Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data

• URL: http://arxiv.org/abs/2505.04566v1
• Date: Wed, 07 May 2025 16:58:18 GMT
• Title: Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data
• Authors: Lucas R. C. Farias, Talita P. Silva, Pedro H. M. Araujo,
• Abstract summary: This paper presents a multitask learning approach for the joint prediction of arboviral outbreaks and case counts in Recife, Brazil.<n>The proposed model concurrently performs binary classification (outbreak detection) and regression (case forecasting) tasks.<n>The architecture delivers competitive performance across diseases and tasks, demonstrating the feasibility and advantages of unified modeling strategies for scalable epidemic forecasting in data-limited public health scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a multitask learning approach based on long-short-term memory (LSTM) networks for the joint prediction of arboviral outbreaks and case counts of dengue, chikungunya, and Zika in Recife, Brazil. Leveraging historical public health data from DataSUS (2017-2023), the proposed model concurrently performs binary classification (outbreak detection) and regression (case forecasting) tasks. A sliding window strategy was adopted to construct temporal features using varying input lengths (60, 90, and 120 days), with hyperparameter optimization carried out using Keras Tuner. Model evaluation used time series cross-validation for robustness and a held-out test from 2023 for generalization assessment. The results show that longer windows improve dengue regression accuracy, while classification performance peaked at intermediate windows, suggesting an optimal trade-off between sequence length and generalization. The multitask architecture delivers competitive performance across diseases and tasks, demonstrating the feasibility and advantages of unified modeling strategies for scalable epidemic forecasting in data-limited public health scenarios.

Related papers

• Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting [64.45587649141842]
  Time-series forecasting plays a critical role in many real-world applications.<n>No single model consistently outperforms others across different test samples, but instead (ii) each model excels in specific cases.<n>We introduce TimeFuse, a framework for collective time-series forecasting with sample-level adaptive fusion of heterogeneous models.
  arXiv  Detail & Related papers  (2025-05-24T00:45:07Z)
• Epidemic Forecasting with a Hybrid Deep Learning Method Using CNN-LSTM With WOA-GWO Parameter Optimization: Global COVID-19 Case Study [0.0]
  This study introduces a novel deep learning framework that advances time series forecasting for infectious diseases.<n>Our framework is applied to COVID 19 case data from 24 countries across six continents.
  arXiv  Detail & Related papers  (2025-03-17T04:41:26Z)
• Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series [45.76310830281876]
  We propose Quantile Sub-Ensembles, a novel method to estimate uncertainty with ensemble of quantile-regression-based task networks. Our method not only produces accurate imputations that is robust to high missing rates, but also is computationally efficient due to the fast training of its non-generative model.
  arXiv  Detail & Related papers  (2023-12-03T05:52:30Z)
• Forecast reconciliation for vaccine supply chain optimization [61.13962963550403]
  Vaccine supply chain optimization can benefit from hierarchical time series forecasting. Forecasts of different hierarchy levels become incoherent when higher levels do not match the sum of the lower levels forecasts. We tackle the vaccine sale forecasting problem by modeling sales data from GSK between 2010 and 2021 as a hierarchical time series.
  arXiv  Detail & Related papers  (2023-05-02T14:34:34Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [107.63407690972139]
  Conditional Score-based Diffusion models for Imputation (CSDI) is a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. CSDI improves by 40-70% over existing probabilistic imputation methods on popular performance metrics. In addition, C reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods.
  arXiv  Detail & Related papers  (2021-07-07T22:20:24Z)
• Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases [0.5849513679510832]
  The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread on. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times.
  arXiv  Detail & Related papers  (2021-05-05T14:56:27Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions [2.30238915794052]
  We propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties.
  arXiv  Detail & Related papers  (2020-07-31T23:37:38Z)
• Forecasting the Spread of Covid-19 Under Control Scenarios Using LSTM and Dynamic Behavioral Models [2.11622808613962]
  This study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries and Australia.
  arXiv  Detail & Related papers  (2020-05-24T10:43:55Z)
• COVID-19 forecasting based on an improved interior search algorithm and multi-layer feed forward neural network [0.0]
  A new forecasting model is presented to analyze and forecast the confirmed cases of COVID-19 for the coming days. The ISACL-MFNN model integrates an improved interior search algorithm (ISA) based on chaotic learning (CL) strategy into a multi-layer feed-forward neural network (MFNN) The proposed model is investigated in the most affected countries (i.e., USA, Italy, and Spain)
  arXiv  Detail & Related papers  (2020-04-06T12:08:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.