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

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

Modeling of the Glycolysis Pathway in Plasmodium falciparum using Petri Nets

Malaria is one of the deadly diseases, which affects a large number of the world’s population. The Plasmodium falciparum parasite during erythrocyte stages produces its energy mainly through anaerobic glycolysis, with pyruvate being converted into lactate. The glycolysis metabolism in P. falci-parum is one of the important metabolic pathways of the parasite because the parasite is entirely dependent on it for energy. Also, several glycolytic enzymes have been proposed as drug targets. Petri nets (PNs) have been recognized as one of the important models for representing biological pathways. In this work, we built a qualitative PN model for the glycolysis pathway in P. falciparum and analyzed the model for its structural and quantitative properties using PN theory. From PlasmoCyc files, a total of 11 reactions were extracted; 6 of these were reversible and 5 were irreversible. These reactions were catalyzed by a total number of 13 enzymes. We extracted some of the essential reactions in the pathway using PN model, which are the possible drug targets without which the pathway cannot function. This model also helps to improve the understanding of the biological processes within this pathway

Measurement of Plasmodium falciparum transmission intensity using serological cohort data from Indonesian schoolchildren.

BACKGROUND: As malaria transmission intensity approaches zero, measuring it becomes progressively more difficult and inefficient because parasite-positive individuals are hard to detect. This situation may arise shortly before achieving local elimination, or during surveillance post-elimination to prevent reintroduction. Antibody responses against the parasite last longer than the infections themselves. This "footprint" of infection may thus be used for assessing transmission intensity. A statistical approach is presented for measuring the seroconversion rate (SCR), a correlate of the force of infection, from individual-level longitudinal data on antibody titres in an area of low Plasmodium falciparum transmission. METHODS: Blood samples were collected from 160 Indonesian schoolchildren every month for six months. Titres of antibodies against AMA-1 and MSP-1(19) antigens of P. falciparum were measured using ELISA. The distribution of antibody titres among seronegative and -positive individuals, respectively, was estimated by comparing the titres from the study data (a mixture of both seropositive and -negative individuals) with titres from a (unexposed) negative control group of Indonesian individuals. Two Markov-Chain models for the transition of individuals between serological states were fitted to individual anti-PfAMA-1 or anti-PfMSP-1 titre time series using Bayesian Markov-Chain-Monte-Carlo (MCMC). This yielded estimates of SCR as well as of the duration of seropositivity. RESULTS: A posterior median SCR of 0.02 (Pf AMA-1) and 0.09 (PfMSP-1) person(-1) year(-1) was estimated, with credible intervals ranging from 1E-4 to 0.2 person(-1) year(-1). This level of transmission intensity is at the lower range of what can reliably be measured with the present study size. A Bayesian test for seroconversion of an individual between two observations is presented and used to identify the subjects who have most likely experienced an infection. Furthermore, the theoretical limits of measuring transmission intensity, and how these depend on duration and size of a study as well as on transmission intensity itself, is illustrated. CONCLUSIONS: This analysis shows that it is possible to measure SCR's from individual-level longitudinal data on antibody titres. In addition, individual seroconversion events can be identified, which can be useful in assessing interruption of transmission. Analyses of further serological datasets using the present method are required to improve and validate it. This includes measurement of the duration of antibody responses, how it depends on host age or cumulative exposure, or on the particular antigen used

SIMULATION AND ANALYSIS OF PENTOSE PHOSPHATE PATHWAY IN PLASMODIUM FALCIPARUM USING COLORED PETRI NETS MODEL

Plasmodium falciparum is a protozoan parasite and the deadliest of five human malaria species which is responsible for the majority of malaria related deaths in humans. The erythrocytes’ stage of Plasmodium falciparum depend on Pentose Pathway as an alternative source of energy and it releases electrons used in protecting the Plasmodium falciparum from its host. Colored Petri Net has been recognized as one of the important models in modelling and analyzing biological pathways. It is an accurate qualitative and quantitative modelling tool for modeling complex biological systems. In this work, the modeling of the pentose phosphate pathway in Plasmodium falciparum is presented using the Petri Net Markup Language (PNML). The Colored Petri Net (CPN) models based on the Petri Net representation and the conservation and kinetic equations were used to examine the dynamic behavior of the metabolic pathway. The usefulness of Petri Nets is demonstrated for the quantitative analysis of the pathway. We obtained data from Biocyc database. The constructed model was viewed through the Colored Petri Net Tool (CPN tool 4.0). Specific drug targets called the essential reactions within the pathway were identified, listed and proposed. These essential reactions would alter the functioning of the pathway which would affect the energy and protection needs of the parasite therefore leading to the death of the parasite in the human red blood cell

Evaluating the performance of malaria genomics for inferring changes in transmission intensity using transmission modelling

AbstractAdvances in genetic sequencing and accompanying methodological approaches have resulted in pathogen genetics being used in the control of infectious diseases. To utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment. Here we develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The best performing model successfully predicted malaria prevalence with mean absolute error of 0.055, suggesting genetic tools could be used for monitoring the impact of malaria interventions.</jats:p

Evaluating the performance of malaria genetics for inferring changes in transmission intensity using transmission modelling

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance

Towards a comprehensive simulation model of malaria epidemiology and control

Planning of the control of Plasmodium falciparum malaria leads to a need for models of malaria epidemiology that provide realistic quantitative prediction of likely epidemiological outcomes of a wide range of control strategies. Predictions of the effects of control often ignore medium- and long-term dynamics. The complexities of the Plasmodium life-cycle, and of within-host dynamics, limit the applicability of conventional deterministic malaria models. We use individual-based stochastic simulations of malaria epidemiology to predict the impacts of interventions on infection, morbidity, mortality, health services use and costs. Individual infections are simulated by stochastic series of parasite densities, and naturally acquired immunity acts by reducing densities. Morbidity and mortality risks, and infectiousness to vectors, depend on parasite densities. The simulated infections are nested within simulations of individuals in human populations, and linked to models of interventions and health systems. We use numerous field datasets to optimise parameter estimates. By using a volunteer computing system we obtain the enormous computational power required for model fitting, sensitivity analysis, and exploration of many different intervention strategies. The project thus provides a general platform for comparing, fitting, and evaluating different model structures, and for quantitative prediction of effects of different interventions and integrated control programme

Predictability of epidemic malaria under non-stationary conditions with process-based models combining epidemiological updates and climate variability.

