Seasonal Mathematical Model of Salmonellosis Transmission and the Role of Contaminated Environments and Food Products
Mathematics·
- DOI
- 10.3390/math13020322
- PMID
- —
- PMCID
- —
- OpenAlex
- W4406629375
- Study type
- Mathematical modelling
- Publisher
- MDPI AG
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The model highlights the relevance of environmental and food contamination pathways in salmonellosis transmission and emphasizes accurate parameter estimation as important for understanding and controlling disease dynamics.
Structured evidence summary
Research question
The study develops a seasonal mathematical model to explore how environmental and food contamination interact with human and cattle hosts to drive Salmonella typhimurium transmission dynamics.
Study design
A non-autonomous compartmental mathematical model with seasonal forcing, supplemented by analytical derivation of the basic reproduction number (R0), parameter estimation via Latin hypercube sampling and least squares fitting, and sensitivity analyses.
Population and setting
The model is parameterized using Salmonella outbreak data from Saudi Arabia covering 2018 to 2021, with compartments representing humans, cattle, and bacteria in environmental and food reservoirs.
Main findings
The estimated R0 of 0.606 suggests disease is controllable, with model fits to cumulative and new case data and a predicted gradual decline stabilizing near 40,000 cumulative cases. Analytical results indicate disease extinction when R0 < 1 and persistence with recurrent outbreaks when R0 > 1. Sensitivity analysis identified mortality, infection, and decay rates as the parameters most influencing model outcomes.
Public-health relevance
The model highlights the relevance of environmental and food contamination pathways in salmonellosis transmission and emphasizes accurate parameter estimation as important for understanding and controlling disease dynamics.
Important limitations
No explicit limitations are stated in the supplied abstract/metadata. Accordingly, this summary is limited to the supplied single-article abstract and metadata and requires the original paper for decision-grade interpretation.
GIDS interpretation
The paper contributes to discoverability of modelling evidence on salmonellosis transmission across humans, animals, and the environment; it does not, on its own, establish or confirm any live surveillance signal.
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 10 auditable classifier relationships to diseases, places, topics, and study design.