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Due to the exponential growth of the number of subjects affected by coronavirus disease 2019 (COVID-19), the entire Italian health care system had to respond promptly and in a very short time with the need of semi-intensive and intensive care units. Moreover, trained dedicated COVID-19 teams consisting of physicians were coming from different specialties (intensivists or pneumologists and infectiologists), while respiratory therapists and nurses have been recruited to work on and on without rest. However, due to still limited and evolving knowledge of COVID-19, there are few recommendations concerning the need in respiratory rehabilitation and physiotherapy interventions. The presentation of this paper is the result of a consensus promoted by the Italian societies of respiratory health care professionals who contacted pulmonologists directly involved in the treatment and rehabilitation of COVID-19. The aim was to formulate the more proper and common suggestions to be applied in different hospital settings in offering rehabilitative programs and physiotherapy workforce planning for COVID-19 patients. Two main areas of intervention were identified: organization and treatment, which are described in this paper to face the emergency. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive dataset on West Nile virus outbreaks that have occurred in Italy from September 2012 to November 2022. We have digitized bulletins published by the Italian National Institute of Health to demonstrate the potential utilization of this data for the research community. Our aim is to establish a centralized open access repository that facilitates analysis and monitoring of the disease. We have collected and curated data on the type of infected host, along with additional information whenever available, including the type of infection, age, and geographic details at different levels of spatial aggregation. By combining our data with other sources of information such as weather data, it becomes possible to assess potential relationships between West Nile virus outbreaks and environmental factors. We strongly believe in supporting public oversight of government epidemic management, and we emphasize that open data play a crucial role in generating reliable results by enabling greater transparency. [ABSTRACT FROM AUTHOR]

Purpose: After the identification of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Wuhan, China, a pandemic was widely spread worldwide. In Italy, about 240,000 people were infected because of this virus including 34,721 deaths until the end of June 2020. To control this new pandemic, epidemiologists recommend the enforcement of serious mitigation measures like country lockdown, contact tracing or testing, social distancing and self-isolation. Design/methodology/approach: This paper presents the most popular epidemic model of susceptible (S), exposed (E), infected (I) and recovered (R) collectively called SEIR to understand the virus spreading among the Italian population. Findings: Developed SEIR model explains the infection growth across Italy and presents epidemic rates after and before country lockdown. The results demonstrated that follow-up of strict measures such that country lockdown along with high testing is making Italy practically a pandemic-free country. Originality/value: These models largely help to estimate and understand how an infectious agent spreads in a particular country and how individual factors can affect the dynamics. Further studies like classical SEIR modeling can improve the quality of data and implementation of this modeling could represent a novelty of epidemic models. [ABSTRACT FROM AUTHOR]

The paper reports the results of an analysis of COVID-19 diffusion in Italy. The analysis was carried out with a new method based on the combined use of a 3 Way Decisions model and graph theory. Specifically, the data about infected people in the Italian regions is assessed by means of an evaluation function which allows the tri-partitioning of Italy and the identification of high, medium or low critical regions. The tri-partition is performed, along the temporal evolution of the COVID-19 diffusion, by calculating two threshold values which take into account the containment actions that, from time to time, the decision makers have implemented. The effects of a containment action are related to a reduction in the centrality value of a region. To estimate the effect of containment actions, we evaluated two approaches. The first is based on a uniform reduction in the centrality values of the regions, the second estimates the effects of containment actions starting from the mobility changes data provided by the Google Community Mobility reports. The results of our evaluation based on real data of the COVID-19 diffusion in Italy are encouraging and represent a good starting point for future extensions of the method. [ABSTRACT FROM AUTHOR]

In a recent paper by one of the authors and collaborators, motivated by the Olive Quick Decline Syndrome (OQDS) outbreak, which has been ongoing in Southern Italy since 2013, a simple epidemiological model describing this epidemic was presented. Beside the bacterium Xylella fastidiosa, the main players considered in the model are its insect vectors, Philaenus spumarius, and the host plants (olive trees and weeds) of the insects and of the bacterium. The model was based on a system of ordinary differential equations, the analysis of which provided interesting results about possible equilibria of the epidemic system and guidelines for its numerical simulations. Although the model presented there was mathematically rather simplified, its analysis has highlighted threshold parameters that could be the target of control strategies within an integrated pest management framework, not requiring the removal of the productive resource represented by the olive trees. Indeed, numerical simulations support the outcomes of the mathematical analysis, according to which the removal of a suitable amount of weed biomass (reservoir of Xylella fastidiosa) from olive orchards and surrounding areas resulted in the most efficient strategy to control the spread of the OQDS. In addition, as expected, the adoption of more resistant olive tree cultivars has been shown to be a good strategy, though less cost-effective, in controlling the pathogen. In this paper for a more realistic description and a clearer interpretation of the proposed control measures, a spatial structure of the epidemic system has been included, but, in order to keep mathematical technicalities to a minimum, only two players have been described in a dynamical way, trees and insects, while the weed biomass is taken to be a given quantity. The control measures have been introduced only on a subregion of the whole habitat, in order to contain costs of intervention. We show that such a practice can lead to the eradication of an epidemic outbreak. Numerical simulations confirm both the results of the previous paper and the theoretical results of the model with a spatial structure, though subject to regional control only. [ABSTRACT FROM AUTHOR]

In this paper, the fractional-order generalization of the susceptible-infected-recovered (SIR) epidemic model for predicting the spread of the COVID-19 disease is presented. The time-domain model implementation is based on the fixed-step method using the nabla fractional-order difference defined by Grünwald-Letnikov formula. We study the influence of fractional order values on the dynamic properties of the proposed fractional-order SIR model. In modeling the COVID-19 transmission, the model's parameters are estimated while using the genetic algorithm. The model prediction results for the spread of COVID-19 in Italy and Spain confirm the usefulness of the introduced methodology. [ABSTRACT FROM AUTHOR]

The paper aims to relate the observations of some historians who have dealt with epidemic events in early modern Italy to some central features of Norbert Elias' approach, and to observe some social phenomena related to such events in the perspective of Eliasian sociology. In this way, we will try to suggest that the social implications of great epidemic events that occurred "in the past" can be included among the various fields of investigation in which the Eliasian approach can be tested, and we will attempt to highlight the usefulness of some key concepts of the figurational sociology for the interpretation of what can be observed in that field. With particular reference to seventeenth-century Tuscany, attention will be focused on some dynamics of change that involved - at the same time - both certain institutions engaged in the fight against the plague and certain dimensions of people's attitudes and "sensibilities". Following Norbert Elias, we will also try to suggest that the role played by the plague in defining the direction of those dynamics of change was part of a broader (civilizing) process. [ABSTRACT FROM AUTHOR]

