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Elucidating relationships between P.falciparum prevalence and measures of genetic diversity with a combined genetic-epidemiological model of malaria
- Source :
- PLoS Computational Biology, Vol 17, Iss 8, p e1009287 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- There is an abundance of malaria genetic data being collected from the field, yet using these data to understand the drivers of regional epidemiology remains a challenge. A key issue is the lack of models that relate parasite genetic diversity to epidemiological parameters. Classical models in population genetics characterize changes in genetic diversity in relation to demographic parameters, but fail to account for the unique features of the malaria life cycle. In contrast, epidemiological models, such as the Ross-Macdonald model, capture malaria transmission dynamics but do not consider genetics. Here, we have developed an integrated model encompassing both parasite evolution and regional epidemiology. We achieve this by combining the Ross-Macdonald model with an intra-host continuous-time Moran model, thus explicitly representing the evolution of individual parasite genomes in a traditional epidemiological framework. Implemented as a stochastic simulation, we use the model to explore relationships between measures of parasite genetic diversity and parasite prevalence, a widely-used metric of transmission intensity. First, we explore how varying parasite prevalence influences genetic diversity at equilibrium. We find that multiple genetic diversity statistics are correlated with prevalence, but the strength of the relationships depends on whether variation in prevalence is driven by host- or vector-related factors. Next, we assess the responsiveness of a variety of statistics to malaria control interventions, finding that those related to mixed infections respond quickly (∼months) whereas other statistics, such as nucleotide diversity, may take decades to respond. These findings provide insights into the opportunities and challenges associated with using genetic data to monitor malaria epidemiology.<br />Author Summary Knowledge of how the prevalence of P.falciparum malaria varies, either between regions or through time, is critical to the operation of malaria control programs. Yet obtaining this information through traditional methods presents many challenges. Parasite genetic data is increasingly accessible, and may provide an alternative means to estimate P.falciparum prevalence in the field. However, our understanding of how the genetic diversity of parasite populations relates to prevalence is limited, and suitable models to guide our understanding are largely lacking. Here, we merge two classical models—the Ross-Macondald and the Moran—to produce a framework in which the relationships between parasite genetic diversity and prevalence can be explored. We find that several genetic diversity statistics are correlated with prevalence, although to differing degrees, and over different time scales. Overall, statistics related to mixed infection are robustly and rapidly responsive to changes in prevalence, suggesting they may be a useful focal point for the development of malaria surveillance methods that harness genetic data.
- Subjects :
- Epidemiology
Population genetics
Nucleotide diversity
0302 clinical medicine
Medical Conditions
Epidemiological Statistics
Prevalence
Medicine and Health Sciences
Malaria, Falciparum
Biology (General)
Protozoans
0303 health sciences
Ecology
Malarial Parasites
Contrast (statistics)
Eukaryota
Genomics
3. Good health
Infectious Diseases
Computational Theory and Mathematics
Modeling and Simulation
Genetic Epidemiology
Epidemiological Methods and Statistics
Research Article
medicine.medical_specialty
QH301-705.5
030231 tropical medicine
Plasmodium falciparum
Biology
03 medical and health sciences
Cellular and Molecular Neuroscience
parasitic diseases
medicine
Parasitic Diseases
Genetics
Animals
Humans
Molecular Biology
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Genetic diversity
Evolutionary Biology
Population Biology
Organisms
Genetic Variation
Biology and Life Sciences
Models, Theoretical
medicine.disease
Tropical Diseases
Parasitic Protozoans
Malaria
Vector-Borne Diseases
Genetic epidemiology
Evolutionary biology
Population Genetics
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 17
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....53b4fe87c55e334a6c7a87e4acb90c34