Malaria treatment for prevention: a modelling study of the impact of routine case management on malaria prevalence and burden.

Background: Testing and treating symptomatic malaria cases is crucial for case management, but it may also prevent future illness by reducing mean infection duration. Measuring the impact of effective treatment on burden and transmission via field studies or routine surveillance systems is difficult and potentially unethical. This project uses mathematical modeling to explore how increasing treatment of symptomatic cases impacts malaria prevalence and incidence.

Genotyping methods to distinguish Plasmodium falciparum recrudescence from new infection for the assessment of antimalarial drug efficacy: an observational, single-centre, comparison study.

Background: Distinguishing Plasmodium falciparum recrudescence from new infections is crucial for the assessment of antimalarial drug efficacy against P falciparum. We aimed to compare the efficacy of different genotyping methods to assess their effect on drug efficacy estimates, particularly in patients from high-transmission settings with polyclonal infections.

Methods: In this head-to-head comparison study, we compared five different genotyping methods currently used: fast capillary electrophoresis (F-CE) using msp1, msp2, and glurp; high-resolution capillary electrophoresis (H-CE) using msp1, msp2, and glurp; H-CE using microsatellites; targeted amplicon deep sequencing (TADS) using single nucleotide polymorphism (SNP)-rich markers; and high-resolution melting (HRM) analysis using msp1 and msp2.

AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact.

In recent decades, field and semi-field studies of malaria transmission have gathered geographic-specific information about mosquito ecology, behaviour and their sensitivity to interventions. Mathematical models of malaria transmission can incorporate such data to infer the likely impact of vector control interventions and hence guide malaria control strategies in various geographies. To facilitate this process and make model predictions of intervention impact available for different geographical regions, we developed AnophelesModel.

Additional blood meals increase sporozoite infection in Anopheles mosquitoes but not Plasmodium falciparum genetic diversity.

The availability of nutrients from mosquito blood meals accelerates the development of Plasmodium falciparum laboratory strains in artificially infected Anopheles gambiae mosquitoes. The impact of multiple blood meals on the number of P. falciparum genotypes developing from polyclonal natural human malaria infections (field-isolates) remains unexplored.

Bridging the Gap from Molecular Surveillance to Programmatic Decisions for Malaria Control and Elimination.

An increasing number of molecular and genomic assays are available to study malaria parasite populations. However, so far they have played a marginal role in informing policy and programmatic decision-making. Currently, molecular data are mainly used for monitoring drug efficacy against Plasmodium falciparum; assessing molecular markers of drug and insecticide resistance; and assessing P.

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania.

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions.

Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention.

Seasonal malaria chemoprevention (SMC) has proven highly efficacious in reducing malaria incidence. However, the continued success of SMC is threatened by the spread of resistance against one of its main preventive ingredients, Sulfadoxine-Pyrimethamine (SP), operational challenges in delivery, and incomplete adherence to the regimens. Via a simulation study with an individual-based model of malaria dynamics, we provide quantitative evidence to assess long-acting injectables (LAIs) as potential alternatives to SMC.

The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania.

Background: Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2).

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant .

The effectiveness of artemisinin-based combination therapies (ACTs) to treat malaria is threatened by resistance. The complex interplay between sources of selective pressure-treatment properties, biological factors, transmission intensity, and access to treatment-obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance.

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions.

Background: Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning.

Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria.

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression.

Proteo-Transcriptomic Dynamics of Cellular Response to HIV-1 Infection.

Throughout the HIV-1 replication cycle, complex host-pathogen interactions take place in the infected cell, leading to the production of new virions. The virus modulates the host cellular machinery in order to support its life cycle, while counteracting intracellular defense mechanisms. We investigated the dynamic host response to HIV-1 infection by systematically measuring transcriptomic, proteomic, and phosphoproteomic expression changes in infected and uninfected SupT1 CD4+ T cells at five time points of the viral replication process.

Single-Cell RNA-Seq Reveals Transcriptional Heterogeneity in Latent and Reactivated HIV-Infected Cells.

Despite effective treatment, HIV can persist in latent reservoirs, which represent a major obstacle toward HIV eradication. Targeting and reactivating latent cells is challenging due to the heterogeneous nature of HIV-infected cells. Here, we used a primary model of HIV latency and single-cell RNA sequencing to characterize transcriptional heterogeneity during HIV latency and reactivation.

Exploring viral infection using single-cell sequencing.

Single-cell sequencing (SCS) has emerged as a valuable tool to study cellular heterogeneity in diverse fields, including virology. By studying the viral and cellular genome and/or transcriptome, the dynamics of viral infection can be investigated at single cell level. Most studies have explored the impact of cell-to-cell variation on the viral life cycle from the point of view of the virus, by analyzing viral sequences, and from the point of view of the cell, mainly by analyzing the cellular host transcriptome.

BMix: probabilistic modeling of occurring substitutions in PAR-CLIP data.

Motivation: Photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) is an experimental method based on next-generation sequencing for identifying the RNA interaction sites of a given protein. The method deliberately inserts T-to-C substitutions at the RNA-protein interaction sites, which provides a second layer of evidence compared with other CLIP methods. However, the experiment includes several sources of noise which cause both low-frequency errors and spurious high-frequency alterations.

Bioinformatics and HIV latency.

Despite effective treatment, HIV is not completely eliminated from the infected organism because of the existence of viral reservoirs. A major reservoir consists of infected resting CD4+ T cells, mostly of memory type, that persist over time due to the stable proviral insertion and a long cellular lifespan. Resting cells do not produce viral particles and are protected from viral-induced cytotoxicity or immune killing.