Nonlinear dynamics perspective is an interesting approach to describe COVID-19 epidemics, providing information to support strategic decisions. This paper proposes a dynamical map to describe COVID-19 epidemics based on the classical susceptible-exposed-infected-recovered (SEIR) differential model, incorporating vaccinated population. On this basis, the novel map represents COVID-19 discrete-time dynamics by adopting three populations: infected, cumulative infected and vaccinated. The map promotes a dynamical description based on algebraic equations with a reduced number of variables and, due to its simplicity, it is easier to perform parameter adjustments. In addition, the map description allows analytical calculations of useful information to evaluate the epidemic scenario, being important to support strategic decisions. In this regard, it should be pointed out the estimation of the number deaths, infection rate and the herd immunization point. Numerical simulations show the model capability to describe COVID-19 dynamics, capturing the main features of the epidemic evolution. Reported data from Germany, Italy and Brazil are of concern showing the map ability to describe different scenario patterns that include multi-wave pattern with bell shape and plateaus characteristics. The effect of vaccination is analyzed considering different campaign strategies, showing its importance to control the epidemics. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 epidemic in Italy has severely affected people aged more than 80, especially socially isolated. Aim of this paper is to assess whether a social and health program reduced mortality associated to the epidemic. Methods: An observational retrospective cohort analysis of deaths recorded among >80 years in three Italian cities has been carried out to compare death rate of the general population and "Long Live the Elderly!" (LLE) program. Parametric and non-parametric tests have been performed to assess differences of means between the two populations. A multivariable analysis to assess the impact of covariates on weekly mortality has been carried out by setting up a linear mixed model. Results: The total number of services delivered to the LLE population (including phone calls and home visits) was 34,528, 1 every 20 day per person on average, one every 15 days during March and April. From January to April 2019, the same population received one service every 41 days on average, without differences between January-February and March-April. The January-April 2020 cumulative crude death rate was 34.8‰ (9,718 deaths out of 279,249 individuals; CI95%: 34.1–35.5) and 28.9‰ (166 deaths out of 5,727 individuals; CI95%:24.7–33.7) for the general population and the LLE sample respectively. The general population weekly death rate increased after the 11th calendar week that was not the case among the LLE program participants (p<0.001). The Standardized Mortality Ratio was 0.83; (CI95%: 0.71–0.97). Mortality adjusted for age, gender, COVID-19 weekly incidence and prevalence of people living in nursing homes was lower in the LLE program than in the general population (p<0.001). Conclusions: LLE program is likely to limit mortality associated with COVID-19. Further studies are needed to establish whether it is due to the impact of social care that allows a better clients' adherence to the recommendations of physical distancing or to an improved surveillance of older adults that prevents negative outcomes associated with COVID-19. [ABSTRACT FROM AUTHOR]

Copyright of Investigaciones Geograficas is the property of Universidad de Alicante, Instituto Universitario de Geografia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks. [ABSTRACT FROM AUTHOR]

The importance of spatial networks in the spread of an epidemic is an essential aspect in modeling the dynamics of an infectious disease. Additionally, any realistic data-driven model must take into account the large uncertainty in the values reported by official sources such as the amount of infectious individuals. In this paper, we address the above aspects through a hyperbolic compartmental model on networks, in which nodes identify locations of interest such as cities or regions, and arcs represent the ensemble of main mobility paths. The model describes the spatial movement and interactions of a population partitioned, from an epidemiological point of view, on the basis of an extended compartmental structure and divided into commuters, moving on a suburban scale, and non-commuters, acting on an urban scale. Through a diffusive rescaling, the model allows us to recover classical diffusion equations related to commuting dynamics. The numerical solution of the resulting multiscale hyperbolic system with uncertainty is then tackled using a stochastic collocation approach in combination with a finite volume Implicit–Explicit (IMEX) method. The ability of the model to correctly describe the spatial heterogeneity underlying the spread of an epidemic in a realistic city network is confirmed with a study of the outbreak of COVID-19 in Italy and its spread in the Lombardy Region. [ABSTRACT FROM AUTHOR]

Background: Healthcare associated foodborne outbreaks (HA-FBO) can result from exposure to contaminated food in premises where highly vulnerable patients are cared, including hospitals and nursery homes. The probability of developing severe foodborne diseases such as invasive listeriosis is much higher for susceptible populations. There is limited knowledge on the data on food exposures that could be used to investigate HAFBOs. There is also lack of data relating to specific policies in the health facilities aimed to prevent the administration of potentially risky foods, possibly bringing them from home. Methods: This study was in the framework of One Health EJP Project NOVA. A semistructured questionnaire was administered to 4 general hospitals, 1 specialist clinic and 3 nursing homes located in the Abruzzo and Molise regions, Italy. The questionnaire included 25 questions in 3 sections (I. General organization of the catering service; II. Food menu data; III. Food from home). Results: In most facilities (75%) external catering companies used inhouse kitchens. Trace-back of food to individual patients was possible in 5 premises out of 8 (62.5%), but only in 1 nursery home out of 3 (33.3%). Data were collected in electronic format (hospitals) or paper (nursery homes) and stored 1-12 months. Food at risk of Listeria monocytogenes were not excluded from the menus, such as pre-sliced cooked meat products (served in 4 out of 8 premises, 50%), smoked fish (20%), soft cheese (20%). The consumption of food brought from home was possible in ‘‘special occasions’’, mostly in the nursery homes (66.6%). Conclusions: In case of HA-FBO it could be difficult to trace back the food particularly in the nursery homes where food data are not individually collected and usually on paper. More studies are needed in order to understand the effective exposure to foodborne pathogens, given that some types of foods considered by EFSA as at high risk of microbial contamination are commonly served. Key messages: · Served food could not be traced back to the patient mostly in nursery homes. · Foods at risk of Listeria monocytogenes contamination are commonly served to patients and residents. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 in early 2020 created dangerous public health conditions which pressured governments and health systems to respond in a rapid and effective manner. However, this type of rapid response required many governments to bypass standing; bureaucratic structures of health sector administration and political governance to quickly take; essential measures against a rapidly evolving public health threat. Each government's particular; configuration of governmental and health system decision-making created specific structural and functional challenges to these necessary centrally developed and coordinated strategies. Most East Asian governments (except Japan) succeeded relatively quickly in centralizing essential disease control and treatment initiatives in a timely manner. In contrast, a number of European countries, especially those with predominantly tax-based financing and politically managed health delivery systems, had greater difficulty in escaping bureaucratic governance and management constraints. Drawing on data about these governments' early stage COVID-19 control experiences, this article suggests that structural changes will be necessary if low-performing governments are to better respond to a pandemic. This paper also summarizes other relatively successful strategies. By adopting such strategies, nations can help overcome structural bureaucratic and administrative obstacles in responding to further waves of COVID-19 or similar future pandemic events. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic is the latest in a long list of pandemics that have affected humankind in the last century. In this paper, we propose a novel mathematical epidemiological model named SUIHTER from the names of the seven compartments that it comprises: susceptible uninfected individuals (S), undetected (both asymptomatic and symptomatic) infected (U), isolated infected (I), hospitalized (H), threatened (T), extinct (E) and recovered (R). A suitable parameter calibration that is based on the combined use of the least-squares method and the Markov chain Monte Carlo method is proposed with the aim of reproducing the past history of the epidemic in Italy, which surfaced in late February and is still ongoing to date, and of validating SUIHTER in terms of its predicting capabilities. A distinctive feature of the new model is that it allows a one-to-one calibration strategy between the model compartments and the data that are made available daily by the Italian Civil Protection Department. The new model is then applied to the analysis of the Italian epidemic with emphasis on the second outbreak, which emerged in autumn 2020. In particular, we show that the epidemiological model SUIHTER can be suitably used in a predictive manner to perform scenario analysis at a national level. [ABSTRACT FROM AUTHOR]

