The COVID-19 Infection in Italy: A Statistical Study of an Abnormally Severe Disease

Acute stress symptoms in general population during the first wave of COVID lockdown in Italy: Results from the COMET trial

BACKGROUND

The coronavirus disease of 2019 (COVID-19) pandemic is an unprecedented traumatic event that has severely impacted social, economic, and health well-being worldwide. The COvid Mental hEalth Trial was specifically designed to evaluate the impact of the COVID-19 pandemic and its containment measures on the mental health of the Italian general population in terms of COVID-19-related acute stress disorder (ASD) symptoms.

METHODS

The present cross-sectional study is based on an online survey carried out in the period March-May 2020. Italian general adult population was invited to compile an anonymous survey, which included the severity of acute stress symptoms scale/National Stressful Events Survey Short Scale to investigate the occurrence and severity of ASD symptoms.

RESULTS

The final sample consisted of 20,720 participants. During the lockdown, subjects with pre-existing mental health problems reported a statistically significant higher risk of acute post-traumatic symptoms compared to the general population (B: 2.57; 95% CI:2.04-3.09; p < .0001) and health care professionals (B: .37; 95% CI: .02-0.72; p < .05). According to multivariate regression models, the levels of acute post-traumatic symptoms (p < .0001) were higher in younger and female respondents. Social isolation and sleep disorder/insomnia represented positive predictors of acute stress (B = 3.32, 95% CI = 3.08-3.57).

CONCLUSIONS

Concerns about the risk of infection as well as social isolation caused a higher incidence of acute post-traumatic stress symptoms that may predict the subsequent development of post-traumatic stress disorder symptoms in the long term.

The first 2 years of COVID-19 in Italy: Incidence, lethality, and health policies

Background

The novel coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was first recognized in China in December 2019. This paper aims to provide a detailed overview of the first 2 years of the pandemic in Italy.

Design and methods

Using the negative binomial distribution, the daily incidence of infections was estimated through the virus's lethality and the moving-averaged deaths. The lethality of the original strain (estimated through national sero-surveys) was adjusted daily for age of infections, hazard ratios of virus variants, and the cumulative distribution of vaccinated individuals.

Results

From February 24, 2020, to February 28, 2022, there were 20,833,018 (20,728,924-20,937,375) cases distributed over five waves. The overall lethality rate was 0.73%, but daily it ranged from 2.78% (in the first wave) to 0.15% (in the last wave). The first two waves had the highest number of daily deaths (about 710) and the last wave showed the highest peak of daily infections (220,487). Restriction measures of population mobility strongly slowed the viral spread. During the 2nd year of the pandemic, vaccines prevented 10,000,000 infections and 115,000 deaths.

Conclusion

Almost 40% of COVID-19 infections have gone undetected and they were mostly concentrated in the first year of the pandemic. From the second year, a massive test campaign made it possible to detect more asymptomatic cases, especially among the youngest. Mobility restriction measures were an effective suppression strategy while distance learning and smart working were effective mitigation strategies. Despite the variants of concern, vaccines strongly reduced the pandemic impact on the healthcare system avoiding strong restriction measures.

Exposure-lag response of air temperature on COVID-19 incidence in twelve Italian cities: A meta-analysis

The exposure-lag response of air temperature on daily COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. Utilising nearly two years' worth of data, we fit statistical models to twelve Italian cities to quantify the delayed effect of air temperature on daily COVID-19 incidence, accounting for several categories of potential confounders (meteorological, air quality and non-pharmaceutical interventions). Coefficients and covariance matrices for the temperature term were then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response with effects presented as the relative risk (RR) and cumulative RR (RR_cum). The cumulative exposure response curve was non-linear, with peak risk at 15.1 °C and declining risk at progressively lower and higher temperatures. The lowest RR_cum at 0.2 °C is 0.72 [0.56,0.91] times that of the highest risk. Due to this non-linearity, the shape of the lag response curve necessarily varied by temperature. This work suggests that on a given day, air temperature approximately 15 °C maximises the incidence of COVID-19, with the effects distributed in the subsequent ten days or more.

Exposure-lag response of air temperature on COVID-19 incidence in twelve Italian cities: A meta-analysis

The exposure-lag response of air temperature on daily COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. Utilising nearly two years' worth of data, we fit statistical models to twelve Italian cities to quantify the delayed effect of air temperature on daily COVID-19 incidence, accounting for several categories of potential confounders (meteorological, air quality and non-pharmaceutical interventions). Coefficients and covariance matrices for the temperature term were then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response with effects presented as the relative risk (RR) and cumulative RR (RR_cum). The cumulative exposure response curve was non-linear, with peak risk at 15.1 °C and declining risk at progressively lower and higher temperatures. The lowest RR_cum at 0.2 °C is 0.72 [0.56,0.91] times that of the highest risk. Due to this non-linearity, the shape of the lag response curve necessarily varied by temperature. This work suggests that on a given day, air temperature approximately 15 °C maximises the incidence of COVID-19, with the effects distributed in the subsequent ten days or more.

