A team of Ethiopian researchers has harnessed some of the most advanced tools in artificial intelligence to chart the future of one of the world’s deadliest neglected tropical diseases, and the results carry both reassurance and a stark warning. By training deep learning models on more than three decades of epidemiological data, the scientists projected that leishmaniasis incidence in Ethiopia will continue its long decline through 2030, falling to roughly 84 cases per 100,000 people. Yet even that optimistic trajectory, the analysis concludes, will leave the country short of the World Health Organization’s ambitious target of cutting leishmaniasis incidence by at least 60 percent by the end of the decade.
The study, published in Discover Artificial Intelligence, drew on the Global Burden of Disease 2021 database, which compiles standardized estimates of disease incidence, prevalence and mortality for 204 countries and territories. The researchers extracted annual leishmaniasis incidence rates for Ethiopia spanning 1990 to 2021, covering all clinical forms of the disease, including the visceral form known as kala-azar, which is fatal without timely treatment. The data revealed a dramatic long-term downward trend: incidence plummeted from 768.091 cases per 100,000 people in 1990 to 86.820 by 2021, a decline of nearly 89 percent over three decades.
What distinguishes this analysis is its head-to-head comparison of five forecasting architectures, ranging from classical statistics to cutting-edge neural networks. The team built an autoregressive integrated moving average model, the workhorse of traditional time-series analysis, using the statsmodels and pmdarima libraries in Python. Against it they pitted a standard Long Short-Term Memory network, a multistep LSTM variant designed for multi-horizon prediction, a hybrid ARIMA-LSTM framework in which the statistical model captures linear structure while the neural network learns nonlinear residuals, and finally a Transformer model equipped with multi-head self-attention mechanisms and positional encoding to capture long-range temporal dependencies across the entire series.
The technical pipeline was rigorous. Raw incidence values were rescaled to a range between zero and one using MinMax normalization to stabilize neural network training, and the univariate series was converted into a supervised learning format through a sliding-window approach that generated input-target pairs while preserving temporal dependencies. Before fitting the ARIMA model, the researchers tested stationarity with the Augmented Dickey-Fuller test, obtaining a statistic of minus 1.51 and a p-value of 0.52, confirming the series was non-stationary and required differencing. Autocorrelation and partial autocorrelation plots then identified ARIMA(1, 1, 0) as the optimal specification. Deep learning components were constructed in TensorFlow 2.x with the Keras API, with hyperparameters tuned through grid search and time-series cross-validation, and overfitting controlled through early stopping, dropout regularization and temporally ordered train-test splits.
When the models were evaluated on held-out test data, the Transformer emerged as the clear winner. It achieved a mean absolute error of 9.55, a root mean square error of 10.075, a mean absolute percentage error of 12.78 percent and a symmetric mean absolute percentage error of 13.04 percent, meaning its predictions deviated from observed values by fewer than ten cases per 100,000 on average. The multistep LSTM came second with a mean absolute error of 12.13, while the basic LSTM performed worst among the neural approaches at 17.79. The classical ARIMA model trailed the field entirely, with a mean absolute error of 13.52 and a mean absolute percentage error of 16.55 percent, underscoring the limits of linear assumptions when confronted with the complex, nonlinear dynamics of an epidemic time series.
Applying the best-performing Transformer to the future, the researchers forecast a continued but modest decline, with incidence reaching approximately 84.198 cases per 100,000 people by 2030. That trajectory, while encouraging, falls short of the WHO Neglected Tropical Diseases Roadmap 2021-2030, which calls for reducing fatalities from primary visceral leishmaniasis to below 1 percent in 56 countries by 2025 and in 64 countries by 2030, alongside a 60 percent reduction in incidence. The authors frame this shortfall not merely as a statistical projection but as a pressing call to action, arguing that current interventions must be significantly expanded in scale and intensity to meet the global objective.
