Potential short-term outcome of an uncontrolled COVID-19 epidemic in Lombardy, Italy, February to March 2020

Month-to-month all-cause mortality forecasting: a method allowing for changes in seasonal patterns

Forecasting of seasonal mortality patterns can provide useful information for planning health-care demand and capacity. Timely mortality forecasts are needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. In this article, we propose a flexible method for forecasting all-cause mortality in real time considering short-term changes in seasonal patterns within an epidemiologic year. All-cause mortality data have the advantage of being available with less delay than cause-specific mortality data. In this study, we use all-cause monthly death counts obtained from the national statistical offices of Denmark, France, Spain, and Sweden from epidemic seasons 2012-2013 through 2021-2022 to demonstrate the performance of the proposed approach. The method forecasts deaths 1 month ahead, based on their expected ratio to the next month. Prediction intervals are obtained via bootstrapping. The forecasts accurately predict the winter mortality peaks before the COVID-19 pandemic. Although the method predicts mortality less accurately during the first wave of the COVID-19 pandemic, it captures the aspects of later waves better than other traditional methods. The method is attractive for health researchers and governmental offices for aiding public health responses because it uses minimal input data, makes simple and intuitive assumptions, and provides accurate forecasts both during seasonal influenza epidemics and during novel virus pandemics.

Piloting a new method to estimate action thresholds in medicine through intuitive weighing

OBJECTIVES

In clinical decision-making, physicians take actions such as prescribing treatment only when the probability of disease is sufficiently high. The lowest probability at which the action will be considered, is the action threshold. Such thresholds play an important role whenever decisions have to be taken under uncertainty. However, while several methods to estimate action thresholds exist, few methods give satisfactory results or have been adopted in clinical practice. We piloted the adapted nominal group technique (aNGT), a new prescriptive method based on a formal consensus technique adapted for use in clinical decision-making.

DESIGN, SETTING AND PARTICIPANTS

We applied this method in groups of postgraduate students using three scenarios: treat for rifampicin-resistant tuberculosis (RR-TB), switch to second-line HIV treatment and isolate for SARS-CoV-2 infection.

INTERVENTIONS

The participants first summarise all harms of wrongly taking action when none is required and wrongly not taking action when it would have been useful. Then they rate the statements on these harms, discuss their importance in the decision-making process, and finally weigh the statements against each other.

MAIN OUTCOME MEASURES

The resulting consensus threshold is estimated as the relative weights of the harms of the false positives divided by the total harm, and averaged out over participants. In some applications, the thresholds are compared with an existing method based on clinical vignettes.

RESULTS

The resulting action thresholds were just over 50% for RR-TB treatment, between 20% and 50% for switching HIV treatment and 43% for COVID-19 isolation. These results were considered acceptable to all participants. Between sessions variation was low for RR-TB and moderate for HIV. Threshold estimates were moderately lower with the method based on clinical vignettes.

CONCLUSIONS

The aNGT gives sensible results in our pilot and has the potential to estimate action thresholds, in an efficient manner, while involving all relevant stakeholders. Further research is needed to study the value of the method in clinical decision-making and its ability to generate acceptable thresholds that stakeholders can agree on.

Investigating the relationship between interventions, contact patterns, and SARS-CoV-2 transmissibility

BACKGROUND

After a rapid upsurge of COVID-19 cases in Italy during the fall of 2020, the government introduced a three-tiered restriction system aimed at increasing physical distancing. The Ministry of Health, after periodic epidemiological risk assessments, assigned a tier to each of the 21 Italian regions and autonomous provinces. It is still unclear to what extent these different sets of measures altered the number of daily interactions and the social mixing patterns.

METHODS AND FINDINGS

We conducted a survey between July 2020 and March 2021 to monitor changes in social contact patterns among individuals in the metropolitan city of Milan, Italy, which was hardly hit by the second wave of the COVID-19 pandemic. The number of daily contacts during periods characterized by different levels of restrictions was analyzed through negative binomial regression models and age-specific contact matrices were estimated under the different tiers of restrictions. By relying on the empirically estimated mixing patterns, we quantified relative changes in SARS-CoV-2 transmission potential associated with the different tiers. As tighter restrictions were implemented during the fall of 2020, a progressive reduction in the mean number of daily contacts recorded by study participants was observed: from 15.9 % under mild restrictions (yellow tier), to 41.8 % under strong restrictions (red tier). Higher restrictions levels were also found to increase the relative contribution of contacts occurring within the household. The SARS-CoV-2 reproduction number was estimated to decrease by 17.1 % (95 %CI: 1.5-30.1), 25.1 % (95 %CI: 13.0-36.0) and 44.7 % (95 %CI: 33.9-53.0) under the yellow, orange, and red tiers, respectively.

