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We aimed to develop and validate a COVID-19 specific scoring system, also including some ECG features, to predict all-cause in-hospital mortality at admission. Patients were retrieved from the ELCOVID study (ClinicalTrials.gov identifier: NCT04367129), a prospective, multicenter Italian study enrolling COVID-19 patients between May to September 2020. For the model validation, we randomly selected two-thirds of participants to create a derivation dataset and we used the remaining one-third of participants as the validation set. Over the study period, 1014 hospitalized COVID-19 patients (mean age 74 years, 61% males) met the inclusion criteria and were included in this analysis. During a median follow-up of 12 (IQR 7-22) days, 359 (35%) patients died. Age (HR 2.25 [95%CI 1.72-2.94], p < 0.001), delirium (HR 2.03 [2.14-3.61], p = 0.012), platelets (HR 0.91 [0.83-0.98], p = 0.018), D-dimer level (HR 1.18 [1.01-1.31], p = 0.002), signs of right ventricular strain (RVS) (HR 1.47 [1.02-2.13], p = 0.039) and ECG signs of previous myocardial necrosis (HR 2.28 [1.23-4.21], p = 0.009) were independently associated to in-hospital all-cause mortality. The derived risk-scoring system, namely EL COVID score, showed a moderate discriminatory capacity and good calibration. A cut-off score of ≥ 4 had a sensitivity of 78.4% and 65.2% specificity in predicting all-cause in-hospital mortality. ELCOVID score represents a valid, reliable, sensitive, and inexpensive scoring system that can be used for the prognostication of COVID-19 patients at admission and may allow the earlier identification of patients having a higher mortality risk who may be benefit from more aggressive treatments and closer monitoring., (© 2024. The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI).)

Background: In March 2020, with the scope to reduce the spread of COVID-19, most national governments around the world canceled in-person education and moved to online learning. Therefore, teachers and students had to adapt a new way of teaching. Most of Italian teachers never had such an experience before and encountered difficulties in effectively carrying out this process on their own. Difficulties that can naturally increase anxiety and stress, leading, in situations perceived as extreme, to burnout syndrome., Objectives: This paper endeavored to verify levels of job stress and burnout of Italian teachers caused by the COVID-19 pandemic using the validated Maslach Burnout Inventory-General. This study aimed to measure the association among the three main dimensions of burnout and the variables of teachers' personal and working lives that changed due to COVID-19., Method: The aim of this paper was to verify burnout state and to measure the association among the three dimensions of burnout and the personal and working lives of Italian teachers using structural equation model analysis. The analysis was conducted in December 2021 and considered the situation in which the Italian teachers (from primary to middle and upper school) are working since March 2020., Results: The results showed that teachers were emotionally exhausted; they did not feel able to fully fulfill their task towards the students. This involved a high absenteeism, a lower quality of work performance and the impossibility of making an objective evaluation of the students with an inevitable flattening of the class level. In contrast, the study shows that teachers who experienced few problems had relatively low levels of burnout., Conclusion: The findings brought out some proposals to reduce the risk of burnout and increase the individual well-being of schoolwork organization with positive effects on the lives of students: to strengthen social identity, to avoid a full-time online connection, to promote a psychological support service and to promote resilience training.

Background: Cardiovascular comorbidities are a common cause of death in COVID-19 and the aim of this study is to evaluate the effect of comorbidities on mortality in COVID-19 patients., Methods: In this retrospective observational study we enrolled 1049 patients hospitalized with confirmed SARS-CoV-2 infection in a single Italian Center from 21 February to 20 March 2020 Evaluated risk factors (RFs) were: advanced age, gender, hypertension, diabetes, atrial fibrillation, hyperlipidemia, chronic kidney disease, thyroid disease, chronic obstructive pulmonary disease, malignancy, stroke, cardiovascular disease, and peripheral vascular disease. Endpoint of the study was death from any cause. A multivariate logistic regression model was built using covariates that showed as statistically significant at univariate regression analysis., Results: Median age at presentation was 71.1 years (IQR: 59.1-80.5); 244 (72.2%) were males. Primary outcome occurred in 338 patients (32.2%). In decedents, median survival from Hospitalization was 6 (IQR: 3-10) days. 264 decedents had 1 RF, 120 had 2 RFs and 39 had ≥3 RFs. At multivariate logistic regression model, variables associated with primary outcome were: age class (64-69 years) (OR 3.03, CI: 1.75-5.31, P<0.001), age class (70-88 years) (OR 10.08, CI: 6.67-15.72, P<0.001), age class (≥88 years) (OR 23.99, CI: 13.21-44.82, P<0.001), male gender (OR 1.88, CI: 1.36-2.62, P<0.001), diabetes (OR 1.56, CI: 1.07-2.26, P=0.02), stroke (OR 3.41, CI: 1.33-9.91, P=0.015)., Conclusions: Age, male gender, presence of diabetes and stroke appeared as independent predictors of mortality in COVID-19 patients. A table for risk of 30 days-mortality in SARS-CoV-2 infection was built, based on odds ratios derived from multivariate regression analysis.