The analysis also dissected the burden by age and geography, revealing sharp disparities that carry direct policy implications. Children under five experienced the highest incidence rates of any age group, exceeding 900 cases per 100,000 people on average between 1990 and 2021, with the 5-to-9-year group close behind at nearly 700. Regionally, the northern highlands of Amhara and Tigray recorded dramatically elevated incidence in the early 1990s, with rates declining sharply after 1995, while areas such as Addis Ababa, Afar and Dire Dawa remained consistently low throughout. The forecast suggests a possible slight uptick in the Somali region, while most other regions are projected to remain stable through 2030.
The concentration of disease is striking. More than 60 percent of Ethiopia’s visceral leishmaniasis cases occur in the northern lowland foci of Amhara and Tigray, and the country reports an estimated 3,700 to 7,400 visceral cases annually, along with roughly 50,000 new cutaneous cases. Seasonal migrant laborers who travel to endemic zones for planting and harvesting represent the highest-risk group and also exhibit the world’s highest rate of HIV-leishmaniasis coinfection, at approximately 20 percent. In long-standing endemic areas, around 60 percent of cases occur in children under 15. Historical treatment data from 2009 to 2015 showed an overall case fatality rate of 4 percent, ranging from 0.9 percent in 2014 to 7.4 percent in 2013, figures that remain far above the WHO’s sub-1 percent goal.
The study’s findings align with a growing international literature demonstrating the superiority of deep learning for epidemic forecasting. Comparable analyses in India and Sudan have shown that LSTM and hybrid models outperform classical methods in predicting visceral leishmaniasis incidence by capturing nonlinear trends and seasonality, while studies in Brazil found that deep learning surpassed ARIMA for both cutaneous and visceral forms in settings with complex environmental drivers. East Africa now accounts for nearly 45 percent of global visceral leishmaniasis cases, and the disease remains endemic in 78 countries, disproportionately affecting impoverished and conflict-affected communities. Disruptions since 2019, including migration, political instability and the COVID-19 pandemic, have further hampered diagnosis and treatment across the region.
The authors are candid about the limitations of their approach. The analysis relies on historical incidence data that may be affected by underreporting, particularly in remote or conflict-affected areas, and the models are univariate, meaning they exclude climatic conditions, vector control efforts and political instability, all of which influence transmission. National-level aggregation may also mask localized heterogeneity, and the absence of established deep learning benchmarks for Ethiopia complicates direct performance comparisons. Even so, the researchers argue that the work validates advanced forecasting tools for public health planning and offers health ministries a data-driven baseline for tracking progress. Their recommendations are concrete: scaled-up targeted mass screening in Amhara and Tigray, strengthened surveillance networks, improved diagnostics, expanded vector control, and conflict-sensitive programming that builds health systems capable of withstanding the shocks that have repeatedly reversed hard-won gains against this neglected disease.
Subject of Research: Forecasting leishmaniasis incidence in Ethiopia using deep learning models and Global Burden of Disease 2021 data
Article Title: Forecasting the incidence of leishmaniasis in Ethiopia using global burden of disease 2021 data
Article References: Kelkay, J. M., Alemu, E. A., Alemayehu, M. A., Arage, F. G., Gessese, R. T., Tadese, Z. B., Tsegaw, T. K., & Taye, E. A. (2026). Forecasting the incidence of leishmaniasis in Ethiopia using global burden of disease 2021 data. Discover Artificial Intelligence, 6(1), Article 1196. https://doi.org/10.1007/s44163-026-02283-z
Image Credits: AI Generated
DOI: 10.1007/s44163-026-02283-z
Keywords: leishmaniasis, visceral leishmaniasis, Ethiopia, deep learning, Transformer model, LSTM, ARIMA, time series forecasting, Global Burden of Disease, neglected tropical diseases, WHO roadmap 2030, epidemiology
News Source: Blake Davidson. (October 19, 2026). AI Models Forecast Leishmaniasis Decline in Ethiopia but Warn WHO 2030 Target Will Be Missed. Scienmag.
