Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents.

The estimation of transmission parameters has been problematic for diseases that rely predominantly on transmission of pathogens from person to person through small infectious droplets. Age-specific transmission parameters determine how such respiratory agents will spread among different age groups in a human population. Estimating the values of these parameters is essential in planning an effective response to potentially devastating pandemics of smallpox or influenza and in designing control strategies for diseases such as measles or mumps. In this study, the authors estimated age-specific transmission parameters by augmenting infectious disease data with auxiliary data on self-reported numbers of conversational partners per person. They show that models that use transmission parameters based on these self-reported social contacts are better able to capture the observed patterns of infection of endemically circulating mumps, as well as observed patterns of spread of pandemic influenza. The estimated age-specific transmission parameters suggested that school-aged children and young adults will experience the highest incidence of infection and will contribute most to further spread of infections during the initial phase of an emerging respiratory-spread epidemic in a completely susceptible population. These findings have important implications for controlling future outbreaks of novel respiratory-spread infectious agents.

[1]  S. Miller,et al.  Control of communicable diseases. , 1949, The Journal-lancet.

[2]  W. S. Jordan,et al.  A study of illness in a group of Cleveland families. XVII. The occurrence of Asian influenza. , 1958, American journal of hygiene.

[3]  B. Sigurdsson,et al.  Experience with influenza vaccination in Iceland, 1957. , 1959, Bulletin of the World Health Organization.

[4]  H FUKUMI,et al.  Summary report on the Asian influenza epidemic in Japan, 1957. , 1959, Bulletin of the World Health Organization.

[5]  K. Warren,et al.  A study of illness in a group of Cleveland families. XXII. Antibodies to Toxoplasma gondii in 40 families observed for ten years. , 1953, The New England journal of medicine.

[6]  K. Stýblo,et al.  Results of contact examination in Rotterdam, 1967-1969. , 1975, Bulletin of the International Union against Tuberculosis.

[7]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[8]  P. Killworth,et al.  Informant accuracy in social-network data V. An experimental attempt to predict actual communication from recall data☆ , 1982 .

[9]  D. Schenzle An age-structured model of pre- and post-vaccination measles transmission. , 1984, IMA journal of mathematics applied in medicine and biology.

[10]  R M May,et al.  Age-related changes in the rate of disease transmission: implications for the design of vaccination programmes , 1985, Journal of Hygiene.

[11]  O. Diekmann,et al.  On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations , 1990, Journal of mathematical biology.

[12]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.

[13]  M. Morris,et al.  A log-linear modeling framework for selective mixing. , 1991, Mathematical biosciences.

[14]  G. Berbers,et al.  Comparison of a neutralization enzyme immunoassay and an enzyme-linked immunosorbent assay for evaluation of immune status of children vaccinated for mumps , 1992, Journal of clinical microbiology.

[15]  L. Sattenspiel,et al.  Geographic spread of measles on the island of Dominica, West Indies. , 1993, Human biology.

[16]  H. Hethcote Models for Infectious Human Diseases: Modeling heterogeneous mixing in infectious disease dynamics , 1996 .

[17]  Valerie Isham,et al.  Models for Infectious Human Diseases: Their Structure and Relation to Data , 1996 .

[18]  W. Edmunds,et al.  Who mixes with whom? A method to determine the contact patterns of adults that may lead to the spread of airborne infections , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[19]  M. Kretzschmar,et al.  Perspective: human contact patterns and the spread of airborne infectious diseases. , 1999, Trends in microbiology.

[20]  H. D. de Melker,et al.  Non-participation in a population-based seroprevalence study of vaccine-preventable diseases , 2000, Epidemiology and Infection.

[21]  J. Wallinga,et al.  Estimation of measles reproduction ratios and prospects for elimination of measles by vaccination in some Western European countries , 2001, Epidemiology and Infection.

[22]  C. P. Farrington,et al.  Estimation of the basic reproduction number for infectious diseases from age‐stratified serological survey data , 2001 .

[23]  A. Nizam,et al.  Containing Bioterrorist Smallpox , 2002, Science.

[24]  N. Ferguson,et al.  Planning for smallpox outbreaks , 2003, Nature.

[25]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[26]  C. Fraser,et al.  Public Health Risk from the Avian H5N1 Influenza Epidemic , 2004, Science.

[27]  S. Leach,et al.  Epidemiologic Determinants for Modeling Pneumonic Plague Outbreaks , 2004, Emerging infectious diseases.

[28]  D. Heymann Control of Communicable Diseases Manual , 2004 .

[29]  A. Nizam,et al.  Containing pandemic influenza with antiviral agents. , 2004, American journal of epidemiology.

[30]  A. Nizam,et al.  Containing Pandemic Influenza at the Source , 2005, Science.

[31]  C P Farrington,et al.  Matrix models for childhood infections: a Bayesian approach with applications to rubella and mumps , 2005, Epidemiology and Infection.

[32]  D. Cummings,et al.  Strategies for containing an emerging influenza pandemic in Southeast Asia , 2005, Nature.

[33]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.