Dependency‑Light ML Forecasts of ASEAN Smoking Prevalence with Uncertainty & Policy Scenarios (1990‑2035)

Introduction

Smoking is one of the major causes of preventable death in the ASEAN region, accounting for over 10% of all global smoking related deaths (Dai et al., 2025). Despite the World Health Organization’s (WHO) target to reduce adult smoking prevalence by 30% from 2010 to 2030, there has been limited progress across ASEAN countries (World Health Organization, 2023). The smoking trends for the ASEAN countries were clustered ( Figure 1) and forecasted aggregate smoking prevalence can also be seen ( Figure 2). Few studies have explored the impact of future policies on this goal and have used complex methods that are difficult to reproduce.

Figure 1. Smoking prevalence trends in 11 ASEAN countries, 1990–2022.

The age standardized smoking prevalence rates for all 11 ASEAN countries can be seen over the period of 1990–2022 for both sexes. Each coloured line represents a different country indicated by the key and illustrates historical trends in smoking prevalence.

Figure 2. ASEAN aggregate smoking prevalence: observed vs forecast.

The forecasted age standardized smoking prevalence rates for both sexes were generated using the stacked LSTM model based on previous trends. The dashed lines represent the predicted average prevalence across all ASEAN countries from 2023 to 2035. The solid line illustrates the actual observed average smoking prevalence for all 11 ASEAN countries from 1990 to 2022.

Long short-term memory (LSTM) networks are types of machine learning (ML) models that have proven to be promising in forecasting public health trends and predicting future behaviours (Hochreiter and Schmidhuber, 1997). These models have performed well in predicting infectious disease outbreaks but have not been used in tobacco control.

This study aimed to build a ML tool that could be operated without additional software and could: help group ASEAN countries based on past smoking trends, produce reliable forecasts up to 2035 and thirdly simulate the impact on health of introducing new policies such as increased tobacco tax.

Method

Results

The 11 ASEAN countries were categorized into three main groups based on their smoking trends:

Early Convergers – Singapore, Brunei: had already reduced smoking prevalence to below 15% by 2022.

Mid Decliners – Malaysia, Philippines, Thailand: have experienced a steady decline of 1.2 percentage points (pp) per year since 2005.

Late Stagnators – Indonesia, Myanmar, Vietnam: which still have high smoking rates of around 28% with little recent progress.

Discussion

This lightweight machine learning tool is the first that has been developed to forecast smoking prevalence across ASEAN countries. The study creates opportunities for shared learning and policy collaboration as it groups countries based on past smoking trends. For example, Malaysia’s trajectory closely mirrors that of the Philippines and Thailand, while Indonesia’s trends resemble those of Myanmar and Vietnam.

The results show that LSTM models have higher accuracy when compared to traditional ARIMA methods which is also consistent with what has been seen in infectious disease forecasting. The LSTM model is highly practical in low-resource settings such as government health departments or public health NGOs due to it not requiring external software installations.

Based on current policies, without stronger action, most ASEAN countries will clearly not meet the WHO target of a 30% reduction in smoking prevalence by 2030. Smoking rates could be significantly reduced, and over 1 million years of life lost to smoking-related disease could be prevented with interventions such as a 15% increase in tobacco taxes.

Conclusion

We developed a simple machine learning tool that does not require installation and can predict smoking trends while assessing policy impacts across ASEAN. Based on our analysis, most countries will not achieve the WHO target unless stronger tobacco control measures are implemented. Even a modest tax increase could avoid over a million DALYs. The overall framework is also adaptable to other public health areas without external software installation.

Software availability

Source code available from: https://github.com/awabahmad469/smoking-analysis

Archived source code at: https://doi.org/10.5281/zenodo.17499927

License: MIT.

This software is based on code originally published by Mahmood Ahmad (DOI: https://doi.org/10.5281/zenodo.17095791) and has been reused and extended with permission under the MIT License.

Ethics and consent

Ethical approval and consent were not required for this study as it used publicly available, deidentified datasets and there was no direct involvement from human participants.

The primary data on smoking prevalence and disease burden were obtained from the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease Study 2021, accessible at https://ghdx.healthdata.org/record/ihme-data/gbd-2021-asean-smoking-prevalence-burden-1990-2021 (accessed 30 July 2025).

Additional variables and processed datasets generated for analysis, including extrapolations to 2022 and policy simulation inputs, are publicly archived on Zenodo at https://doi.org/10.5281/zenodo.17095791, enabling full reproducibility by readers and reviewers.