Background: Acute right ventricular (RV) failure is common in patients hospitalized with COVID-19. Compared to the conventional echocardiographic parameters, right ventricular longitudinal strain (RVLS) is more sensitive and accurate for the diagnosis of RV systolic dysfunction., Objective: Our purpose was to investigate the sustained RV dysfunction echo-quantified by RVLS in patients recovered from severe COVID-19. Furthermore, we aimed to assess whether disseminated intravascular coagulation (DIC) has a key role to predict the impaired RV strain., Methods: Of 198 consecutive COVID-19 patients hospitalized from March 1, 2020, to April 15, 2020, 45 selected patients who survived from severe COVID-19 were enrolled in the study and referred to our echo-lab for transthoracic echocardiography 6-months after discharge. RVLS was calculated as the mean of the strain values of RV free wall. DIC was defined with a validated scoring system: DIC score equal to or more than 5 is compatible with overt-DIC. Categories of acute respiratory distress syndrome (ARDS) were defined based on PaO 2 /FiO 2 ratio., Results: A total 26 of 45 patients showed impaired RVLS at 6-months' follow-up. DIC score was significantly higher in patients with worse RVLS than in those with better RVLS (4.8 ± .5 vs. 3.6 ± .6, p =.03). Stages of ARDS did not modulate this relationship. Finally, overt-DIC results the only independent predictor of sustained RV dysfunction (OR 1.233, 95% CI 1.041-1.934, p =.043)., Conclusions: Sustained RV impairment frequently occurs in patients recovered from severe COVID-19. DIC plays a key role, resulting in an independent predictor of sustained RV dysfunction., (© 2022 Wiley Periodicals LLC.)

Considerable uncertainty surrounds the timeline of introductions and onsets of local transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) globally 1-7 . Although a limited number of SARS-CoV-2 introductions were reported in January and February 2020 (refs. 8,9 ), the narrowness of the initial testing criteria, combined with a slow growth in testing capacity and porous travel screening 10 , left many countries vulnerable to unmitigated, cryptic transmission. Here we use a global metapopulation epidemic model to provide a mechanistic understanding of the early dispersal of infections and the temporal windows of the introduction of SARS-CoV-2 and onset of local transmission in Europe and the USA. We find that community transmission of SARS-CoV-2 was likely to have been present in several areas of Europe and the USA by January 2020, and estimate that by early March, only 1 to 4 in 100 SARS-CoV-2 infections were detected by surveillance systems. The modelling results highlight international travel as the key driver of the introduction of SARS-CoV-2, with possible introductions and transmission events as early as December 2019 to January 2020. We find a heterogeneous geographic distribution of cumulative infection attack rates by 4 July 2020, ranging from 0.78% to 15.2% across US states and 0.19% to 13.2% in European countries. Our approach complements phylogenetic analyses and other surveillance approaches and provides insights that can be used to design innovative, model-driven surveillance systems that guide enhanced testing and response strategies., (© 2021. The Author(s).)

Background: Patients with coronavirus disease 2019 (COVID-19) exhibit high thrombotic risk. The evidence on a potential independent prognostic role of antiplatelet treatment in those patients is limited. The aim of the study was to evaluate the prognostic impact of pre-admission low-dose acetylsalicylic acid (ASA) in a wide series of hospitalized patients with COVID-19., Methods: This cohort study included 984 COVID-19 patients stratified according to ASA intake before hospitalization: ASA + (n = 253) and ASA - (n = 731). Patients were included in ASA + group if they received it daily in the 7 days before admission. 213 (83%) were on ASA 100 mg daily. Primary endpoint was a composite of in-hospital death and/or need for respiratory support upgrade, secondary endpoints were in-hospital death and need for respiratory support upgrade., Results: Mean age was 72 [62; 81] with 69% of male patients. ASA + patients were significantly older, with higher prevalence of comorbidities. No significant differences regarding the degree of respiratory dysfunction were observed. At 30-day Kaplan-Meier analysis, ASA + patients had higher survival free from the primary endpoint and need for respiratory support upgrade, conversely in-hospital death did not significantly differ between groups. At multivariate analysis ASA intake was independently associated with a lower probability of reaching primary endpoint (HR 0.697, 95% C.I. 0.525-0.924; p = 0.012)., Conclusions: In COVID-19 patients undergoing hospitalization, pre-admission treatment with ASA is associated with better in-hospital outcome, mainly driven by less respiratory support upgrade., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling., Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia., Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO 2 /FiO 2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori., Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively., Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia., (©Geza Halasz, Michela Sperti, Matteo Villani, Umberto Michelucci, Piergiuseppe Agostoni, Andrea Biagi, Luca Rossi, Andrea Botti, Chiara Mari, Marco Maccarini, Filippo Pura, Loris Roveda, Alessia Nardecchia, Emanuele Mottola, Massimo Nolli, Elisabetta Salvioni, Massimo Mapelli, Marco Agostino Deriu, Dario Piga, Massimo Piepoli. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2021.)