CONCLUSIONS

Our results give an important quantification of the expected contribution of different restriction levels in shaping social contacts and decreasing the transmission potential of SARS-CoV-2. These estimates can find an operational use in anticipating the effect that the implementation of these tiered restriction can have on SARS-CoV-2 reproduction number under an evolving epidemiological situation.

Social Network Analysis of COVID-19 Sentiments: 10 Metropolitan Cities in Italy

The pandemic spread rapidly across Italy, putting the region’s health system on the brink of collapse, and generating concern regarding the government’s capacity to respond to the needs of patients considering isolation measures. This study developed a sentiment analysis using millions of Twitter data during the first wave of the COVID-19 pandemic in 10 metropolitan cities in Italy’s (1) north: Milan, Venice, Turin, Bologna; (2) central: Florence, Rome; (3) south: Naples, Bari; and (4) islands: Palermo, Cagliari. Questions addressed are as follows: (1) How did tweet-related sentiments change over the course of the COVID-19 pandemic, and (2) How did sentiments change when lagged with policy shifts and/or specific events? Findings show an assortment of differences and connections across Twitter sentiments (fear, anger, and joy) based on policy measures and geographies during the COVID-19 pandemic. Results can be used by policy makers to quantify the satisfactory level of positive/negative acceptance of decision makers and identify important topics related to COVID-19 policy measures, which can be useful for imposing geographically varying lockdowns and protective measures using historical data.

Pressure on the Health-Care System and Intensive Care Utilization During the COVID-19 Outbreak in the Lombardy Region of Italy: A Retrospective Observational Study in 43,538 Hospitalized Patients

During the spring of 2020, the COVID-19 epidemic caused an unprecedented demand for intensive care resources in Lombardy, Italy. Using data on 43,538 hospitalized patients admitted between February 21 and July 12, 2020, we evaluated variations in intensive care unit (ICU) admissions and mortality over three periods: the early phase (February 20-March 13), the period of highest pressure on healthcare (March 14-April 25, when COVID-19 patients exceeded the ICU pre-pandemic bed capacity), and the declining phase (April 26-July 12).

Compared to the early phase, patients above 70 years of age were admitted less often to an ICU during highest pressure on healthcare (odds ratio OR 0.47, 95%CI: 0.41-0.54) with longer delays (incidence rate ratio IRR 1.82, 95%CI: 1.52-2.18), and lower chances of death in ICU (OR 0.47, 95%CI: 0.34-0.64). Patients under 56 years of age reported more limited changes in the probability (OR 0.65, 95%CI: 0.56-0.76) and delay to ICU admission (IRR 1.16, 95%CI: 0.95-1.42) and an increased mortality (OR 1.43, 95%CI: 1.00-2.07). In the declining phase, all quantities decreased for all age groups.

These patterns may suggest that limited healthcare resources during the peak epidemic phase in Lombardy forced a shift in ICU admission criteria to prioritize patients with higher chances of survival.

Modeling and predicting the spread of COVID-19

The world is currently overwhelmed with the perils of the outbreak of the coronavirus disease 2019 (COVID-19) pandemic. As of May 18, 2020, there were 4,819,102 confirmed cases, of which there were 316,959 deaths worldwide. The devastating effects of the COVID-19 pandemic on the world economy are more grievous than many natural disasters like earthquakes and tsunamis in history. Understanding the spread pattern of COVID-19 and predicting the disease dynamics have been essential to assist policymakers and health practitioners in the public and private health sector in providing an efficient way of alleviating the effects of the pandemic across continents. Scholars have steadily worked to provide timely information. Nevertheless, there is a lack of information on which insights can be derived from all these endeavors, especially with regard to modeling and prediction techniques. In this study, we used a literature synthesis approach to provide a narrative review of the current research efforts geared toward predicting the spread of COVID-19 across continents. Such information is useful to provide a global perspective of the virus particularly with regard to modeling and prediction techniques and their outcomes. A total of 69 peer-reviewed articles were reviewed. We found that most articles were from Asia (34.8%) and Europe (23.2%), followed by North America (14.5%), and very few emanated from other continents including Africa and Australia (6.8% each), while no study was reported in Antarctica. Most of the modeling and predictions were based on compartmental epidemiologic models and a few used advanced machine learning techniques. While some models have accurately predicted the end of the epidemic in some countries, other predictions strongly deviate from reality. Interestingly, some studies showed that combining artificial intelligence with classical compartmental models provides a better prediction of the disease spread. Assumptions made when parameterizing the models might be wrong and might not suit the local contexts and might partly explain the observed deviation from the reality on the ground. Furthermore, lack of publicly available key data such as age, gender, comorbidity, and historical medical data of cases and deaths in some continents could limit researchers in addressing some essential aspects of the virus spread and its consequences.

