Italian Ablation Registry 2020. Italian Association of Arrhythmology and Cardiostimulation

Background. This report describes the findings of the 2020 Italian Catheter Ablation Registry of the Italian Association of Arrhythmology and Cardiac Pacing (AIAC). Methods. Data collection was retrospective. A standardized questionnaire was completed by each of the participating centers. Results. A total of 10 378 ablation procedures were performed by 66 institutions. Most centers (70%) have an electrophysiology laboratory, and 23% a hybrid cardiac surgery laboratory. All centers have a 3D mapping system. The median number of electrophysiologists and nurses involved in the electrophysiology laboratory was 3.5 and 3, respectively. An electrophysiology technician was involved in 35% of all centers. In 88.2% of cases, catheter ablation was performed for supraventricular arrhythmias;the most frequently treated arrhythmia was atrial fibrillation (39.4%), followed by atrioventricular nodal reentrant tachycardia (18.6%), and common atrial flutter (10.6%). In 72.9% of patients, catheter ablation was performed using a 3D mapping system, with a "near-zero" fluoroscopic approach in 37.7% of all patients. Conclusions. The 2020 Italian Catheter Ablation Registry confirmed that the electrophysiology activity was markedly affected by the COVID-19 pandemic;atrial fibrillation is the most frequently treated arrhythmia with an increasing number of procedures performed with a 3D mapping system and a "near-zero" approach.

Tiered social distancing policies and epidemic control

Tiered social distancing policies have been adopted by many governments to mitigate the harmful consequences of COVID-19. Such policies have a number of well-established features, i.e. they are short-term, adaptive (to the changing epidemiological conditions), and based on a multiplicity of indicators of the prevailing epidemic activity. Here, we use ideas from Behavioural Epidemiology to represent tiered policies in an SEIRS model by using a composite information index including multiple indicators of current and past epidemic activity mimicking those used by governments during the COVID-19 pandemic, such as transmission intensity, infection incidence and hospitals' occupancy. In its turn, the dynamics of the information index is assumed to endogenously inform the governmental social distancing interventions. The resulting model is described by a hereditary system showing a noteworthy property, i.e. a dependency of the endemic levels of epidemiological variables from initial conditions. This is a consequence of the need to normalize the different indicators to pool them into a single index. Simulations suggest a rich spectrum of possible results. These include policy suggestions and identify pitfalls and undesired outcomes, such as a worsening of epidemic control, that can arise following such types of approaches to epidemic responses.

‘Period doubling’ induced by optimal control in a behavioral SIR epidemic model

We consider a behavioral SIR epidemic model to describe the action of the public health system aimed at enhancing the social distancing during an epidemic outbreak. An optimal control problem is proposed where the control acts in a specific way on the contact rate. We show that the optimal control of social distancing is able to generate a period doubling–like phenomenon. Namely, the ‘period’ of the prevalence is the double of the ‘period’ of the control, and an alternation of small and large peaks of disease prevalence can be observed. © 2022 Elsevier Ltd

Behavioral SIR models with incidence-based social-distancing

Most available behavioral epidemiology models have linked the behavioral responses of individuals to infection prevalence. However, this is a crude approximation of reality because prevalence is typically an unobserved quantity. This work considers a general endemic SIR epidemiological model where behavioral responses are incidence-based i.e., the agents perceptions of risks are based on available information on infection incidence. The differences of this modeling approach with respect to the standard ‘prevalence-based’ formulations are discussed and its dynamical implications are investigated. Both current and delayed behavioral responses are considered. We show that depending on the form of the ‘memory’ (i.e., in mathematical language, of the information delaying kernel), the endemic equilibrium can either be globally stable or destabilized via Hopf bifurcations yielding to stable recurrent oscillations. These oscillations can have a very long inter-epidemic periods and a very wide amplitude. Finally, a numerical investigation of the interplay between these behavior-related oscillations and seasonality of the contact rate reveals a strong synergic effect yielding to a dramatic amplification of oscillations. © 2021 Elsevier Ltd

Responsible Research and Innovation Associated With Risk Communication and Public Engagement on Health Emergency Preparedness at the Local Level

Responsible Research and Innovation (RRI) associated with public health emergency preparedness (PHEP) and response pose major challenges to the scientific community and civil society because a multistakeholder and interdisciplinary methodology is needed to foster public engagement. In 2017, within “Action plan on Science in Society related issues in Epidemics and Total pandemics”, twenty-three initiatives in eleven cities—Athens, Brussels, Bucharest, Dublin, Geneva, Haifa, Lyon, Milan, Oslo, Rome, and Sofia—represented effective opportunities for Mobilization and Mutual Learning on RRI issues in the matter of PHEP with different community-level groups. These experiences show that to effectively address a discourse on RRI-related issues in PHEP it is necessary to engage the local population and stakeholders, which is challenging because of needed competencies and resources. Under coronavirus disease 2019 (COVID-19) pandemic, we are proven that such a diversified multistakeholder engagement on RRI related to PHEP locally needs further elaboration and practical development. Copyright © 2022 Possenti, De Mei, Kurchatova, Green, Drager, Villa, d'Onofrio, Saadatian-Elahi, Moore, Brattekas, Karnaki, Beresniak, Popa and Greco.

