The Effects of Different Deployment Strategies of Artemisinin Combination Therapies on Slowing Down the Spread of Antimalarial Drug Resistence: Investigation With Individual-Based Simulations

Abstract

Despite the success of recent global malaria control efforts, which have halved global malaria mortality since 2000, malaria is still one of the world’s most deadly diseases causing an estimated half a million deaths, mostly among African children, and around a quarter of billion clinical episodes every year as reported in 2014. Drug resistance is one of the most important challenges to malaria elimination. To contain drug resistance, many efforts have been put forth including improvement of surveillance systems and mass treatment in order to stop or slow down the transmission of the resistant strain. To find out whether a population-level treatment strategy can have any benefit in containing drug resistance, mathematical models are an appropriate approach to this problem and individual-based models allow us to have a better understanding of the effect of individual heterogeneities on the outcome. The first part of the thesis is about building and validating an individual based microsimulation. The model is implemented as an individual-based discrete-time event simulation model in C++. The behaviors and the state changes of human individuals are determined by relevant events and mathematical formulas. This integrated model combines components that reproduce the most important features of malaria transmission and epidemiology: the infectiousness of human populations; clinical model of acute illness; heterogeneities in individuals’ age, biting-rate level, drug absorption, drug action, multiple parasite populations, and human immunity. To validate this individual-based model, two types of validation have been done. The model’s parameters were obtained from field or clinical data were used directly in the model. For those parameters that cannot be obtained directly from literature review, sensitivity analysis has been done to find how variation in parameter values affects certain key features of malaria epidemiology. The second part of the thesis focused on the comparison between population-scaled treatment strategies. The results showed that using multiple first-line therapies (MFT) results in a lower number of treatment failures compared to other strategies where a single first-line ACT is recommended. This result is robust to various epidemiological, pharmacological, and evolutionary features of malaria transmission. In addition, including non-ACT therapy in an MFT strategy seems to have a significant benefit in reducing the pressure on artemisinin-resistance evolution, delaying its emergence and slowing its spread. The third part of the thesis focused on individual-level treatment strategies to combat artemisinin resistance. The results showed that lengthening an ACT course or using multiple courses of ACT can reduce the long-term number of treatment failures significantly. The work reported here introduces a novel individual-based simulation that includes drug resistance evolution and the ability to be scaled up to millions number of individuals. The challenge that remains is to evaluate the feasibility of these novel treatment strategies given that they will need to be implemented in the real world of malaria control programs, their operations, human behavior, and economic realities.