[Impact of COVID-19 on mortality in the autonomous community of Castilla y León (Spain)]

OBJECTIVE

To estimate the increase in mortality associated with the SARS-CoV-2 coronavirus pandemic in the autonomous community of Castilla y León (Spain).

METHOD

Ecological study based on population and death data for the months of March 2016 to 2020 in Castilla y León. The general and provincial standardized rates, the relative risks of the year 2020 with respect to previous years and the risks adjusted by sex, periods and province, using Poisson regression, were calculated. Trend analysis was performed using joinpoint linear regression.

RESULTS

An increase in mortality was observed in March 2020 with respect to previous years, with an increase of 39% for men (relative risk [RR]: 1.39; 95% confidence interval [95%CI]: 1.32-1.47) and 28% for women (RR: 1.28; 95%CI: 1.21-1.35). The model predicts excess mortality of 775 deaths. In the trend analysis there is a significant turning point in 2019 in men, globally and for almost all provinces. The increase in mortality is general, although heterogeneous by sex, age group and province.

CONCLUSIONS

Although the observed increase in mortality cannot be totally attributed to the disease, it is the best estimate we have of the real impact on deaths directly or indirectly related to it. The number of declared deaths only reaches two thirds of the increase in mortality observed.

Use of telemedicine during the COVID-19 pandemic in patients with inflammatory arthritis: a retrospective study on feasibility and impact on patient-reported outcomes in a real-life setting

Close follow-up is mandatory in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS). During the Coronavirus Disease 2019 (COVID-19) pandemic, rheumatological care was rapidly reorganized during the first peak from March 1, 2020 to May 31, 2020, and all patients with RA, PsA, and AS being treated with a subcutaneous biologic disease-modifying anti-rheumatic drug or oral targeted synthetic disease-modifying anti-rheumatic drug were followed remotely. A retrospective database analysis of these 431 patients before and after this period is presented herein. A rheumatologist directly contacted all patients by telephone. Patients could also enter data on patient-reported outcomes remotely using the digital platform iAR Plus. General health (GH) and visual analog scale (VAS) pain were the main outcomes along with FACIT and disease-specific questionnaires (RADAI, ROAD, PROCLARA for RA, and BASDAI, BASGI, BASFI for AS). In all, 449 visits were postponed (69.9% of all scheduled visits); telephone evaluation was deemed inadequate in 193 instances, and patients underwent a standard outpatient visit. Comparing patients on telemedicine to those who underwent hospital visits, we found no statistically significant differences in GH (35.3 vs 39.3; p = 0.24), VAS (33.3 vs 37.1; p = 0.29), or other specific outcome measures in patients with RA, PsA, or AS. These results show that telemedicine has undoubted benefits, and in light of the ongoing COVID-19 pandemic, it is likely that many patients with these diseases may prefer it.

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity

BACKGROUND

The pressures exerted by the coronavirus disease 2019 (COVID-19) pandemic pose an unprecedented demand on healthcare services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities.

OBJECTIVE

We describe methods used by a university hospital to forecast case loads and time to peak incidence.

METHODS

We developed a set of models to forecast incidence among the hospital catchment population and to describe the COVID-19 patient hospital-care pathway. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care-pathway model according to expert opinion (ie, the static model). Once sufficient local data were available, trends for the time-dependent effective reproduction number were fitted, and the care pathway was reparameterized using hazards for real patient admission, referrals, and discharge (ie, the dynamic model).

RESULTS

The static model, deployed before the epidemic, exaggerated the bed occupancy for general wards (116 forecasted vs 66 observed), ICUs (47 forecasted vs 34 observed), and predicted the peak too late: general ward forecast April 9 and observed April 8 and ICU forecast April 19 and observed April 8. After April 5, the dynamic model could be run daily, and its precision improved with increasing availability of empirical local data.

