Covid-19 Epidemiological Factor Analysis: Identifying Principal Factors with Machine Learning

Dolgikh, Serge

doi:10.1101/2020.06.01.20119560

Cited by 6 publications

(4 citation statements)

References 3 publications

(3 reference statements)

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“…In [465] , machine learning algorithms have been used to identify the dominant factors of the epidemiological factors apart from management policies. The finding suggest that BCG and smoking are among the most important factors.…”

Section: Applications Of Ai In Epidemiologymentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

148

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Section: Applications Of Ai In Epidemiologymentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

148

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show abstract

“…Hasan et al [90] utilized Q-Q plot and ARIMA model to present the death rate based on time-series data of different states of India. Many other researchers [91][92][93][94][95][96][97][98][99] have presented the applications of Machine Learning approaches for early diagnosis and predicting the trends of COVID-19. Wang et al [100] presented the epidemic trend using logistic and time-series prediction model called FbProphet.…”

Section: Machine-learning-basedmentioning

confidence: 99%

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey

Sinwar

Dhaka

Tesfaye

et al. 2022

Contrast Media & Molecular Imaging

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Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

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“…Using neural network forecasting methods requires some analyst experience in setting neural network parameters but makes it possible to get more accurate forecast results. Paper [8] analyzes the tools used in the study of time series. The peculiarities of the application of different methods in the study of the influence of external factors on the time series are shown.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Devising a method for constructing the optimal model of time series forecasting based on the principles of competition

Mulesa

Povkhan

Радівілова

et al. 2021

EEJET

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This paper reports the analysis of a forecasting problem based on time series. It is noted that the forecasting stage itself is preceded by the stages of selection of forecasting methods, determining the criterion for the forecast quality, and setting the optimal prehistory step. As one of the criteria for a forecast quality, its volatility has been considered. Improving the volatility of the forecast could ensure a decrease in the absolute value of the deviation of forecast values from actual data. Such a criterion should be used in forecasting in medicine and other socially important sectors. To implement the principle of competition between forecasting methods, it is proposed to categorize them based on the values of deviations in the forecast results from the exact values of the elements of the time series. The concept of dominance among forecasting methods has been introduced; rules for the selection of dominant and accurate enough predictive models have been defined. Applying the devised rules could make it possible, at the preceding stages of the analysis of the time series, to reject in advance the models that would surely fail from the list of predictive models available to use. In accordance with the devised method, after applying those rules, a system of functions is built. The functions differ in the sets of predictive models whose forecasting results are taken into consideration. Variables in the functions are the weight coefficients with which predictive models are included. Optimal values for the variables, as well as the optimal model, are selected as a result of minimizing the functions built. The devised method was experimentally verified. It has been shown that the constructed method made it possible to reduce the forecast error from 0.477 and 0.427 for basic models to 0.395 and to improve the volatility of the forecast from 1969.489 and 1974.002 to 1607.065

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Covid-19 Epidemiological Factor Analysis: Identifying Principal Factors with Machine Learning

Cited by 6 publications

References 3 publications

Applications of artificial intelligence in battling against covid-19: A literature review

Applications of artificial intelligence in battling against covid-19: A literature review

Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey

Devising a method for constructing the optimal model of time series forecasting based on the principles of competition

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