Analytical and data-driven fractional-order malaria transmission model with vector and non-vector pathways
BMC Infectious Diseases·
- DOI
- 10.1186/s12879-025-12219-0
- PMID
- 41366737
- PMCID
- PMC12801622
- OpenAlex
- W4417161488
- Study type
- Mathematical modelling
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The model framework supports reproducibility through MATLAB implementation and offers potential application to malaria forecasting and control strategy development.
Structured evidence summary
Research question
The study aims to develop a fractional-order malaria model that captures memory effects and incorporates both vector-borne and non-vector transmission pathways, addressing limitations in classical integer-order models that neglect these factors.
Study design
This is a mathematical modeling study using a Caputo fractional-order approach, with analytical derivation of model properties and numerical simulation via the Adams-Bashforth-Moulton predictor-corrector scheme, validated against Nigerian surveillance data.
Population and setting
Model parameter estimation and validation utilized weekly malaria incidence data from the Nigeria Centre for Disease Control (NCDC).
Main findings
Lower fractional order values delayed epidemic peaks, prolonged oscillatory persistence, and amplified long-term infection memory; incorporating non-vector exposure pathways increased infection persistence and improved alignment with field observations; the model reproduced outbreak patterns consistent with NCDC data.
Public-health relevance
The model framework supports reproducibility through MATLAB implementation and offers potential application to malaria forecasting and control strategy development.
Important limitations
This summary is limited to the supplied single-article abstract/metadata and requires access to the original paper for decision-grade interpretation; no explicit limitations were stated in the provided abstract.
GIDS interpretation
This modeling study addresses malaria transmission dynamics in Nigeria, a setting where ongoing surveillance and outbreak investigation are relevant; the fractional-order framework provides a computational approach to understanding memory-dependent transmission effects.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 6 auditable classifier relationships to diseases, places, topics, and study design.