SEIRS model for TB transmission dynamics incorporating the environment and optimal control

Tuberculosis (TB) remains a significant global health challenge, claiming more than 2 million lives annually, predominantly among adults. Existing studies often neglect the environment, reinfection, relapse/reactivation, and model calibration, thus limiting their applicability. This study presents a novel data-driven model that incorporates these factors to analyze the dynamics of TB transmission. Using the next-generation matrix approach, a basic reproduction number ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>R</mml:mi> <mml:mn>0</mml:mn></mml:msub> </mml:math> ) of 1.737266 was calculated, indicating that active TB disease will persist in the human population without robust public health interventions. The model equations were numerically solved using fourth- and fifth-order Runge-Kutta methods. The model was calibrated to the historical TB incidence data for Kenya, spanning 2000 to 2022, using least squares curve fitting. The fitting algorithm yielded a mean absolute error (MAE) of 0.01% when comparing the actual data points with the results of the simulated model. This finding indicates that the proposed mathematical model closely aligns with the recorded TB incidence data. The optimal values of the model parameters were estimated from the fitting algorithm, and future TB transmission dynamics was projected for the next two decades. Key findings indicate that a 10% decrease in transmission rate, while maintaining other parameters constant, would result in a 10% reduction in TB transmission in Kenya. In addition, the incidence of tuberculosis in Kenya is expected to decrease to an estimated 35 cases per 100,000 people by 2045 with sustained efforts in Bacillus Calmette-Guérin (BCG) vaccination programs and public awareness campaigns. BCG vaccination emerges as the most cost-effective strategy to combat TB transmission in Kenya. Policymakers should prioritize investing in BCG vaccination programs to achieve optimal public health outcomes and economic benefits, aligning with Kenya's Vision 2030.

Authors

Institutions

Publication Details

Journal
BMC Infectious Diseases
Published
2025-04-09
DOI
https://doi.org/10.1186/s12879-025-10710-2
Citations
8
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
9.72
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SEIRS model for TB transmission dynamics incorporating the environment and optimal control

Francis Oketch Ochieng
8 citations
BMC Infectious Diseases
Smart Grid Security and Resilience
9.72
article

SEIRS model for TB transmission dynamics incorporating the environment and optimal control

Francis Oketch Ochieng
article en
8 citations

Abstract

Tuberculosis (TB) remains a significant global health challenge, claiming more than 2 million lives annually, predominantly among adults. Existing studies often neglect the environment, reinfection, relapse/reactivation, and model calibration, thus limiting their applicability. This study presents a novel data-driven model that incorporates these factors to analyze the dynamics of TB transmission. Using the next-generation matrix approach, a basic reproduction number ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>R</mml:mi> <mml:mn>0</mml:mn></mml:msub> </mml:math> ) of 1.737266 was calculated, indicating that active TB disease will persist in the human population without robust public health interventions. The model equations were numerically solved using fourth- and fifth-order Runge-Kutta methods. The model was calibrated to the historical TB incidence data for Kenya, spanning 2000 to 2022, using least squares curve fitting. The fitting algorithm yielded a mean absolute error (MAE) of 0.01% when comparing the actual data points with the results of the simulated model. This finding indicates that the proposed mathematical model closely aligns with the recorded TB incidence data. The optimal values of the model parameters were estimated from the fitting algorithm, and future TB transmission dynamics was projected for the next two decades. Key findings indicate that a 10% decrease in transmission rate, while maintaining other parameters constant, would result in a 10% reduction in TB transmission in Kenya. In addition, the incidence of tuberculosis in Kenya is expected to decrease to an estimated 35 cases per 100,000 people by 2045 with sustained efforts in Bacillus Calmette-Guérin (BCG) vaccination programs and public awareness campaigns. BCG vaccination emerges as the most cost-effective strategy to combat TB transmission in Kenya. Policymakers should prioritize investing in BCG vaccination programs to achieve optimal public health outcomes and economic benefits, aligning with Kenya's Vision 2030.

BMC Infectious DiseasesVol. 25(1)
Jomo Kenyatta University of Agriculture and Technology (KE)
Climate action
Openalex Percentile: Top 2%
Smart Grid Security and Resilience
9.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

SEIRS model for TB transmission dynamics incorporating the environment and optimal control — Francis Oketch Ochieng · BMC Infectious Diseases (2025) | TGRS Research Map | TGRS