Mathematical Modeling of Malaria: Theories of Malaria Elimination

This dissertation describes the development and application of a new mathematical model for simulating the progression of Plasmodium falciparum infections in individuals with no malarial acquired immunity. The model allows for stochastic simulation of asexual and sexual parasitemias as well as the onset of fever and human to mosquito infectivity on a daily time scale. The model components for the asexual and sexual stages were developed elsewhere but are here extended to allow for simulation of the full range of dynamics observed in a subset of malaria therapy patients. As a first application of the model, I calculate the human component of malarial R0, the basic reproductive number. I then compare this value to those from three other models and describe how this quantity can be used to model malaria transmission. The second application of the model incorporates the effects of drug treatment on progression of infection by utilizing modeled pharmacokinetic and pharmacodynamic properties of a variety of antimalarials. I utilize a stage specific proportional killing model for sexual stages, informed from recent in vitro data. The relationship of effect sizes to treatment coverage and type of treatment in both early and late treatment seeking settings is calculated. In the third chapter, I consider the economic and epidemiological ramifications of antimalarial and rapid diagnostic subsidization for malaria control. For the epidemiological modeling I utilize a semi-mechanistic model of the spread of drug resistance parameterized from historical malaria mortality data; for the economic model I consider the effect of rapid diagnostics on the intensive and extensive margins of antibiotics and antimalarials, as well as the benefits to improved targeting of both. I find that rapid diagnostic testing is justified given our baseline assumptions for areas with low proportions of malarious individuals among all treatment-seekers, but that caution is necessary before deployment worldwide. For antimalarial subsidization, we find that this is a cost-effective method for reducing mortality in developing countries, though efforts to delay the onset and slow the spread of resistance are urgently needed

Mitotic Evolution of Plasmodium falciparum Shows a Stable Core Genome but Recombination in Antigen Families

Malaria parasites elude eradication attempts both within the human host and across nations. At the individual level, parasites evade the host immune responses through antigenic variation. At the global level, parasites escape drug pressure through single nucleotide variants and gene copy amplification events conferring drug resistance. Despite their importance to global health, the rates at which these genomic alterations emerge have not been determined. We studied the complete genomes of different Plasmodium falciparum clones that had been propagated asexually over one year in the presence and absence of drug pressure. A combination of whole-genome microarray analysis and next-generation deep resequencing (totaling 14 terabases) revealed a stable core genome with only 38 novel single nucleotide variants appearing in seventeen evolved clones (avg. 5.4 per clone). In clones exposed to atovaquone, we found cytochrome b mutations as well as an amplification event encompassing the P. falciparum multidrug resistance associated protein (mrp1) on chromosome 1. We observed 18 large-scale (greater than 1 kb on average) deletions of telomere-proximal regions encoding multigene families, involved in immune evasion (9.5×10−6 structural variants per base pair per generation). Six of these deletions were associated with chromosomal crossovers generated during mitosis. We found only minor differences in rates between genetically distinct strains and between parasites cultured in the presence or absence of drug. Using these derived mutation rates for P. falciparum (1.0–9.7×10−9 mutations per base pair per generation), we can now model the frequency at which drug or immune resistance alleles will emerge under a well-defined set of assumptions. Further, the detection of mitotic recombination events in var gene families illustrates how multigene families can arise and change over time in P. falciparum. These results will help improve our understanding of how P. falciparum evolves to evade control efforts within both the individual hosts and large populations

Risk for Major Bleeding in Patients Receiving Ticagrelor Compared With Aspirin After Transient Ischemic Attack or Acute Ischemic Stroke in the SOCRATES Study (Acute Stroke or Transient Ischemic Attack Treated With Aspirin or Ticagrelor and Patient Outcomes)

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Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation