Predictive Analytics of COVID-19 Using Information, Communication and Technologies

Mahalle, Parikshit N.; Sable, Nilesh; Mahalle, Namita; Shinde, Gitanjali Rahul

doi:10.20944/preprints202004.0257.v1

Cited by 17 publications

(16 citation statements)

References 15 publications

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“…Tremendous work has been going on the COVID-19 apart from the above discussed work [77][78][79][80][81][82]. Researchers are working to investigate efficient and accurate models in order to predict the death count.…”

Section: Data Science/machine Learning Techniquesmentioning

confidence: 99%

Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

et al. 2020

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COVID-19 is a pandemic that has affected over 170 countries around the world. The number of infected and deceased patients has been increasing at an alarming rate in almost all the affected nations. Forecasting techniques can be inculcated thereby assisting in designing better strategies and in taking productive decisions. These techniques assess the situations of the past thereby enabling better predictions about the situation to occur in the future. These predictions might help to prepare against possible threats and consequences. Forecasting techniques play a very important role in yielding accurate predictions. This study categorizes forecasting techniques into two types, namely, stochastic theory mathematical models and data science/ machine learning techniques. Data collected from various platforms also play a vital role in forecasting. In this study, two categories of datasets have been discussed, i.e., big data accessed from World Health Organization/National databases and data from a social media communication. Forecasting of a pandemic can be done based on various parameters such as the impact of environmental factors, incubation period, the impact of quarantine, age, gender and many more. These techniques and parameters used for forecasting are extensively studied in this work. However, forecasting techniques come with their own set of challenges (technical and generic). This study discusses these challenges and also provides a set of recommendations for the people who are currently fighting the global COVID-19 pandemic.

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Section: Data Science/machine Learning Techniquesmentioning

confidence: 99%

Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

et al. 2020

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“…M. P.N. et al, 2020 [14] To present different predictive analytics techniques available for trend analysis, different models and algorithms and their comparison.…”

Section: A-sir Modelmentioning

confidence: 99%

Machine learning model estimating number of COVID-19 infection cases over coming 24 days in every province of South Korea (XGBoost and MultiOutputRegressor)

Suzuki

Suzuki²,

Nakamura³

et al. 2020

Preprint

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We built a machine learning model (ML model) which input the number of daily infection cases and the other information related to COVID-19 over the past 24 days in each of 17 provinces in South Korea, and output the total increase in the number of infection cases in each of 17 provinces over the coming 24 days. We employ a combination of XGBoost andMultiOutputRegressor as machine learning model (ML model). For each province, we conduct a binary classification whether our ML model can classify provinces where total infection cases over the coming 24 days is more than 100. The result is Sensitivity = 3/3 = 100%, Specificity = 11/14 = 78.6%, False Positive Rate = 3/11 = 21.4%, Accuracy = 14/17 = 82.4%. Sensitivity = 100% means that we did not overlook the three provinces where the number of COVID-19 infection cases increased by more than100. In addition, as for the provinces where the actual number of new COVID-19 infection cases is less than 100, the ratio (Specificity) that our ML model can correctly estimate was 78.6%, which is relatively high. From the above all, it is demonstrated that there is a sufficient possibility that our ML model can support the following four points. (1) Promotion of behavior modification of residents in dangerous areas, (2) Assistance for decision to resume economic activities in each province, (3) Assistance in determining infectious disease control measures in each province, (4) Search for factors that are highly correlated with the future increase in the number of COVID-19 infection cases. IntroductionThe new-type corona virus (SARS-CoV-2), which suddenly appeared at the end of 2019, caused global epidemic of COVID-19 in 2020. As of May 2020, industry, government, and academia from each country are cooperating to take measures to prevent the spreading of COVID-19 infection and develop therapeutic drugs. This is the third time in this century that humans have been threatened by corona virus. The first was SARS in 2003, the second was MERS in 2012, and the third is COVID-19. Comparing SARS and MERS with COVID-19, it can be said that computer science including artificial intelligence (AI) and machine learning (ML) has made great progress. In fact, many computer science approaches have been developed to prevent the spreading of COVID-19 infection. For example, AI and ML are used for infection spreading analysis, drug discovery assistance, automatic diagnosis, diagnosis assistance, social trend analysis, and infection route analysis. This paper proposes an ML technology for infection spreading analysis. To be more specific, we input the number of COVID-19 infection cases per day and other related information in each of 17 provinces in South Korea for the last 24 days, and output the total increase in the number of infection cases in each of 17 provinces over the coming 24 days. The result of conducting binary classification whether the total number of COVID-19 infection cases exceeds 100 over coming 24 days is Sensitivity = 3/3 = 100%, Specificity = 11/14 = 78.6%, False Positive Rate = ...

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“…In this study, they shown that if fever is yes, cough is yes then the probability of coronavirus infection seems to be positive, similarly, if fever is greater than 38 and cough is dry which comes in the category of yes and patient is facing difficulty in breathing and he has also a travel history to some infected countries than the result shows the positive probability of coronavirus infection. Mahalle P. N. et al [79] proposed a model on April 16. They proposed different predictive analytics techniques available for trend analysis, different models and algorithms, and their comparison.…”

Section: Machine Learning Based Modelsmentioning

confidence: 99%

“…Al. [79]. They have used the model to study the data related to Madhya Pradesh, India, and tried to estimate the number of days in which the cases will be removed based on different recovery rates.…”

Section: Other Modelsmentioning

confidence: 99%

Epidemiological Models of SARS-CoV-2 (COVID-19) to Control the Transmission Based on Current Evidence: A Systematic Review

Arora¹,

Jain²,

Singh³

2020

Preprint

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In Wuhan city of China, an episode of novel coronavirus (COVID-19) happened. during late December and it has quickly spread to all places in the world. Until May 29, 2020, cases were high in the USA with 1.7 Million, Russia with approximately 387 thousand, the UK with 271 thousand confirmed cases. Everybody on the planet is anxious to know when the coronavirus pandemic will end. In this scourge, most nations force extreme medication measures to contain the spread of COVID-19. Modeling has been utilized broadly by every national government and the World Health Organization in choosing the best procedures to seek after in relieving the impacts of COVID-19. Many epidemiological models are studied to understand the spread of the illness and its prediction to find maximum capacity for human-to-human transmission so that control techniques can be adopted. Also, arrangements for the medical facilities required such as hospital beds and medical supplies can be made in advance. Many models are used to anticipate the results keeping in view the present scenario. There is an urgent need to study the various models and their impacts. In this study, we present a systematic literature review on epidemiological models for the outbreak of novel coronavirus in India. The epidemiological dynamics of COVID-19 is also studied. Here, In addition, an attempt to take out the results from the exploration and comparing it with the real data. The study helps to choose the models that are progressive and dependable to predict and give legitimate methods for various strategies.

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Predictive Analytics of COVID-19 Using Information, Communication and Technologies

Cited by 17 publications

References 15 publications

Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art

Machine learning model estimating number of COVID-19 infection cases over coming 24 days in every province of South Korea (XGBoost and MultiOutputRegressor)

Epidemiological Models of SARS-CoV-2 (COVID-19) to Control the Transmission Based on Current Evidence: A Systematic Review

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