An activation-clearance model for Plasmodium vivax malaria

TL;DR

This paper introduces a dynamic probabilistic model for the activation and clearance of hypnozoites in Plasmodium vivax malaria, providing analytic expressions for relapse timing and hypnozoite clearance, aiding epidemiological understanding and elimination efforts.

Contribution

It develops a novel activation-clearance model for hypnozoites, extending previous work by assuming independent hypnozoite behavior and deriving analytic expressions for key epidemiological quantities.

Findings

Derived formulas for time to first relapse.

Analytic expressions for hypnozoite clearance.

Model extension to multiple hypnozoites per mosquito bite.

Abstract

Malaria is an infectious disease with an immense global health burden. Plasmodium vivax is the most geographically widespread species of malaria. Relapsing infections, caused by the activation of liver-stage parasites known as hypnozoites, are a critical feature of the epidemiology of Plasmodium vivax. Hypnozoites remain dormant in the liver for weeks or months after inoculation, but cause relapsing infections upon activation. Here, we introduce a dynamic probability model of the activation-clearance process governing both potential relapses and the size of the hypnozoite reservoir. We begin by modelling activation-clearance dynamics for a single hypnozoite using a continuous-time Markov chain. We then extend our analysis to consider activation-clearance dynamics for a single mosquito bite, which can simultaneously establish multiple hypnozoites, under the assumption of independent hypnozoite behaviour. We derive analytic expressions for the time to first relapse and the time to hypnozoite clearance for mosquito bites establishing variable numbers of hypnozoites, both of which are quantities of epidemiological significance. Our results extend those in the literature, which were limited due to an assumption of non-independence. Our within-host model can be embedded readily in multi-scale models and epidemiological frameworks, with analytic solutions increasing the tractability of statistical inference and analysis. Our work therefore provides a foundation for further work on immune development and epidemiological-scale analysis, both of which are important for achieving the goal of malaria elimination.