Improvements throughout the Three Waves of COVID-19 Pandemic: Results from 4 Million Inhabitants of North-West Italy

At the very beginning of the European spread of SARS-CoV-2, Piedmont was one of the most affected regions in Italy, with a strong impact on healthcare organizations. In this study, we evaluated the characteristics and outcomes of the COVID-19 patients in an entire region during the first three pandemic waves, identifying similarities and differences in the SARS-CoV-2 epidemic’s timeline. We collected the health-administrative data of all the Piedmont COVID-19 patients infected during the first three pandemic waves (1 March 2020–15 April 2020; 15 October 2020–15 December 2020; 1 March 2021–15 April 2021, respectively). We compared differences among the waves in subjects positive for SARS-CoV-2 and in patients admitted to ICU. Overall, 18.621 subjects tested positive during the first wave (405 patients/day), 144.350 (2366.4 patients/day) in the second, and 81.823 (1778.8 patients/day) in the third. In the second and third waves, we observed a reduction in median age, comorbidity burden, mortality in outpatients, inpatients, and patients admitted to ICU, in intubation, invasive ventilation and tracheostomy, and a parallel increase in the use of CPAP. Our study confirmed a trend towards younger and healthier patients over time but also showed an independent effect of the period on mortality and ICU admission. The appearance of new viral variants, the starting of vaccination, and organizational improvements in tracking, outpatients and inpatients management could have influenced these trends.

Needs and Perceptions of Patients With Dystonia During the COVID-19 Pandemic: A Qualitative Framework Analysis of Survey Responses From Italy

Introduction:

The COVID-19 pandemic and its countermeasures have created changes in both life and healthcare. With the prioritization of COVID-19-related management, the risks and experiences of patients suffering from rare conditions, such as dystonia, during the pandemic remain understudied.

Materials and Methods

Using a framework analysis of a nationwide qualitative online survey, we sought to explore the perspectives of patients with dystonia on their clinical assistance and possible unmet needs during the first pandemic wave. An online survey consisting of 37 items (such as demographic characteristics, dystonia-related features, neurological service provision, therapeutic relationship with the neurologist, perceptions related to virus infection, perceptions about healthcare-related needs, work-related questions, requesting information, and seeking support during the pandemic) was carried out using both close and open-ended questions.

Results

Responses from 62 participants were collected, with most of them from the red zones in Italy, where they were confined indoors. Social isolation was a relevant stressor. Motor and non-motor symptoms increased with detrimental consequences for patients' job and daily functionality. Outpatient clinics and rehabilitation sessions were temporarily shut down, and even telephone/mail support was sparse. Despite efforts, patients felt alone in dealing with dystonia.

Conclusion

The first wave of the pandemic and its related restrictions had detrimental consequences for people living with dystonia, and their relevant needs remained unmet. These findings may contribute to implementing remedial healthcare provisions in this pandemic or in future pandemics.

Analyzing the factors affecting the attitude of public toward lockdown, institutional trust, and civic engagement activities

The early 2020 witnessed Coronavirus disease 2019 (COVID-19) pandemic followed by a nationwide lockdown in the whole history for the first time. The entire world had to go for nationwide lockdown to curb the spread of such contagious and deadly disease. In short, the coronavirus outbreak and the subsequent lockdown had created a storm in the world giving rise to change in public with respect to parameters like trust in the government institutions, civic engagement, and so on. In this raising dilemma, multiple countries have acknowledged the significance of trusting institutions, especially during lockdown. It is also widely accepted that lives of individuals had been undergoing change ever since the spread of COVID-19. Likewise, public's trust in the institutions is directly related to the civic engagement. Civic engagement is commonly understood as a phenomenon that develops a difference in an individual's civic life by combining values, skills, motivation, as well as knowledge. Different forms of civic engagement include national service, volunteering, societal services, and so forth. The main focus of this article is to examine the influence of COVID-19 outbreak on general attitude of the citizens of Malaysia and India, their trust on the governing institutions, and the civic engagement. In other words, this study tries to assess the impact of the pandemic on variables such as attitude toward the lockdown, trust in institutions, and civic engagement. For this, the study adopted cross-sectional community questionnaire survey in two countries-Malaysia and India. The respondents selected for the study was 1437. The respondent's demographics, attitude toward lockdown, trust in institutions, and civic engagement during the lockdown were collected by means of convenience sampling technique. Later the collected data were measured in terms of descriptive statistics and regression analysis. Findings of the study stated that public's trust in the institution can be increased by enhancing the civic activities and implementing policies that govern and build the society.

Policy responses to COVID-19 pandemic waves: Cross-region and cross-sector economic impact

This paper proposes a modelling approach to assess the cross-region and cross-sector economic impacts of the restrictions imposed by governments to contain the COVID-19 pandemic. The nationwide lockdown imposed in Italy during the first wave of the pandemic is used as a benchmark. However, the adopted approach allows an ex-ante assessment of alternative policy responses, in the event of successive pandemic waves, in order to rationalise the policy intervention and reach the best possible compromise between containing the risk of contagion and reducing economic losses. The used approach consists of a non-linear programming model based on a multiregional Input-Output (I-O) table, which guarantees greater flexibility than traditional I-O analysis. It is applied to estimate both direct and indirect losses of GDP and employment produced by alternative policy responses represented by general and differentiated lockdowns. The evidence deriving from the Italian experience shows a sort of learning process through successive waves based on the introduction of increasingly flexible and tailored policy responses to the pandemic.

First and second waves among hospitalised patients with COVID-19 with severe pneumonia: a comparison of 28-day mortality over the 1-year pandemic in a tertiary university hospital in Italy

OBJECTIVE

The first COVID-19-19 epidemic wave was over the period of February-May 2020. Since 1 October 2020, Italy, as many other European countries, faced a second wave. The aim of this analysis was to compare the 28-day mortality between the two waves among COVID-19 hospitalised patients.

DESIGN

Observational cohort study. Standard survival analysis was performed to compare all-cause mortality within 28 days after hospital admission in the two waves. Kaplan-Meier curves as well as Cox regression model analysis were used. The effect of wave on risk of death was shown by means of HRs with 95% CIs. A sensitivity analysis around the impact of the circulating variant as a potential unmeasured confounder was performed.

