Elucidating relationships between P.falciparum prevalence and measures of genetic diversity with a combined genetic-epidemiological model of malaria

Journal article

Abstract Due to the interaction between human mobility and the genetic complexity of Plasmodium falciparum, malaria elimination faces ongoing challenges. While genomic surveillance has made progress in tracking viral pathogens, parasitic diseases like malaria face unique challenges, that prevent the direct translation of these methods including complex infections and recombination. This study presents a novel modeling framework that combines individual-based epidemiological dynamics while directly recording the genetic haplotype of parasites during simulated transmission events. Further, the individual human and vector cover multiple spatial units that allow for interaction between different transmission settings. Here we used this model to explore how mobility between patches can drive genetic relatedness between populations. We considered two mobility scenarios, uniform and skewed travel patterns, which described the different distributions in the probability of travel for infected individuals. We then investigated how these behaviors influence local transmission and the genetic structure of infections. Parasite genomes are explicitly tracked in the simulation, allowing inference of transmission relationships to be subsequently inferred from genetic data. Simulation results indicate that increased migration from rural to urban areas amplified genetic mixing. Furthermore, when the probability of travel is skewed, i.e. there are few individuals who take the majority of trips ,genetic patterns between the populations are more distinct. By linking observable genetic markers to underlying transmission processes, this study provides a mechanistic foundation for interpreting genomic data in malaria epidemic contexts. This framework offers a practical tool for assessing the impact of interventions, optimizing monitoring strategies, and identifying hotspots for reintroduction risks. The integration of genetic and mobility data lays the foundation for more sensitive and tailored malaria elimination efforts

Authors: Hendry, JA; Kwiatkowski, D; McVean, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Computational Biology
• Volume: 17
• Issue: 8
• Pages: e1009287-e1009287
• Publication date: 2021-08-19
• DOI: 10.1371/journal.pcbi.1009287
• EISSN: 1553-7358
• ISSN: 1553-734X
• Language: English
• Keywords: Nucleotide diversity; Genetic epidemiology; Biology; Medicine; Genetic diversity; Malaria; Evolutionary biology; Population; Genetic variation; Environmental health; Epidemiology; Genetics; Diversity (politics); Genotype