Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series

• URL: http://arxiv.org/abs/2009.12325v1
• Date: Thu, 10 Sep 2020 12:53:05 GMT
• Title: Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series
• Authors: Ahmed Ben Said, Abdelkarim Erradi, Hussein Aly, Abdelmonem Mohamed
• Abstract summary: This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases. Data of multiple countries in addition to lockdown measures improve accuracy of the forecast of daily cumulative COVID-19 cases.
• Score: 1.8352113484137624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: To assist policy makers in taking adequate decisions to stop the spread of COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. Materials and Methods: This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using Bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-Means clustering algorithm. The cumulative cases data for each clustered countries enriched with data related to the lockdown measures are fed to the Bidirectional LSTM to train the forecasting model. Results: We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar. Quantitative evaluation, using multiple evaluation metrics, shows that the proposed technique outperforms state-of-art forecasting approaches. Conclusion: Using data of multiple countries in addition to lockdown measures improve accuracy of the forecast of daily cumulative COVID-19 cases.

Related papers

• A Multilateral Attention-enhanced Deep Neural Network for Disease Outbreak Forecasting: A Case Study on COVID-19 [0.6874745415692134]
  We propose a novel approach to address the challenges of infectious disease forecasting. We introduce a Multilateral Attention-enhanced GRU model that leverages information from multiple sources. By incorporating attention mechanisms within a GRU framework, our model can effectively capture complex relationships and temporal dependencies in the data.
  arXiv  Detail & Related papers  (2024-08-26T06:31:53Z)
• 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)
• Time Associated Meta Learning for Clinical Prediction [78.99422473394029]
  We propose a novel time associated meta learning (TAML) method to make effective predictions at multiple future time points. To address the sparsity problem after task splitting, TAML employs a temporal information sharing strategy to augment the number of positive samples. We demonstrate the effectiveness of TAML on multiple clinical datasets, where it consistently outperforms a range of strong baselines.
  arXiv  Detail & Related papers  (2023-03-05T03:54:54Z)
• Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia [105.54048699217668]
  Covid-19 allows researchers to gather datasets accumulated over 2 years and to use them in predictive analysis. We present the results of a consistent comparative study of different types of methods for predicting the dynamics of the spread of Covid-19 based on regional data for two countries: the United States and Russia.
  arXiv  Detail & Related papers  (2022-07-15T18:21:36Z)
• A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the United States [2.9822184411723645]
  We present COVID-LSTM, a data-driven model based on a Long Short-term memory architecture for forecasting COVID-19 incidence at the county-level in the US. We use the weekly number of new cases as temporal input, and hand-engineered spatial features from Facebook to capture the spread of the disease in time and space. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble.
  arXiv  Detail & Related papers  (2021-09-24T17:40:08Z)
• 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)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak [10.307715136465056]
  We present a comparative analysis of various machine learning (ML) approaches in predicting the COVID-19 outbreak in the epidemiological domain. The results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.
  arXiv  Detail & Related papers  (2021-02-11T11:57:33Z)
• 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)
• When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes [111.69190108272133]
  coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures. Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential. This paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context.
  arXiv  Detail & Related papers  (2020-05-13T18:21:50Z)

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.