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.

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.

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.

A Systematic Review and a Meta-Analysis Comparing Prophylactic and Therapeutic Low Molecular Weight Heparins for Mortality Reduction in 32,688 COVID-19 Patients.

Background: Antithrombotic treatment, including low molecular weight heparin (LMWH) or unfractionated heparin (UFH), has been proposed as a potential therapy for coronavirus disease 2019 (COVID-19) to lower diffuse intravascular clotting activation. However, it is unclear whether prophylactic or therapeutic doses have similar efficacy in reducing mortality. Methods: We performed a systematic review (PROSPERO registration CRD42020179955) and meta-analysis including observational cohort studies and randomized controlled trials (RCT) evaluating the effectiveness of heparins (either LMWH, UFH, or fondaparinux) in COVID-19 patients. Heparin treatment was compared to no anticoagulation. A subgroup analysis on prophylactic or therapeutic doses compared to no anticoagulation was performed. Prophylactic dose was also compared to full dose anticoagulation. Primary endpoint was all-cause mortality. Secondary endpoints were major bleeding and length of hospital stay (LOS). Results: 33 studies (31 observational, 2 RCT) were included for a total overall population of 32,688 patients. Of these, 21,723 (66.5%) were on heparins. 31 studies reported data on all-cause mortality, showing that both prophylactic and full dose reduced mortality (pooled Hazard Ratio [HR] 0.63, 95% confidence interval [CI] 0.57-0.69 and HR 0.56, 95% CI 0.47-0.66, respectively). However, the full dose was associated with a higher risk of major bleeding (Odds Ratio [OR] 2.01, 95% CI 1.14-3.53) compared to prophylactic dose. Finally, LOS was evaluated in 3 studies; no difference was observed between patients with and without heparins (0.98, -3.87, 5.83 days). Conclusion: Heparin at both full and prophylactic dose is effective in reducing mortality in hospitalized COVID-19 patients, compared to no treatment. However, full dose was associated with an increased risk of bleeding. Systematic Review Registration: https://clinicaltrials.gov/, identifier CRD42020179955.

JAK inhibition reduces SARS-CoV-2 liver infectivity and modulates inflammatory responses to reduce morbidity and mortality.

Using AI, we identified baricitinib as having antiviral and anticytokine efficacy. We now show a 71% (95% CI 0.15 to 0.58) mortality benefit in 83 patients with moderate-severe SARS-CoV-2 pneumonia with few drug-induced adverse events, including a large elderly cohort (median age, 81 years). An additional 48 cases with mild-moderate pneumonia recovered uneventfully. Using organotypic 3D cultures of primary human liver cells, we demonstrate that interferon-α2 increases ACE2 expression and SARS-CoV-2 infectivity in parenchymal cells by greater than fivefold. RNA-seq reveals gene response signatures associated with platelet activation, fully inhibited by baricitinib. Using viral load quantifications and superresolution microscopy, we found that baricitinib exerts activity rapidly through the inhibition of host proteins (numb-associated kinases), uniquely among antivirals. This reveals mechanistic actions of a Janus kinase-1/2 inhibitor targeting viral entry, replication, and the cytokine storm and is associated with beneficial outcomes including in severely ill elderly patients, data that incentivize further randomized controlled trials.

Nowcasting COVID-19 incidence indicators during the Italian first outbreak.

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.

A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence.

We propose a model based on discrete latent variables, which are spatially associated and time specific, for the analysis of incident cases of SARS-CoV-2 infections. We assume that for each area the sequence of latent variables across time follows a Markov chain with initial and transition probabilities that also depend on latent variables in neighboring areas. The model is estimated by a Markov chain Monte Carlo algorithm based on a data augmentation scheme, in which the latent states are drawn together with the model parameters for each area and time. As an illustration we analyze incident cases of SARS-CoV-2 collected in Italy at regional level for the period from February 24, 2020, to January 17, 2021, corresponding to 48 weeks, where we use number of swabs as an offset. Our model identifies a common trend and, for every week, assigns each region to one among five distinct risk groups.

