ArticleIEEE transactions on computational social systems2021
COVID-19 Patient Count Prediction Using LSTM.
Article in IEEE transactions on computational social systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Predicting the prevalence and estimating the economic burden of ischemic heart disease in China: Based on long short-term memory model.Medicine · 2025Article
- Prediction of traumatic hemorrhagic shock using a Multi-scale exogenous variable model (MS-TimeXer-MoE).European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Article
- An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction.PeerJ. Computer science · 2024Article
- Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases.Frontiers in public health · 2024Article
- Multi-weight susceptible-infected model for predicting COVID-19 in China.Neurocomputing · 2023Article
- Nonstationary time series forecasting using optimized-EVDHM-ARIMA for COVID-19.Frontiers in big data · 2023Article
- Temporal deep learning architecture for prediction of COVID-19 cases in India.Expert systems with applications · 2022Article
- An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19.Neural processing letters · 2022Article
- Adapting recurrent neural networks for classifying public discourse on COVID-19 symptoms in Twitter content.Soft computing · 2022Article
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Authors and funding
8 authors.
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Abstract
In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients' estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model's prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients' count of the proposed model is much closer to the actual patient count.
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