Estimating the undetected infections in the Monkeypox outbreak.

While the number of detected Monkeypox infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of its spread. The aim of this study is to estimate the true number of Monkeypox (detected and undetected) infections in most affected countries. The question being asked is: How many cases have actually occurred? We propose a lower bound estimator for the true number of Monkeypox cases. The estimator is data-driven and can be easily computed from the cumulative distributions of weekly cases. We focused on the ratio of the total estimated cases to the observed cases on July 31, 2022: The proportion of undetected cases was relevant in all countries, with countries whose estimated true number of infections could be more than three times the observed one. We provided a practical contribution to the understanding of the current Monkeypox wave and reliable estimates on how many undetected cases are going around in several countries, where the epidemic spreads differently.

Short-term forecasts of Monkeypox cases in multiple countries: keep calm and don't panic.

We propose a parametric regression model for incidence indicators based on the use of the Richards' curve (a generalized logistic function) to analyse the current Monkeypox epidemic data for the most 10 affected countries worldwide. At present, results show that the outbreak is under control in most countries.

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.

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.

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.

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.

Multilevel Hidden Markov Models for Behavioral Data: A Hawk-and-Dove Experiment.

Motivated by the analysis of behavioral data taken from an economic experiment based on the Hawk-and-Dove game, this article describes a multilevel hidden Markov model, that includes covariates, autoregression, and endogenous initial conditions under a unified framework. The data at hand are affected by multiple sources of latent heterogeneity, due to multilevel unobserved factors that operate in conjunction with observed covariates at all the levels of the data hierarchy. We fit a multilevel logistic regression model for repeated measurements of player behaviors, nested within groups of interacting players. The model integrates discrete random effects at the group level and Markovian sequences of discrete random effects at the player level. Parameters are estimated by a computationally feasible expectation-maximization algorithm. We model the probability of playing the Hawk strategy, which implies fighting aggressively for controlling an asset, and test the role played by initial possession, property, and other player-specific characteristics in driving hawkish behaviors. The results from our study suggest that crucial factors in determining hawkish behavior are both the way possession is achieved - which depends on our treatment manipulation- and possession itself. Furthermore, a clear time-dependence is observed in the data at the player level as accounted for by the Markovian random effects.

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.

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.

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.

Cardiovascular Death Risk in Recovered Mid-Range Ejection Fraction Heart Failure: Insights From Cardiopulmonary Exercise Test.

BACKGROUND: Heart failure with midrange ejection fraction (HFmrEF) represents a heterogeneous category where phenotype, as well as prognostic assessment, remains debated. The present study explores a specific HFmrEF subset, namely those who recovered from a reduced EF (rec-HFmrEF) and, particularly, it focuses on the possible additive prognostic role of cardiopulmonary exercise testing. METHODS AND RESULTS: We analyzed data from 4535 patients with HFrEF and 1176 patients with rec-HFmrEF from the Metabolic Exercise combined with Cardiac and Kidney Indexes database. The end point was cardiovascular death at 5 years. The median follow-up was 1343 days (25th-75th range 627-2403 days). Cardiovascular death occurred in 552 HFrEF and 61 rec-HFmrEF patients. The multivariate analysis confirmed an independent role of the MECKI score's variables in HFrEF (C-index = 0.744) whereas, in the rec-HFmrEF group, only age and peak oxygen uptake (pVO2) remained associated to the end point (C-index = 0.745). A peak oxygen uptake of ≤55% of predicted and a ventilatory efficiency of ≥31 resulted as the most accurate cut-off values in the outcome prediction. CONCLUSIONS: Present data support the cardiopulmonary exercise test and, particularly, the peak oxygen uptake, as a useful tool in the rec-HFmrEF prognostic assessment. A peak VO2 of ≤55% predicted and ventilatory efficiency of ≥31 might help to identify a high-risk rec-HFmrEF subgroup.

Temporary Staffing and Patient Death in Acute Care Hospitals: A Retrospective Longitudinal Study.

PURPOSE: To explore the association between the levels of temporary nurse staffing and patient mortality. Achieving adequate nurse staffing levels plays a vital role in keeping patients safe from harm. The evidence around deploying temporary staffing to maintain safe staffing levels is mixed, with some studies reporting no adverse effects on patient mortality. DESIGN: A retrospective longitudinal observational study using routinely collected data on 138,133 patients admitted to a large hospital in the south of England. Data were collected between April 2012 and April 2015. METHODS: We used multilevel survival models to explore the association between in-hospital deaths and daily variation in registered nurse (RN) and nursing assistant (NA) temporary staffing, measured as hours per patient per day. Analyses controlled for unit and patient risk. FINDINGS: Use of temporary staffing was common, with only 24% (n = 7,529) of the 30,980 unit-days having no temporary RN staff and 13% (n = 3,951) having no temporary NAs. The hazard of death was increased by 12% for every day a patient experienced high levels (1.5 hr or more per day) of RN temporary staffing (adjusted hazard ratio [aHR] 1.12, 95% confidence interval [CI] 1.03-1.21). The hazard of death was increased on days when NA temporary staffing was more than 0.5 hr per patient (aHR 1.06; 95% CI 1.03-1.08). CONCLUSIONS: Days with more than 1.5 hr per patient of temporary RNs and days with more than 0.5 hr of temporary NAs were associated with increased hazard of death. CLINICAL RELEVANCE: Heavy reliance on temporary staff is associated with higher risk for patients dying. There is no evidence of harm associated with modest use of temporary RNs so that required staffing levels can be maintained.

