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Estimation of the basic reproduction number for infectious diseases from age‐stratified serological survey data

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  • C. P. Farrington
  • M. N. Kanaan
  • N. J. Gay

Abstract

The basic reproduction number of an infection, R0, is the average number of secondary infections generated by a single typical infective individual in a totally susceptible population. It is directly related to the effort required to eliminate infection. We consider statistical methods for estimating R0 from age‐stratified serological survey data. The main difficulty is indeterminacy, since the contacts between individuals of different ages are not observed. We show that, given an estimate of the average age‐specific hazard of infection, a particular leading left eigenfunction is required to specify R0. We review existing methods of estimation in the light of this indeterminacy. We suggest using data from several infections transmitted via the same route, and we propose that the choice of model be guided by a criterion based on similarity of their contact functions. This approach also allows model uncertainty to be taken into account. If one infection induces no lasting immunity, we show that the only additional assumption required to estimate R0 is that the contact function is symmetric. When matched data on two or more infections transmitted by the same route are available, the methods may be extended to incorporate the effect of individual heterogeneity. The approach can also be applied in partially vaccinated populations and to populations comprising loosely linked communities. The methods are illustrated with data on hepatitis A, mumps, rubella, parvovirus, Haemophilus influenzae type b and measles infection.

Suggested Citation

  • C. P. Farrington & M. N. Kanaan & N. J. Gay, 2001. "Estimation of the basic reproduction number for infectious diseases from age‐stratified serological survey data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 251-292.
  • Handle: RePEc:bla:jorssc:v:50:y:2001:i:3:p:251-292
    DOI: 10.1111/1467-9876.00233
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    Cited by:

    1. Schimit, P.H.T. & Monteiro, L.H.A., 2012. "On estimating the basic reproduction number in distinct stages of a contagious disease spreading," Ecological Modelling, Elsevier, vol. 240(C), pages 156-160.
    2. Steven Abrams & Marc Aerts & Geert Molenberghs & Niel Hens, 2017. "Parametric overdispersed frailty models for current status data," Biometrics, The International Biometric Society, vol. 73(4), pages 1388-1400, December.
    3. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    4. Ziv Shkedy & Marc Aerts & Geert Molenberghs & Philippe Beutels & Pierre Van Damme, 2003. "Modelling forces of infection by using monotone local polynomials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 469-485, October.
    5. H. J. Whitaker & C. P. Farrington, 2004. "Infections with Varying Contact Rates: Application to Varicella," Biometrics, The International Biometric Society, vol. 60(3), pages 615-623, September.
    6. Lahrouz, A. & El Mahjour, H. & Settati, A. & Bernoussi, A., 2018. "Dynamics and optimal control of a non-linear epidemic model with relapse and cure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 299-317.
    7. P. Kriwy, 2012. "Similarity of parents and physicians in the decision to vaccinate children against measles, mumps and rubella," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(2), pages 333-340, April.
    8. Steffen Unkel & C. Paddy Farrington & Heather J. Whitaker & Richard Pebody, 2014. "Time varying frailty models and the estimation of heterogeneities in transmission of infectious diseases," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 141-158, January.
    9. Nele Goeyvaerts & Niel Hens & Benson Ogunjimi & Marc Aerts & Ziv Shkedy & Pierre Van Damme & Philippe Beutels, 2010. "Estimating infectious disease parameters from data on social contacts and serological status," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 255-277, March.
    10. Kimberly M. Thompson & Cassie L. Odahowski, 2016. "Systematic Review of Measles and Rubella Serology Studies," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1459-1486, July.
    11. Jair Andrade & Jim Duggan, 2021. "A Bayesian approach to calibrate system dynamics models using Hamiltonian Monte Carlo," System Dynamics Review, System Dynamics Society, vol. 37(4), pages 283-309, October.
    12. Wen, Chi-Chung & Chen, Yi-Hau, 2011. "Nonparametric maximum likelihood analysis of clustered current status data with the gamma-frailty Cox model," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1053-1060, February.
    13. Tao Hu & Qingyun Du & Fu Ren & Shi Liang & Denan Lin & Jiajia Li & Yan Chen, 2014. "Spatial Analysis of the Home Addresses of Hospital Patients with Hepatitis B Infection or Hepatoma in Shenzhen, China from 2010 to 2012," IJERPH, MDPI, vol. 11(3), pages 1-13, March.

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