Aug 2026· International Journal For Multidisciplinary Research· Vol 8· 0 citations· 23 references
TL;DR
The Hybrid ARIMA–LSTM model consistently achieved superior forecasting accuracy, with lower errors and better goodness-of-fit than the standalone models, while supporting the Hybrid ARIMA–LSTM framework as a robust tool for long-term epidemiological forecasting.
Abstract
Hypertension is a major global risk factor for cardiovascular disease and premature mortality, with increasing public health implications in India. This study compares ARIMA, LSTM, and Hybrid ARIMA–LSTM models for forecasting India’s hypertension burden using annual data from 1990–2025 across six epidemiological indicators: prevalence, prevalence cases, ASPR, incidence, deaths, and DALYs. After data pre-processing and stationarity testing, ARIMA captured linear trends, LSTM modeled nonlinear patterns, and the hybrid model integrated both approaches. Model performance was evaluated using MAE, RMSE, MAPE, R², and residual diagnostics. The Hybrid ARIMA–LSTM model consistently achieved superior forecasting accuracy, with lower errors and better goodness-of-fit than the standalone models. Forecasts for 2026–2045 suggest that hypertension will remain a significant health challenge, with prevalence reaching 30.87% and affecting approximately 350.11 million people by 2045, along with 13.83 million incident cases, 217.77 thousand deaths, and 5.54 million DALYs. The findings highlight the need for strengthened prevention, early detection, disease management, and evidence-based health policies, while supporting the Hybrid ARIMA–LSTM framework as a robust tool for long-term epidemiological forecasting.
Accurate forecasting of influenza-like illness (ILI) incidence is essential for public health surveillance and early epidemic intervention. Traditional statistical models effectively capture linear temporal trends but often fail to model the complex nonlinear dynamics of disease transmission. To address this limitation, this study proposes a hybrid forecasting framework that integrates Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), temporal attention mechanisms, and Transformer architecture. Weekly ILI surveillance data collected from Xinjiang and Shandong provinces, China, between 2023 and 2025 were used for model development and validation. Meteorological variables, including weekly average temperature and relative humidity, were incorporated as exogenous features. The proposed framework decomposes the forecasting task into linear and nonlinear components, where ARIMA captures long-term linear trends and an attention-enhanced LSTM–Transformer branch models nonlinear temporal dependencies. Experimental results demonstrated that the proposed model achieved superior predictive performance on the Xinjiang Production and Construction Corps dataset, with an MAE of 29.371, RMSE of 41.334, MAPE of 10.744%, and R2 of 0.579. Diebold–Mariano tests confirmed statistically significant improvements over standalone ARIMA, LSTM, and ARIMA– LSTM models (P < 0.05). Furthermore, cross-regional validation on the Yantai High-Tech Zone dataset showed comparable forecasting accuracy, indicating strong generalization capability under different climatic conditions. These findings suggest that the proposed hybrid framework effectively captures both linear and nonlinear transmission patterns of ILI and provides a practical tool for early warning and public health resource allocation in grassroots disease surveillance systems.
Jing-Long Chen, Li-Min Xu, Q. Yuan et al.· 2026 8th International Confe...· 0 citations
Accurately forecasting the transmission of infectious diseases, such as Dengue Haemorrhagic Fever (DHF), is essential for robust public health planning. This study assesses the performance of three time-series forecasting models—Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA)—in predicting annual DHF cases in Malaysia from 2011 to 2021. Model accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). Results indicate that ARIMA and SARIMA yielded the lowest error rates (73.34%), whereas Exponential Smoothing performed less effectively (76.79%). ARIMA was preferred due to its simplicity and comparable predictive power. Although these error rates exceed the standard 10–20% threshold for reliable forecasting, the study demonstrates that these models have significant potential when integrated with more granular data and external covariates. Furthermore, a three-segment piecewise nonlinear regression was employed to identify structural changes in DHF trends. This combined analytical approach provides deeper insights into disease progression and strengthens the evidence base for effective public health interventions.
Unknown authors· International journal of res...· 0 citations
Cardiovascular disease (CVD) is a major public health concern, and its burden and trends require further clarification. This study aimed to compare the CVD burden between China and the global population and provide projections.
Data on CVD metrics from the Global Burden of Disease (GBD) 2021. The Joinpoint regression model was applied to analyze long-term trends in age-standardized incidence (ASIR), prevalence (ASPR), mortality (ASMR), and age-standardized disability-adjusted life year (ASDR) rates. Decomposition analysis quantified drivers of change, and autoregressive integrated moving average models projected future trends.