Are long nursing shifts on hospital wards associated with sickness absence? A longitudinal retrospective observational study.

OBJECTIVE: To investigate whether working 12 hr shifts is associated with increased sickness absence among registered nurses and health care assistants. BACKGROUND: Previous studies reported negative impacts on nurses' 12 hr shifts; however, these studies used cross-sectional techniques and subjective nurse-reported data. METHODS: A retrospective longitudinal study using routinely collected data across 32 general inpatient wards at an acute hospital in England. We used generalized linear mixed models to explore the association between shift patterns and the subsequent occurrence of short (<7 days) or long-term (≥7 days) sickness absence. RESULTS: We analysed 601,282 shifts and 8,090 distinct episodes of sickness absence. When more than 75% of shifts worked in the past 7 days were 12 hr in length, the odds of both a short-term (adjusted odds ratio = 1.28; 95% confidence index: 1.18-1.39) and long-term sickness episode (adjusted odds ratio = 1.22; 95% confidence index: 1.08-1.37) were increased compared with working none. CONCLUSION: Working long shifts on hospital wards is associated with a higher risk of sickness absence for registered nurses and health care assistants. IMPLICATIONS FOR NURSING MANAGEMENT: The higher sickness absence rates associated with long shifts could result in additional costs or loss of productivity for hospitals. The routine implementation of long shifts should be avoided.

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data.

The association between nurse staffing and omissions in nursing care: A systematic review.

AIMS: To identify nursing care most frequently missed in acute adult inpatient wards and to determine evidence for the association of missed care with nurse staffing. BACKGROUND: Research has established associations between nurse staffing levels and adverse patient outcomes including in-hospital mortality. However, the causal nature of this relationship is uncertain and omissions of nursing care (referred as missed care, care left undone or rationed care) have been proposed as a factor which may provide a more direct indicator of nurse staffing adequacy. DESIGN: Systematic review. DATA SOURCES: We searched the Cochrane Library, CINAHL, Embase and Medline for quantitative studies of associations between staffing and missed care. We searched key journals, personal libraries and reference lists of articles. REVIEW METHODS: Two reviewers independently selected studies. Quality appraisal was based on the National Institute for Health and Care Excellence quality appraisal checklist for studies reporting correlations and associations. Data were abstracted on study design, missed care prevalence and measures of association. Synthesis was narrative. RESULTS: Eighteen studies gave subjective reports of missed care. Seventy-five per cent or more nurses reported omitting some care. Fourteen studies found low nurse staffing levels were significantly associated with higher reports of missed care. There was little evidence that adding support workers to the team reduced missed care. CONCLUSIONS: Low Registered Nurse staffing is associated with reports of missed nursing care in hospitals. Missed care is a promising indicator of nurse staffing adequacy. The extent to which the relationships observed represent actual failures, is yet to be investigated.

What impact does nursing care left undone have on patient outcomes? Review of the literature.

AIMS AND OBJECTIVES: Systematic review of the impact of missed nursing care on outcomes in adults, on acute hospital wards and in nursing homes. BACKGROUND: A considerable body of evidence supports the hypothesis that lower levels of registered nurses on duty increase the likelihood of patients dying on hospital wards, and the risk of many aspects of care being either delayed or left undone (missed). However, the direct consequence of missed care remains unclear. DESIGN: Systematic review. METHODS: We searched Medline (via Ovid), CINAHL (EBSCOhost) and Scopus for studies examining the association of missed nursing care and at least one patient outcome. Studies regarding registered nurses, healthcare assistants/support workers/nurses' aides were retained. Only adult settings were included. Because of the nature of the review, qualitative studies, editorials, letters and commentaries were excluded. PRISMA guidelines were followed in reporting the review. RESULTS: Fourteen studies reported associations between missed care and patient outcomes. Some studies were secondary analyses of a large parent study. Most of the studies used nurse or patient reports to capture outcomes, with some using administrative data. Four studies found significantly decreased patient satisfaction associated with missed care. Seven studies reported associations with one or more patient outcomes including medication errors, urinary tract infections, patient falls, pressure ulcers, critical incidents, quality of care and patient readmissions. Three studies investigated whether there was a link between missed care and mortality and from these results no clear associations emerged. CONCLUSIONS: The review shows the modest evidence base of studies exploring missed care and patient outcomes generated mostly from nurse and patient self-reported data. To support the assertion that nurse staffing levels and skill mix are associated with adverse outcomes as a result of missed care, more research that uses objective staffing and outcome measures is required. RELEVANCE TO CLINICAL PRACTICE: Although nurses may exercise judgements in rationing care in the face of pressure, there are nonetheless adverse consequences for patients (ranging from poor experience of care to increased risk of infection, readmissions and complications due to critical incidents from undetected physiological deterioration). Hospitals should pay attention to nurses' reports of missed care and consider routine monitoring as a quality and safety indicator.