Purpose: Early on in the COVID-19 pandemic, the scientific community highlighted a potential risk of epidemics occurring inside prisons. Consequently, specific operational guidelines were promptly released, and containment measures were quickly implemented in prisons. This paper aims to describe the spread of COVID-19 in detention facilities within the Lombardy region of Italy during March to July 2020, and the impact of the prevention and control measures implemented.Design/methodology/approach: A descriptive retrospective analysis of case distribution was performed for all COVID-19 cases identified among people in detention (PiD) and prison officers (POs). A comparison of the epidemic burden affecting different populations and a correlation analysis between the number of cases that occurred and prevention measures implemented were also carried out.Findings: From this study, it emerged that POs were at a high risk of contracting COVID-19. This study observed a delay in the occurrence of cases among PiD and substantial heterogeneity in the size of outbreaks across different prisons. Correlation between reported cases among PiD and registered sick leave taken by POs suggested the latter contributed to introducing the infection into prison settings. Finally, number of cases among PiD inversely correlated with the capacity of each prison to identify and set up dedicated areas for medical isolation.Originality/value: Prevention and control measures when adopted in a timely manner were effective in protecting PiD. According to the findings, POs are a population at high risk for acquiring and transmitting COVID-19 and should be prioritized for testing, active case finding and vaccination. This study highlights the critical importance of including prison settings within emergency preparedness plans. [ABSTRACT FROM AUTHOR]

There is evidence that adoption of non-pharmaceutical containment measures (NPMs) may have had a major impact on Covid-19 epidemic dynamics, and mitigated its effect on healthcare system. Optimal timing of implementation of these measures however is not known. In Italy, a national lockdown was decided on March 11th 2020 and ended 4th of May. At that time, cumulative incidence (CI) was different in Italian regions which ranged from <5 cases/100,000 to >11 cases/100,000 inhabitants. In this paper, we aim to evaluate how level of incidence in different regions at the time of implementation of NPMs affected CI and had an impact on the healthcare system in terms of ICU bed occupancy and mortality rates. We used regional daily new COVID-19 diagnosed cases as well number of people hospitalized in ICU and number of deaths for period February 24-May 11 from all the 19 Italian regions and two autonomous provinces. For each region we calculated: temporal daily trend of cumulative cases of Covid-19/100,000 inhabitants, daily trend of ICU bed occupancy and mortality rate at the end of period. We found that the epidemic curves show similar trends for all regions and all tend to flatten between 11–32 days. However, after 2 months, regions with lower CI at lockdown remained at substantially lower CI (<265 cases/100,000), had a peak of percentage of cases hospitalized in ICU which did not exceed 79.4% and a mortality<0.27/1,000. On the other hand, in regions with higher incidence at lockdown, CI reached 382–921 cases/100,000, the peak of percentage of cases hospitalized in ICU and mortality rate reached 270%, and 1.5/1,000, respectively. Our data suggests that level of CI at the moment of lockdown is important to control the subsequent spread of infection so NPMs should be adopted very early during the course of Covid-19 epidemic, in order to mitigate the impact on the healthcare system and to reduce related mortality. [ABSTRACT FROM AUTHOR]

During the Italian campaigns of the French Revolutionary Wars (1796-1801), several epidemic outbreaks sparked acrimonious aetiological debates: were the fevers spread by soldiers and prisoners of war, or produced by environmental factors? This debate was not only a scientific issue, but also a political one, for causation was linked to accountability. Looking at a series of medical investigations written by French military practitioners, this paper argues that theories of contagion were used by civilians to accuse the army of spreading disease, in what I describe as an "aetiology of blame." Likewise, military officials attempted to absolve themselves of responsibility for the spread of disease by focusing on unwholesome environments, depicting diseases as unavoidable fatalities. Military doctors thus supported French imperial endeavours by obscuring the army's responsibility for the spread of diseases, putting forward aetiologies of fate and blaming individual behaviour. Even when they did not radically dismiss contagionist perspectives, military practitioners insisted on a wide range of pathogenic causes. Their discourses were in line with medical theories of the time, but they downplayed the responsibility of the army for the spread of diseases, which they depicted as a factor among others. Military doctors were thus not always involved in the direct and deliberate production of ignorance, but they did sustain controversies and uncertainty. [ABSTRACT FROM AUTHOR]