Spatial behavioural responses to the spread of an infectious disease can suppress Turing and Turing-Hopf patterning of the disease

Reducing risky behaviour and/or avoiding sites where the risk of infection is perceived as higher (by social and/or spatial distancing) represent the two main forms of non-pharmaceutical behavioural responses of humans to the threats of infectious diseases. Here we investigate, within a reaction-diffusion setting, a family of new models for an endemic SIR (susceptible-infective-removed) infectious disease for which no vaccine is available and individuals' responses to the infection threat are entirely based on changes either in their social behaviour or in their mobility behaviour, that is avoiding to visit sites with a large infection prevalence. First, we derive general conditions for the onset of Turing patterns for a general family of spatially inhomogeneous SIR models with a prevalence-dependent contact rate and constant recruitment. Then, we characterize our main family of models where the behavioural response also includes a spatial component, and show the condition bringing to the mitigation, or even the destruction, of Turing patterns. The same conditions can allow the transition from Turing-Hopf spatio-temporal patterns to pure Hopf temporal patterns. The same is also done for two SIS models. These results bring an inference of interest: the reduction of spatial clustering typically observed during the course of an epidemics might be related to a combination of agents' spontaneous social and spatial distancing. To validate our theoretical results and further explore other spatio-temporal effects of the proposed spatial behavioural responses, numerical simulations of a specific instance of the above-mentioned family of SIR models have been performed. (C) 2019 Published by Elsevier B.V.

Implantable cardioverter defibrillator multisensor monitoring during home confinement caused by the covid-19 pandemic

Background: Utilization of remote monitoring platforms was recommended amidst the COVID-19 pandemic. The HeartLogic algorithm combines data from multiple implantable cardioverter defibrillator (ICD) sensors (first and third heart sounds, intrathoracic impedance, respirations, night heart rate, and patient activity) to provide integrated data that may allow for detection of early signs of worsening HF. Purpose: We examined whether the HeartLogic platform may elucidate behavioral changes that impact HF decompensation, and the possible consequences of home confinement caused by the COVID-19 pandemic. Methods: The Italian lockdown was imposed from March 8th to May 18th. On March 8th 2020, the HeartLogic feature was active in 349 ICD and cardiac resynchronization therapy ICD patients at 20 Italian centers. The period from January 1st to July 19th was divided in 3 phases: Pre-Lockdown (weeks 1-11), Lockdown (weeks 12-20), Post-Lockdown (weeks 21-29). Results: Immediately after the implementation of stay at home orders (week 12) we observed a significant drop in median activity level (65min [36-103] in week 12 vs. 101min [61-140] in Pre-Lockdown;p < 0.001), while there was no difference in the other contributing sensors. The median composite HeartLogic index increased at the end of Lockdown (4.7 [1.3-10.2] in week 20 vs. 2.5 [0.7-7.0] in Pre-Lockdown;p = 0.019). The weekly rate of HeartLogic alerts was significantly higher during Lockdown (1.56 alerts/week/100pts, 95%CI:1.15-2.06;IRR = 1.71, p = 0.014) and Post-Lockdown (1.37 alerts/week/100pts, 95%CI:0.99-1.84;IRR = 1.50, p = 0.072) than that reported in Pre-Lockdown (0.91 alerts/week/100pts, 95%CI:0.64-1.27). However, the median duration of alert state and the maximum index value did not change among phases, as well as the proportion of alerts followed by clinical actions at the centers (Pre-Lockdown: 31%, Lockdown: 22%, Post- Lockdown: 28%), and the proportion of alerts fully managed remotely (i.e. no in-clinic visits) (Pre-Lockdown: 89%, Lockdown: 90%, Post- Lockdown: 88%). Conclusions: The system was sensitive to the behavioral changes occurred during the lockdown, i.e. decrease in activity. However, the home confinement had no impact on the other sensors. The higher rate of HeartLogic alerts during lockdown and the increase in the median index after 8 weeks of home confinement suggest the worsening of the HF status, possibly explained by the behavioral changes. Nonetheless, the management of the HF detected events (actions performed and management strategy) was not impacted by the restrictions.

A new approach to the dynamic modeling of an infectious disease

In this work we propose a delay differential equation as a lumped parameter or compartmental infectious disease model featuring high descriptive and predictive capability, extremely high adaptability and low computational requirement. Whereas the model has been developed in the context of COVID-19, it is general enough to be applicable with such changes as necessary to other diseases as well. Our fundamental modeling philosophy consists of a decoupling of public health intervention effects, immune response effects and intrinsic infection properties into separate terms. All parameters in the model are directly related to the disease and its management;we can measure or calculate their values a priori basis our knowledge of the phenomena involved, instead of having to extrapolate them from solution curves. Our model can accurately predict the effects of applying or withdrawing interventions, individually or in combination, and can quickly accommodate any newly released information regarding, for example, the infection properties and the immune response to an emerging infectious disease. After demonstrating that the baseline model can successfully explain the COVID-19 case trajectories observed all over the world, we systematically show how the model can be expanded to account for heterogeneous transmissibility, detailed contact tracing drives, mass testing endeavours and immune responses featuring different combinations of temporary sterilizing immunity, severity-reducing immunity and antibody dependent enhancement. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Modelling of Natural Phenomena is the property of EDP Sciences 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.)

The effects of containment measures in the Italian outbreak of COVID-19.

The COVID-19 pandemic is spreading worldwide. Italy emerged early on as the country with the largest outbreak outside Asia. The outbreak in Northern Italy demonstrates that it is fundamental to contain the virus' spread at a very early stage of diffusion. At later stages, no containment measure, even if strict, can prevent the saturation of the hospitals and of the intensive care units in any country. Here we show that it is possible to predict when the intensive care units will saturate, within a few days from the beginning of the exponential growth of COVID-19 intensive care patients. Using early counts of intensive care patients, we predict the saturation for Lombardy, Italy. We also assess short-term and long-term lockdown effects on intensive care units and number of deaths. Governments should use the Italian outbreak as a precedent and implement appropriate containment measures to prevent the saturation of their intensive care units and protect their population, also, and above all, in anticipation of a possible second exponential spread of infections.