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We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand., (© 2024. The Author(s).)

Objectives: to describe studies that evaluated the screening programmes implemented in the school during the COVID-19 pandemic., Design: a systematic literature review was conducted according to the PRISMA 2020 Guidelines. Studies published until December 2021 were included. The methodological quality of the studies was assessed with validated scales. Study selection, data extraction, and quality assessment were carried out by two authors independently., Setting and Participants: teachers and students belonging to schools of all levels, including universities., Main Outcomes Measures: a. transmission-related outcomes (such as the number or proportion of cases, cumulative frequency, incidence); b. feasibility/acceptability of the screening strategies; c. socioeconomic outcomes (such as testing cost, number of days spent in school, quarantine)., Results: after having removed duplicate articles, 2,822 records were retrieved. Thirty-six studies were included (15 used an observational design and 21 modelling study). Regarding the former, the methodological quality has been rated as high in 2 studies, intermediate in 6 and low in 2; in the remaining ones, it was not evaluated because only descriptive. Screenings were quite different in terms of school study population, types of tests used, methods of submission and analysis, and level of incidence in the community at the time of implementation. Outcome indicators were also varied, a heterogeneity that, on the one hand, did not allow for meta-analysis of results and, on the other, allowed for testing the performance of the screenings in very different settings. All of the field studies claim that the screenings reduced SARS-CoV-2 exposure and infection among children, adolescents, and college students, curbing at-school transmission and helping to reduce the number of closing school days. Studies that evaluated the cost of the intervention emphasized its cost-effectiveness, while those that focused on the acceptability of the instrument showed a preference among children, adolescents, and parents for minimally invasive, self-administered tests with high sensitivity and lower frequency of repetition. Simulation-based studies are mostly based on compartmental and agent-based models. Their quality is quite high methodologically, although uncertainty quantification and external validation, aimed at verifying the model ability to reproduce observed data, are lacking in many cases. The contexts to which the simulations refer are all school-based, although 7 studies consider residential situations, which are poorly suited to the Italian context. All simulation-based models indicate the importance of planning repeated testing on asymptomatic individuals to limit contagion. However, the costs of these procedures can be high unless assessments are spaced out or pool testing procedures are used. Obtaining high student adherence to the screening programme is extremely important to maximize results., Conclusions: school-based screenings, especially when combined with other preventive measures, have been important public health tools to contain infections during COVID-19 waves and to ensure children's and adolescents' right to education and to prevent the fallout in physical and mental health (with strong equity consequences) associated with school closures.

The emergence of hyper-transmissible SARS-CoV-2 variants that rapidly became prevalent throughout the world in 2022 made it clear that extensive vaccination campaigns cannot represent the sole measure to stop COVID-19. However, the effectiveness of control and mitigation strategies, such as the closure of non-essential businesses and services, is debated. To assess the individual behaviours mostly associated with SARS-CoV-2 infection, a questionnaire-based case-control study was carried out in Tuscany, Central Italy, from May to October 2021. At the testing sites, individuals were invited to answer an online questionnaire after being notified regarding the test result. The questionnaire collected information about test result, general characteristics of the respondents, and behaviours and places attended in the week prior to the test/symptoms onset. We analysed 440 questionnaires. Behavioural differences between positive and negative subjects were assessed through logistic regression models, adjusting for a fixed set of confounders. A ridge regression model was also specified. Attending nightclubs, open-air bars or restaurants and crowded clubs, outdoor sporting events, crowded public transportation, and working in healthcare were associated with an increased infection risk. A negative association with infection, besides face mask use, was observed for attending open-air shows and sporting events in indoor spaces, visiting and hosting friends, attending courses in indoor spaces, performing sport activities (both indoor and outdoor), attending private parties, religious ceremonies, libraries, and indoor restaurants. These results might suggest that during the study period people maintained a particularly responsible and prudent approach when engaging in everyday activities to avoid spreading the virus.

During autumn 2020, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number, R 0 ( t ), and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID-19-related deaths, fixing at values from literature Infection Fatality Rate (IFR) and average infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R 0 ( t ) curves were similar in the Northern and Central regions, while a less peaked curve was estimated for the South. The maximum R 0 ( t ) ranged from 2.15 (South) to 2.61 (North) with an increase following school reopening and a decline at the end of October. The predictive performance of the regional models, assessed through cross validation, was good, with a Mean Absolute Percentage Error of 7.2% and 10.9% when considering prediction horizons of 7 and 14 days, respectively. Average temperature, urbanization, characteristics of family medicine and healthcare system, economic dynamism, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R 0 ( t ) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results, but without compromising between-region comparisons., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cereda, Viscardi and Baccini.)

With the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The confidence intervals are estimated by a parametric bootstrap procedure and a Global Sensitivity Analysis is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions., Competing Interests: The authors have declared that no competing interests exist.

Objectives: about two months after the end of the lockdown imposed for the containment of the SARS-CoV-2 epidemic, the contagion dynamics in the Tuscany Region (Central Italy) have been assessed from the beginning of the emergency to the end of June through a compartmental model, and future medium-long term projections have been produced., Data and Methods: this study used a SIRD model in which the infection reproduction number R0 varied over time, according to a piecewise constant function. The fatality parameter and the time from contagion to infection resolution (death or recovery) were fixed to ensure parameter identifiability, and the model was calibrated on the Covid-19 deaths notified from March 9th to June 30th 2020. The uncertainty around the estimates was quantified through parametric bootstrap. Finally, the resulting model was used to produce medium-long term projections of the epidemic dynamics., Results: the date of the first infection in Tuscany was estimated as February 21st 2020. The value of R0(t) ranged from 7.78 (95%CI 7.55-7.89), at the beginning of the outbreak, to a value very close to 0 between April 27th and May 17th. Finally, R0(t) rose, reaching an average of 0.66 (0.32, 0.88) between May 18th and June 30th. At the epidemic peak, estimated at the beginning of April, the notified infected people circulating in the region were just 22% of those predicted by the model. According to the estimated SIRD, under the hypothetical scenario that R0(t) slightly exceeds 1 from the beginning of October 2020, a new wave of contagion could arise by next spring., Conclusions: the estimated trend of R0(t) is suggestive of a strong effect of the lockdown in Tuscany and of a mild increase of the contagion potentially attributable to the easing of the containment measures. Medium-long term projections unequivocally indicate that the danger of a new epidemic wave has not been averted.