Analysis of COVID-19 spread in South Korea using the SIR model with time-dependent parameters and deep learning

Jo, Hyeontae; Son, Hwijae; Hwang, Hyung Ju; Jung, Sung Il

doi:10.1101/2020.04.13.20063412

Cited by 45 publications

(51 citation statements)

References 19 publications

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“…being the number of effective reproduction, in a time interval T, given by R t =exp ( ∫ ( − ) . ) (JO et al, 2020). It is important to note that this means that the reproduction factor changes over time, growing exponentially to low t, reaching a peak (at the peak of infection) and decreasing more slowly after that, as predicted by the work of JO et al (2020).…”

Section: Model and Methodsmentioning

confidence: 88%

“…(JO et al, 2020). It is important to note that this means that the reproduction factor changes over time, growing exponentially to low t, reaching a peak (at the peak of infection) and decreasing more slowly after that, as predicted by the work of JO et al (2020). The faster the reproduction factor falls, the faster the epidemic will be controlled, an important data for the adoption of measures that will mitigate the effects of the pandemic in the state and that needs to be taken into account by health authorities.…”

Section: Model and Methodsmentioning

confidence: 99%

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New S.I.R. model used in the projection of COVID 19 cases in Brazil

Freitas¹,

Silva

Sandes

2020

Preprint

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In this work, we proposed a variant of the SIR model, taking as based on models used to describe the epidemic outbreak in South Korea and Portugal, to study the COVID-19epidemic curve in Brazil. The model presented here describes with reasonable agreement the number of COVID-19 cases registered in Brazil between February 26 and May 18, 2020 based on the hypothesis that there a large number no notified cases (11 to 1) and variation in contagion rate according to social isolation measures and greater or lesser exposure to the virus (highest rate in beginning from epidemic). To this end, we introduced an exposure factor, called β 1 /β 2 , which allows us to describe the influence of factors such as social isolation on dispersal from disease. The results also corroborate a phenomenon observed in countries that registered a high growth in cases in short period of time, to example of Italy, Spain and USA: if isolation measures are imposed late, the total number of cases explodes when the epidemic is approaching from peak, which implies a higher exposure rate in the first days of case registration.From the data collected, we made the linear adjustment of the infected curve according to the time between the 41st and 74th days since the official notification of the first case and we obtained a high infection rate in the period, close to 4.0. The result indicates that the relaxation of social exclusion measures contributed to the high increase in cases in the period. This result reinforces the adoption of the model that differentiates unexposed susceptible from those that are most exposed to contagion. The model also predicts that the peak epidemic outbreak in Brazil, based on the number of cases, will occur around in late May and early June.

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Section: Model and Methodsmentioning

confidence: 88%

Section: Model and Methodsmentioning

confidence: 99%

New S.I.R. model used in the projection of COVID 19 cases in Brazil

Freitas¹,

Silva

Sandes

2020

Preprint

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“…Generic Deep Neural Networks: In [420] , the data on South Korea are collected and a DNN is used to to find the best time-varying parameters for the model.…”

Section: Applications Of Ai In Epidemiologymentioning

confidence: 99%

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

2021

Chaos, Solitons & Fractals

156

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“…H. Jo et al, 2020 [12] To analyze the novel coronavirus infection (COVID-19) spreading in South Korea.…”

Section: ) Linear Regression 2) Mlpmentioning

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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Analysis of COVID-19 spread in South Korea using the SIR model with time-dependent parameters and deep learning

Cited by 45 publications

References 19 publications

New S.I.R. model used in the projection of COVID 19 cases in Brazil

New S.I.R. model used in the projection of COVID 19 cases in Brazil

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

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

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