SETTING

University Hospital of Modena, Italy. Patients admitted to the hospital for severe COVID-19 pneumonia during the first (22 February-31 May 2020) and second (1 October-31 December 2020) waves were included.

RESULTS

During the two study periods, a total of 1472 patients with severe COVID-19 pneumonia were admitted to our hospital, 449 during the first wave and 1023 during the second. Median age was 70 years (IQR 56-80), 37% women, 49% with PaO2/FiO2 <250 mm Hg, 82% with ≥1 comorbidity, median duration of symptoms was 6 days. 28-day mortality rate was 20.0% (95% CI 16.3 to 23.7) during the first wave vs 14.2% (95% CI 12.0 to 16.3) in the second (log-rank test p value=0.03). After including key predictors of death in the multivariable Cox regression model, the data still strongly suggested a lower 28-day mortality rate in the second wave (aHR=0.64, 95% CI 0.45 to 0.90, p value=0.01).

CONCLUSIONS

In our hospitalised patients with COVID-19 with severe pneumonia, the 28-day mortality appeared to be reduced by 36% during the second as compared with the first wave. Further studies are needed to identify factors that may have contributed to this improved survival.

Overview of dreadful consequences of SARS-CoV-2 invasion in Italy from March 2020 to March 2022

BACKGROUND

The unpredicted pandemic disease COVID-19 first flared up adversely in Europe by imparting interminable force of infected and fatality cases to Italy. In late February 2020, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in northern Italy and swiftly proliferated to the entire country, albeit continuous to date (23 March 2022) with a lesser extent of deadliness. Current review focused on the invasions and the associated consequences by SARS-CoV-2 during the period of March 2020-March 2022.

MAIN BODY OF THE ABSTRACT

Initially, the lethality and transmissibility of the novel virus made Italy stunned within 1 month, the number of death cases reached 12,428 at the end of March 2020. The Italian Government announced an immediate emergency phase in entire country, educational institutions to local businesses, manufacturing works, cultural activities to elective activities were rescinded and all the hospitals to morgues were swamped, ensuing that fear of epidemic was impended. Besides, the Italian National Health System and Service coordinated massive public health interventions and conferred unprecedented efforts to limit the high mortality rate of the first wave of infection. Amidst 2 years of epidemic (as of 23 March 2022), Italy has documented 14,070,450 (23.74% of the population) confirmed infected cases, 12,685,306 (21.41% of the population) healed cases, 158,254 death cases (0.27% of the population) and ranking 9th worldwide in the number of deaths.

SHORT CONCLUSION

Based on publicly available Italian Ministry of Health COVID-19 data, current review has comprehended region-wise total infected cases, death cases and healed cases for three consecutive years 2020-2022 to foresee different patterns of the regional outbreak and gradual subservience. At a glance, we highlighted the overview of the exhaustion and exertion of COVID-19 crisis throughout the periods in Italy.

A novel analysis approach to determining the case fatality rate of COVID-19 in Italy

BACKGROUND

The Coronavirus Disease 2019 (COVID-19), which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate.

OBJECTIVE

The main objective of this study was to explore the value of the discharged case fatality rate (DCFR) to make more accurate forecasts of epidemic trends the of COVID-19 in Italy.

METHODS

We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of daily deaths and total deaths to calculate the discharged case fatality rate (tDCFR), monthly discharged case fatality rate (mDCFR), and stage discharged case fatality rate (sDCFR). Furthermore, we analyzed the trend in mDCFR between January and December 2020 using Joinpoint Regression Analysis and used the ArcGIS version 10.7 software to visualize the spatial distribution of epidemic case fatality rate and assigned different colors to each province based on the CFR or tDCFR.

RESULTS

We calculate the number and obtain the new index tDCFR and mDCFR for calculating the fatality rate. The results show that the overall trend of tDCFR and mDCFR fluctuates greatly from January to May. After reaching the peak, it first rises rapidly, then falls rapidly, and finally stabilizes. The map shows that the provinces with high tDCFR were Emilia-Romagna, Puglia and Lombardia. The change trend of mDCFR over time was divided into two stages, the first stage (from January to May) and the second stage (from June to December). Among the six selected countries, the United States has the highest tDCFR (4.26%), while the tDCFR of the remaining countries is between 0.98% and 2.72%.

CONCLUSIONS

We provide a new perspective for assessing the mortality of COVID-19 in Italy,which can use these ever-changing data to calculate a more accurate case fatality rate and scientifically predict the development trend of the epidemic.

CLINICALTRIAL

Survey on COVID-19-related mortality associated with occupational infection during the first phase of the pandemic: A systematic review

According to the Centre for Disease Control and Prevention in 2020, a cluster of pneumonia cases of unknown etiology caused by the severe acute respiratory syndrome (SARS)-coronavirus 2 was reported in Wuhan, China. The present review examined the literature to reveal the incidence of novel coronavirus-2019 disease (COVID-19) infections, underlying comorbidities, workplace infections and case fatality rates. A review was performed to identify the relevant publications available up to May 15, 2020. Since the early stages of the COVID-19 outbreak, the case fatality rate among healthcare workers (HCWs) has stood at 0.69% worldwide and 0.4% in Italy. Based on the current information, most patients have exhibited good prognoses in terms of after-effects or sequelae and low mortality rate. Patients that became critically ill were primarily in the elderly population or had chronic underlying diseases, including diabetes and hypertension. Among all working sectors, HCWs, since they are front-line caregivers for patients with COVID-19, are considered to be in the high-risk population. Increased age and a number of comorbidity factors have been associated with increased risk of mortality in patients with COVID-19. The most frequent complications of COVID-19 reported that can cause fatality in patients were SARS, cardiac arrest, secondary infections and septic shock, in addition to acute kidney failure and liver failure. Overcoming the COVID-19 pandemic is an ongoing challenge, which poses a threat to global health that requires close surveillance and prompt diagnosis, in coordination with research efforts to understand this pathogen and develop effective countermeasures.