Association of renin-angiotensin system inhibitors with risk of death in patients with hypertension (HTN) and coronavirus disease 2019 (COVID-19) is not well characterized. The aim of this study was to evaluate the outcomes of patients with HTN and COVID-19 with respect to different chronic antihypertensive drug intake. We performed a retrospective, observational study from a large cohort of patients with HTN and with a laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection admitted to the Emergency Rooms (ER) of the Piacenza Hospital network from February 21, 2020 to March 20, 2020. There were 1050 patients admitted to the ERs of the Piacenza Hospital network with COVID-19. HTN was present in 590 patients [median age, 76.2 years (IQR 68.2-82.6)]; 399 (66.1%) patients were male. Of them, 248 patients were chronically treated with ACEi, 181 with ARBs, and 161 with other drugs (O-drugs) including beta blockers, diuretics and calcium-channel inhibitors. With respect to the antihypertensive use, there was no difference between comorbid conditions. During a follow-up of 38 days (IQR 7.0-46.0), 256 patients (43.4%) died, without any difference stratifying for antihypertensive drugs. Of them, 107 (43.1%) were in ACEi group vs 67 (37%) in ARBs group vs 82 (50.7%) in O-drugs group, (log-rank test: p = 0.066). In patients with HTN and COVID-19, neither ACEi nor ARBs were independently associated with mortality. After adjusting for potential confounders in risk prediction, the rate of death was similar. Our data confirm Specialty Societal recommendations, suggesting that treatment with ACEIs or ARBs should not be discontinued because of COVID-19.

Aims: Our aim was to describe the electrocardiographic features of critical COVID-19 patients., Methods and Results: We carried out a multicentric, cross-sectional, retrospective analysis of 431 consecutive COVID-19 patients hospitalized between 10 March and 14 April 2020 who died or were treated with invasive mechanical ventilation. This project is registered on ClinicalTrials.gov (identifier: NCT04367129). Standard ECG was recorded at hospital admission. ECG was abnormal in 93% of the patients. Atrial fibrillation/flutter was detected in 22% of the patients. ECG signs suggesting acute right ventricular pressure overload (RVPO) were detected in 30% of the patients. In particular, 43 (10%) patients had the S1Q3T3 pattern, 38 (9%) had incomplete right bundle branch block (RBBB), and 49 (11%) had complete RBBB. ECG signs of acute RVPO were not statistically different between patients with (n = 104) or without (n=327) invasive mechanical ventilation during ECG recording (36% vs. 28%, P = 0.10). Non-specific repolarization abnormalities and low QRS voltage in peripheral leads were present in 176 (41%) and 23 (5%), respectively. In four patients showing ST-segment elevation, acute myocardial infarction was confirmed with coronary angiography. No ST-T abnormalities suggestive of acute myocarditis were detected. In the subgroup of 110 patients where high-sensitivity troponin I was available, ECG features were not statistically different when stratified for above or below the 5 times upper reference limit value., Conclusions: The ECG is abnormal in almost all critically ill COVID-19 patients and shows a large spectrum of abnormalities, with signs of acute RVPO in 30% of the patients. Rapid and simple identification of these cases with ECG at hospital admission can facilitate classification of the patients and provide pathophysiological insights., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions, please email: journals.permissions@oup.com.)

