Predicting COVID-19 Using Hybrid AI Model

Du, Shaoyi; Wang, Jianji; He, Zhongshi; Cui, Wenting; Kang, Zijian; Yang, Tao; Lou, Bin; Chi, Yuting; Li, Hong; Ma, Mei; Yuan, Qi; Zhang, Shupei; Zhang, Dong; Xin, Jingmin; Zheng, Nanning

doi:10.2139/ssrn.3555202

Cited by 27 publications

(22 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was because the data and features were not su cient, and the models lack epidemic rationality. Deep neural network sequence models like long shortterm memory (LSTM) had weak capability to predict the long-term trends and the turning point [18].…”

Section: Discussionmentioning

confidence: 99%

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model

Sun

Chen

Zhang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that applied the traditional epidemic models or machine learning models were subject to underfitting or overfitting problems.Methods We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from 27 January, 2020.Results We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the 40 days long trend and the exact date of turning point. The predicted cumulative number (12,506) by 10, March 2020 was only 3·8% different with the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China.Conclusions The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of COVID-19 outbreak. The learned parameters suggested the effectiveness of intervention measures taken in China.

show abstract

Section: Discussionmentioning

confidence: 99%

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model

Sun

Chen

Zhang

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Jin et al [12] built and deployed an artificial intelligence (AI)-assisted system for automatic computed tomography (CT) image analysis and recognition of COVID-19 within 4 weeks, which has been applied in many hospitals, greatly reducing the pressure of radiologists. Du et al [13] proposed a hybrid AI model for COVID-19 prediction that fully considered the effects of prevention and control measures, and the improvement of public prevention awareness, and has been applied in a number of Chinese cities. Moreover, Reeves et al [14] built a series of standardized tools based on the existing electronic health record (EHR) system for the COVID-19 epidemic to support outbreak management, including scripted triaging, electronic check-in, standard ordering and documentation, secure messaging, real time data analytics, and telemedicine capabilities.…”

Section: The Role Of Telemedicine In the Era Of Covid-19mentioning

confidence: 99%

A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study

Liu¹,

Wang²,

Ren³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

Background The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. Objective The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. Methods Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model. Results DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients’ demographic information, clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro–area under the curve were all above 0.71 in each scenario. Conclusions DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.

show abstract

“…They use a variety of features such as maximum, minimum, and average daily temperature, the density of city, humidity and wind speed as inputs to the ANN, with the aim of predicting the confirmed number of COVID-19 patients in the next 30 days. Du et al [15] propose a hybrid AI model that combines the strengths of both a natural language processing module and a long short-term memory network. Their objective is to analyze the change in the infectious capacity of COVID-19 patients within a few days after they catch the virus.…”

Section: Related Workmentioning

confidence: 99%

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

Hamed

Sobhy

Nassar

2020

Preprint

View full text Add to dashboard Cite

The coronavirus 2019 disease (COVID-19) is wreaking havoc around the world, and great efforts are underway to control it. Millions of people are now being tested and their data keeps accumulating in large volumes. This data can be used to classify newly tested persons as whether they have the disease or not. However, normal classification techniques are hampered by the fact that the data is typically both incomplete and heterogeneous. To address this two-fold obstacle, we propose a KNN variant (KNNV) algorithm which accurately and efficiently classifies COVID-19. The main two ideas behind the proposed algorithm are that for each instance to be classified it chooses the parameter K adaptively and calculates the distances to other instances in a novel way. The KNNV was implemented and tested on a COVID-19 dataset from the Italian society of medical and intervention radiology society. It was also compared to three algorithms of its category. The test results show that the KNNV can efficiently and accurately classify COVID-19 patients. The comparison results show that the algorithm greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score.

show abstract

Predicting COVID-19 Using Hybrid AI Model

Cited by 27 publications

References 0 publications

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model

A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

Contact Info

Product

Resources

About