From 1990 to 2021, ASMR and ASDR showed significant downward trends in China and globally. However, the ASIR for CVD in China increased from 783.897 to 811.813 per 100,000, while globally it declined from 878.787 to 787.04 per 100,000, with average annual percent change of 0.105% (95% CI: 0.067%, 0.144%) and –0.353% (95% CI: –0.404%, –0.301%), respectively. The upward trend of ASPR in China exceeded the global level. The crude rates of CVD metrics were low among individuals younger than 40 years and then increased with advancing age; sex differences were also observed. Decomposition analysis highlighted population aging as an important driver of increased CVD incidence in China (total population: 41.88%, male: 24.17%, female: 47.81%). Projections suggested that ASIR in China and globally will continue to rise to 825.94 and 807.96 per 100,000, respectively, by 2036, whereas ASMR and ASDR will continue to decline.
This study revealed a complex CVD epidemiological landscape in China and globally, with increasing ASPR alongside declining ASMR and ASDR. The ASIR of CVD in China remains significant and is projected to continue increasing.
Yue Zhang, Ning Zhang, Chang-Jian Lin· Archives of Medical Science· 0 citations
Background: The Accuracy of mortality rate forecasting plays an important role in various decision-making processes in the life insurance sector, including determining premium amounts. In addition, it also contributes to assessing the readiness of human resources to support national defense, as well as to conducting risk assessments aimed at maintaining demographic stability. The United States mortality data was selected as the study object due to the availability of comprehensive and high-quality.
Aims: This study explores five hybrid approaches that combine stochastic models and machine learning, along with one non-hybrid approach to assess their potential to improve forecasting accuracy.
Method: In this study, the Lee-Carter–ARIMA, Lee-Carter–Random Forest, Lee-Carter–ANN, Lee-Carter–ARIMA–Random Forest, Lee-Carter–ARIMA–ANN, and ANN models were evaluated. These models were applied to mortality rate data from nine divisions in the United States (US), stratified by gender, using training data from 1966 to 2005 and test data from 2006 to 2015. The best model is determined based on the smallest Mean Absolute Percentage Error (MAPE) value while also considering the interpretability of the model.
Result: The study's results show that, across the number of divisions, the Lee-Carter–ARIMA–Random Forest model produces the smallest MAPE values most often. However, in terms of average MAPE, the Lee-Carter–ARIMA–ANN model performs better, with MAPEs of 9.66% for females and 9.28% for males. Furthermore, neither of these models yields a substantial improvement in predictive accuracy compared with the Lee-Carter–ARIMA model.
Conclusion: Considering the relatively small decrease in MAPE and the difficulty of interpreting machine learning models due to their black box nature, the Lee-Carter–ARIMA model demonstrates the best overall performance relative to the other models. Nevertheless, the Lee-Carter–ARIMA–Random Forest and Lee-Carter–ARIMA–ANN models show potential as alternative approaches that merit further investigation and may contribute to national defense planning and support the maintenance of demographic stability.
Vita Nuarini, Mahmudi, Nina Fitriyati et al.· International Journal of App...· 0 citations
These findings support selecting forecasting models according to operating conditions rather than relying on a single universally preferred approach, and provide a practical framework for combining complementary statistical and machine-learning forecasts.
Background: The Philippines is experiencing one of the fastest-growing HIV epidemics in the Western Pacific region, with Cebu City among the most affected urban centers. However, localized forecasting studies remain limited, constraining evidence-based public health planning and resource allocation. This study aims to develop and evaluate an Autoregressive Integrated Moving Average (ARIMA) model for forecasting monthly HIV incidence in Cebu City. Methods: Monthly HIV surveillance data from January 2022 to March 2025 were analyzed using time-series forecasting. Candidate ARIMA models were assessed using the Akaike Information Criterion (AIC), parameter significance, residual diagnostics, and out-of-sample forecasting accuracy. Results: ARIMA (0,1,1) demonstrated the best predictive performance, achieving a Mean Absolute Percentage Error (MAPE) of 25.04% and outperforming Naïve, Simple Moving Average, and Exponential Smoothing models. Forecasts for April to December 2025 project monthly HIV cases to increase from 33 to 36. Although forecasts suggest a persistent rise in HIV incidence, widening confidence intervals indicate greater uncertainty over longer forecasting horizons. Conclusion: The projected increase in HIV incidence signals a continuing public health challenge in Cebu City. ARIMA-based forecasting provides a practical decision-support tool for anticipating disease trends and informing surveillance, targeted prevention, and healthcare resource allocation.
Jonecis A. Dayap, John Michael B. Genterolizo, Miguelito A. Ruelan et al.· Recoletos Multidisciplinary...· 0 citations
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