CoVID-19流行軽減に学校閉鎖は無効だった ｰ ベイジアン推論を使用した時系列分析
Was school closure effective in mitigating coronavirus disease 2019 (COVID-19)? Time series analysis using Bayesian inference
Available online 31 July 2020
Fig. 2. Local linear trend model showing the number of predicted cases of coronavirus infection in Japan until March 31 (red line for predicted median with red area for 80%). The assumption is that the intervention began on February 29 (blue solid line), and the effectiveness started to appear on March 7. Black and white dots denote reported cases actually. Black dots are data until March 17 and used in our estimation.
Fig. 3. Local linear trend model showing the number of predicted cases of coronavirus infection in Japan until March 31 (red line for predicted median with red area for 80%). The assumption is that the intervention began on February 29 (blue solid line), and the effectiveness started to appear on March 16 (blue dotted line). Black and white dots denote reported cases actually. Black dots are data until March 17 and used in our estimation.
Table 1. Estimated α(coefficient for intervention effects)using our model with 95% credible intervals. Note if α is less than zero, the intervention is presumed to be effective in decreasing the number of newly reported cases, and if α is more than zero, the number continues to increase.
Our analysis did not demonstrate the effectiveness of the school closure that occurred in Japan in mitigating the risk of coronavirus infection in the nation. Although the effectiveness could have occurred in some scenario on sensitivity analyses, most scenarios in our sensitivity analyses also did not demonstrate its effectiveness.
The effectiveness of school closure has been studied for other infections such as influenza, and these studies have suggested that school closure may be effective in reducing or delaying the epidemic peak. However, there were large heterogeneity in data and generalization of the findings remains difficult. Reopening of schools sometimes reversed the effects, and the optimal timing and duration of school closure, as well as the target age range and the ideal scale of closure remain unknown , , . School children are liable to suffer from influenza every year, and the rationale for school closure to mitigate its epidemic appears sound. Decreasing influenza in children by vaccination could even decrease the disease burden among the elderly . However, children are not a major population that suffers from COVID-19, and young children less than 20-year-old comprised only about 2% of all infected according to a large-scale epidemiological study in China . A study using mathematical models suggested that school closure, including universities, is indeed effective in mitigating the impact of the COVID-19 outbreak, but only when combined with other measures . While it is theoretically possible that school closure among children could reduce transmission among them and potentially to other generations, its impact is likely to be much less than the one conducted for influenza. Therefore, our findings that school closure in Japan did not demonstrate its effectiveness to mitigate the transmission of coronavirus infection are not surprising.
An epidemiological study on COVID-19 in children identified 2,143 pediatric patients in China, and it found that infants may be vulnerable to coronavirus infection. The proportion of severe and critical cases among those less than 1-year-old was 10.6% followed by 7.3% in those aged 1-5 years . However, the school closure done in Japan was only for those aged 6-18, and those vulnerable were not likely to be protected by the measure. A cluster of outbreaks was found in Kyoto, Japan among college students , but again college/university students were not included in the school closure in Japan. When school closure was to be implemented to prevent the spread of COVID-19, those who are vulnerable to this disease and those who are likely to spread it also should be included.
Our study has several limitations. First, our local linear trend model might not be an appropriate model for the current epidemic of COVID-19 in Japan. One might argue that the school closure could have prevented rather stochastic clusters or outbreaks among school children, which could have not happened thanks to the measure. However, if we accept such theory of stochasticity in justifying the school closure, we will end up carrying on the measure until the coronavirus pandemic comes to an end, since we will never know when we can discontinue the measure while fearing unpredictable stochastic outbreaks occurring. Second, the estimated α value using data by the time of intervention effectiveness might not be accurately predicting the α value afterwards, i.e., the α value after March 18. Third, our estimations resulted in rather wide confidence intervals, and the results should be interpreted cautiously. Fourth, school closures in other forms might be effective in mitigating the epidemic, such as ones including infants and small children, or university students. Fifth, school closures combined with other measures such as traffic limitations or even city lock down might be effective. Therefore, we are not claiming that school closures overall are ineffective in mitigating the COVID-19 epidemic in a nation. However, we would like to suggest that the school closure carried out in Japan in March did not demonstrate meaningful effectiveness in controlling the COVID-19 epidemic. Further studies will be necessary to investigate the effectiveness of school closures in other forms at different settings.