Pandemic infection by COVID‐19 could be changing the public image of the nursing profession in Italy. Recently, as in any western country, we were being registered with an increase in the number of violence against healthcare professionals. Nevertheless, due to pandemic in the social media, the nursing profession is remembered for competence, determination, courage, and humanity, and it is continually remercied by people, politicians, and journalists. In this paper, we will conduct a phenomenological argument that proposes both phenomena can be explained by Paul Ricoeur's courses of recognition. In cases of violence, patients and their family members reacting because they feel betrayed for a real or alleged injustice—primarily not to be listened to by health professionals. Nurses and other professionals are often unable to take the right grade of involvement for understanding patient's needs. On the other hand, during COVID‐19 pandemic, patients and family members perceive the extraordinary daily work of caring, and they react thanking. The principle of the gift is implicated in both cases: the crisis of gift received or donated can be explaining violence; gratitude and thanks can be explaining by gratuitousness of care. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019(COVID-19) has broken out worldwide. Up to Apr.21,2020, more than 2.45 million confirmed cases have been diagnosed, and more than 0.17 million deaths have been reported. Based on the development process of China’s epidemic, this paper proposes an improved discrete time multi-stage time-delay dynamic model to extract the transmission characteristics of epidemic, the impact of prevention and control interventions (prevention and control intervention effect) and the impact of medical resource availability, and establishes an empirical transmission dynamics method of COVID 19 analysis. Among them, the experience extraction is based on the model and the epidemic data released by World Health Organization(WHO）， and realized by parameter inversion. Furthermore, by using the proposed method, this paper analyzes the epidemic stages in Italy, Spain, Germany and the United States, predicts the possible trend of epidemic situation in different countries under different measures, and gives suggestions on rapid control. Analysis results show that the China’s epidemic has been basically controlled, Spain, Germany and Italy have reached the peak, and the United States is in a rapid growth period. According to the empirical transfer dynamics method, Spain, Germany and Italy should continue to adhere to the current prevention and control intervention mode, and the United States should intensify the efforts of prevention and control and make the epidemic enter the controllable stage as soon as possible. [ABSTRACT FROM AUTHOR]

The article aims to identify good practices in epidemic risk management. Thus, by knowing and applying these good practices, results can be obtained to mitigate the negative consequences produced by extremely risky events such as epidemics. In order to achieve the proposed goal, the following were specified as objectives of the article: identification of measures adopted in the four countries analyzed, namely Spain, Italy, Romania and Poland, to prevent the spread of the coronavirus pandemic and of solutions taken to reduce economic losses which arose as consequences of the imposition of these measures in the field of tourism. This paper combines elements of qualitative research, namely the use of methods such as data processing and analysis, comparative analysis, documentation of reports, and the presentation of conclusions. [ABSTRACT FROM AUTHOR]

Copyright of Regional Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the main title 'COVID-19 revolution: a new challenge for the internist' states, the global coronavirus infection disease 2019 (COVID-19) pandemic represented a new challenge for the internists. This paper is part of a series of articles written during the difficult period of the ongoing global pandemic and published all together in this fourth issue of the Italian Journal of Medicine, with the aim of sharing the direct experiences of those who were the first to face this severe emergency, expressing each point of view in the management of COVID-19 in relation to other diseases. Each article is therefore the result of many efforts and a joint collaboration between many colleagues from the Departments of Internal Medicine or Emergency Medicine of several Italian hospitals, engaged in the front line during the pandemic. These preliminary studies therefore cover diagnostic tools available to health care personnel, epidemiological reflections, possible new therapeutic approaches, discharge and reintegration procedures to daily life, the involvement of the disease not only in the lung, aspects related to various comorbidities, such as: coagulopathies, vasculitis, vitamin D deficiency, gender differences, etc.. The goal is to offer a perspective, as broad as possible, of everything that has been done to initially face the pandemic in its first phase and provide the tools for an increasingly better approach, in the hope of not arriving unprepared to a possible second wave. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus (RSV) bronchiolitis remains a significant global health burden, particularly in newborns and infants during their first year of life. The quest for an effective preventive strategy against RSV has long been sought, and recent developments have shown promise in the form of nirsevimab, a monoclonal antibody specifically designed for RSV prophylaxis. Valle d'Aosta was the first Italian region to propose universal prophylaxis with nirsevimab for newborns and infants in their first epidemic season as early as 2023–2024. This study describes the effectiveness and safety of the universal prevention program of RSV bronchiolitis using the monoclonal antibody nirsevimab in children resident in Valle d'Aosta born during the 2023–2024 epidemic season. There were 556 neonates born from 1 May 2023 to 15 February 2024. The risk of hospitalization for RSV bronchiolitis in 2023–2024 was 3.2%, compared to 7% in the 2022–2023 epidemic season (p < 0.001). After the start of the prophylaxis campaign with nirsevimab, the risk of hospitalization was 8.3% in the sample of infants who did not adhere to the prophylaxis, while no child in the sample of those treated (p < 0.001) was hospitalized for bronchiolitis. Few mild transient side effects were reported. This study shows the efficacy and safety of universal prophylaxis with nirsevimab in neonates, making Valle d'Aosta the first Italian region to offer universal prophylaxis to newborns without risk factors for RSV complications. Future research could further explore its long-term impact and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Ruth Paterson provides an overview of recently published articles that may be of interest to non-medical prescribers. Should you wish to look at any of the papers in more detail, a full reference is provided [ABSTRACT FROM AUTHOR]

Introduction: This paper aims to measure the performance of early detection methods, which are usually used for infectious diseases. Methodology: By using real data of confirmed Coronavirus cases from the Kingdom of Saudi Arabia and Italy, the moving epidemic method (MEM) and the moving average cumulative sums (Mov. Avg Cusum) methods are used in our simulation study. Results: Our results suggested that the CUSUM method outperforms the MEM in detecting the start of the Coronavirus outbreak. [ABSTRACT FROM AUTHOR]

An analysis of the COVID‐19 epidemic is proposed on the basis of the epiMOX dashboard (publicly accessible at https://www.epimox.polimi.it) that deals with data of the epidemic trends and outbreaks in Italy from late February 2020. Our analysis provides an immediate appreciation of the past epidemic development, together with its current trends by fostering a deeper interpretation of available data through several critical epidemic indicators. In addition, we complement the epiMOX dashboard with a predictive tool based on an epidemiological compartmental model, named SUIHTER, for the forecast on the near future epidemic evolution. [ABSTRACT FROM AUTHOR]