Estimating the epidemic growth dynamics within the first week

Information about the early growth of infectious outbreaks is indispensable to estimate the epidemic spreading. A large number of mathematical tools have been developed to this end, facing as much large number of different dynamic evolutions, ranging from sub-linear to super-exponential growth. Of course, the crucial point is that we do not have enough data during the initial outbreak phase to make reliable inferences. Here we propose a straightforward methodology to estimate the epidemic growth dynamic from the cumulative infected data of just a week, provided a surveillance system is available over the whole territory. The methodology, based on the Newcomb-Benford Law, is applied to the Italian covid 19 case-study. Results show that it is possible to discriminate the epidemic dynamics using the first seven data points collected in fifty Italian cities. Moreover, the most probable approximating function of the growth within a six-week epidemic scenario is identified.

Characterization of epidemiological distribution and outcome of COVID-19 in patients with hereditary hemorrhagic telangiectasia: a nationwide retrospective multi-centre study during first wave in Italy

Background

Coronavirus Disease 2019 (COVID-19) continues to have a devastating impact across the world. A number of pre-existing common clinical conditions were reported to represent risk factors for more severe COVID-19 outcomes. Hereditary Hemorrhagic Telangiectasia (HHT) is a rare vascular heritable disorders, characterized by complications secondary to visceral Arterio-Venous Malformations. The impact of HHT, as well as for many Rare Diseases (RDs) on infection susceptibility profile and clinical adverse outcome risk is an unresolved issue.

Objectives

The main objectives were: to assess the clinical features and outcomes of HHT patients infected with COVID-19; to compare the relative infection risk in these patients with the Italian general population throughout the first pandemic wave; to investigate the factors potentially associated with severe COVID-19 outcome in HHT patients, and the possible impact of COVID-19 infection on HHT-related symptoms/complications. Finally, we aimed to estimate how the lockdown-associated wearing of personal protective equipment/individual protection devices could affect HHT-related telangiectasia bleeding frequency.

Methods

The study is a nation-wide questionnaire-based survey, with a multi-Center retrospective cross-sectional design, addressed to the whole Italian HHT population. COVID-19 cases, occurring throughout the first pandemic wave, were collected by a questionnaire-based semi-structured interview. Only the cases ascertained by laboratory confirmation (molecular/serological) were included for epidemiological estimates. Information concerning eventual SarS-Cov-2 infection, as well as regarding HHT-related manifestations and HHT-unrelated co-morbidities were collected by the questionnaire. Prevalence data were compared to Italian general population in the same period.

Results

The survey disclosed 9/296 (3.04%) COVID-19 cases, 8/9 of them being resident in Lombardy, the main epidemic epicenter. Pneumonia was reported by 4/9 patients, which prompted hospital admission and intensive care management in 2 cases. No fatal outcome was recorded. After careful refinement of epidemiological analysis, the survey evidenced overlapping infection risk in HHT compared to general population.

Conclusions

COVID-19 infection profile parallels geographical distribution of epidemic foci. COVID-19 in HHT patients can lead to highly variable clinical profile, likely overlapping with that of general population. The HHT disease does not seem to involve a different approach in terms of hospital admission and access to intensive care with respect to general population.

Underdetection in a stochastic SIR model for the analysis of the COVID-19 Italian epidemic

We propose a way to model the underdetection of infected and removed individuals in a compartmental model for estimating the COVID-19 epidemic. The proposed approach is demonstrated on a stochastic SIR model, specified as a system of stochastic differential equations, to analyse data from the Italian COVID-19 epidemic. We find that a correct assessment of the amount of underdetection is important to obtain reliable estimates of the critical model parameters. The adaptation of the model in each time interval between relevant government decrees implementing contagion mitigation measures provides short-term predictions and a continuously updated assessment of the basic reproduction number.

A Continuous Model of Three Scenarios of the Infection Process with Delayed Immune Response Factors

The course of an infection was modeled as a controlled nonlinear process. Understanding the substantial differences observed in the trajectory of the disease caused by the new coronavirus SARS-CoV-2 is of critical importance at the moment. Numerous factors have been considered to explain the fact that symptoms vary highly among different people and the infection transmission rate varies among local populations. Virus replication within the host cell and the development of an immune response to virus antigens in the body are two interdependent processes, which have aftereffects and depend on the preexisting states of the cell and virus populations. Different scenarios with the same input parameters are necessary to consider for modeling the properties of the states. The efficiency of the immune response is the most important factor, including the time it takes to develop defense responses from three levels of the immune system, which is a complex system of the body. A computational description of infection scenarios was proposed on the basis of a delay differential equation with two values of the time lag. In the new model, transitions between phases of infectious disease depend on the initial virus dose and the delayed immune response to infection. A variation in the dose of the virus and response time can lead to a transition from an acute phase of the disease with overt symptoms to a chronic phase or fatal outcome. Asymptomatic transmission of viral infection was calculated and described in the model as a situation where the virus is rapidly and efficiently suppressed after a short replication phase, while still persisting in the body in minor amounts. An analysis of the model behavior is consistent with the theory that the initial number of virions can affect the quality of the immune response. The reasons that high individual differences are observed in the trajectory of COVID-19 disease and the formation of types of the immune response to coronavirus are still poorly understood. Known trajectories of hepatitis C virus (HCV) infection were used as a basis for model scenarios.

