Opening schools and trends in SARS-CoV-2 transmission in European countries

BackgroundIt is important to understand the role of schools in the community transmission of SARS-CoV-2, bearing in mind that children and adolescents can spread the infection within families, even when their own symptoms are mild. The aim of this study was to examine the trends of contagion before and after schools reopened across 27 countries in the European Union. MethodsAll data on the number of people testing positive for COVID-19 in each European country were collected from 20 days before schools reopened to 45 days afterwards. The Joinpoint regression method was used to detect single change points on the trend of contagion. The Bayesian Information Criterion (BIC) was used for model selection purposes. ResultsWe calculated 27 linear regression models for the daily case numbers of SARS-CoV-2 infection in the 27 countries from 20 days before schools reopened to 45 days afterwards. A significant increase in the number of daily infections was seen for 21 countries after a change point in the linear regression lines. The change points in different countries varied, ranging from 10 to 42 days after schools reopened, with the majority occurring beyond the 21st day. ConclusionThis study analysed the trend of SARS-CoV-2 transmission before and after schools reopened in Europe. We observed a significant increase in the number of new daily cases in most countries. This issue poses a public health problem that needs to be taken into account in deciding strategies to contain the spread of COVID-19.

Title: Health and Healthcare Variables Associated with Italy's Excess Mortality during the First Wave of the COVID-19 pandemic: An Ecological Study.

BackgroundHealthcare factors have strongly influenced the propagation of COVID-19. The present study aims to examine whether excess mortality during the first phase of the COVID-19 outbreak in Italy was associated with health, healthcare, demographic, and socioeconomic indicators measured at a provincial level. MethodsThe present ecological study concerns the raw number of deaths from Jan. 1 to Apr. 30, 2020 and the mean number of deaths in the same months of 2015 to 2019, per province. Information on socioeconomic factors and healthcare settings were extracted from the most recently updated databases on the ISTAT website. Two multilevel, multivariate models were constructed to test whether excess mortality was associated with the indicators across 107 provinces in Italy. ResultsOn linear multilevel, multivariate analysis, AIDS mortality rate (p-value <0.05) correlates positively with excess mortality, while a higher density of General Practitioners (number of GPs per 1,000 population) is associated with lower excess mortality (p-value <0.05). After controlling for the diffusion of COVID-19 in each province, the significance of GP density increases (p-value <0.001) and the rate of hospitalization in long-term care wards is positively associated (p-value <0.05) with excess mortality. ConclusionSome health and healthcare variables are strongly associated with excess mortality caused by COVID-19 in Italy and should be considered to implement mitigation policies and increase healthcare resilience.

Demographic and Socio-Economic Factors, and Healthcare Resource Indicators Associated with the Rapid Spread of COVID-19 in Northern Italy: An Ecological Study

BackgroundCOVID-19 rapidly escalated into a pandemic, threatening 213 countries, areas, and territories the world over. We aimed to identify potential province-level socioeconomic determinants of the viruss dissemination, and explain between-province differences in the speed of its spread, based on data from 36 provinces of Northern Italy. MethodsThis is an ecological study. We included all confirmed cases of SARS-CoV-2 reported between February 24th and March 30th, 2020. For each province, we calculated the trend of contagion as the relative increase in the number of individuals infected between two time endpoints, assuming an exponential growth. Pearsons test was used to correlate the trend of contagion with a set of healthcare-associated, economic, and demographic parameters by province. The viruss spread was input as a dependent variable in a stepwise OLS regression model to test the association between rate of spread and province-level indicators. FindingsMultivariate analysis showed that the spread of COVID-19 was correlated negatively with aging index (p-value=0.003), and positively with public transportation per capita (p-value=0.012), the % of private long-term care hospital beds and, to a lesser extent (p-value=0.070), the % of private acute care hospital beds (p-value=0.006). InterpretationDemographic and socioeconomic factors, and healthcare organization variables were found associated with a significant difference in the rate of COVID-19 spread in 36 provinces of Northern Italy. An aging population seemed to naturally contain social contacts. The availability of healthcare resources and their coordination could play an important part in spreading infection.

Suppression of COVID-19 outbreak in the municipality of Vo, Italy

On the 21st of February 2020 a resident of the municipality of Vo, a small town near Padua, died of pneumonia due to SARS-CoV-2 infection1. This was the first COVID-19 death detected in Italy since the emergence of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province2. In response, the regional authorities imposed the lockdown of the whole municipality for 14 days3. We collected information on the demography, clinical presentation, hospitalization, contact network and presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo at two consecutive time points. On the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI) 2.1-3.3%). On the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI 0.8-1.8%). Notably, 43.2% (95% CI 32.2-54.7%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic. The mean serial interval was 6.9 days (95% CI 2.6-13.4). We found no statistically significant difference in the viral load (as measured by genome equivalents inferred from cycle threshold data) of symptomatic versus asymptomatic infections (p-values 0.6 and 0.2 for E and RdRp genes, respectively, Exact Wilcoxon-Mann-Whitney test). Contact tracing of the newly infected cases and transmission chain reconstruction revealed that most new infections in the second survey were infected in the community before the lockdown or from asymptomatic infections living in the same household. This study sheds new light on the frequency of asymptomatic SARS-CoV-2 infection and their infectivity (as measured by the viral load) and provides new insights into its transmission dynamics, the duration of viral load detectability and the efficacy of the implemented control measures.