Spatial dynamics of malaria transmission

Wu, S. L. et al. (2023) Spatial dynamics of malaria transmission. PLoS Computational Biology, 19(6), e1010684. (doi: 10.1371/journal.pcbi.1010684) (PMID:37307282) (PMCID:PMC10289676)

Abstract

The Ross-Macdonald model has exerted enormous influence over the study of malaria transmission dynamics and control, but it lacked features to describe parasite dispersal, travel, and other important aspects of heterogeneous transmission. Here, we present a patch-based differential equation modeling framework that extends the Ross-Macdonald model with sufficient skill and complexity to support planning, monitoring and evaluation for Plasmodium falciparum malaria control. We designed a generic interface for building structured, spatial models of malaria transmission based on a new algorithm for mosquito blood feeding. We developed new algorithms to simulate adult mosquito demography, dispersal, and egg laying in response to resource availability. The core dynamical components describing mosquito ecology and malaria transmission were decomposed, redesigned and reassembled into a modular framework. Structural elements in the framework—human population strata, patches, and aquatic habitats—interact through a flexible design that facilitates construction of ensembles of models with scalable complexity to support robust analytics for malaria policy and adaptive malaria control. We propose updated definitions for the human biting rate and entomological inoculation rates. We present new formulas to describe parasite dispersal and spatial dynamics under steady state conditions, including the human biting rates, parasite dispersal, the “vectorial capacity matrix,” a human transmitting capacity distribution matrix, and threshold conditions. An package that implements the framework, solves the differential equations, and computes spatial metrics for models developed in this framework has been developed. Development of the model and metrics have focused on malaria, but since the framework is modular, the same ideas and software can be applied to other mosquito-borne pathogen systems.

• Item Type: Articles
• Additional Information: Funding: This research was supported by a grant from the National Institute of Allergies and Infectious Diseases (R01 AI163398), which supported DLS, RCR, JMH, and ARC; the grant also funds an active collaboration with the Bioko Island Malaria Elimination Program (BIMEP), which employs CAG and GAG. Additional funding support comes from: a grant from the National Institutes of Allergies and Infectious Diseases (2U19AI089674) that partially supported DLS; a grant from the Bill and Melinda Gates Foundation (INV 030600) that supported DLS, DMS, and JNN; a grant from the National Science Foundation, Directorate for Technology, Innovation, and Partnerships (TIP) as part of the Convergence Accelerator Program (NSF 2040688) that supported DLS and SLW; a UK Medical Research Council Career Development Award (MR/V031112/1) that supported OJB.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ferguson, Professor Heather
• Authors: Wu, S. L., Henry, J. M., Citron, D. T., Mbabazi Ssebuliba, D., Nakakawa Nsumba, J., Sánchez C., H. M., Brady, O. J., Guerra, C. A., García, G. A., Carter, A. R., Ferguson, H. M., Afolabi, B. E., Hay, S. I., Reiner, R. C., Kiware, S., and Smith, D. L.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
• Journal Name: PLoS Computational Biology
• Publisher: Public Library of Science
• ISSN: 1553-734X
• ISSN (Online): 1553-7358
• Published Online: 12 June 2023
• Copyright Holders: Copyright © 2023 Wu et al.
• First Published: First published in PLoS Computational Biology 19(6):e1010684
• Publisher Policy: Reproduced under a Creative Commons license