A simple but complex enough θ -SIR type model to be used with COVID-19 real data. Application to the case of Italy

Since the start of the COVID-19 pandemic in China many models have appeared in the literature, trying to simulate its dynamics. Focusing on modeling the biological and sociological mechanisms which influence the disease spread, the basic reference example is the SIR model. However, it is too simple to be able to model those mechanisms (including the three main types of control measures: social distancing, contact tracing and health system measures) to fit real data and to simulate possible future scenarios. A question, then, arises: how much and how do we need to complexify a SIR model? We develop a θ -SEIHQRD model, which may be the simplest one satisfying the mentioned requirements for arbitrary territories and can be simplified in particular cases. We show its very good performance in the Italian case and study different future scenarios.

Ag-Based Synergistic Antimicrobial Composites. A Critical Review

The emerging problem of the antibiotic resistance development and the consequences that the health, food and other sectors face stimulate researchers to find safe and effective alternative methods to fight antimicrobial resistance (AMR) and biofilm formation. One of the most promising and efficient groups of materials known for robust antimicrobial performance is noble metal nanoparticles. Notably, silver nanoparticles (AgNPs) have been already widely investigated and applied as antimicrobial agents. However, it has been proposed to create synergistic composites, because pathogens can find their way to develop resistance against metal nanophases; therefore, it could be important to strengthen and secure their antipathogen potency. These complex materials are comprised of individual components with intrinsic antimicrobial action against a wide range of pathogens. One part consists of inorganic AgNPs, and the other, of active organic molecules with pronounced germicidal effects: both phases complement each other, and the effect might just be the sum of the individual effects, or it can be reinforced by the simultaneous application. Many organic molecules have been proposed as potential candidates and successfully united with inorganic counterparts: polysaccharides, with chitosan being the most used component; phenols and organic acids; and peptides and other agents of animal and synthetic origin. In this review, we overview the available literature and critically discuss the findings, including the mechanisms of action, efficacy and application of the silver-based synergistic antimicrobial composites. Hence, we provide a structured summary of the current state of the research direction and give an opinion on perspectives on the development of hybrid Ag-based nanoantimicrobials (NAMs).

Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic

Estimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000–62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15–52%) in Lombardy, and 72% (95% confidence interval 36–100%) in Bergamo.

Estimates of COVID-19-related mortality are limited by incomplete testing. Here, the authors perform counterfactual analyses and estimate that there were 59,000–62,000 deaths from COVID-19 in Italy until 9^th September 2020, approximately 1.5 times higher than official statistics.

Evaluating Social Distancing Measures and Their Association with the Covid-19 Pandemic in South America

Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in South America during the pandemic (15 February to 27 October 2020). We used a data-driven approach to create a community mobility index from the Google Covid-19 Community Mobility and relate it to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT). Two hypotheses were established: countries which have adopted stricter social distancing measures have also a lower level of circulation (H1), and mobility is occurring randomly in space (H2). Considering a transient period, a low capacity of governments to respond to the pandemic with more stringent measures of social distancing was observed at the beginning of the crisis. In turn, considering a steady-state period, the results showed an inverse relationship between the Covid stringency index and the community mobility index for at least three countries (H1 rejected). Regarding the spatial analysis, global and local Moran indices revealed regional mobility patterns for Argentina, Brazil, and Chile (H1 rejected). In Brazil, the absence of coordinated policies between the federal government and states regarding social distancing may have played an important role for several and extensive clusters formation. On the other hand, the results for Argentina and Chile could be signals for the difficulties of governments in keeping their population under control, and for long periods, even under stricter decrees.

The first 8 weeks of the Austrian SARS-CoV-2 epidemic

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV‑2) reached Austria in February 2020. This study aims to describe the first 8 weeks of the Austrian epidemic and reflect on the potential mental health consequences as known at that time.

Methods

Data on Austrian Coronavirus Disease 19 (COVID-19) epidemiological indicators and number of tests were obtained from official registers. Relative risks (RRs) of infection and death from COVID-19 were calculated for sex and age groups (< 65 years and ≥ 65 years). Public health measures introduced to reduce the spread of COVID-19 were identified via online media research. A rapid review of initial evidence on mental health consequences of the pandemic was performed in PubMed and medRxiv.

Results

By 21 April 2020 the case count in Austria was 14,810 after a peak of new daily infections mid-March. The RR of death for age ≥ 65 years was 80.07 (95% confidence interval, CI 52.64–121.80; p < 0.0001) compared to those aged < 65 years. In men the RR of death was 1.44 (95% CI 1.20–1.73; p < 0.0001) compared to women. Wide-ranging public health measures included avoidance of case importation, limitation of social contacts, hygiene measures, testing, case tracking, and the call for COVID-19-related research. International rates of psychiatric symptoms during the initial lockdowns exceeded typical levels: anxiety (6%–51%), depression (17%–48%) and posttraumatic stress (5%–54%).

Conclusion

Data show great vulnerability of older people also in Austria. Severe mental health impacts can be expected with need for proper assessment of the long-term consequences of this pandemic.

Parkinson's disease patients' needs during the COVID-19 pandemic in a red zone: A framework analysis of open-ended survey questions

Background and purpose

During the first phase of the COVID‐19 pandemic, a lockdown was imposed in Italy. The aim of this study was to investigate the perceptions, feelings and unmet needs of Parkinson's disease (PD) patients who experienced the 2‐month lockdown in a “red zone” in the northern part of Italy during the COVID‐19 outbreak.

