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.