Dose-response models for selected respiratory infectious agents: Bordetella pertussis, group a Streptococcus, rhinovirus and respiratory syncytial virus

BMC Infectious Diseases

Research Article

Rachael M Jones¹  Yu-Min Su¹ 

[1] Division of Environmental and Occupational Health Sciences, School of Public Health University of Illinois, Chicago, USA;

关键词: Risk assessment;    Dose-response;    Respiratory infections;    Pharyngitis;    Common cold;   

DOI  :  10.1186/s12879-015-0832-0

 received in 2014-05-22, accepted in 2015-02-12,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundDose-response assessment is one step in quantitative microbial risk assessment (QMRA). Four infectious microbes capable of causing respiratory diseases important to public health, and for which dose-response functions have not been available are: Bordetella pertussis (whooping cough), group A Streptococcus (pharyngitis), rhinovirus (common cold) and respiratory syncytial virus (common cold). The objective of this study was to fit dose-response functions for these microbes to published experimental data.MethodsExperimental infectivity data in human subjects and/or animal models were identified from the peer-reviewed literature. The exponential and beta-Poisson dose-response functions were fitted using the method of maximum likelihood, and models compared by Akaike’s Information Criterion.ResultsDose-response functions were identified for each appropriate data set for the four infectious microbes. Statistical and graphical measures of fit are presented.ConclusionsWith the fitted dose-response functions it will be possible to perform QMRA for these microbes. The dose-response functions, however, have a number of limitations associated with the route of exposure, use of animal hosts, and quality of fit. As a result, thoughtfulness must be used in selecting one dose-response function for a QMRA, and the function should be recognized as a significant source of uncertainty. Nonetheless, QMRA offers a transparent, systematic framework within which to understand the mechanisms of disease transmission, and evaluate interventions.

【 授权许可】

CC BY   
© Jones and Su; licensee BioMed Central. 2015

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