On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.

Molecular In-Depth on the Epidemiological Expansion of SARS-CoV-2 XBB.1.5.

Since the beginning of the pandemic, the generation of new variants periodically recurs. The XBB.1.5 SARS-CoV-2 variant is one of the most recent. This research was aimed at verifying the potential hazard of this new subvariant. To achieve this objective, we performed a genome-based integrative approach, integrating results from genetic variability/phylodynamics with structural and immunoinformatic analyses to obtain as comprehensive a viewpoint as possible. The Bayesian Skyline Plot (BSP) shows that the viral population size reached the plateau phase on 24 November 2022, and the number of lineages peaked at the same time. The evolutionary rate is relatively low, amounting to 6.9 × 10-4 subs/sites/years. The NTD domain is identical for XBB.1 and XBB.1.5 whereas their RBDs only differ for the mutations at position 486, where the Phe (in the original Wuhan) is replaced by a Ser in XBB and XBB.1, and by a Pro in XBB.1.5. The variant XBB.1.5 seems to spread more slowly than sub-variants that have caused concerns in 2022. The multidisciplinary molecular in-depth analyses on XBB.1.5 performed here does not provide evidence for a particularly high risk of viral expansion. Results indicate that XBB.1.5 does not possess features to become a new, global, public health threat. As of now, in its current molecular make-up, XBB.1.5 does not represent the most dangerous variant.

Genome-based survey of the SARS-CoV-2 BF.7 variant from Asia.

The SARS-CoV-2 BF.7 variant represents one of the most recent subvariant under monitoring. At the beginning of the 2023 it caused several concerns especially in Asia because of a resurge in COVID-19 cases. Here we perform a genome-based integrative approach on SARS-CoV-2 BF.7 to shed light on this emerging lineage and produce some consideration on its real dangerousness. Both genetic and structural data suggest that this new variant currently does not show evidence of an high expansion capability. It is very common in Asia, but it appears less virulent than other Omicron variants as proved by its relatively low evolutionary rate (5.62 × 10-4 subs/sites/years). The last plateau has been reached around December 14, 2022 and then the genetic variability, and thus the viral population size, no longer increased. As already seen for several previous variants, the features that may be theoretically related to advantages are due to genetic drift that allows to the virus a constant adaptability to the host, but is not strictly connected to a fitness advantage. These results have further pointed that the genome-based monitoring must continue uninterruptedly to be prepared and well documented on the real situation.

Assessing COVID-19-Related Excess Mortality Using Multiple Approaches-Italy, 2020-2021.

INTRODUCTION: Excess mortality (EM) is a valid indicator of COVID-19's impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations. METHODS: We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons. RESULTS: We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE. DISCUSSION: Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

Postexposure-vaccine-prophylaxis against COVID-19.

During the COVID-19 pandemic, postexposure-vaccine-prophylaxis is not a practice. Following exposure, only patient isolation is imposed. Moreover, no therapeutic prevention approach is applied. We asked whether evidence exists for reduced mortality rate following postexposure-vaccine-prophylaxis. To estimate the effectiveness of postexposure-vaccine-prophylaxis, we obtained data from the Israeli Ministry of Health registry. The study population consisted of Israeli residents aged 12 years and older, identified for the first time as PCR-positive for SARS-CoV-2, between December 20th, 2020 (the beginning of the vaccination campaign) and October 7th, 2021. We compared "recently injected" patients-that proved PCR-positive on the same day or on 1 of the 5 consecutive days after first vaccination (representing an unintended postexposure-vaccine-prophylaxis)s-to unvaccinated control group. Among Israeli residents identified PCR-positive for SARS-CoV-2, 11 687 were found positive on the day they received their first vaccine injection (BNT162b2) or on 1 of the 5 days thereafter. In patients over 65 years, 143 deaths occurred among 1412 recently injected (10.13%) compared to 255 deaths among the 1412 unvaccinated (18.06%), odd ratio (OR) 0.51 (95% confidence interval [CI]: 0.41-0.64; p < 0.001). A significant reduction in the death toll was observed among the 55-64 age group, with 8 deaths occurring among the 1320 recently injected (0.61%) compared to 24 deaths among the 1320 unvaccinated control (1.82%), OR 0.33 (95% CI: 0.13-0.76; p = 0.007). Postexposure-vaccine-prophylaxis is effective against death in COVID-19 infection.