Role of Low-Molecular-Weight Heparin in Hospitalized Patients With Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia: A Prospective Observational Study.

BACKGROUND: This study was conducted to evaluate the impact of low-molecular-weight heparin (LMWH) on the outcome of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. METHODS: This is a prospective observational study including consecutive patients with laboratory-confirmed SARS-CoV-2 pneumonia admitted to the University Hospital of Pisa (March 4-April 30, 2020). Demographic, clinical, and outcome data were collected. The primary endpoint was 30-day mortality. The secondary endpoint was a composite of death or severe acute respiratory distress syndrome (ARDS). Low-molecular-weight heparin, hydroxychloroquine, doxycycline, macrolides, antiretrovirals, remdesivir, baricitinib, tocilizumab, and steroids were evaluated as treatment exposures of interest. First, a Cox regression analysis, in which treatments were introduced as time-dependent variables, was performed to evaluate the association of exposures and outcomes. Then, a time-dependent propensity score (PS) was calculated and a PS matching was performed for each treatment variable. RESULTS: Among 315 patients with SARS-CoV-2 pneumonia, 70 (22.2%) died during hospital stay. The composite endpoint was achieved by 114 (36.2%) patients. Overall, 244 (77.5%) patients received LMWH, 238 (75.5%) received hydroxychloroquine, 201 (63.8%) received proteases inhibitors, 150 (47.6%) received doxycycline, 141 (44.8%) received steroids, 42 (13.3%) received macrolides, 40 (12.7%) received baricitinib, 13 (4.1%) received tocilizumab, and 13 (4.1%) received remdesivir. At multivariate analysis, LMWH was associated with a reduced risk of 30-day mortality (hazard ratio [HR], 0.36; 95% confidence interval [CI], 0.21-0.6; P < .001) and composite endpoint (HR, 0.61; 95% CI, 0.39-0.95; P = .029). The PS-matched cohort of 55 couples confirmed the same results for both primary and secondary endpoint. CONCLUSIONS: This study suggests that LMWH might reduce the risk of in-hospital mortality and severe ARDS in coronavirus disease 2019. Randomized controlled trials are warranted to confirm these preliminary findings.

An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian regions.

The availability of intensive care beds during the COVID-19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short-term prediction of COVID-19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area-specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave-last-out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.

Features of severe COVID-19: A systematic review and meta-analysis.

BACKGROUND: To systematically review clinical and biochemical characteristics associated with the severity of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related disease (COVID-19). MATERIALS AND METHODS: Systematic review of observational studies from PubMed, ISI Web of Science, SCOPUS and Cochrane databases including people affected by COVID-19 and reporting data according to the severity of the disease. Data were combined with odds ratio (OR) and metanalysed. Severe COVID-19 was defined by acute respiratory distress syndrome, intensive care unit admission and death. RESULTS: We included 12 studies with 2794 patients, of whom 596 (21.33%) had severe disease. A slightly higher age was found in severe vs non-severe disease. We found that prevalent cerebrovascular disease (odds ratio [OR] 3.66, 95% confidence interval [CI] 1.73-7.72), chronic obstructive pulmonary disease (OR: 2.39, 95% CI 1.10-5.19), prevalent cardiovascular disease (OR: 2.84, 95% CI 1.59-5.10), diabetes (OR: 2.78, 95% CI 2.09-3.72), hypertension (OR: 2.24, 95% CI 1.63-3.08), smoking (OR: 1.54, 95% CI 1.07-2.22) and male sex (OR: 1.22, 95% CI 1.01-1.49) were associated with severe disease. Furthermore, increased procalcitonin (OR: 8.21, 95% CI 4.48-15.07), increased D-Dimer (OR: 5.67, 95% CI 1.45-22.16) and thrombocytopenia (OR: 3.61, 95% CI 2.62-4.97) predicted severe infection. CONCLUSION: Characteristics associated with the severity of SARS-CoV-2 infection may allow an early identification and management of patients with poor outcomes.