Simple Summary: West Nile virus (WNV) infection is a significant public health concern in Europe and Italy is one of the most affected countries since 2008. This brief report describes circulation cases of West Nile virus lineage 1 and lineage 2 strains occurring at the end of the 2022 vector season in Sicily and Tuscany, regions where no strains had ever been sequenced. The genetic characterization of the WNV strains responsible for these cases confirmed the peculiar WNV Italian scenario, marked by endemic Italian circulation spreading to new areas of the country (Tuscany case) and novel introductions from different European and Italian areas (Sicilian episode). It highlights the importance of continuous molecular surveillance for the early detection of viral circulation throughout the country and the monitoring of the arrival of new strains and the evolution of novel variants, in order to trigger adequate measures and limit its spread and human infections. West Nile virus (WNV) (Flaviviridae, Flavivirus) infection is a mosquito-borne zoonosis able of causing disease and death in humans and animals. Over the past decade, WNV infections have been a significant public health concern in Europe, and Italy has been among the most affected countries since 2008. The 2022 vector season has been characterized by an intense and early circulation of WNV. This report describes cases of co-circulation of WNV L1 and of WNV L2 occurring at the end of the 2022 vector season in Sicily and Tuscany, regions where no strains had ever been sequenced. The phylogenetic analysis of the detected strains confirmed the peculiar WNV scenario that has characterized the Italian West Nile disease (WND) epidemic since its appearance. The circulation observed in Tuscany was in fact a consequence of the spread of endemic strains to new areas while the Sicilian episodes were linked to new introductions of WNV L1 and L2 strains likely from other European countries. [ABSTRACT FROM AUTHOR]

The aim of this study was to describe the 2022–2023 bronchiolitis epidemic season (the second after COVID-19 pandemic and the first without social restriction), focusing on patients discharged home from a pediatric emergency department (PED) and on those revisited within 72 h. We performed a retrospective observational study in an Italian tertiary care children's hospital, reviewing PED accesses from 1 October 2022 to 31 March 2023. The number of hospitalizations for bronchiolitis was extracted from hospital discharge forms. A total of 512 patients diagnosed with bronchiolitis were admitted to PED (2.8% of total admissions). Accesses increased sharply from November to January, with a peak in December, in both admissions and hospitalizations. More than half of the patients (55.5%) were safely discharged home, while 38 (13.4%) came back to PED for a revisit. Overall PED accesses and hospitalizations for bronchiolitis increased since the previous epidemic season, and particularly compared to the pandemic and pre-pandemic eras. Empowering the collaboration between all healthcare provisioners is fundamental to suitable management of patients. Monitoring the epidemiology and seasonality of bronchiolitis is a starting point for an effective internal organization of pediatric departments and to further evaluate its socio-economic burden. [ABSTRACT FROM AUTHOR]

The second world cholera pandemic in Europe (1829-1849) was significant because of its geographic extent and the enormous numbers of people who fell ill or died. It was also singularly important because it demonstrated the profound levels of ignorance in both Europe and North America concerning the cause, modes of transmission, and treatment of cholera. This paper discusses the pandemic in the Kingdom of the Two Sicilies in great detail. Even though medical and public health authorities in this kingdom had several years to prepare for cholera's eventual arrival in 1836-1837, their elaborate preventive and therapeutic measures proved no more successful than elsewhere. Despite their efforts, it was estimated that there were 32,145 cases of cholera in the city of Naples by July 1837. Some 19,470 people were estimated to have died among the city's then 357,283 population. This amounted to a cholera-specific mortality rate of 54.5/1000 population. Sicily was also severely affected by the epidemic. It was estimated that 69,000 people died of cholera in Sicily, 24,000 of them in the city of Palermo. Two rural towns in the kingdom, San Prisco and Forio d'Ischia, were selected for in-depth epidemiologic study. The former had a population of 3700 in 1836-1837, while the latter had a population of 5500. The economic basis of both towns was agriculture. However, because Forio is located on an island, fishing and sea transport were then also important industries. Cholera appeared in San Prisco in July 1837 and quickly swept through the population. By August, the epidemic was essentially over. It is estimated that some 109 people died from cholera in San Prisco for a disease-specific mortality rate of 29.5/1000 population. The age range of those who died from cholera was 1 to 90 years. The majority of deaths (60.6 %) were among women. The first cases of cholera appeared in Forio d'Ischia in June 1837. The epidemic then peaked in July. It is estimated that approximately 316 people died from cholera in Forio out of a population of 5500. This resulted in a cholera-specific mortality rate of 57.5/1000 population. Among the first 42 fatal cases in whom the disease was documented on their death certificates, ages ranged from 15 to 88 years. The mean age was 52.4 years. The majority of deaths (57.1 %) were among women. We reached beyond the statistics of this epidemic by presenting an in-depth study of the first person to die from cholera in Forio d'Ischia, Nicola Antonio Insante. By focusing on him, we were able to develop a broad account of the social and economic consequences of his death on his family. At the same time, our research demonstrated the resiliency of his immediate and distant descendants. Similarly, we discuss the D'Ambra and Scola families of Forio d'Ischia, and the Caruso and Valenziano families of San Prisco, among whom a number died from cholera in 1837. [ABSTRACT FROM AUTHOR]

This paper is aimed to point out the current state of the COVID-19 pandemics in Italy. Currently, COVID-19 is an international crisis and all the continents have recorded a certain number of cases to date. After the first report of such infectious threat in China, the disease has progressively widespread thorough the world, and all the countries have attempted to point out specific protocols and therapies to face this new, unknown condition. The authors report an overview of how the Italian health service is facing this crisis after two months from the first internal reported cases. At last, special considerations highlight the non-sanitary issues of COVID-19 outbreak in Italy. [ABSTRACT FROM AUTHOR]

Background: As the novel SARS-CoV-2 pandemic occurred, no specific treatment was yet available. Inflammatory response secondary to viral infection might be the driver of severe diseases. We report the safety and efficacy (in terms of overall survival and hospital discharge) of the anti-IL6 tocilizumab (TCZ) in subjects with COVID-19.Methods: This retrospective, single-center analysis included all the patients consecutively admitted to our Hospital with severe or critical COVID-19 who started TCZ treatment from March 13th to April 03rd, 2020. A 1:2 matching to patients not treated with TCZ was performed according to age, sex, severity of disease, P/F, Charlson Comorbidity Index and length of time between symptoms onset and hospital admittance. Descriptive statistics and non-parametric tests to compare the groups were applied. Kaplan Meier probability curves and Cox regression models for survival, hospital discharge and orotracheal intubation were used.Results: Seventy-four patients treated with TCZ were matched with 148 matched controls. They were mainly males (81.5%), Caucasian (82.0%) and with a median age of 59 years. The majority (69.8%) showed critical stage COVID-19 disease. TCZ use was associated with a better overall survival (HR 0.499 [95% CI 0.262-0.952], p = 0.035) compared to controls but with a longer hospital stay (HR 1.658 [95% CI 1.088-2.524], p = 0.019) mainly due to biochemical, respiratory and infectious adverse events.Discussion: TCZ use resulted potentially effective on COVID-19 in terms of overall survival. Caution is warranted given the potential occurrence of adverse events.Financial Support: Some of the tocilizumab doses used in the subjects included in this analysis were provided by the "Multicenter study on the efficacy and tolerability of tocilizumab in the treatment of patients with COVID-19 pneumonia" (EudraCT Number: 2020-001110-38) supported by the Italian National Agency for Drugs (AIFA). No specific funding support was planned for study design, data collection and analysis and manuscript writing of this paper. [ABSTRACT FROM AUTHOR]