SARS-CoV-2 Variants in COVID-19 Disease: A Focus on Disease Severity and Vaccine Immunity in Patients Admitted to the Emergency Department.

Tracking SARS-CoV-2 variants along with vaccinations are fundamental for severe COVID-19 disease prevention. A study was performed that focused on 43 patients with the SARS-CoV-2 infection who were admitted to the Emergency Department. RT-PCR-positive nasopharyngeal samples were sequenced using the MiSeq II system for variant detection. The main reason for Emergency Department admission was COVID-19 (67%), followed by other causes (33%); 51% patients were unvaccinated or vaccinated with a single dose and 49% had completed the vaccination course with two or three doses. Among the vaccinated group, 38% were admitted for COVID-19, versus 94.5% of the unvaccinated group. After admission, 50% of the vaccinated group and 36% of the unvaccinated group were discharged and allowed to go home, and 80% of the unvaccinated had no major comorbidities; 63% needed hospital admission and 5% required a stay in the Intensive Care Unit. Of these, 37% were vaccinated with 3 doses, 11% with two doses, 4% with a single dose, and 48% were unvaccinated. The 70% of the vaccinated patients who were admitted to hospital presented major comorbidities versus 38% of the unvaccinated group. Two unvaccinated patients that needed intensive care had relevant comorbidities and died. Genome sequencing showed the circulation of three omicron and two pure sub-lineages of omicron, including 22 BA.1, 12 BA.1.1, and 7 BA.2. Data showed the SARS-CoV-2 national and international migration patterns and how vaccination was useful for severe COVID-19 disease prevention.

Genetic and Structural Data on the SARS-CoV-2 Omicron BQ.1 Variant Reveal Its Low Potential for Epidemiological Expansion.

The BQ.1 SARS-CoV-2 variant, also known as Cerberus, is one of the most recent Omicron descendant lineages. Compared to its direct progenitor BA.5, BQ.1 has some additional spike mutations in some key antigenic sites, which confer further immune escape ability over other circulating lineages. In such a context, here, we perform a genome-based survey aimed at obtaining a complete-as-possible nuance of this rapidly evolving Omicron subvariant. Genetic data suggest that BQ.1 represents an evolutionary blind background, lacking the rapid diversification that is typical of a dangerous lineage. Indeed, the evolutionary rate of BQ.1 is very similar to that of BA.5 (7.6 × 10-4 and 7 × 10-4 subs/site/year, respectively), which has been circulating for several months. The Bayesian Skyline Plot reconstruction indicates a low level of genetic variability, suggesting that the peak was reached around 3 September 2022. Concerning the affinity for ACE2, structure analyses (also performed by comparing the properties of BQ.1 and BA.5 RBD) indicate that the impact of the BQ.1 mutations may be modest. Likewise, immunoinformatic analyses showed moderate differences between the BQ.1 and BA5 potential B-cell epitopes. In conclusion, genetic and structural analyses on SARS-CoV-2 BQ.1 suggest no evidence of a particularly dangerous or high expansion capability. Genome-based monitoring must continue uninterrupted for a better understanding of its descendants and all other lineages.

Two years of COVID-19 pandemic: The Italian experience of Statgroup-19.