Methods

The study had a descriptive design that used a cross‐sectional online survey which included open‐ended questions to elicit responses on the participant's feelings concerning their risk of contracting coronavirus, how their physical activity had changed, and their personal needs, dictated by their condition, which were not met in this pandemic period as compared to previous periods. Demographic data were analysed using descriptive frequencies, while the open‐ended questions were analysed using thematic framework analysis.

Results

The study included 103 participants (63 men/40 women [61.17 vs. 38.83%]). Framework analysis led to the identification of four main themes: (i) fearing the risk of contracting coronavirus; (ii) reduction of physical activity; (iii) perception of the risk of not being able to access outpatient clinics or support services; and (iv) negative experiences of the important reduction in socialization. The perceptions of unmet needs appeared to be greater than the actual experience, particularly for the reduction in physical activity and the interruption of contacts with the neurologist and other specialists.

Conclusions

This study highlights how perceptions and actual experience shape the meaning of living with PD during the pandemic. Worth noting is the divergence between perceptions and real impact in some aspects of the COVID‐19 outbreak.

Air pollutants and risk of death due to COVID-19 in Italy

The present work aims to study the role of air pollutants in relation to the number of deaths per each Italian province affected by COVID-19. To do that, specific mortality from COVID-19 has been standardized for each Italian province and per age group (10 groups) ranging from 0 to 9 years to >90 years, based on the 2019 national population figures. The link between air pollutants and COVID-19 mortality among Italian provinces was studied implementing a linear regression model, whereas the wide set of variables were examined by means of LISA (Local Indicators of Spatial Autocorrelation), relating the spatial component of COVID-19 related data with a mix of environmental variables as explanatory variables. As results, in some provinces, namely the Western Po Valley provinces, the SMR (Standardized Mortality Ratio) is much higher than expected, and the presence of PM₁₀ was independently associated with the case status. Furthermore, the results for LISA on SMR and PM₁₀ demonstrate clusters of high-high values in the wide Metropolitan area of Milan and the Po Valley area respectively, with a certain level of overlap of the two distributions in the area strictly considered Milan. In conclusion, this research appears to find elements to confirm the existence of a link between pollution and the risk of death due to the disease, in particular, considering land take and air pollution, this latter referred to particulate (PM₁₀). For this reason, we can reiterate the need to act in favour of policies aimed at reducing pollutants in the atmosphere, by means of speeding up the already existing plans and policies, targeting all sources of atmospheric pollution: industries, home heating and traffic.

How to manage with telemedicine people with neuromuscular diseases?

INTRODUCTION

COVID-19 pandemic radically transformed our daily clinical practice, raising the need not to lose close contact with patients without being able to see them face-to-face. These issues are even more felt and evident in fragile patients, as those affected by neuromuscular disease. An important help came from new digital technologies that allow clinicians to remotely monitor health status and any deterioration of chronically ill patients.

METHODS

In this mini-review, an initiative of the "Digital Technologies, Web and Social Media Study Group" of the Italian Society of Neurology, we propose to analyze the approach to neuromuscular patients by looking over raising evidence on the main cornerstones of Telemedicine (TM): clinician-patient interaction, remote clinical assessment, remote monitoring, and digital therapeutics. In particular, we explored the strategies developed by researchers and their impact on the physical and emotional status of the patients, with particular focusing on their adherence to the program of virtual monitoring.

RESULTS

TM plays an important role in each of four stages of approach to neuromuscular disease, having demonstrated validity in keep close clinical patient interaction, clinical assessment, remote monitoring, and telerehabilitation. Nevertheless, there is no remote alternative to electrophysiological testing neither validate tools to assess disability.

CONCLUSION

The role of TM in neuromuscular care is yet underestimated but is crucial, beyond the pandemic era. Further development of TM is advisable, through making specific apps, remotely controlled by clinicians, and making more engaging clinicians-patients interaction. Last, it is necessary to ensure adequate internet access to everyone.

COVID-19: a review of current knowledge regarding exposure, quarantine, isolation and other preventive measures

Deeper understanding of the spread, morbidity, fatality, and development of immune response associated with coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, is necessary in order to establish an appropriate epidemiological and clinical response. Exposure control represents a key part of the combat against COVID-19, as the effectiveness of current therapeutic options remains partial. Since the preventive measures have not been sufficiently able to slow down this pandemic, in this article we explore some of the pertinent knowledge gaps, while overall looking to effective vaccination strategies as a way out. Early on, such strategies may need to rely on counting the convalescents as protected in order to speed up the immunization of the whole population.

Prevalence of SARS-CoV-2, Verona, Italy, April-May 2020

We used random sampling to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 infection in Verona, Italy. Of 1,515 participants, 2.6% tested positive by serologic assay and 0.7% by reverse transcription PCR. We used latent class analysis to estimate a 3.0% probability of infection and 2.0% death rate.

The Evolution of Covid-19 in Italy after the Spring of 2020: An Unpredicted Summer Respite Followed by a Second Wave

The coronavirus (COVID-19) pandemic was particularly invasive in Italy during the period between March and late April 2020, then decreased in both the number of infections and in the seriousness of the illness throughout the summer of 2020. In this work, we measure the severity of the disease by the ratio of Intensive Care Units (ICU) spaces occupied by COVID-19 patients and the number of Active Cases (AC) each month from April to October 2020. We also use the ratio of the number of Deaths (D) to the number of Active Cases. What clearly emerges, from rigorous statistical analysis, is a progressive decrease in both ratios until August, indicating progressive mitigation of the disease. This is particularly evident when comparing March-April with July-August; during the summer period the two ratios became roughly 18 times lower. We test such sharp decreases against possible bias in counting active cases and we confirm their statistical significance. We then interpret such evidence in terms of the well-known seasonality of the human immune system and the virus-inactivating effect of stronger UV rays in the summer. Both ratios, however, increased again in October, as ICU/AC began to increase in September 2020. These ratios and the exponential growth of infections in October indicate that the virus-if not contained by strict measures-will lead to unsustainable challenges for the Italian health system in the winter of 2020-2021.