CONCLUSIONS

The models provided data-based guidance for the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when the population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.

Sars-Cov2 Not Detected in a Pediatric Population With Acute Respiratory Infection in Primary Care in Central and Southern Italy From November 2019 to Early March 2020

Background: In December 2019, a novel coronavirus named SARS-CoV-2 started circulating in China and this led to a major epidemic in Northern Italy between February and May 2020. Young children (aged <5 years) seem to be less affected by this coronavirus disease (COVID-19) compared to adults, although there is very little information on the circulation of this new virus among children in Italy. We retrospectively tested nasopharyngeal swabs for SARS-CoV-2 in samples collected in young children between November, 2019 and March, 2020 in the context of the RSV ComNet study. Methods: Two networks of primary care pediatricians in Lazio (Central Italy) and Puglia (Southern Italy) collected nasopharyngeal swabs from children, aged <5 years, presenting with symptoms for an acute respiratory infection (ARI). The RSV ComNet study is a multicenter study implemented to estimate the burden of RSV in young children (aged <5 years) in the community. Swabs were sent to a central reference laboratory and tested for 14 respiratory viruses through RT-PCR. All collected samples were retrospectively tested for SARS-CoV-2 using RT-PCR (Istituto Superiore di Sanità protocol). Results: A total of 293 children with ARI were identified in the two participating networks. The highest number of cases were recruited in weeks 51/2019 and 3/2020. The majority of patients (57%) came from the Lazio region. All of the 293 samples tested negative for SARS-Cov2. Rhinovirus was the most frequently detected virus (44%), followed by RSV (41%) and influenza viruses (14%). Conclusions: Our study shows that in Lazio (a region of intermediate SARS-COV-2 incidence) and Puglia (a region of low incidence), the SARS-Cov2 virus did not circulate in a sample of ARI pediatric cases consulting primary care pediatricians between November 2019 and March 2020.

Modeling the interplay between demography, social contact patterns, and SARS-CoV-2 transmission in the South West Shewa Zone of Oromia Region, Ethiopia

BACKGROUND

COVID-19 spread may have a dramatic impact in countries with vulnerable economies and limited availability of, and access to, healthcare resources and infrastructures. However, in sub-Saharan Africa, a low prevalence and mortality have been observed so far.

METHODS

We collected data on individuals' social contacts in the South West Shewa Zone (SWSZ) of Ethiopia across geographical contexts characterized by heterogeneous population density, work and travel opportunities, and access to primary care. We assessed how socio-demographic factors and observed mixing patterns can influence the COVID-19 disease burden, by simulating SARS-CoV-2 transmission in remote settlements, rural villages, and urban neighborhoods, under school closure mandate.

RESULTS

From national surveillance data, we estimated a net reproduction number of 1.62 (95% CI 1.55-1.70). We found that, at the end of an epidemic mitigated by school closure alone, 10-15% of the population residing in the SWSZ would have been symptomatic and 0.3-0.4% of the population would require mechanical ventilation and/or possibly result in a fatal outcome. Higher infection attack rates are expected in more urbanized areas, but the highest incidence of critical disease is expected in remote subsistence farming settlements. School closure contributed to reduce the reproduction number by 49% and the attack rate of infections by 28-34%.

CONCLUSIONS

Our results suggest that the relatively low burden of COVID-19 in Ethiopia observed so far may depend on social mixing patterns, underlying demography, and the enacted school closures. Our findings highlight that socio-demographic factors can also determine marked heterogeneities across different geographical contexts within the same region, and they contribute to understand why sub-Saharan Africa is experiencing a relatively lower attack rate of severe cases compared to high-income countries.

Knowledge and Perception of COVID-19 Pandemic during the First Wave (Feb-May 2020): A Cross-Sectional Study among Italian Healthcare Workers

Italy was the first country in Europe to face the coronavirus pandemic. The aim of the study was to analyze healthcare workers’ (HCWs) level of information, practice, and risk perception towards COVID-19. We set up a cross-sectional study through SurveyMonkey^® and distributed the link through Facebook and Whatsapp closed groups. The research instrument was a 31 items questionnaire distributed using Facebook and Whatsapp. It was conducted in Italy from February to May 2020. The study participants were general practitioners, pediatricians and other health professionals. A total of 958 participants were included: 320 (33.4%) general practitioners, 248 (25.9%) pediatricians and 390 (40.7%) other health professionals. The highest response rate was from Northern Italy (48.1%), followed by Central Italy (29.9%) and Southern Italy (22.0%). Less than a half (46%) of respondents felt they had a good level of information of COVID-19 case definition and of national prevention guidelines. Respondents reported to have changed their clinical practice; particularly, they increased the use of masks (87.1%, p < 0.001), disinfection and sanitization of doctors’ offices (75.8%, p < 0.001), the use of protective glasses (71.2%, p < 0.001), alcoholic hand solution (71.2%, p < 0.001), and hand washing (31.8%, p = 0.028). HCWs are at high risk of infection; less than a half of them felt adequately prepared to face COVID-19 pandemic, so they need extensive information and awareness of the disease to take adequate precautionary measures, and they are crucial to disseminate good practices.

