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bayesian approach

Uncovering drivers of dose-dependence and individual variation in malaria infection outcomes

October 15, 2020 - 08:52 -- Open Access
Kamiya T, Greischar MA, Schneider DS, Mideo N
PLoS Comput Biol. 2020 Oct 8;16(10):e1008211

To understand why some hosts get sicker than others from the same type of infection, it is essential to explain how key processes, such as host responses to infection and parasite growth, are influenced by various biotic and abiotic factors. In many disease systems, the initial infection dose impacts host morbidity and mortality. To explore drivers of dose-dependence and individual variation in infection outcomes, we devised a mathematical model of malaria infection that allowed host and parasite traits to be linear functions (reaction norms) of the initial dose.

NOT Open Access | Modeling an association between malaria cases and climate variables for Keonjhar district of Odisha, India: a Bayesian approach

June 9, 2020 - 11:36 -- NOT Open Access
Kumar P, Vatsa R, Sarthi PP, Kumar M, Gangare V
J Parasit Dis. 2020 Jun;44(2):319-331

Malaria, a vector-borne disease, is a significant public health problem in Keonjhar district of Odisha (the malaria capital of India). Prediction of malaria, in advance, is an urgent need for reporting rolling cases of disease throughout the year. The climate condition do play an essential role in the transmission of malaria. Hence, the current study aims to develop and assess a simple and straightforward statistical model of an association between malaria cases and climate variates.

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