Predictive Model for the Total Daily COVID-19 Cases with Herd Immunity Policy

Sunthornwat, Rapin; Areepong, Yupaporn

doi:10.12982/cmujns.2021.016

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…The parameters for the exponential growth curve were estimated by the least square method by using the number of total COVID-19 cases in Thailand from February 15 th , 2020 to March 25 th , 2020 which is the period before the emergency decree announcement. The results showed that the parameters 0 16.07, 0.08 C r = =…”

Section: Resultsmentioning

confidence: 99%

“…Comparisons and examples between the flattened curve policy and the herd immunity policy are discussed. Sweden is the country in which the herd immunity policy has firstly been used to protect and control the COVID-10 outbreak since the worldwide pandemic time of coronavirus disease 2019 [16]. The other Nordic countries-Norway, Finland, and Denmark-including Thailand have applied the flattened curve policy to fight the COVID-19 outbreak.…”

Section: Figure 1 Estimated Exponential Growth Curve Before and After The Emergency Decree (Ed)mentioning

confidence: 99%

“…Moreover, the predictive models of the number of total COVID-19 cases for the herd immunity policy without vaccination in Sweden were presented. The herd immunity policy in Sweden was compared amongst the other Nordic countries (Norway, Finland, and Denmark) which were applied to the flattened curve policy [16].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Number of Cumulative COVID-19 Cases by Day in Thailand Based on a Flattened Curve Policy

Areepong

Sunthornwat²

2021

Walailak J Sci & Tech

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Since December 2019, the world has been facing an emerging infectious disease named coronavirus disease 2019. Thailand has also been affected by the spread of the coronavirus. The Thai government have announced policies to protect people, based on the emergency decree and curfew law for flattening the curve of the number of the coronavirus disease 2019 cases without vaccination in Thailand. This research estimated of the number of total infectious cases of coronavirus disease 2019 in Thailand. Two growth curves, including an exponential growth curve under a non-flattened curve policy (herd immunity policy without vaccination), and a logistic growth curve under a flattened curve policy without vaccination, were selected to estimate the parameters of the curves by the least square method to represent the number of the total infectious cases in Thailand. Moreover, the maximum infectious cases of coronavirus disease 2019 and the speed of spreading for coronavirus disease 2019 in Thailand were also explored. Based on the number of the total infectious cases of coronavirus disease 2019 in Thailand, the findings demonstrated that the coefficient of determination of the logistic growth curve was greater than the exponential growth curve and the root means squared percentage error of the logistic growth curve was less than the exponential growth curve. These results suggest that the logistic growth curve is suitable for describing the number of total infectious cases of coronavirus disease 2019 in Thailand under the fattened curve policy. GRAPHICAL ABSTRACT

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Section: Resultsmentioning

confidence: 99%

Section: Figure 1 Estimated Exponential Growth Curve Before and After The Emergency Decree (Ed)mentioning

confidence: 99%

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Estimation of the Number of Cumulative COVID-19 Cases by Day in Thailand Based on a Flattened Curve Policy

Areepong

Sunthornwat²

2021

Walailak J Sci & Tech

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“…The optimal parameters or estimated parameters K, r, C can be solved by the least square error method. The validation and accuracy of the predictive growth curve time series are based on statistic the coefficient of determination (R 2 ) and the root mean squared percentage error (RMSPE) [13][14].…”

Section: Predictive Time Series and Its Parameter Estimation For The Covid-19 Outbreakmentioning

confidence: 99%

Delay Time Parameter and Its Confidence Interval of Predictive Time Series of COVID-19 Outbreak Between the First and the Second Wave

Sunthornwat¹,

Sookkhee

2021

J. Math. Fund. Sci.

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The outbreak of coronavirus disease 2019 (COVID-19) has become a major problem facing humans all around the world. For governments, in order to deal with the outbreak and protect the population, it is important to predict the number of infectious cases in the future to monitor the COVID-19 situation. This research aimed to compare the effectiveness of the logistic and the delay logistic time series in predicting the total number of infectious cases by using actual data from four countries, i.e. Thailand, South Korea, Egypt, and Nigeria. The total number of COVID-19 cases was collected during the first and the second wave of the COVID-19 outbreak. The validation and accuracy of the predictive growth curve time series were determined based on statistical values, i.e. the coefficient of determination and the root mean squared percentage error. It was found that the logistic time series was more appropriate for predicting the first wave in the four countries. For the second wave, the delay logistic time series was preferable. Moreover, the confidence interval based on Chebyshev’s inequality of delay time between the first and the second wave of the COVID-19 outbreak is also proposed.

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“…As the above literature reviews, the models for describing the COVID-19 outbreak are based on time independent variable. For example, the forecasting models are the models with time independent variables such as the logistic and Gompertz models [1][2][3], the Bertalanffy model [4], the Boltzmann growth curve [5,6], and Regression analysis [7,8]. The mathematical models, a system of differential equations, are the models with time independent variable such SEIQCRW model [19], SIR model [20], SEIR model [21], and SEIQRD [24].…”

mentioning

confidence: 99%

Probabilistic Analysis Depending on the Distance from A COVID-19 Outbreak

Areepong

Sunthornwat²

2023

Emerg Sci J

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COVID-19 has been affecting human beings since the end of 2019. Studying the characteristics of a COVID-19 outbreak is significant because it will add to the knowledge that is necessary for protecting the general public and controlling future viral outbreaks. The aims of the present research are to analyze COVID-19 outbreaks in Thailand depending on the distance from the outbreak center by using a differential equation, to construct a probability density function from the solution of the differential equation, and to prove the theorem for the probability density function depending on the distance from the outbreak. The least-squares-error method is adopted to estimate the parameters of the function describing the COVID-19 outbreak. Moreover, a cumulative distribution function, a quantile function, a sojourn function, a hazard function, the median, the expected value, variance, skewness, and kurtosis are derived, and their practicability is shown. Applying the exponentially weighted moving average control chart to monitor a COVID-19 outbreak based on distance is proposed and compared with monitoring the COVID-19 outbreak based on time. The results show that using the former more quickly detected the out-of-control first passage time of the COVID-19 outbreak than the latter. Doi: 10.28991/ESJ-2023-SPER-012 Full Text: PDF

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Predictive Model for the Total Daily COVID-19 Cases with Herd Immunity Policy

Cited by 4 publications

References 7 publications

Estimation of the Number of Cumulative COVID-19 Cases by Day in Thailand Based on a Flattened Curve Policy

Estimation of the Number of Cumulative COVID-19 Cases by Day in Thailand Based on a Flattened Curve Policy

Delay Time Parameter and Its Confidence Interval of Predictive Time Series of COVID-19 Outbreak Between the First and the Second Wave

Probabilistic Analysis Depending on the Distance from A COVID-19 Outbreak

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