Determining parameter distribution in within-host severe P. falciparum malaria

Area of Science:

• * Infectious Diseases
• * Mathematical Biology
• * Parasitology

Background:

• * Existing models of within-host malaria dynamics often assume uninfected red blood cell (RBC) availability limits parasite propagation.
• * Studies on Plasmodium falciparum malaria have yielded varying results regarding parasite growth and regulation.
• * Mathematical models are crucial for understanding the complex evolution of malaria within a host.

Purpose of the Study:

• * To review classical models of blood-stage malaria.
• * To propose a novel mathematical model incorporating double infection of red blood cells (RBCs).
• * To analyze model behavior and parameter identifiability using Markov chain Monte Carlo (MCMC).

Main Methods:

• * Review of established mathematical models for within-host malaria.
• * Development of a new model that accounts for the possibility of double infection within a single red blood cell (RBC).
• * Application of Markov chain Monte Carlo (MCMC) for model analysis and parameter estimation.

Main Results:

• * The proposed model suggests that infected RBCs may rupture more rapidly in severe malaria.
• * This accelerated rupture rate could be linked to the phenomenon of double infection.
• * Findings may explain the occurrence of anemia and non-uniform oscillations observed in malaria infections.

Conclusions:

• * Double infection is a potential factor influencing the dynamics of severe malaria.
• * Accelerated RBC rupture due to double infection could contribute to anemia in malaria patients.
• * Further research is needed to elucidate the step-by-step evolution of malaria within a host.