The complexity of interaction patterns among individuals in social systems plays a fundamental role on the inception and spreading of epidemic outbreaks. Empirical evidence has shown that the network of social interactions may co-evolve with the spread of the disease at comparable time-scales. Time-varying features have also been documented in the study of the propensity of individuals toward social activity, leading to the emergence of burstiness and temporal clustering. These temporal network dynamics are not independent of the disease evolution, whereby infected individuals could experience changes in their tendency to form connections, spontaneously or due to exogenous control policies. Neglecting these phenomena in modeling epidemics could lead to dangerous mispredictions of an outbreak and ineffective control interventions. In this paper, we propose a mathematically tractable modeling framework that relies on a limited number of parameters and encapsulates all these instances of complex phenomena through the lens of activity driven networks. Hawkes processes, Markov chains, and stability theory are leveraged to assist in the analysis of the framework and the formulation of theory-based control interventions. Our mathematical findings confirm the intuition that bursty activity patterns, typical of humans, facilitate epidemic spreading, while behavioral changes aiming at individual isolation could accelerate the eradication of epidemics. The proposed tools are demonstrated on a real-world case of influenza spreading in Italy. Overall, this work contributes new insight into the theory of temporal networks, laying the foundations for the analysis and control of spreading processes over networks with complex interaction patterns. [ABSTRACT FROM AUTHOR]

Background: During the global pandemic of A/H1N1/California/07/2009 (A/H1N1/Cal) influenza, many governments signed contracts with vaccine producers for a universal influenza immunization program and bought hundreds of millions of vaccines doses. We argue that, as Health Ministers assumed the occurrence of the worst possible scenario (generalized pandemic influenza) and followed the strong version of the Precautionary Principle, they undervalued the possibility of mild or weak pandemic wave. Methodology: An alternative decision rule, based on the non-extensive entropy principle, is introduced, and a different Precautionary Principle characterization is applied. This approach values extreme negative results (catastrophic events) in a different way and predicts more plausible and mild events. It introduces less pessimistic forecasts in the case of uncertain influenza pandemic outbreaks. A simplified application is presented using seasonal data of morbidity and severity among Italian children influenza-like illness for the period 2003–10. Principal Findings: Established literature results predict an average attack rate of not less than 15% for the next pandemic influenza [Meltzer M, Cox N, Fukuda K. The economic impact of pandemic influenza in the United States: implications for setting priorities for interventions. Emerg Infect Dis 1999;5:659–71; Meltzer M, Cox N, Fukuda K. Modeling the Economic Impact of Pandemic Influenza in the United States: Implications for Setting Priorities for Intervention. Background paper. Atlanta, GA: CDC, 1999. Available at: http://www.cdc.gov/ncidod/eid/vol5no5/melt_back.htm (7 January 2011, date last accessed))]. The strong version of the Precautionary Principle would suggest using this prediction for vaccination campaigns. On the contrary, the non-extensive maximum entropy principle predicts a lower attack rate, which induces a 20% saving in public funding for vaccines doses. Conclusions: The need for an effective influenza pandemic prevention program, coupled with an efficient use of public funding, calls for a rethinking of the Precautionary Principle. The non-extensive maximum entropy principle, which incorporates vague and incomplete information available to decision makers, produces a more coherent forecast of possible influenza pandemic and a conservative spending in public funding. [ABSTRACT FROM PUBLISHER]

Viral liver infections with parenteral transmission in Western countries are mostly caused by hepatitis B and hepatitis C viruses (HBV and HCV). This paper presents a mathematical model that describes the history of the spread of HBV and HCV infections in the general population in Italy. The analysis of the model and the results also provide some new insight into the mechanisms of the epidemics. The model structure is based on an underlying analysis of the various effects of the infection progression in the host, in order to incorporate into its parameters most of the information available from the literature. Moreover, incidence and prevalence curves of both HBV and HCV infections and of HBV/HCV co-infections are generated and qualitative aspects of the epidemic, such as possible endemic steady states and the basic reproduction number, are also analyzed. [ABSTRACT FROM AUTHOR]

Italian medical history in the age of positivism showed a strong interest in epidemics. This can be seen in Alfonso Corradi's monumental Annali (1865-1895) and in works of other 19th-century historians who addressed major public health issues in the newly unified country. Local history was also widely practiced in Italy, and it was instrumental in discovering and publishing a wealth of documentation on past epidemic and endemic diseases, as well as on measures such as quarantines that were invented or introduced in the peninsula as early as the late Middle Ages. The way Italian historians looked at epidemics in the 20th century was shaped by politics, religion and literature more than by demography, epidemiology, or technical knowledge in the medical field. This article and its accompanying bibliography will focus on regional historiography and deal with the history of plague, smallpox, cholera and malaria, and other diseases addressed in works published after the 1980s. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To evaluate the impact of the professional transition of new graduate nurses during the COVID‐19 pandemic. Background: The transition from the role of student to the professional role can be challenging for new graduate nurses for the acquisition of higher autonomy and responsibility. The COVID‐19 pandemic impacted the quality of the professional transition. Design: This was a cross‐sectional observational study following the Strengthening and Reporting of Observational Studies in Epidemiology checklist. Methods: One hundred and two nurses who graduated in three consecutive sessions (November 2019—pre‐pandemic, March 2020—pandemic outbreak, and November 2020—2nd wave) in a north Italian university located in the most affected Italian region by the COVID‐19 pandemic, completed an online survey assessing well‐being, risk of burnout, resilience, perceived stigma, strengths and limitations and quality of the professional transition. The study was performed between March and May 2021. Results: 81.4% of participants described the professional transition as worse than expected, and new graduate nurses who worked in COVID‐19 settings reported a more difficult transition to professional life. No differences emerged in burnout, mental well‐being and perceived stigma between new graduate nurses who worked in COVID‐19 settings and those who did not. Similarly, no differences emerged amongst the three graduated cohort sessions. The most commonly mentioned challenges faced during the transition were organisational aspects, suddenly acquired autonomy and lack of suitable coaching. Conclusion: New graduate nurses reported a challenging academic‐professional transition, in particular, those who worked in COVID‐19 settings. The mid‐ and long‐term impact of experiencing an academic‐professional transition in COVID‐19 settings should be assessed and monitored. Relevance to clinical practice: The professional transition of new graduate students should be adequately planned and monitored, new graduates should be assisted to develop realistic expectations about the transition, and an adequate coaching period should be guaranteed all the more during health emergencies. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two‐dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID‐19, which includes the effect of nonpharmaceutical interventions and a concurrent two‐dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio‐economic costs associated with the epidemics. We calibrate our model to the 2021 COVID‐19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first‐dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first‐dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID‐19 vaccination campaign, and increase preparedness for future epidemics. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is the most widely discussed topic worldwide in 2020, and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers.Objective: In this paper, a data analytics study on the diffusion of COVID-19 in Italy and the Lombardy Region is developed to define a predictive model tailored to forecast the evolution of the diffusion over time.Methods: Starting with all available official data collected worldwide about the diffusion of COVID-19, we defined a predictive model at the beginning of March 2020 for the Italian country.Results: This paper aims at showing how this predictive model was able to forecast the behavior of the COVID-19 diffusion and how it predicted the total number of positive cases in Italy over time. The predictive model forecasted, for the Italian country, the end of the COVID-19 first wave by the beginning of June.Conclusions: This paper shows that big data and data analytics can help medical experts and epidemiologists in promptly designing accurate and generalized models to predict the different COVID-19 evolutionary phases in other countries and regions, and for second and third possible epidemic waves. [ABSTRACT FROM AUTHOR]

