Forecasting <scp>COVID</scp>‐19 cases using dynamic time warping and incremental machine learning methods

Miralles-Pechuán, Luis; Kumar, Ankit; Suárez‐Cetrulo, Andrés L.

doi:10.1111/exsy.13237

Cited by 8 publications

(3 citation statements)

References 61 publications

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“…Previous work has been done on COVID-19 forecasting using both classical and machine learning methods. Miralles-Pechuán et al compared the performance of state-of-the-art machine learning algorithms, such as long-short-term memory networks, against that of online incremental machine learning algorithms to predict the coronavirus cases for the 50 countries with the most cases during 2020 27 . Kasilingam et al used exponential growth modelling studies to understand the spreading patterns of SARS-CoV-2 and identify countries that showed early signs of containment until March 26, 2020 28 .…”

Section: Discussionmentioning

confidence: 99%

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting

Aung,

Pang,

Chua

et al. 2023

Sci Rep

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COVID-19 has resulted in significant morbidity and mortality globally. We develop a model that uses data from thirty days before a fixed time point to forecast the daily number of new COVID-19 cases fourteen days later in the early stages of the pandemic. Various time-dependent factors including the number of daily confirmed cases, reproduction number, policy measures, mobility and flight numbers were collected. A deep-learning model using Bidirectional Long-Short Term Memory (Bi-LSTM) architecture was trained on data from 22nd Jan 2020 to 8 Jan 2021 to forecast the new daily number of COVID-19 cases 14 days in advance across 190 countries, from 9 to 31 Jan 2021. A second model with fewer variables but similar architecture was developed. Results were summarised by mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and total absolute percentage error and compared against results from a classical ARIMA model. Median MAE was 157 daily cases (IQR: 26–666) under the first model, and 150 (IQR: 26–716) under the second. Countries with more accurate forecasts had more daily cases and experienced more waves of COVID-19 infections. Among countries with over 10,000 cases over the prediction period, median total absolute percentage error was 33% (IQR: 18–59%) and 34% (IQR: 16–66%) for the first and second models respectively. Both models had comparable median total absolute percentage errors but lower maximum total absolute percentage errors as compared to the classical ARIMA model. A deep-learning approach using Bi-LSTM architecture and open-source data was validated on 190 countries to forecast the daily number of cases in the early stages of the COVID-19 outbreak. Fewer variables could potentially be used without impacting prediction accuracy.

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Section: Discussionmentioning

confidence: 99%

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting

Aung,

Pang,

Chua

et al. 2023

Sci Rep

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“…These methods have shown promising performance in modeling and forecasting COVID-19 cases. [12][13][14] In other research, methods for forecasting COVID-19 cases and trends have included manual fitting, where initial model parameters are chosen based on historical data, and automated fitting, where parameters are chosen based on candidate case trajectory simulations. 15 Polynomial regression, ARIMA, deep learning techniques, such as recurrent neural network (RNN), and generalized space-time (GST) ARIMA models have been used for COVID-19 forecasting.…”

Section: Previous Studies On Covid-19 Forecastingmentioning

confidence: 99%

COVID-19 trends across borders: Identifying correlations among countries

Muhaidat,

Albatayneh

2024

J Int Med Res

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Objectives To enhance the accuracy of forecasting future coronavirus disease 2019 (COVID-19) cases and trends by identifying and analyzing correlations between the daily case counts of different countries reported between January 2020 and January 2023, to uncover significant links in COVID-19 patterns between nations, allowing for real-time, precise predictions of disease spread based on observed trends in correlated countries. Methods Daily COVID-19 cases for each country were tracked between January 2020 and January 2023 to identify correlations between nations. Current case data were obtained from reliable sources, such as Johns Hopkins University and the World Health Organization. Data were analyzed in Microsoft Excel using Pearson’s correlation coefficient to assess the strength of connections. Results Strong correlations (r > 0.80) were revealed between the daily reported COVID-19 case counts of numerous countries across various continents. Specifically, 62 nations showed significant correlations with at least one correlated (connected) country per nation. These correlations indicate a similarity in COVID-19 trends over the past 3 or more years. Conclusion This study addresses the gap in country-specific correlations within COVID-19 forecasting methodologies. The proposed method offers essential real-time insights to aid effective government and organizational planning in response to the pandemic.

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“…The authors claimed that the proposed model can significantly reduce the prediction errors as compared to the traditional epidemic models. Researchers have investigated many machine learning and deep learning models to train prediction algorithms on COVID-19 data [1], [21], [33], [36]. The research studies are able to provide effective directions to control and manage the spread of a pandemic like COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning

Kumar,

Susan

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Epidemiological models are best suitable to model an epidemic if the spread pattern is stationary. To deal with nonstationary patterns and multiple waves of an epidemic, we develop a hybrid model encompassing epidemic modeling, particle swarm optimization, and deep learning. The model mainly caters to three objectives for better prediction: 1. Periodic estimation of the model parameters. 2. Incorporating impact of all the aspects using data fitting and parameter optimization 3. Deep learning based prediction of the model parameters. In our model, we use a system of ordinary differential equations (ODEs) for Susceptible-Infected-Recovered-Dead (SIRD) epidemic modeling, Particle Swarm Optimization (PSO) for model parameter optimization, and stacked-LSTM for forecasting the model parameters. Initial or one time estimation of model parameters is not able to model multiple waves of an epidemic. So, we estimate the model parameters periodically (weekly). We use PSO to identify the optimum values of the model parameters. We next train the stacked-LSTM on the optimized parameters, and perform forecasting of the model parameters for upcoming four weeks. Further, we fed the LSTM forecasted parameters into the SIRD model to forecast the number of COVID-19 cases. We evaluate the model for highly affected three countries namely; the USA, India, and the UK. The proposed hybrid model is able to deal with multiple waves, and has outperformed existing methods on all the three datasets.

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Forecasting COVID‐19 cases using dynamic time warping and incremental machine learning methods

Cited by 8 publications

References 61 publications

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting

COVID-19 trends across borders: Identifying correlations among countries

Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning

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