BACKGROUND: Previous studies have demonstrated the feasibility of early-warning systems for epidemic malaria informed by climate variability. Whereas modelling approaches typically assume stationary conditions, epidemiological systems are characterized by changes in intervention measures over time, at scales typically longer than inter-epidemic periods. These trends in control efforts preclude simple application of early-warning systems validated by retrospective surveillance data; their effects are also difficult to distinguish from those of climate variability itself. METHODS: Rainfall-driven transmission models for falciparum and vivax malaria are fitted to long-term retrospective surveillance data from four districts in northwest India. Maximum-likelihood estimates (MLEs) of model parameters are obtained for each district via a recently introduced iterated filtering method for partially observed Markov processes. The resulting MLE model is then used to generate simulated yearly forecasts in two different ways, and these forecasts are compared with more recent (out-of-fit) data. In the first approach, initial conditions for generating the predictions are repeatedly updated on a yearly basis, based on the new epidemiological data and the inference method that naturally lends itself to this purpose, given its time-sequential application. In the second approach, the transmission parameters themselves are also updated by refitting the model over a moving window of time. RESULTS: Application of these two approaches to examine the predictability of epidemic malaria in the different districts reveals differences in the effectiveness of intervention for the two parasites, and illustrates how the 'failure' of predictions can be informative to evaluate and quantify the effect of control efforts in the context of climate variability. The first approach performs adequately, and sometimes even better than the second one, when the climate remains the major driver of malaria dynamics, as found for Plasmodium vivax for which an effective clinical intervention is lacking. The second approach offers more skillful forecasts when the dynamics shift over time, as is the case of Plasmodium falciparum in recent years with declining incidence under improved control. CONCLUSIONS: Predictive systems for infectious diseases such as malaria, based on process-based models and climate variables, can be informative and applicable under non-stationary conditions

Integration of parasite genetic information in malaria transmission modelling

Mathematical models of malaria transmission are increasingly used to quantify the impact of malaria control efforts and to assist in the development and costing of future initiatives such as the WHO Global Technical Strategy for Malaria 2016-2030. These models have highlighted both the progress made so far, but also how continued investment is needed to reach the milestones required. However, the increase in global malaria cases reported in 2018 suggests that new tools may be required to continue the gains made and to address the growing risk of antimalarial resistance threatening to reverse the recent declines in malaria burden. The proliferation of genetic sequencing and the publication of the Plasmodium falciparum reference genome in 2002 has facilitated a greater understanding of the genetic determinants of resistance and molecular tools are subsequently poised to become a routine tool for malaria control. Consequently, integrating parasite genetic information into established models of malaria transmission models can contribute to both our understanding of the drivers and optimum policies for addressing resistance and detailing the potential of molecular tools within malaria control. Plasmodium falciparum is known to have evolved several times in response to first line antimalarials. However, recent evidence has shown evolution to rapid diagnostic tests. The WHO has consequently issued guidance advising national malaria control programmes to conduct surveillance for pfhrp2/3 deletions. The timing of this policy recommendation and my previous work modelling pfhrp2 deletions necessitated a timely extension of our previous model to evaluate the implications of seasonality in malaria transmission on estimates of the prevalence of pfhrp2/3 deletions. Recent studies have suggested that malaria genotyping could be a useful tool for epidemiological surveillance. By developing an extended version of an established model of malaria transmission, which now models individual mosquitoes affording the full parasite life cycle to be represented, I characterise the potential utility of malaria genomics for inferring changes in transmission intensity. I conclude that although molecular tools could enable accurate estimation of malaria prevalence, greater attention needs to be placed on the chosen sampling scheme, recording patient metadata and developing the statistical toolkit for analysing polyclonal infected individuals. In 2015, health ministers in the Greater Mekong Subregion (GMS) adopted the WHO strategy for malaria elimination in the GMS 2016-2030. The strategy was developed to accelerate elimination in South-East Asia, which is currently the best approach to address the growing threat of artemisinin resistance and the emergence of multidrug resistant parasite lineages. In response, I demonstrate how the therapeutic lifespan of the five currently recommended artemisinin combination therapies can be prolonged by reducing antimalarial overprescription by ensuring that all suspected malaria fevers are tested before administering antimalarials. I conclude by comparing different cycling and mixing strategies before reviewing how each strategy can be improved to slow the spread of antimalarial resistance. Elimination in the GMS is undoubtedly an effective mechanism for preventing the spread of artemisinin resistance to Africa. However, if efforts to eliminate by 2030 have failed it will be imperative to understand the mechanisms with which resistance may continue to spread. To this extent, the capability of resistant strains to invade susceptible populations is evaluated using data from standard membrane feeding assays. Findings are incorporated in the transmission model to quantify the transmission advantage of artemisinin resistance at the population level.Open Acces