Global mitigation strategies to tackle the threat posed by SARS-CoV-2 have produced a significant decrease of the severity of 2020/21 seasonal influenza, which might result in a reduced population natural immunity for the upcoming 2021/22 influenza season. To predict the spread of influenza virus in Italy and the impact of prevention and control measures, we present an age-structured Susceptible-Exposed-Infectious-Removed (SEIR) model including the role of social mixing patterns and the impact of age-stratified vaccination strategies and Non-Pharmaceutical Interventions (NPIs) such as school closures, partial lockdown, as well as the adoption of personal protective equipment and the practice of hand hygiene. We find that vaccination campaigns with standard coverage would produce a remarkable mitigation of the spread of the disease in moderate influenza seasons, making the adoption of NPIs unnecessary. However, in case of severe seasonal epidemics, a standard vaccination coverage would not be sufficiently effective in fighting the epidemic, thus implying that a combination with the adoption of NPIs is necessary to contain the disease. Alternatively, our results show that the enhancement of the vaccination coverage would reduce the need to adopt NPIs, thus limiting the economic and social impacts that NPIs might produce. Our results highlight the need to respond to the influenza epidemic by strengthening the vaccination coverage. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

In the last two years, there have been three major epidemic seasons in the territory of the European Union and the HPAI epizootic in 2021–2022 is the most severe in recent history. In Italy, the disease was introduced to dense poultry areas with serious economic consequences for the entire sector. In Lombardy, the analysis of the risk factors was carried out, also taking into account the density of domestic birds. In the most affected areas, 66.7% of the outbreaks occurred in the areas with the highest poultry density and the likelihood of an outbreak occurring increased with an increase in the density of birds per km2. In cells 10 × 10 km with a density greater than 10,000 birds/km2, the probability of outbreak occurrence was over 66.7%. The provinces involved in the last epidemic were the same involved in previous epidemics and, given the risk factors present in the area, it is plausible that the risk remains high also for future epidemic seasons. Therefore, to avoid the repetition of similar events, certain control measures shall be strengthened and vaccination considered as a complementary tool for the control of HPAI virus in risk areas. [ABSTRACT FROM AUTHOR]

This study describes a SARS-CoV-2 outbreak caused by the Delta (B.1.617.2) variant in a nursing home in Central Italy during October–November 2021. Trained interviewers collected data from residents, staff, and administration officers with an agreed informed consent procedure. Thirty-two (44.5%) out of 72 residents (median age 89 years) and six (26.1%) of 23 healthcare workers were found to be infected with SARS-CoV-2. Infections occurred more often among residents with a higher index of independence in daily living activities, suggesting an increased risk for those with more interactions. Twenty-five infected residents (78.1%) received the booster dose of mRNA anti-COVID-19 vaccine > 7 days before SARS-CoV-2 onset. Half of the infected residents had mild symptoms, and only three required hospitalisation, one of whom died from COVID-19 complications. The study underlines the effectiveness of a booster dose in providing a high protection against severe disease and hospitalisation even among vulnerable individuals infected with the Delta variant of concern. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, several countries have resorted to self-adaptive mechanisms that tailor non-pharmaceutical interventions to local epidemiological and health care indicators. These mechanisms reinforce the mutual influence between containment measures and the evolution of the epidemic. To account for such interplay, we develop an epidemiological model that embeds an algorithm mimicking the self-adaptive policy mechanism effective in Italy between November 2020 and March 2022. This extension is key to tracking the historical evolution of health outcomes and restrictions in Italy. Focusing on the epidemic wave that started in mid-2021 after the diffusion of Delta, we compare the functioning of alternative mechanisms to show how the policy framework may affect the trade-off between health outcomes and the restrictiveness of mitigation measures. Mechanisms based on the reproduction number are generally highly responsive to early signs of a surging wave but entail severe restrictions. The emerging trade-off varies considerably depending on specific conditions (e.g., vaccination coverage), with less-reactive mechanisms (e.g., those based on occupancy rates) becoming more appealing in favorable contexts. [ABSTRACT FROM AUTHOR]

• Prediction and control of the socio-economic important Olive Quick Decline Syndrome (OQDS). • Mathematical/computational model based on ordinary differential equations. • Qualitative analysis of the mathematical model • Threshold agronomic parameters to tackle OQDS. • Identification of cost-effective and socially sustainable solutions for OQDS. In Southern Italy, since 2013, there has been an ongoing Olive Quick Decline Syndrome (OQDS) outbreak, due to the bacterium Xylella fastidiosa , which has caused a dramatic impact from both socio-economic and environmental points of view. The main players involved in OQDS are represented by the insect vector, Philaenus spumarius , its host plants (olive trees and weeds) and the bacterium, X. fastidiosa. Current agronomic practices are mainly based on uprooting the sick olive trees and their surrounding ones, with later installment of olive cultivars more resistant to the bacterium infection. Unfortunately, both of these practices are having an undesirable impact on the environment (most of these olive trees were monumental ones) and on the economy. Based on a mathematical model expressed in terms of a nontrivial system of ordinary differential equations, our analysis has provided a clear picture of all possible steady states (feasible equilibria) and their stability properties, corresponding to a variety of different parameter scenarios; all of this has been illustrated by a set of computational experiments. A significant original contribution of this paper is the proof of the global asymptotic stability of each of the feasible equilibria under its existence assumptions, a fact that excludes multiple equilibria under the given conditions. It has emerged that the removal of a suitable amount of weed biomass (host plants of the juvenile stages of the insect vector of X. fastidiosa) from olive orchards and surrounding areas leads to the eradication of the epidemic, without requiring neither the removal nor the substitution of the existing olive trees. [ABSTRACT FROM AUTHOR]

