A study on the transmission dynamics of COVID-19 considering the impact of asymptomatic infection

Xu, Chuanqing; Zhang, Zonghao; Huang, Xiaotong; Cheng, Kedeng; Guo, Songbai; Wang, Xiaojing; Liu, Maoxing; Liu, Xiaoling

doi:10.1080/17513758.2023.2244980

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…However, because COVID-19 is an emerging infectious disease, risk factors that affect the spread of infection have not been clearly elucidated. Additionally, COVID-19 infection may be transmitted from asymptomatic or pre-symptomatic individuals [23] and many cases of reinfection have been reported [24]. Owing to these issues, it is considered difficult to predict COVID-19 outbreaks with high accuracy and in a timely fashion using conventional SIR models and models using the basic reproduction number or effective reproduction number.…”

Section: Discussionmentioning

confidence: 99%

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend

Manabe,

Honda

et al. 2024

BMC Infect Dis

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Background Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. Methods We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the “rising trend line”; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. Results Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. Conclusion Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

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Section: Discussionmentioning

confidence: 99%

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend

Manabe,

Honda

et al. 2024

BMC Infect Dis

View full text Add to dashboard Cite

show abstract

“…However, because COVID-19 is an emerging infectious disease, risk factors that affect the spread of infection have not been clearly elucidated. Additionally, COVID-19 infection may be transmitted from asymptomatic or pre-symptomatic individuals [19] and many cases of reinfection have been reported [20]. Owing to these issues, it is considered di cult to predict COVID-19 outbreaks with high accuracy and in a timely fashion using conventional SIR models and models using the basic reproduction number or effective reproduction number.…”

Section: Discussionmentioning

confidence: 99%

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend

Manabe,

Honda

et al. 2024

Preprint

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Background Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. Methods We used data from the Ministry of Health, Labour and Welfare - Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data; then, epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques was used to depict the regression lines for each epidemic wave, denoted the “rising trend line”; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. Results Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. In the predicted seventh wave, although the starting time and peak time of the epidemic were slightly behind, our developed prediction model had a fairly high degree of accuracy. Conclusion Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 when an epidemic wave has high periodicity.

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“…Indeed, transmission due to asymptomatic infections cannot be effectively identified, making it more difficult to contain and control [6][7][8][9]. New and complex mathematical models are needed to represent epidemic dynamics due to asymptomatic carriers [10][11][12][13][14][15], also considering infection transmission prior to symptom onset [16]. Most importantly, early detection of aberrations becomes a crucial point [1,17].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Early Warning Method for Infectious Diseases with Asymptomatic Carriers

Gaspari

2024

Healthcare

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At the beginning of 2023, the Italian former prime minister, the former health minister and 17 others including the current president of the Lombardy region were placed under investigation on suspicion of aggravated culpable epidemic in connection with the government’s response at the start of the COVID-19 pandemic. The charges revolve around the failure by authorities to take adequate measures to prevent the spread of the virus in the Bergamo area, which experienced a significant excess of deaths during the initial outbreak. The aim of this paper is to analyse the pandemic data of Italy and the Lombardy region in the first 10 days of the pandemic, spanning from the 24th of February 2020 to the 4th of March 2020. The objective is to determine whether the use of early warning indicators could have facilitated the identification of a critical increase in infections. This identification, in turn, would have enabled the timely formulation of strategies for pandemic containment, thereby reducing the number of deaths. In conclusion, to translate our findings into practical guidelines, we propose a low-cost early warning method for infectious respiratory diseases with asymptomatic carriers.

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A study on the transmission dynamics of COVID-19 considering the impact of asymptomatic infection

Cited by 3 publications

References 22 publications

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend

A Low-Cost Early Warning Method for Infectious Diseases with Asymptomatic Carriers

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