Impact estimation on COVID-19 infections following school reopening in September 2020 in Italy
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Abstract
Background
Since its outbreak, CoViD-19 (formally known as 2019-nCoV) has been triggering many questions among public authorities, social organisms and school officials, as to when students should be allowed to return to school. Such a decision is critical and must take into account, other than its beneficial effects, also those associated with an increased exposition of the students to the virus, which, as a result, might spread at a faster rate. To date, in Italy, a few studies have rigorously investigated the correlation between school reopening and number of people tested positive to CoViD-19. Therefore, this paper aims to provide an assessment of such an impact as well as to illustrate the methodology followed.
Methods
Official daily data on the cumulative number of people tested positive to CoViD-19 – in conjunction with external information accounting for the different points in time schools reopened in the various Italian regions – have been employed to build a stochastic model of the type Seasonal Autoregressive Moving Average embodying external information.
Results
There was a statistically significant increase in the number of positive cases in all the Italian regions related to schools reopening. Such an increase occurred, in average, about 18.9 days after the schools have been reopened. Schools reopening have been significantly contributed to the diffusion of the pandemic, with an overall estimated impact of about 228,724 positive cases.
Conclusions
The results suggest the need for strict control of all in-school activities. This could be done by using, to a variable extent, all the non-pharmaceutical interventions available, such as limited access to school spaces, no overlapping practices between different sports in the same space, universal masking, bubble-size classroom. However, in many cases, such measures might not be a viable option, at least in the short run, nor be reasonably applicable. Therefore, whenever the established safety criteria could not be met, school buildings should remain closed.
Key Messages
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Due to CoViD-19 pandemic, physical school attendance is at the center of an intense political and social debates;
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schools reopening decision should be based on reliable and well maintained data-sets;
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in the lack of quality data, it is advisable to use a portion of them, to reduce uncertainty.
Article activity feed
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SciScore for 10.1101/2021.06.11.21258733: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Lack of reliable information and intrinsic limitations of the S-ARIMA-REG scheme, prevented us from designing a more complex model, e.g. able to test and discriminate among different triggering factors. With that said, we believe that the methodological approach chosen has one main advantage over the methods based on multiple data sources: we used one single variable (the cumulative positives cases) and one auxiliary information (the …
SciScore for 10.1101/2021.06.11.21258733: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Lack of reliable information and intrinsic limitations of the S-ARIMA-REG scheme, prevented us from designing a more complex model, e.g. able to test and discriminate among different triggering factors. With that said, we believe that the methodological approach chosen has one main advantage over the methods based on multiple data sources: we used one single variable (the cumulative positives cases) and one auxiliary information (the schools starting date). As a result of that, the amount of uncertainty surrounding the whole analysis has been drastically reduced. In addition, the theoretical framework chosen makes possible the extraction of useful information – including counterfactual scenarios, as mentioned in Section 3 – on the dynamical behavior of the diseases and an easier interpretation of the results.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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