Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

Satu, Md. Shahriare; Howlader, Koushik Chandra; Mahmud, Mufti; Kaiser, M. Shamim; Islam, Sheikh Mohammed Shariful; Quinn, Julian M.W.; Alyami, Salem A.; Moni, Mohammad Ali

doi:10.3390/app11094266

Cited by 73 publications

(27 citation statements)

References 53 publications

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“…We solved the ordinary NMPC in Problem 1 to find the candidate mean functions for x and û. Then, we computed the gain matrices and the variances of the joint distribution (13). Using the obtained feasible solution as an initial guess, we solved the µΣ-NMPC in Problem 2.…”

Section: Resultsmentioning

confidence: 99%

“…The expanded Formula ( 14) of the expectation of cost ( 7) is derived in Appendix A. 14) is typically a nonquadratic function of the mean and the variance of the joint distribution (13). This term introduces a potential difficulty to the optimization, which is addressed later in Section 4.4.…”

Section: Probabilistic Cost and Input Constraintmentioning

confidence: 99%

“…Logistic wavelets were used in [11] to compute the possible cumulative number of people infected with COVID. The application of artificial intelligence and machine learning has also been successful in COVID-related modeling and prediction [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

2022

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In this paper, we propose a model-based method for the reconstruction of not directly measured epidemiological data. To solve this task, we developed a generic optimization-based approach to compute unknown time-dependent quantities (such as states, inputs, and parameters) of discrete-time stochastic nonlinear models using a sequence of output measurements. The problem was reformulated as a stochastic nonlinear model predictive control computation, where the unknown inputs and parameters were searched as functions of the uncertain states, such that the model output followed the observations. The unknown data were approximated by Gaussian distributions. The predictive control problem was solved over a relatively long time window in three steps. First, we approximated the expected trajectories of the unknown quantities through a nonlinear deterministic problem. In the next step, we fixed the expected trajectories and computed the corresponding variances using closed-form expressions. Finally, the obtained mean and variance values were used as an initial guess to solve the stochastic problem. To reduce the estimated uncertainty of the computed states, a closed-loop input policy was considered during the optimization, where the state-dependent gain values were determined heuristically. The applicability of the approach is illustrated through the estimation of the epidemiological data of the COVID-19 pandemic in Hungary. To describe the epidemic spread, we used a slightly modified version of a previously published and validated compartmental model, in which the vaccination process was taken into account. The mean and the variance of the unknown data (e.g., the number of susceptible, infected, or recovered people) were estimated using only the daily number of hospitalized patients. The problem was reformulated as a finite-horizon predictive control problem, where the unknown time-dependent parameter, the daily transmission rate of the disease, was computed such that the expected value of the computed number of hospitalized patients fit the truly observed data as much as possible.

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Section: Resultsmentioning

confidence: 99%

Section: Probabilistic Cost and Input Constraintmentioning

confidence: 99%

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Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

2022

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“…Stevenson et al [81] applied three models (LSTM, naïve, and seasonal naïve forecast), to predict COVID-19 and they used RMSE metrics to evaluate the obtained results for 7-day and 14-day time intervals to predict future daily cases in South Africa. Satu et al [82] proposed polynomial multi-layer perceptron (Poly-MLP), support vector regression (SVR), and Prophet models to predict confirmed and death cases using two evaluation metrics, RMSE and R2, to examine the proposed models. The datasets were collected using the Bangladesh surveillance system for the time period of 185 days from 8 March 2020 to 28 November 2020 to predict confirmed infection, death, and recovered cases.…”

Section: Discussionmentioning

confidence: 99%

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Aldhyani

Alkahtani

2021

Life

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Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.

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“…In addition to using mathematical models to study the spread of infectious diseases, there are some researchers [36][37][38] that used the statistical phylogeography to track the spread of the highly pathogenic H5N1, HIV-1, and H9N2, and some researchers [39] used machine learning models to speculate on the transmission and evolution mechanism of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analyzing Transmission of Infectious Diseases Using Generalized Stochastic Petri Nets

et al. 2021

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Some infectious diseases such as COVID-19 have the characteristics of long incubation period, high infectivity during the incubation period, and carriers with mild or no symptoms which are more likely to cause negligence. Global researchers are working to find out more about the transmission of infectious diseases. Modeling plays a crucial role in understanding the transmission of the new virus and helps show the evolution of the epidemic in stages. In this paper, we propose a new general transmission model of infectious diseases based on the generalized stochastic Petri net (GSPN). First, we qualitatively analyze the transmission mode of each stage of infectious diseases such as COVID-19 and explain the factors that affect the spread of the epidemic. Second, the GSPN model is built to simulate the evolution of the epidemic. Based on this model’s isomorphic Markov chain, the equilibrium state of the system and its changing laws under different influencing factors are analyzed. Our paper demonstrates that the proposed GSPN model is a compelling tool for representing and analyzing the transmission of infectious diseases from system-level understanding, and thus contributes to providing decision support for effective surveillance and response to epidemic development.

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Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

Cited by 73 publications

References 53 publications

Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Modeling and Analyzing Transmission of Infectious Diseases Using Generalized Stochastic Petri Nets

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