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The time varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks, but delays between infection and reporting hinder its accurate estimation in real time. We propose a nowcasting method for improving the timeliness and accuracy of R estimates, based on comparisons of successive versions of surveillance databases. The method was validated against COVID-19 surveillance data collected in Italy over an 18-month period. Compared to traditional methods, the nowcasted reproduction number reduced the estimation delay from 13 to 8 days, while maintaining a better accuracy. Moreover, it allowed anticipating the detection of periods of epidemic growth by between 6 and 23 days. The method offers a simple and generally applicable tool to improve situational awareness during an epidemic outbreak, allowing for informed public health response planning.

We analyzed the first 255 PCR-confirmed cases of monkeypox occurred in Italy in 2022. Preliminary estimates indicate: mean incubation period of 9.1 days (95%CI of the mean: 6.5-10.9); mean generation time of 12.5 days (95%CI of the mean: 7.5-17.3); reproduction number in the MSM community of 2.43 (95%CI: 1.82-3.26)., Comment: 3 pages, 1 figure, 1 table, supplementary material

Background School closures and distance learning have been extensively applied to control SARS-CoV-2 transmission. Despite evidence of viral circulation in schools, the contribution of students and of in-person schooling to the transmission remains poorly quantified. Methods We analyze 976 exposure events, involving 460 positive individuals, as identified in early 2021 by routine surveillance and through an extensive screening conducted on students, school personnel, and their household members during an outbreak in a small municipality of Italy. Results From the analysis of potential transmission chains, we estimated that, on average, 55.1%, 17.3% and 27.6% infection episodes were linked to household, school, and community contacts, respectively. Clusters originated from students or school personnel showed a larger average cluster size (3.32 vs 1.15), a larger average number of generations in the transmission chain (1.56 vs 1.17) and a larger set of associated close contacts (11.3 vs 3.15, on average). We found substantial transmission heterogeneities, with 20% positive individuals seeding 75-80 of all transmission. A higher proportion of infected individuals causing onward transmission was found among students (48.8% vs 29.9%, on average), who also caused a markedly higher number of secondary cases (mean: 1.3 vs 0.5). Conclusions Uncontrolled transmission at school could disrupt the regular conduct of teaching activities, likely seeding the transmission into other settings, and increasing the burden on contact-tracing operations.

Background. During 2021, the COVID-19 pandemic was characterized by the emergence of lineages with increased fitness. For most of these variants, quantitative information is scarce on epidemiological quantities such as the incubation period and generation time, which are critical for both public health decisions and scientific research. Method. We analyzed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period using information on negative PCR tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time (the time between the infection dates of an infector and its secondary cases under a fully susceptible population) using a Bayesian inference approach applied to 4,435 SARS-CoV-2 cases clustered in 1,430 households where at least one secondary case was recorded. Results. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4-5.4; 95 percentile of the mean distribution: 1-12) for Alpha and 4.5 days (95%CrI 4.0-5.0; 95 percentile: 1-10) for Delta. The intrinsic generation time was estimated to have a mean of 6.0 days (95% CrI 5.6-6.4; 95 percentile: 1-15) for Alpha and of 6.6 days (95%CrI 6.0-7.3; 95 percentile: 1-18) for Delta. The household serial interval was 2.6 days (95%CrI 2.4-2.7) for Alpha and 2.4 days (95%CrI 2.2-2.6) for Delta, and the estimated proportion of pre-symptomatic transmission was 54-55% for both variants. Conclusions. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages., Comment: 11 pages, 3 figures, 2 tables, supplementary material

It is estimated that around 80\% of the world's population live in areas susceptible to at-least one major vector borne disease, and approximately 20% of global communicable diseases are spread by mosquitoes. Furthermore, the outbreaks of such diseases are becoming more common and widespread, with much of this driven in recent years by socio-demographic and climatic factors. These trends are causing significant worry to global health organisations, including the CDC and WHO, and-so an important question is the role that technology can play in addressing them. In this work we describe the integration of an epidemiology model, which simulates the spread of mosquito-borne diseases, with the VESTEC urgent computing ecosystem. The intention of this work is to empower human health professionals to exploit this model and more easily explore the progression of mosquito-borne diseases. Traditionally in the domain of the few research scientists, by leveraging state of the art visualisation and analytics techniques, all supported by running the computational workloads on HPC machines in a seamless fashion, we demonstrate the significant advantages that such an integration can provide. Furthermore we demonstrate the benefits of using an ecosystem such as VESTEC, which provides a framework for urgent computing, in supporting the easy adoption of these technologies by the epidemiologists and disaster response professionals more widely., Comment: Preprint of paper in 2021 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)

Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1,965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2-43.6%) likelihood of developing respiratory symptoms or fever >= 37.5 {\deg}C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3-25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4-2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4-30.4%), 8.8% (95%CI: 7.3-10.5%) and 0.4% (95%CI: 0.1-0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0-0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9-19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1-2%) and 19.2% (95% CI: 10.9-30.1%), respectively. The median length of stay in hospital was 10 (IQR 3-21) days; the length of stay in ICU was 11 (IQR 6-19) days. The obtained estimates could be instrumental to refine mathematical modeling work supporting public health decisions., Comment: 20 pages, 4 figures

We analyzed 5,484 close contacts of COVID-19 cases from Italy, all of them tested for SARS-CoV-2 infection. We found an infection fatality ratio of 2.2% (95%CI 1.69-2.81%) and identified male sex, age >70 years, cardiovascular comorbidities, and infection early in the epidemics as risk factors for death.

We quantified the probability of developing symptoms (respiratory or fever \geq 37.5 {\deg}C) and critical disease (requiring intensive care or resulting in death) of SARS-CoV-2 positive subjects. 5,484 contacts of SARS-CoV-2 index cases detected in Lombardy, Italy were analyzed, and positive subjects were ascertained via nasal swabs and serological assays. 73.9% of all infected individuals aged less than 60 years did not develop symptoms (95% confidence interval: 71.8-75.9%). The risk of symptoms increased with age. 6.6% of infected subjects older than 60 years had critical disease, with males at significantly higher risk., Comment: sample increased: results updated with new records coming from the ongoing serological surveys

In 2020, countries affected by the COVID-19 pandemic implemented various non-pharmaceutical interventions to contrast the spread of the virus and its impact on their healthcare systems and economies. Using Italian data at different geographic scales, we investigate the relationship between human mobility, which subsumes many facets of the population's response to the changing situation, and the spread of COVID-19. Leveraging mobile phone data from February through September 2020, we find a striking relationship between the decrease in mobility flows and the net reproduction number. We find that the time needed to switch off mobility and bring the net reproduction number below the critical threshold of 1 is about one week. Moreover, we observe a strong relationship between the number of days spent above such threshold before the lockdown-induced drop in mobility flows and the total number of infections per 100k inhabitants. Estimating the statistical effect of mobility flows on the net reproduction number over time, we document a 2-week lag positive association, strong in March and April, and weaker but still significant in June. Our study demonstrates the value of big mobility data to monitor the epidemic and inform control interventions during its unfolding.

On March 10, 2020, Italy imposed a national lockdown to curtail the spread of COVID-19. Here we estimate that, fourteen days after the implementation of the strategy, the net reproduction number has dropped below the epidemic threshold - estimated range 0.4-0.7. Our findings provide a timeline of the effectiveness of the implemented lockdown, which is relevant for a large number of countries that followed Italy in enforcing similar measures., Comment: 6 pages, 3 figures; submitted