Modeling the second wave of COVID-19 infections in France and Italy via a stochastic SEIR model

COVID-19 has forced quarantine measures in several countries across the world. These measures have proven to be effective in significantly reducing the prevalence of the virus. To date, no effective treatment or vaccine is available. In the effort of preserving both public health and the economical and social textures, France and Italy governments have partially released lockdown measures. Here, we extrapolate the long-term behavior of the epidemic in both countries using a susceptible-exposed-infected-recovered model, where parameters are stochastically perturbed with a lognormal distribution to handle the uncertainty in the estimates of COVID-19 prevalence and to simulate the presence of super-spreaders. Our results suggest that uncertainties in both parameters and initial conditions rapidly propagate in the model and can result in different outcomes of the epidemic leading or not to a second wave of infections. Furthermore, the presence of super-spreaders adds instability to the dynamics, making the control of the epidemic more difficult. Using actual knowledge, asymptotic estimates of COVID-19 prevalence can fluctuate of the order of 10×10⁶ units in both countries.

Modelling the Potential Impact of Social Distancing on the COVID-19 Epidemic in South Africa

The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. With the global spread increasing and infecting millions, containment efforts by countries have largely focused on lockdowns and social distancing to minimise contact between persons. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to describe the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to data from March 5 to April 13, 2020, on the cumulative number of infected cases, and a scenario analysis on different levels of social distancing is presented. The model shows that with the levels of social distancing under the initial lockdown level between March 26 and April 13, 2020, there would be a projected continued rise in the number of infected cases. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lockdown. It is shown that relaxation of social distancing by 2% can result in a 23% rise in the number of cumulative cases whilst an increase in the level of social distancing by 2% would reduce the number of cumulative cases by about 18%. The model results accurately predicted the number of cases after the initial lockdown level was relaxed towards the end of April 2020. These results have implications on the management and policy direction in the early phase of the epidemic.

Assessing concerns for the economic consequence of the COVID-19 response and mental health problems associated with economic vulnerability and negative economic shock in Italy, Spain, and the United Kingdom

Many different countries have been under lockdown or extreme social distancing measures to control the spread of COVID-19. The potentially far-reaching side effects of these measures have not yet been fully understood. In this study we analyse the results of a multi-country survey conducted in Italy (N = 3,504), Spain (N = 3,524) and the United Kingdom (N = 3,523), with two separate analyses. In the first analysis, we examine the elicitation of citizens' concerns over the downplaying of the economic consequences of the lockdown during the COVID-19 pandemic. We control for Social Desirability Bias through a list experiment included in the survey. In the second analysis, we examine the data from the same survey to predict the level of stress, anxiety and depression associated with being economically vulnerable and having been affected by a negative economic shock. To accomplish this, we have used a prediction algorithm based on machine learning techniques. To quantify the size of this affected population, we compare its magnitude with the number of people affected by COVID-19 using measures of susceptibility, vulnerability and behavioural change collected in the same questionnaire. We find that the concern for the economy and for "the way out" of the lockdown is diffuse and there is evidence of minor underreporting. Additionally, we estimate that around 42.8% of the populations in the three countries are at high risk of stress, anxiety, and depression, based on their level of economic vulnerability and their exposure to a negative economic shock.

Prevalence of asymptomatic SARS-CoV-2-positive individuals in the general population of northern Italy and evaluation of a diagnostic serological ELISA test: a cross-sectional study protocol

INTRODUCTION

As of 30 April 2020, the novel betacoronavirus SARS-CoV-2 had infected more than 3 172 000 individuals, killing over 224 000 people and spreading to more than 200 countries. Italy was the most affected country in Europe and the third most affected in the world in terms of the number of cases. Therefore, the aims of this study are: (1) to estimate the prevalence of asymptomatic SARS-CoV-2-positive individuals among the general population of Verona; (2) to assess the accuracy (sensitivity, specificity and predictive values) of an ELISA serological test for the screening of SARS-CoV-2.

METHODS AND ANALYSIS

The study will be carried out on a random sample of subjects aged at least 10 years from the general population of Verona. Participants will undergo the measurement of vital parameters (oxygen saturation measured by oximeter, respiratory rate and body temperature detected by laser thermometer), the administration of a COVID-19-related symptoms questionnaire, the collection of a blood sample and a nasopharyngeal swab. Our evaluation will include the statistical technique of Latent Class Analysis, which will be the basis for the estimation of prevalence.

ETHICS AND DISSEMINATION

The study protocol has been approved by the Ethics Committee of Verona and Rovigo provinces on 15 April 2020 (internal protocol number 2641CESC). The study results will be submitted for publication in international, peer-reviewed journals and the complete dataset will be deposited in a public repository. Most relevant data will be made available to policy-makers as well as disseminated to stakeholders and to the community.

COVID-19 in systemic lupus erythematosus: Data from a survey on 417 patients

BACKGROUND

Systemic lupus erythematosus (SLE) is a chronic disease characterised by autoimmunity and increased susceptibility to infections. COVID-19 is a systemic viral disease currently spreading as a pandemic. Little is known about the impact of COVID-19 in patients with SLE.