The amount and poor quality of available data and the need of appropriate modeling of the main epidemic indicators require specific skills. In this context, the statistician plays a key role in the process that leads to policy decisions, starting with monitoring changes and evaluating risks. The "what" and the "why" of these changes represent fundamental research questions to provide timely and effective tools to manage the evolution of the epidemic. Answers to such questions need appropriate statistical models and visualization tools. Here, we give an overview of the role played by Statgroup-19, an independent Italian research group born in March 2020. The group includes seven statisticians from different Italian universities, each with different backgrounds but with a shared interest in data analysis, statistical modeling, and biostatistics. Since the beginning of the COVID-19 pandemic the group has interacted with authorities and journalists to support policy decisions and inform the general public about the evolution of the epidemic. This collaboration led to several scientific papers and an accrued visibility across various media, all made possible by the continuous interaction across the group members that shared their unique expertise.

Genomic Epidemiology Unveil the Omicron Transmission Dynamics in Rome, Italy.

Since 2020, the COVID-19 pandemic represented an important worldwide burden. Well-structured surveillance by reliable and timely genomic data collection is crucial. In this study, a genomic monitoring analysis of all SARS-CoV-2 positive samples retrieved at the Fondazione Policlinico Universitario Campus Bio-Medico, in Rome, Italy, between December 2021 and June 2022, was performed. Two hundred and seventy-four SARS-CoV-2-positive samples were submitted to viral genomic sequencing by Illumina MiSeqII. Consensus sequences were generated by de novo assembling using the iVar tool and deposited on the GISAID database. Lineage assignment was performed using the Pangolin lineage classification. Sequences were aligned using ViralMSA and maximum-likelihood phylogenetic analysis was performed by IQ-TREE2. TreeTime tool was used to obtain dated trees. Our genomic monitoring revealed that starting from December 2021, all Omicron sub-lineages (BA.1, BA.2, BA.3, BA.4, and BA.5) were circulating, although BA.1 was still the one with the highest prevalence thought time in this early period. Phylogeny revealed that Omicron isolates were scattered throughout the trees, suggesting multiple independent viral introductions following national and international human mobility. This data represents a sort of thermometer of what happened from July 2021 to June 2022 in Italy. Genomic monitoring of the circulating variants should be encouraged considering that SARS-CoV-2 variants or sub-variants emerged stochastically and unexpectedly.

COVID-19-induced excess mortality in Italy during the Omicron wave.

Background: The existing literature estimates a significantly reduced odds of hospitalisation and death among individuals. However, though less severe than other variants, the Omicron variant may still lead to excess mortality compared to pre-pandemic years. Methods: A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, excess of mortality is estimated. Results: In Italy, 14 and 11 regions suffered from relevant excess mortality in January and February, respectively. However, the situation is far from being as critical as during previous waves. Conclusions: We can conclude that no matter which variant (or multiple inter-variant recombination) we are facing, excess mortality will appear in correspondence of any incidence peak.

Estimating COVID-19-induced excess mortality in Lombardy, Italy.

We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy. The first case in Italy was found in Lombardy in early 2020, and the first wave was mainly centered in Lombardy. The other three waves, in Autumn 2020, March 2021 and Summer 2021 are also characterized by a high number of cases in absolute terms. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15-64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes.

The unresolved question on COVID-19 virus origin: The three cards game?

The ongoing discussion about the real origin of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) feeds acrimonious debates. Where did SARS-CoV-2 come from? Was SARS-CoV-2 transmitted in the wild from an animal to a person before exploding in Wuhan or was it an engineered virus that escaped from research or a laboratory in Wuhan? Right now, we still don't know enough whether SARS-CoV-2 is human-made or not, and lab-leak theories remain essentially speculative. Many recent studies have pointed out several plausible scenarios. Anyhow, currently, even if suspicions by some about the possibility of lab-leak hypothesis still remain, the consensus view is that the pandemic probably started from a natural source and, to determine the real origin of the SARS-CoV-2 virus, further research is needed.

Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions.

We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of both the first and the second wave of COVID-19 in Italy, i.e. from February 2020 to July 2020 and from July 2020 to December 2020, respectively. We analyse data at the regional level and, interestingly enough, prove that substantial spatial and temporal dependence occurred in both waves, although strong restrictive measures were implemented during the first wave. Accurate predictions are obtained, improving those of the model where independence across regions is assumed.