Dataset Information

Was school closure effective in mitigating coronavirus disease 2019 (COVID-19)? Time series analysis using Bayesian inference.

ABSTRACT:

Objectives

The Coronavirus disease 2019 (COVID-19) pandemic is causing significant damage to many nations. For mitigating its risk, Japan called on all elementary, junior high, and high schools nationwide to close beginning March 1, 2020. However, its effectiveness in decreasing the disease burden has not been investigated.

Methods

We used daily data of the COVID-19 and coronavirus infection incidence in Japan until March 31, 2020. Time-series analyses were conducted using the Bayesian method. Local linear trend models with interventional effects were constructed for the number of newly reported cases of COVID-19, including asymptomatic infections. We considered that the effects of the intervention started to appear nine days after the school closure.

Results

The intervention of school closure did not appear to decrease the incidence of coronavirus infection. If the effectiveness of school closure began on March 9, the mean coefficient α for the effectiveness of the measure was calculated to be 0.08 (95% confidence interval -0.36 to 0.65), and the actual reported cases were more than predicted, yet with a rather wide confidence interval. Sensitivity analyses using different dates also did not demonstrate the effectiveness of the school closure.

Discussion

School closure carried out in Japan did not show any mitigating effect on the transmission of novel coronavirus infection.

SUBMITTER: Iwata K

PROVIDER: S-EPMC7836901 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Was school closure effective in mitigating coronavirus disease 2019 (COVID-19)? Time series analysis using Bayesian inference.

Iwata Kentaro K Doi Asako A Miyakoshi Chisato C

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 20200731

<h4>Objectives</h4>The Coronavirus disease 2019 (COVID-19) pandemic is causing significant damage to many nations. For mitigating its risk, Japan called on all elementary, junior high, and high schools nationwide to close beginning March 1, 2020. However, its effectiveness in decreasing the disease burden has not been investigated.<h4>Methods</h4>We used daily data of the COVID-19 and coronavirus infection incidence in Japan until March 31, 2020. Time-series analyses were conducted using the Bay ...[more]

PMID: 32745628

Similar Datasets

Project description:BackgroundNearly every year Influenza affects most countries worldwide and the risk of a new pandemic is always present. Therefore, influenza is a major concern for public health. School-age individuals are often the most affected group, suggesting that the inclusion in preparedness plans of school closure policies may represent an option for influenza mitigation. However, their applicability remains uncertain and their implementation should carefully be weighed on the basis of cost-benefit considerations.MethodsWe developed an individual-based model for influenza transmission integrating data on sociodemography and time use of the Italian population, face-to-face contacts in schools, and influenza natural history. The model was calibrated on the basis of epidemiological data from the 2009 influenza pandemic and was used to evaluate the effectiveness of three reactive school closure strategies, all based on school absenteeism.ResultsIn the case of a new influenza pandemic sharing similar features with the 2009 H1N1 pandemic, gradual school closure strategies (i.e., strategies closing classes first, then grades or the entire school) could lead to attack rate reduction up to 20-25 % and to peak weekly incidence reduction up to 50-55 %, at the cost of about three school weeks lost per student. Gradual strategies are quite stable to variations in the start of policy application and to the threshold on student absenteeism triggering class (and school) closures. In the case of a new influenza pandemic showing different characteristics with respect to the 2009 H1N1 pandemic, we found that the most critical features determining the effectiveness of school closure policies are the reproduction number and the age-specific susceptibility to infection, suggesting that these two epidemiological quantities should be estimated early on in the spread of a new pandemic for properly informing response planners.ConclusionsOur results highlight a potential beneficial effect of reactive gradual school closure policies in mitigating influenza spread, conditioned on the effort that decision makers are willing to afford. Moreover, the suggested strategies are solely based on routinely collected and easily accessible data (such as student absenteeism irrespective of the cause and ILI incidence) and thus they appear to be applicable in real world situations.

Dataset Information

Was school closure effective in mitigating coronavirus disease 2019 (COVID-19)? Time series analysis using Bayesian inference.

Objectives

Methods

Results

Discussion

Publications

Was school closure effective in mitigating coronavirus disease 2019 (COVID-19)? Time series analysis using Bayesian inference.

Similar Datasets

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