OBJECTIVE

to acquire information on the impact of COVID-19 in SLE.

METHODS

A 26-item anonymous questionnaire investigating demographics, SLE clinical features, COVID-19 diagnoses and changes in treatments and daily habits was administered to patients with SLE from three referral centres through www.surveymonkey.com over 10 days. Data from the survey were compared to those from published estimates about the general population.

RESULTS

Four-hundred-seventeen patients responded to the survey. More than 60% of subjects complained of symptoms that are also associated to COVID-19. Fourteen COVID-19 diagnoses (five confirmed by polymerase chain reaction) were reported, in contrast to a 0.73% prevalence of confirmed cases in Lombardy. One hospitalisation was reported. Fever, anosmia, dry cough, a self-reported history of neuropsychiatric SLE and a recent contact with confirmed COVID-19 cases were more strongly associated with COVID-19, as were symptoms and lower compliance to behavioural preventive measures in patients' contacts. No protective effect was seen in subjects on hydroxychloroquine.

CONCLUSION

COVID-19 morbidity might only moderately be increased in most patients with SLE, although limited information can be inferred on more severe cases. Hydroxychloroquine apparently seems not to confer protection to infection per se, although other beneficial roles cannot be excluded. Containment policies and behavioural preventive measures could have a major role in limiting the impact of COVID-19 in patients with SLE.

Effects of the lockdown on the mental health of the general population during the COVID-19 pandemic in Italy: Results from the COMET collaborative network

BACKGROUND

The Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented traumatic event influencing the healthcare, economic, and social welfare systems worldwide. In order to slow the infection rates, lockdown has been implemented almost everywhere. Italy, one of the countries most severely affected, entered the "lockdown" on March 8, 2020.

METHODS

The COvid Mental hEalth Trial (COMET) network includes 10 Italian university sites and the National Institute of Health. The whole study has three different phases. The first phase includes an online survey conducted between March and May 2020 in the Italian population. Recruitment took place through email invitation letters, social media, mailing lists of universities, national medical associations, and associations of stakeholders (e.g., associations of users/carers). In order to evaluate the impact of lockdown on depressive, anxiety and stress symptoms, multivariate linear regression models were performed, weighted for the propensity score.

RESULTS

The final sample consisted of 20,720 participants. Among them, 12.4% of respondents (N = 2,555) reported severe or extremely severe levels of depressive symptoms, 17.6% (N = 3,627) of anxiety symptoms and 41.6% (N = 8,619) reported to feel at least moderately stressed by the situation at the DASS-21.According to the multivariate regression models, the depressive, anxiety and stress symptoms significantly worsened from the week April 9-15 to the week April 30 to May 4 (p < 0.0001). Moreover, female respondents and people with pre-existing mental health problems were at higher risk of developing severe depression and anxiety symptoms (p < 0.0001).

CONCLUSIONS

Although physical isolation and lockdown represent essential public health measures for containing the spread of the COVID-19 pandemic, they are a serious threat for mental health and well-being of the general population. As an integral part of COVID-19 response, mental health needs should be addressed.

Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy

Background

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time.

Methods

Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon.

Results

Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting.

Conclusions

A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished.

Approach to childhood asthma in the era of COVID-19: The official statement endorsed by the Saudi Pediatric Pulmonology Association (SPPA)

Coronaviruses are a large family of viruses that infect humans, which may result in mild symptoms similar to those of the common cold. COVID-19 is most recent subtype similar or even worse than the two previous pandemic strains which were the severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV). The first cases of COVID-19 emerged in December 2019. Since then, the virus causing the disease has infected more than four million people around the globe and led to hundreds of thousands deaths. We think addressing the management of asthma in the era of this pandemic is important for several reasons: high prevalence of asthma in Saudi Arabia, further, majorities were uncontrolled disease. The statement will provide special instructions and answers to common questions of physicians dealing with asthmatic children during this pandemic.

A Preliminary Investigation on the Statistical Correlations between SARS-CoV-2 Spread and Local Meteorology

The statistical correlation between meteorological parameters and the spread of Coronavirus Disease-2019 (COVID-19) was investigated in five provinces of Italy selected according to the number of infected individuals and the different trends of infection in the early stages of the epidemic: Bergamo and Brescia showed some of the highest trends of infections while nearby Cremona and Mantova, showed lower trends. Pesaro-Urbino province was included for further investigation as it was comparably affected by the epidemic despite being the area far from the Po valley. Moving means of the variables were considered to take into account the variability of incubation periods and uncertainties in the epidemiological data. The same analyzes were performed normalizing the number of new daily cases based on the number of checks performed. For each province, the moving mean of adjusted and unadjusted new daily cases were independently plotted versus each meteorological parameter, and linear regressions were determined in the period from 29th of February 2020 to 29th of March 2020. Strong positive correlations were observed between new cases and temperatures within three provinces representing 86.5% of the contagions. Strong negative correlations were observed between the moving means of new cases and relative humidity values for four provinces and more than 90% of the contagions.

Complicity and Synergy Between Bolsonaro and Brazilian Evangelicals in COVID-19 Times: Adherence to Scientific Negationism for Political-Religious Reasons

Since the beginning of the COVID-19 pandemic, the article discusses how a large part of the Brazilian evangelical churches resorted to informational disorder and disinformation as instruments to deny the severity of the disease. It also discusses how the production, endorsement, and support of false news served to establish a support platform for the President of the Republic of Brazil, Jair Messias Bolsonaro, with emphasis on the month of March, when the pandemic hit Brazil, until mid-April 2020, when religious leaders joined Bolsonaro in an Easter celebration.