It has long been known that epidemics can travel along communication lines, such as roads. In the current COVID-19 epidemic, it has been observed that major roads have enhanced its propagation in Italy. We propose a new simple model of propagation of epidemics which exhibits this effect and allows for a quantitative analysis. The model consists of a classical SIR model with diffusion, to which an additional compartment is added, formed by the infected individuals travelling on a line of fast diffusion. The line and the domain interact by constant exchanges of populations. A classical transformation allows us to reduce the proposed model to a system analogous to one we had previously introduced Berestycki et al. (J Math Biol 66:743-766, 2013) to describe the enhancement of biological invasions by lines of fast diffusion. We establish the existence of a minimal spreading speed, and we show that it may be quite large, even when the basic reproduction number [Formula: see text] is close to 1. We also prove here further qualitative features of the final state, showing the influence of the line.

Studies have described clinical features of patients with coronavirus disease (COVID-19). However, limited data concerning the clinical characteristics of the Italian deaths are available. We aim to describe the clinical and epidemiological characteristics of 320 deceased from the Italian experience. We retrospectively collected all consecutive non-survivor patients with laboratory-confirmed COVID-19 infection admitted to the Emergency Rooms (ERs) Piacenza Hospital Network during the first month of COVID-19 pandemic in Italy. Clinical history, comorbidities, laboratory findings and treatment were recorded for each patient. A total of 1050 patients with confirmed COVID-19 pneumonia were admitted to the ERs between 24 February and 22 March 2020. Three hundred and twenty (30.5%) patients died with a median age of 78.0 years, 205 (64%) non-survivors were above 65 years old, 230 (71.9%) were male. Non-survivor patients showed frequently several coexisting medical conditions, with hypertension being the most common comorbidity (235 patients, 73.4%). The in-hospital mortality did not change during the progression of the pandemic. In this retrospective Italian study, most of COVID-19 deceased patients were elderly male aged over than 65 years. Hypertension was the most common coexisting disease. In-hospital mortality was high and showed no variation during the first month of the COVID-19 italian epidemic., (© 2020 Wiley Periodicals LLC.)

A new difficulty presented in the form of COVID-19 has hit the global economy. The tourism market, based on strong interaction and mobility, becomes the main recipient of the pandemic crisis, resulting in the closure and failure of many companies, at the same time changing the relationship with sustainability, causing devastating effects. The purpose of this document, therefore, is to explore the relationship between resilience and complexity within the tourism sector to cope with the COVID-19 crisis. To answer the research questions, the authors conducted a systematic literature review using research papers published in international journals in recent two years. The qualitative analysis on the evaluation of our work was supplemented by the quantitative analysis conducted using statistical criteria and bibliometric indicators. The results explain the link between resilience and complexity in this sector in response to the pandemic crisis, is mainly linked to four main themes: 1) environmental quality, 2) transport and destination, 3) innovation and technology, 4) business organization. In this context, therefore alongside the negative aspects related to the pandemic, this can be exploited to direct the economy towards a more resilient system. Furthermore, our research has shown that the hospitality sector is the most affected in the world. The contribution of this study to research is identified in clarifying the evolutionary development of COVID-19 research and its link with environmental and sustainable factors. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic, "smart working" (hereafter SW) has become the norm for millions of workers around the world. A new way of working for most workers and in particular in Italy, a country where the use of SW was extremely rare before the pandemic. The aim of this paper, was to highlights whether smart working, adopted to face and survive global crises, could be really a suitable tool to generate benefits for companies, society, reduce environmental impacts and guarantee autonomy and flexibility for workers as well as a balance between private life. The analysis was conducted on a sample of 2753 individuals based in Italy during the period January and February 2021 using PLS-SEM model. The contribution of this study to research is identified in clarifying the potential of SW to create sustainable Smart Cities. • Company invests in ICT for smart working model • Smart working offers advantages to companies that apply this operating mode • Smart working do not influences positively the employees' quality of life • Smart working revolutionizes the essential characteristics of a community • The smart working must be regulated avoiding that the use of technological tools alters employment relations and negatively affects the personality of the worker [ABSTRACT FROM AUTHOR]