The classical SEIR model, being an autonomous system of differential equations, has important limitations when representing a pandemic situation. Particularly, the geometric unimodal shape of the epidemic curve is not what is generally observed. This work introduces the βSEIR model, which adds to the classical SEIR model a differential law to model the variation in the transmission rate. It considers two opposite thrives generally found in a population: first, reaction to disease presence that may be linked to mitigation strategies, which tends to decrease transmission, and second, the urge to return to normal conditions that pulls to restore the initial value of the transmission rate. Our results open a wide spectrum of dynamic variabilities in the curve of new infected, which are justified by reaction and restoration thrives that affect disease transmission over time. Some of these dynamics have been observed in the existing COVID-19 disease data. In particular and to further exemplify the potential of the model proposed in this article, we show its capability of capturing the evolution of the number of new confirmed cases of Chile and Italy for several months after epidemic onset, while incorporating a reaction to disease presence with decreasing adherence to mitigation strategies, as well as a seasonal effect on the restoration of the initial transmissibility conditions. [ABSTRACT FROM AUTHOR]

Understanding public risk perception is an essential step to develop effective measures reducing the spread of disease outbreaks. Here we compare epidemic risk perceptions during two different periods of the COVID-19 pandemic in Italy and Sweden. To this end, we analyzed the results of two nationwide surveys carried out in both countries in two periods characterized by different infection rates: August (N = 4154) and November 2020 (N = 4168). Seven domains of epidemic risk perception were considered: likelihood along with (individual and population) impact, preparedness, and knowledge. The role of the context and period was explored in stratified and formal interaction analyses. In both countries, we found an intensification in epidemic risk perception from August to November 2020. Being male, older and having a higher relative income were associated with a lower perception of the likelihood of epidemics, while excess mortality was marginally related to higher odds. Compared to Sweden, Italy had a higher increase in perception of likelihood and impact, and a concurrent decrease in preparedness and knowledge. The different authority response to the COVID-19 pandemic is associated with a different change over time in risk perception. Regional differences in terms of excess mortality only marginally explained differences in risk perception. [ABSTRACT FROM AUTHOR]

Given the urgent informational needs connected with the diffusion of infection with regard to the COVID-19 pandemic, in this article, we propose a sampling design for building a continuous-time surveillance system. Compared with other observational strategies, the proposed method has three important elements of strength and originality: (1) it aims to provide a snapshot of the phenomenon at a single moment in time, and it is designed to be a continuous survey that is repeated in several waves over time, taking different target variables during different stages of the development of the epidemic into account; (2) the statistical optimality properties of the proposed estimators are formally derived and tested with a Monte Carlo experiment; and (3) it is rapidly operational as this property is required by the emergency connected with the diffusion of the virus. The sampling design is thought to be designed with the diffusion of SAR-CoV-2 in Italy during the spring of 2020 in mind. However, it is very general, and we are confident that it can be easily extended to other geographical areas and to possible future epidemic outbreaks. Formal proofs and a Monte Carlo exercise highlight that the estimators are unbiased and have higher efficiency than the simple random sampling scheme. [ABSTRACT FROM AUTHOR]

Early in the COVID-19 pandemic, asymptomatic transmission represented an important challenge for controlling the spread of SARS-CoV-2 through the traditional public health strategies. Further understanding of the contribution of asymptomatic infections to SARS-CoV-2 transmission has been of crucial importance for pandemic control. We conducted a retrospective epidemiological study to characterize asymptomatic COVID-19 cases occurred in the Apulia region, Italy, during the first epidemic wave of COVID-19 outbreak (February 29-July 7, 2020). We analyzed data collected in a regional platform developed to manage surveillance activities, namely, investigation and follow-up of cases and contacts, contact tracing, and laboratory and clinical data collection. We included all asymptomatic cases that were laboratory-confirmed during the appropriate follow-up, defined as persons infected with SARS-CoV-2 who did not develop symptoms/clinical signs of the disease. Between February 29 and July 7, 2020, a total of 4,536 cases were diagnosed with COVID-19 among 193,757 tests performed. The group of persons with asymptomatic SARS-CoV-2 infection consisted of 903 cases; the asymptomatic proportion was 19.9% (95% CI: 18.8-21.1%); this decreased with increasing age (OR: 0.89, 95% CI: 0.83-0.96; p = 0.001), in individuals with underlying comorbidities (OR: 0.55, 95% CI: 0.41-0.73; p < 0.001), and in males (OR: 0.69, 95% CI: 0.54-0.87; p = 0.002). The median asymptomatic SARS-CoV-2 RNA positive period was 19 days (IQR: 14-31) and the cumulative proportion of persons with resolution of infection 14 days after the first positive PCR test was 74%. As the public health community is debating the question of whether asymptomatic and late spreaders could sustain virus transmission in the communities, such cases present unique opportunities to gain insight into SARS-CoV-2 adaptation to human host. This has important implications for future COVID-19 surveillance and prevention. [ABSTRACT FROM AUTHOR]

In Southern Italy, since 2013, there has been an ongoing Olive Quick Decline Syndrome (OQDS) outbreak, due to the bacterium Xylella fastidiosa, which has caused a dramatic impact from both socio-economic and environmental points of view. Current agronomic practices are mainly based on uprooting the sick olive trees and their surrounding ones, with later installment of olive cultivars more resistant to the bacterium infection. Unfortunately, both of these practices are having an undesirable impact on the environment and on the economy. Here, a spatially structured mathematical model has been proposed to include a predator Zelus renardii as a possible biocontrol agent of the Xylella epidemic. The fact that Z. renardii has been reported to be a generalist predator implies that its introduction is not an efficient control strategy to eradicate a Xylella epidemic. Instead, a specialist predator, whenever identified, would lead to the eventual eradication of a Xylella epidemic. In either cases it has been confirmed that a significant reduction of the weed biomass can lead to the eradication of the vector population, hence of a Xylella epidemic, independently of the presence of predators. [ABSTRACT FROM AUTHOR]