The distribution of incubation and relapse times in experimental human infections with the malaria parasite Plasmodium vivax

BMC Infectious Diseases

Alex R Cook⁴  Richard J Coker³  Zheng Gao¹  Xiahong Zhao²  Andrew A Lover² 

[1] Faculty of Engineering, National University of Singapore, Singapore, Singapore;Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore;Communicable Diseases Policy Research Group, London School of Hygiene and Tropical Medicine, Bangkok, Thailand;Communicable Diseases Centre, Tan Tock Seng Hospital, Singapore, Singapore

关键词: Relapse;    Incubation period;    Infectious disease models;    vivax;    Malaria;   

Others  :  1125450
DOI  :  10.1186/1471-2334-14-539

 received in 2014-05-24, accepted in 2014-09-25,  发布年份 2014

【 摘 要 】

Background

The distributions of incubation and relapse periods are key components of infectious disease models for the malaria parasite Plasmodium vivax; however, detailed distributions based upon experimental data are lacking.

Methods

Using a range of historical, experimental mosquito-transmitted human infections, Bayesian estimation with non-informative priors was used to determine parametric distributions that can be readily implemented for the incubation period and time-to-first relapse in P. vivax infections, including global subregions by parasite source. These analyses were complemented with a pooled analysis of observational human infection data with infections that included malaria chemoprophylaxis and long-latencies. The epidemiological impact of these distributional assumptions was explored using stochastic epidemic simulations at a fixed reproductive number while varying the underlying distribution of incubation periods.

Results

Using the Deviance Information Criteria to compare parameterizations, experimental incubation periods are most closely modeled with a shifted log-logistic distribution; a log-logistic mixture is the best fit for incubations in observational studies. The mixture Gompertz distribution was the best fit for experimental times-to-relapse among the tested parameterizations, with some variation by geographic subregions. Simulations suggest underlying distributional assumptions have critically important impacts on both the time-scale and total case counts within epidemics.

Conclusions

These results suggest that the exponential and gamma distributions commonly used for modeling incubation periods and relapse times inadequately capture the complexity in the distributions of event times in P. vivax malaria infections. In future models, log-logistic and Gompertz distributions should be utilized for general incubation periods and relapse times respectively, and region-specific distributions should be considered to accurately model and predict the epidemiology of this important human pathogen.

【 授权许可】

   
2014 Lover et al.; licensee BioMed Central Ltd.

【 图 表 】

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