WITHDRAWN: Machine learning models for covid-19 future forecasting

Mojjada, Ramesh Kumar; Yadav, Arvind; Prabhu, Aparna; Natarajan, Yuvaraj

doi:10.1016/j.matpr.2020.10.962

Cited by 42 publications

(25 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A different line of work replaces epidemiological models with machine learning methods to directly predict the number of new infections [22][23][24][25]. Importantly, Yeung et al [26] added non-pharmaceutical interventions (policies) as features in their models; however, their approach is limited to make predictions up to two weeks in advance, since information about the policies that will be implemented in the future is not available at inference time.…”

Section: Related Workmentioning

confidence: 99%

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Vega

Flores²,

Greiner

2022

Forecasting

View full text Add to dashboard Cite

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.

show abstract

Section: Related Workmentioning

confidence: 99%

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Vega

Flores²,

Greiner

2022

Forecasting

View full text Add to dashboard Cite

show abstract

“…The research goal in Mojjada et al ( 21 ) was to forecast the number of new COVID-19 cases, mortalities, and recoveries using various machine learning regression models, such as the lowest absolute and selective shrinking operator (LASSO), vector supports, such as short message service (SMS), and exponential smoking (ES) models. While the linear regression and LASSO models were more effective in estimating and verifying the death rate, the ES model provided the overall best results.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

Kaliappan

Srinivasan

Qaisar

et al. 2021

Front. Public Health

View full text Add to dashboard Cite

This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.

show abstract

“…Since the outbreak of COVID-19, researchers worldwide have been carrying out a lot of research works on it. These researches can be mainly divided into the following six categories: (1) to study the impact of COVID-19 on human physical and mental health from a biomedical perspective ( Tsamakis et al, 2020 , Xiong et al, 2020 , Pascoal et al, 2021 ); (2) to study the impact of COVID-19 on human production, life, and social and economic development from a sociological perspective ( Takyi and Bentum-Ennin, 2020 , Qian et al, 2021 , Shang et al, 2021 , Beiderbeck et al, 2021 , Jiang et al, 2021 ); (3) to creatively propose new mathematical models or revise some existing models based on relevant data for predicting and analyzing the development of the epidemic in a specific area ( Vianello et al, 2021 , Willis et al, 2021 , Mun and Geng, 2021 , Al-qaness et al, 2021 , Manenti et al, 2020 , Hu et al, 2020 , Cao et al, 2020 , Mojjada et al, 2020 , Yang et al, 2020 ); (4) to analyze the spatial-temporal characteristics of the epidemic in a specific area ( Lv and Cheng, 2020 , Feng et al, 2020 ); (5) to explore related factors which may affect the development of the epidemic ( Hu et al, 2021 ); (6) to evaluate the effects of different epidemic prevention measures ( Leung et al, 2020 , Hasnain et al, 2020 ). In terms of the research purpose and content, the third, the fourth, and the fifth categories are more relevant to the work carried out in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the epidemic data of Hubei province from January 23, 2020, to February 24, 2020, they fitted the parameters of the newly established modified SEIR model. Mojjada et al (2020) commit to demonstrating the ability to predict the number of individuals affected by the COVID-19 as a potential threat to human beings by Machine Learning (ML) modeling. Their work shows that the Linear Regression (LR) effectively predicts new corona cases, death numbers, and recovery.…”

Section: Related Workmentioning

confidence: 99%

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Jin

Yang

et al. 2021

Process Safety and Environmental Protection

View full text Add to dashboard Cite

COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial( Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.

show abstract

WITHDRAWN: Machine learning models for covid-19 future forecasting

Cited by 42 publications

References 5 publications

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Contact Info

Product

Resources

About