Forecasting tuberculosis incidence: a review of time series and machine learning models for prediction and eradication strategies

ABSTRACT

Despite efforts by the World Health Organization (WHO), tuberculosis (TB) remains a leading cause of fatalities globally. This study reviews time series and machine learning models for TB incidence prediction, identifies popular algorithms, and highlights the need for further research to improve accuracy and global scope. SCOPUS, PUBMED, IEEE, Web of Science, and PRISMA were used for search and article selection from 2012 to 2023. The results revealed that ARIMA, SARIMA, ETS, GRNN, BPNN, NARNN, NNAR, and RNN are popular time series and ML algorithms adopted for TB incidence rate predictions. The inaccurate TB incidence prediction and limited global scope of prior studies suggest a need for further research. This review serves as a roadmap for the WHO to focus on regions that require more attention for TB prevention and the need for more sophisticated models for TB incidence predictions.

KEYWORDS:

• Systematic literature review
• tuberculosis
• incidence
• time series
• machine learning

List of Abbreviations

TB	=	Tuberculosis
WHO	=	World Health Organization
ML	=	Machine Learning
ARIMA	=	Autoregressive Integrated Moving Average
SARIMA	=	Seasonal Autoregressive Integrated Moving Average
ETS	=	Error Trend Seasonal
GRNN	=	Generalized Regression Neural Network
BPNN	=	Back-propagation Neural Network
NARNN	=	Nonlinear Autogressive Neural Network
NNAR	=	Neural Network Auto-regression
RNN	=	Recurrent Neural Networks
HIV	=	Human Immunodeficiency Virus
PAM	=	Predictive Analytic Models
IEEE	=	Institute of Electrical and Electronics Engineers

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.