Computer methods and programs in biomedicine · 2026
A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Khouna Abdelkarim, Hssayni El Houssaine, Joudar Nour-Eddine
Аннотация
BACKGROUND AND OBJECTIVE: Epidemiological dynamics require precise mathematical modeling to guide public health actions, especially for viral diseases such as monkeypox, where data uncertainty and nonlinear transmission patterns present significant challenges. In this context, we suggest a novel approach using stochastic epidemic models and deep neural networks.
METHODS: In fact, we introduce the Epidemiologically Informed Neural Network (EINN), which uses the classical SIRD model to capture the dynamics of human-to-human transmission of Mpox. Then, we extend to a novel Milstein stochastic epidemiologically informed neural network (M-SEINN), which integrates stochastic differential equations.
RESULTS: Our results show that M-SEINN outperformed the Euler SEINN and EINN. At 5% noise in the out-of-sample evaluation, it achieves the lowest RMSE of 3.9569 and the best MAPE of 14.92% for cumulative cases, while at 10% noise in the sample evaluation, the daily case RMSE is 11.29, compared to 12.98 and 14.25, respectively. Statistical analysis demonstrated narrow Bootstrap CIs and a medium-large Cohen's d (0.65-1.02).
CONCLUSION: These findings emphasize the necessity of M-SEINN adoption for parameter estimation and public health decisions for epidemic control.
METHODS: In fact, we introduce the Epidemiologically Informed Neural Network (EINN), which uses the classical SIRD model to capture the dynamics of human-to-human transmission of Mpox. Then, we extend to a novel Milstein stochastic epidemiologically informed neural network (M-SEINN), which integrates stochastic differential equations.
RESULTS: Our results show that M-SEINN outperformed the Euler SEINN and EINN. At 5% noise in the out-of-sample evaluation, it achieves the lowest RMSE of 3.9569 and the best MAPE of 14.92% for cumulative cases, while at 10% noise in the sample evaluation, the daily case RMSE is 11.29, compared to 12.98 and 14.25, respectively. Statistical analysis demonstrated narrow Bootstrap CIs and a medium-large Cohen's d (0.65-1.02).
CONCLUSION: These findings emphasize the necessity of M-SEINN adoption for parameter estimation and public health decisions for epidemic control.