This paper analyzes the literature on problematic COVID-19 information published at the onset of the pandemic in 2020. It explores how scientific research has addressed this issue from a disciplinary, methodological and substantive perspective in different world regions. Three hundred seventy-eight articles were analyzed using content analysis and computational methods, including social network analysis and text mining. The study revealed a multidisciplinary field characterized by substantial contributions from medicine and social sciences and with a certain degree of interdisciplinarity and international collaborations. Research devoted particular attention to infodemic and conspiracy theories and their impact on compliance with health-protective behaviors, and showed a general preference for quantitative methods such as surveys. Most contributions focused on European and Americas regions and were from authors working in the same areas. Attention to various topics was also geographically differentiated. For example, conspiracy theories and informational factors that may influence COVID-compliant behaviors characterize the whole corpus, but Western research did the lion's share. Similarly, the attention paid to different social media platforms differed geographically. Worldwide attention was dedicated to global social media platforms such as Twitter, WhatsApp, Facebook and Instagram. In contrast, geographically specific platforms (e.g., VKontakte or WeChat) have been studied less and mainly in regions where they are used the most. Focusing on a crucial period of the COVID-19 infodemic such as the first year of the pandemic, this study contributes to the literature on problematic information by highlighting how misinformation and disinformation research was carried out within and at the crossroads of disciplines and geographic regions. It suggests under-explored areas by contrast with the most relevant trends and establishes a foundation for benchmarking with post-2020 research on this topic. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic carries a high burden of morbidity and mortality worldwide. We aimed to identify possible predictors of in-hospital major cardiovascular (CV) events in COVID-19. Methods: We retrospectively included patients hospitalized for COVID-19 from 10 centers. Clinical, biochemical, electrocardiographic, and imaging data at admission and medications were collected. Primary endpoint was a composite of in-hospital CV death, acute heart failure (AHF), acute myocarditis, arrhythmias, acute coronary syndromes (ACS), cardiocirculatory arrest, and pulmonary embolism (PE). Results: Of the 748 patients included, 141(19%) reached the set endpoint: 49 (7%) CV death, 15 (2%) acute myocarditis, 32 (4%) sustained-supraventricular or ventricular arrhythmias, 14 (2%) cardiocirculatory arrest, 8 (1%) ACS, 41 (5%) AHF, and 39 (5%) PE. Patients with CV events had higher age, body temperature, creatinine, high-sensitivity troponin, white blood cells, and platelet counts at admission and were more likely to have systemic hypertension, renal failure (creatinine ≥ 1.25 mg/dL), chronic obstructive pulmonary disease, atrial fibrillation, and cardiomyopathy. On univariate and multivariate analysis, troponin and renal failure were associated with the composite endpoint. Kaplan–Meier analysis showed a clear divergence of in-hospital composite event-free survival stratified according to median troponin value and the presence of renal failure (Log rank p < 0.001). Conclusions: Our findings, derived from a multicenter data collection study, suggest the routine use of biomarkers, such as cardiac troponin and serum creatinine, for in-hospital prediction of CV events in patients with COVID-19. [ABSTRACT FROM AUTHOR]

Aims. Several studies have unveiled the great heterogeneity of COVID-19 pneumonia. Identification of the "vascular phenotype" (involving both pulmonary parenchyma and its circulation) has prognostic significance. Our aim was to explore the combined role of chest computed tomography (CT) scan and electrocardiogram (ECG) at hospital admission in predicting short-term prognosis and to draw pathophysiological insights. Methods and Results. We analyzed the chest CT scan and ECG performed at admission in 151 consecutive COVID-19 patients admitted between 20 March and 4 April 2020. All-cause mortality within 30 days was the primary endpoint. Median age was 71 years (IQR: 62–76). Severe pneumonia was present in 25 (17%) patients, and 121 (80%) had abnormal ECG. During a median follow-up of 7 days (IQR: 4–13), 54 (36%) patients died. Deceased patients had more severe pneumonia than survivors did (80% vs. 64%, p = 0.044). ECG in deceased patients showed more frequently atrial fibrillation/flutter (17% vs. 6%, p = 0.039) and acute right ventricular (RV) strain (35% vs. 10%, p < 0.001), suggesting the "vascular phenotype". ECG signs of acute RV strain (HR 2.46, 95% CIs 1.36–4.45, p = 0.0028) were independently associated with all-cause mortality in multivariable analysis, and in the likelihood ratio test, showed incremental prognostic value over chest CT scan, age, and C-reactive protein. Conclusions. Combining chest CT scan and ECG data improves risk stratification in COVID-19 pneumonia by identifying a distinctive phenotype with both parenchymal and vascular damage of the lung. Patients with severe pneumonia at chest CT scan plus ECG signs of acute RV strain have an extremely poor short-term prognosis. [ABSTRACT FROM AUTHOR]

Motivated by the rapid spread of coronavirus disease 2019 (COVID-19) in mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated on the basis of internationally reported cases and shows that, at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in mainland China but had a more marked effect on the international scale, where case importations were reduced by nearly 80% until mid-February. Modeling results also indicate that sustained 90% travel restrictions to and from mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community. [ABSTRACT FROM AUTHOR]