Model-consistent estimation of the basic reproduction number from the incidence of an emerging infection

AbstractWe investigate the merit of deriving an estimate of the basic reproduction number $$ \mathcal{R}_0 $$ early in an outbreak of an (emerging) infection from estimates of the incidence and generation interval only. We compare such estimates of $$ \mathcal{R}_0 $$ with estimates incorporating additional model assumptions, and determine the circumstances under which the different estimates are consistent. We show that one has to be careful when using observed exponential growth rates to derive an estimate of $$ \mathcal{R}_0 $$ , and we quantify the discrepancies that arise.

[1]  A. L. Lloyd,et al.  The dependence of viral parameter estimates on the assumed viral life cycle: limitations of studies of viral load data , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[2]  L M Wahl,et al.  Improving estimates of the basic reproductive ratio: Using both the mean and the dispersal of transition times , 2006, Theoretical Population Biology.

[3]  P. E. Kopp,et al.  Superspreading and the effect of individual variation on disease emergence , 2005, Nature.

[4]  M. D. de Jong,et al.  Quantification of the transmission of classical swine fever virus between herds during the 1997-1998 epidemic in The Netherlands. , 1999, Preventive veterinary medicine.

[5]  M. Lipsitch,et al.  How generation intervals shape the relationship between growth rates and reproductive numbers , 2007, Proceedings of the Royal Society B: Biological Sciences.

[6]  L. Matthews,et al.  The construction and analysis of epidemic trees with reference to the 2001 UK foot–and–mouth outbreak , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[7]  M G Roberts,et al.  An integral equation model for the control of a smallpox outbreak. , 2005, Mathematical biosciences.

[8]  Pejman Rohani,et al.  Appropriate Models for the Management of Infectious Diseases , 2005, PLoS medicine.

[9]  P. Kaye Infectious diseases of humans: Dynamics and control , 1993 .

[10]  B. C. Choi,et al.  A simple approximate mathematical model to predict the number of severe acute respiratory syndrome cases and deaths , 2003, Journal of epidemiology and community health.

[11]  Ả. Svensson A note on generation times in epidemic models. , 2007, Mathematical Biosciences.

[12]  J. Wallinga,et al.  Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures , 2004, American journal of epidemiology.

[13]  J. Robins,et al.  Transmissibility of 1918 pandemic influenza , 2004, Nature.

[14]  Matthew J Ferrari,et al.  Estimation and inference of R0 of an infectious pathogen by a removal method. , 2005, Mathematical biosciences.

[15]  J. Robins,et al.  Transmission Dynamics and Control of Severe Acute Respiratory Syndrome , 2003, Science.

[16]  J.A.P. Heesterbeek A Brief History of R0 and a Recipe for its Calculation , 2002, Acta biotheoretica.

[17]  Guiyun Yan,et al.  Severe Acute Respiratory Syndrome Epidemic in Asia , 2003, Emerging infectious diseases.

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

[19]  Ronald Meester,et al.  Modeling and real-time prediction of classical swine fever epidemics. , 2002, Biometrics.

[20]  N Wilson,et al.  A model for the spread and control of pandemic influenza in an isolated geographical region , 2007, Journal of The Royal Society Interface.

[21]  Christl A. Donnelly,et al.  Real-time Estimates in Early Detection of SARS , 2006, Emerging infectious diseases.

[22]  P. Fine The interval between successive cases of an infectious disease. , 2003, American journal of epidemiology.

[23]  M G Roberts,et al.  Modelling strategies for minimizing the impact of an imported exotic infection , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[24]  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.