Reopening Italy’s Schools in September 2020: A Bayesian Estimation of the Change in the Growth Rate of New SARS-CoV-2 Cases
Authors: Casini Luca , Roccetti Marco 
Published: April 2021

Objectives

CoViD-19’s second wave started a debate on the potential role of schools as a primary factor in the contagion resurgence. Two opposite positions appeared: those convinced that schools played a major role in spreading SARS-CoV-2 infections and those who were not. We studied the growth rate of the total number of SARS-CoV-2 infections in all the Italian regions, before and after the school reopening (September - October 2020), investigating the hypothesis of an association between schools and the resurgence of the virus in Italy.

Methods

Using Bayesian piecewise linear regression to scrutinize the number of daily SARS-CoV-2 infections in each Italian, we looked for an estimate of a changepoint in the growth rate of those confirmed cases. We compared the changepoints with the school opening dates, for each Italian region. The regression allows to discuss the change in steepness of the infection curve, before and after the changepoint.

Results

In 15 out of 21 Italian regions (71%), an estimated change in the rate of growth of the total number of daily SARS-CoV-2 infection cases occurred after an average of 16.66 days (CI 95% 14.47 to 18.73) since the school reopening. The number of days required for the SARS-CoV-2 daily cases to double went from an average of 47.50 days (CI 95% 37.18 to 57.61) before the changepoint to an average of 7.72 days (CI 95% 7.00 to 8.48) after it.

Conclusion

Studying the rate of growth of daily SARS-CoV-2 cases in all the Italian regions provides some evidence in favor of a link between school reopening and the resurgence of the virus in Italy. The number of factors that could have played a role are too many to give a definitive answer. Still, the temporal correspondence warrants for a controlled experiment to clarify how much reopening schools mattered.

Design

Not Applicable

Setting

Not Applicable

Participants

Not Applicable

Interventions

Not Applicable

Primary and Secondary Outcome Measures

Not Applicable

Article Summary

Strengths and Limitations of this Study

The use of a Bayesian linear regression model represents a reliable method to account for the uncertainty in the estimation of the changes of the growth rate of the number of daily SARS-CoV-2 infections.

Analyzing the variation of the total number of new daily SARS-CoV-2 confirmed cases per each Italian region, in coincidence with school reopening, has avoided the problems of looking for specific data collected in schools.

The problem has been avoided of many infections missed inside schools as positive children and adolescents tend to display less symptoms, therefore leading to a lower probability to be detected with a passive surveillance methodology.

Data, made available by the Italian Government, used to count the number of daily SARS-CoV-2 infections, amount to aggregated measures, uploaded daily: in some cases, those measures changed meaning over time and/or contained errors that were never corrected.

Many confounding factors, besides schools, may have played a role, nonetheless in September 2020 in Italy those factors were still fewer than in the following months, when several various containment measures were put in place.

Author Contribution

LC and MR equally contributed to conceive, design, write, manage, and revise the manuscript.

Funding

The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests

None declared.

Patient consent for publication

Not required.

Provenance and peer review

Not commissioned, externally peer reviewed.

Data availability statement

Data are available in a public, open access repository (https://github.com/pcm-dpc/COVID-19). All data and code are available upon request to the corresponding author email: luca.casini7@unibo.it

Research Ethics Approval: Human Participants

Not applicable: neither humans nor animals nor personal data are being involved in this study.