Preplanned Studies: Tuberculosis Prevalence Trends from a Predictive Modelling Study — 10 High-Burden Countries, 1980–2035

The fight against tuberculosis (TB) is being severely hampered by the development of drug-resistant strains and increased human mobility, further compounded by the impact of the coronavirus disease 2019 (COVID-19) pandemic (1). The World Health Organization (WHO) Post-2015 End TB Strategy, which was endorsed by the World Health Assembly in 2014 and aligns with the Sustainable Development Goals, has the ambitious objective of eradicating the global TB epidemic (2). Nonetheless, there is growing concern regarding the adequacy of existing TB control strategies to meet these objectives. This study employed four predictive models to evaluate the likelihood of reaching the WHO’s targets within a set timeframe, specifically examining the situation in the 10 high-burden countries (HBCs). Our findings indicate a worsening global TB burden, with numerous countries falling short of the aspirations to eliminate pulmonary TB. Therefore, it is imperative to draw on successful strategies and enact tailored interventions within key nations in conjunction with bolstered international collaboration.

TB case data were extracted from the WHO Global Tuberculosis Programme’s open data repository (https://www.who.int/teams/global-tuberculosis-programme/data), while demographic details, including population size, were gathered from the World Bank’s open data platform. An incidence analysis of TB was carried out for 10 HBCs spanning from 1980 to 2021. It is predicted that China, Congo, India, Indonesia, Mozambique, Myanmar, Nigeria, the Philippines, South Africa, and Zambia will continue to rank among the top 30 HBCs until 2023 due to the persistent challenges of drug-resistant TB and TB/ human immunodeficiency virus (HIV) co-infection (1,3).

In order to predict TB incidence for the years 2023–2035, various predictive models were utilized, including the autoregressive integrated moving average (ARIMA) model (4), neural network model (5), bayesian structural model (6), and a hybrid model integrating ARIMA, exponential smoothing (ETS), and seasonal and trend decomposition using Loess (STL). Details on these models can be found in the Supplementary Material. Data analysis was conducted using R software (version 4.3.3, R Core Team, Vienna, Austria).

Figure 1 depicts the trend of TB incidence in the 10 HBCs from 1980 to 2022. These 10 nations collectively account for 117,314,803 TB cases, representing 60% of the global total. The number of TB cases in the 10 HBCs has been increasing annually, with the exception of a decline in 2020 and 2021 due to the COVID-19 pandemic. Among these countries, nearly 70% of TB cases are concentrated in India and China, highlighting the urgent need to address the TB burden in these two nations. India has consistently shown the highest TB incidence over the past 43 years, with a rising trend since the publication of the TB report in 1997. In contrast, while China has a considerable TB incidence rate, it has been steadily decreasing since 2009, as evidenced in the graph depicting incidence patterns (Figure 1B).

Figure 1. 

The spatiotemporal distribution characteristics of global tuberculosis.

To conduct a thorough analysis of TB incidence trends, we utilized four different models to predict TB cases from 2023 to 2035 (Figure 2). The results revealed a decreasing trend in future TB cases in China, as projected by three of the four models, with the exception of the neural network model. Conversely, Congo is expected to see a rise in TB cases over the next 13 years, regardless of the forecasting model employed. Similar patterns were observed for India, the Philippines, and Zambia, where the bayesian model anticipated a significant increase, while the other models predicted relatively stable case numbers. In the case of Mozambique and Nigeria, the ARIMA, bayesian, and hybrid models foresee a surge in TB cases, while the neural network model predicts minimal changes. South Africa’s future TB incidence shows divergent forecasts, with the ARIMA model suggesting stability and the remaining models indicating an increase. Myanmar’s projections also vary, with the bayesian model forecasting a decline while the other models predict minimal changes. Indonesia poses significant differences among the models, with the ARIMA and bayesian models forecasting a notable increase, while the hybrid and Neural Network models suggest periodic fluctuations in TB cases. When considering forecasts based on pre-COVID-19 data using the four models (Supplementary Figure S1), it was noted that apart from Congo, Mozambique, and Nigeria, the forecast outcomes for other countries were influenced to varying degrees by the COVID-19 pandemic. For instance, the impact of COVID-19 has altered the upward trend in India and lessened the decline in China, and all models for Indonesia now predict an increase in TB cases.

Figure 2. 

Time trends forecasted by four models in ten HBCs. (A) India; (B) Indonesia; (C) China; (D) Philippines; (E) Nigeria; (F) Congo; (G) South Africa; (H) Myanmar; (I) Mozambique; (J) Zambia.

Abbreviation: HBC=high-burden countries.

Figure 3 and Supplementary Figure S2 present a detailed overview of the projected TB case trends in the 10 HBCs aimed at reaching the goal of TB elimination. Besides China, which is forecasted to meet the TB elimination target according to the bayesian model, the other countries are unlikely to reach the desired goal across all forecasting models due to the impact of COVID-19. Excluding the influence of COVID-19, only the forecast results for South Africa based on the bayesian model indicate potential success in achieving this goal.

Figure 3. 

Accessibility projections for ten HBCs to achieve the goal of ending tuberculosis based on four models. (A) China; (B) Congo; (C) India; (D) Indonesia; (E) Mozambique; (F) Myanmar; (G) Nigeria; (H) Philippines; (I) South Africa; (J) Zambia.