Last Updated: 01/07/2024

Genetic data as a signal of changing malaria transmission

Objectives

To design programs that can be used to extract the information present in genetic data, and use this information to estimate the level of infection in a population. Once developed into a user-friendly piece of software, this tool could be used by scientists and policy-makers around the world to make the most of their available data – or to decide whether it is worth the additional cost of collecting new genetic data.

Specific objectives:

Develop the theoretical/computation methods required for efficient simulation of genetic data from P. falciparum transmission models.
Write a user-friendly software package implementing these methods.
Apply this software package to explore how genetic metrics vary with e.g. transmission intensity, and what number of samples/loci is required to answer different research questions.

Principal Institution

Imperial College London, United Kingdom

Principal Investigators / Focal Persons

Robert Verity

Rationale and Abstract

This project aims to develop new mathematical and computational methods to enhance malaria surveillance by leveraging genomic data. Despite significant reductions in malaria mortality, accurately measuring transmission has become increasingly challenging due to the spatial heterogeneity and difficulty in sampling hard-to-reach populations. This project addresses the need for improved surveillance tools by focusing on the genetic signals within malaria parasites that correlate with transmission levels.

The project will integrate modern coalescent methods into existing malaria transmission models to better understand parasite population genetics. It will also develop efficient algorithms and simulation methods to analyze multilocus genetic data, allowing for a more detailed and accurate assessment of malaria transmission patterns. By leveraging the rich information contained in genomic data, the project aims to provide researchers and policymakers with advanced tools for tracking and responding to changes in malaria transmission, ultimately contributing to more effective malaria control and elimination strategies.

Study Design

Mathematical modelling

Project Outputs

Malar J. 2018; Plasmodium falciparum genetic variation of var2csa in the Democratic Republic of the Congo J Infect Dis. 2018; Drug-Resistance and Population Structure of Plasmodium falciparum Across the Democratic Republic of Congo Using High-Throughput Molecular Inversion Probes Elife. 2017; Modelling the drivers of the spread of Plasmodium falciparum hrp2 gene deletions in sub-Saharan Africa

External reference

UK Research & Development - Trial details

Themes

Genetics and Genomics
Measurement of Transmission
Modeling
Surveillance

Date

Apr 2016 — Mar 2019

Total Project Funding

$400,183