Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data

Mohanraj, G; Mohanraj, V.; Marimuthu, M; Sathiyamoorthi, V; Luhach, Ashish Kr.; Kumar, Sandeep

doi:10.1080/0952813x.2022.2058618

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…In the aspect of epidemic models, it has given further consideration to the presence of the asymptomatic and the incidence of population migration based on the SEIR model built by Gopal et al [ 48 ]. and the machine learning model proposed by Mohanraj et al [ 49 ]. Moreover, the model is accurate enough to simulate epidemic transmission, thus disclosing the blocking effects of traffic control measures on COVID-19 under distinct values of intensity and predicting the propagation of COVID-19 in Xi’an City under distinct levels of control intensity.…”

Section: Discussionmentioning

confidence: 99%

“…In the aspect of epidemic models, it has given further consideration to the presence of the asymptomatic and the incidence of population migration based on the SEIR model built by Gopal et al [48]. and the machine learning model proposed by Mohanraj et al [49]. Moreover, the model is accurate enough to simulate In the aspect of balancing epidemic prevention and control and economic development, in view of previous studies focusing on the effectiveness of traffic control measures in blocking the epidemic [27,50], the equally unreasonable epidemic prevention and control measures could negatively impact the society and economy [13].…”

Section: Plos Onementioning

confidence: 99%

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The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi’an, China

Xiang,

Wang,

Pan

et al. 2024

PLoS ONE

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Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi’an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.

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Section: Discussionmentioning

confidence: 99%

“…In the aspect of epidemic models, it has given further consideration to the presence of the asymptomatic and the incidence of population migration based on the SEIR model built by Gopal et al [48]. and the machine learning model proposed by Mohanraj et al [49]. Moreover, the model is accurate enough to simulate In the aspect of balancing epidemic prevention and control and economic development, in view of previous studies focusing on the effectiveness of traffic control measures in blocking the epidemic [27,50], the equally unreasonable epidemic prevention and control measures could negatively impact the society and economy [13].…”

Section: Plos Onementioning

confidence: 99%

The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi’an, China

Xiang,

Wang,

Pan

et al. 2024

PLoS ONE

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A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

Khan

Saif

et al. 2023

Journal of Experimental & Theoretical Artificial Intelligen

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Time series forecasting of the COVID-19 pandemic: a critical assessment in retrospect

Güngör

2023

Alphanumeric Journal

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The COVID-19 pandemic is perceived by many to have run its course, and forecasting its progress is no longer a topic of much interest to policymakers and researchers as it once was. Nevertheless, in order to take lessons from this extraordinary two and a half years, it still makes sense to have a critical look at the vast body of literature formed thereon, and perform comprehensive analyses in retrospect. The present study is directed towards that goal. It is distinguished from others by encompassing all of the following features simultaneously: (i) time series of 10 of the most affected countries are considered; (ii) forecasting for two types of periods, namely days and weeks, are analyzed; (iii) a wide range of exponential smoothing, autoregressive integrated moving average, and neural network autoregression models are compared by means of automatic selection procedures; (iv) basic methods for benchmarking purposes as well as mathematical transformations for data adjustment are taken into account; and (v) several test and training data sizes are examined. Our experiments show that the performance of common time series forecasting methods is highly sensitive to parameter selection, bound to deteriorate dramatically as the forecasting horizon extends, and sometimes fails to be better than that of even the simplest alternatives. We contend that the reliableness of time series forecasting of COVID-19, even for a few weeks ahead, is open to debate. Policymakers must exercise extreme caution before they make their decisions utilizing a time series forecast of such pandemics.

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Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data

Cited by 3 publications

References 23 publications

The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi’an, China

The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi’an, China

A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

Time series forecasting of the COVID-19 pandemic: a critical assessment in retrospect

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