Epidemiol. 2006;164:936-944.

    Not in our library10.1093/aje/kwj317

14. 14.
    Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases · 2008

    No notice
15. 15.
    Mining social mixing patterns for infectious disease models based on a two-day population survey in Belgium · 2009

    Not checked
16. 16.
    Social Contact Patterns in Vietnam and Implications for the Control of Infectious Diseases · 2011

    No notice
17. 17.
    Social mixing patterns in rural and urban areas of southern China · 2014

    No notice
18. 18.

    Ajelli M, Litvinova M. Estimating contact patterns relevant to the spread of infectious diseases in Russia. J. Theor. Biol. 2017;419:1-7.

    Not in our library10.1016/j.jtbi.2017.01.041

19. 19.

    Melegaro A, et al. Social Contact Structures and Time Use Patterns in the Manicaland Province of Zimbabwe. PLoS ONE. 2017;12:e0170459.

    Not in our library10.1371/journal.pone.0170459

20. 20.
    Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks · 2010

    No notice
21. 21.

    Kiti MC, et al. Quantifying social contacts in a household setting of rural kenya using wearable proximity sensors. EPJ Data Sci. 2016;5:21.

    Not in our library10.1140/epjds/s13688-016-0084-2

22. 22.

    Zagheni E, et al. Using time-use data to parameterize models for the spread of close-contact infectious diseases. Am. J. Epidemiol. 2008;168:1082-1090.

    Not in our library10.1093/aje/kwn220

23. 23.
    Inferring the Structure of Social Contacts from Demographic Data in the Analysis of Infectious Diseases Spread · 2012

    No notice
24. 24.
    FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations · 2013

    No notice
25. 25.

    Gallagher S, Richardson LF, Ventura SL, Eddy WF. SPEW: synthetic populations and ecosystems of the world. J. Comput. Graph Stat. 2018;27:773-784.

    Not in our library10.1080/10618600.2018.1442342

26. 26.

    Iozzi F, et al. Little Italy: an agent-based approach to the estimation of contact patterns-fitting predicted matrices to serological data. PLoS Comput. Biol. 2010;6:e1001021.

    Not in our library10.1371/journal.pcbi.1001021

27. 27.

    De Cao E, Zagheni E, Manfredi P, Melegaro A. The relative importance of frequency of contacts and duration of exposure for the spread of directly transmitted infections. Biostatistics. 2014;15:470-483.

    Not in our library10.1093/biostatistics/kxu008

28. 28.
    Projecting social contact matrices in 152 countries using contact surveys and demographic data · 2017

    No notice
29. 29.

    Litvinova M, Liu Q-H, Kulikov ES, Ajelli M. Reactive school closure weakens the network of social interactions and reduces the spread of influenza. Proc. Natl Acad. Sci. USA. 2019;116:13174-13181.

    Not in our library10.1073/pnas.1821298116

30. 30.
    The French Connection: The First Large Population-Based Contact Survey in France Relevant for the Spread of Infectious Diseases · 2015

    No notice
31. 31.

    Munasinghe L, Asai Y, Nishiura H. Quantifying heterogeneous contact patterns in Japan: a social contact survey. Theor. Biol. Med. Model. 2019;16:6.

    Not in our library10.1186/s12976-019-0102-8

32. 32.
    Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles · 2019

    No notice
33. 33.

    Ajelli M, Poletti P, Melegaro A, Merler S. The role of different social contexts in shaping influenza transmission during the 2009 pandemic. Sci. Rep. 2014;4:7218.

    Not in our library10.1038/srep07218

34. 34.
    The impact of the unstructured contacts component in influenza pandemic modeling. · 2008

    Not checked
35. 35.
    Incidence and reproduction numbers of pertussis: estimates from serological and social contact data in five European countries. · 2010

    Not checked
36. 36.

    Kucharski AJ, Gog JR. The role of social contacts and original antigenic sin in shaping the age pattern of immunity to seasonal influenza. PLoS Comput. Biol. 2012;8:e1002741.

    Not in our library10.1371/journal.pcbi.1002741

37. 37.

    Poletti P, et al. Perspectives on the impact of varicella immunization on herpes zoster. A model-based evaluation from three European countries. PLoS ONE. 2013;8:e60732.

    Not in our library10.1371/journal.pone.0060732

38. 38.

    Merler S, Ajelli M. Deciphering the relative weights of demographic transition and vaccination in the decrease of measles incidence in Italy. P. Roy. Soc. B. 2014;281:20132676.

    Not in our library10.1098/rspb.2013.2676

39. 39.

    Kretzschmar ME, et al. Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study. The Lancet Public Health. 2020;5:e452-e459.

    Not in our library10.1016/s2468-2667(20)30157-2

40. 40.

    Weil M, et al. The dynamics of infection and the persistence of immunity to A (H1N1) pdm09 virus in Israel. Influenza Other Respir Viruses. 2013;7:838-846.

    Not in our library10.1111/irv.12071

Cited by papers we hold 5

• Modeling transmission of SARS-CoV-2 Omicron in China · 2022
  No notice
• Covasim: An agent-based model of COVID-19 dynamics and interventions · 2021
  No notice
• The impact of COVID-19 on TB: a review of the data · 2021
  No notice
• Age groups that sustain resurging COVID-19 epidemics in the United States · 2021
  No notice
• Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China · 2020
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.