Challenging management of severe chronic disorders in acute pandemic situation: Chronic liver disease under COVID-19 pandemic as the proof-of-principle model to orchestrate the measures in 3PM context

Chronic liver disease management is a comprehensive approach requiring multi-professional expertise and well-orchestrated healthcare measures thoroughly organized by responsible medical units. Contextually, the corresponding multi-faceted chain of healthcare events is likely to be severely disturbed or even temporarily broken under the force majeure conditions such as global pandemics. Consequently, the chronic liver disease is highly representative for the management of any severe chronic disorder under lasting pandemics with unprecedented numbers of acutely diseased persons who, together with the chronically sick patient cohorts, have to be treated using the given capacity of healthcare systems with their limited resources. Current study aimed at exploring potentially negative impacts of the SARS CoV-2 outbreak on the quality of the advanced chronic liver disease (ACLD) management considering two well-classified parameters, namely, (1) the continuity of the patient registrations and (2) the level of mortality rates, comparing pre-COVID-19 statistics with these under the current pandemic in Slovak Republic. Altogether 1091 registrations to cirrhosis registry (with 60.8% versus 39.2% males to females ratio) were included with a median age of 57 years for patients under consideration. Already within the very first 3 months of the pandemic outbreak in Slovakia (lockdown declared from March 16, 2020, until May 20, 2020), the continuity of the patient registrations has been broken followed by significantly increased ACLD-related death rates. During this period of time, the total number of new registrations decreased by about 60% (15 registrations in 2020 versus 38 in 2018 and 38 in 2019). Corresponding mortality increased by about 52% (23 deaths in 2020 versus 10 in 2018 and 12 in 2019). Based on these results and in line with the framework of 3PM guidelines, the pandemic priority pathways (PPP) are strongly recommended for maintaining tertiary care uninterrupted. For the evidence-based implementation of PPP, creation of predictive algorithms and individualized care strategy tailored to the patient is essential. Resulting classification of measures is summarized as follows:

The Green PPP Line is reserved for prioritized (urgent and comprehensive) treatment of patients at highest risk to die from ACLD (tertiary care) as compared to the risk from possible COVID-19 infection.

The Orange PPP Line considers patients at middle risk of adverse outcomes from ACLD with re-addressing them to the secondary care. As further deterioration of ACLD is still probable, pro-active management is ascertained with tertiary center serving as the 24/7 telemedicine consultation hub for a secondary facility (on a physician-physician level).

The Red PPP Line is related to the patients at low risk to die from ACLD, re-addressing them to the primary care. Since patients with stable chronic liver diseases without advanced fibrosis are at trivial inherent risk, they should be kept out of the healthcare setting as far as possible by the telemedical (patient-nurse or patient- physician) measurements.

The assigned priority has to be monitored and re-evaluated individually—in intervals based on the baseline prognostic score such as MELD. The approach is conform with principles of predictive, preventive and personalized medicine (PPPM / 3PM) and demonstrates a potential of great clinical utility for an optimal management of any severe chronic disorder (cardiovascular, neurological and cancer) under lasting pandemics.

Association of Age With Likelihood of Developing Symptoms and Critical Disease Among Close Contacts Exposed to Patients With Confirmed SARS-CoV-2 Infection in Italy

Importance

Solid estimates of the risk of developing symptoms and of progressing to critical disease in individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are key to interpreting coronavirus disease 2019 (COVID-19) dynamics, identifying the settings and the segments of the population where transmission is more likely to remain undetected, and defining effective control strategies.

Objective

To estimate the association of age with the likelihood of developing symptoms and the association of age with the likelihood of progressing to critical illness after SARS-CoV-2 infection.

Design, Setting, and Participants

This cohort study analyzed quarantined case contacts, identified between February 20 and April 16, 2020, in the Lombardy region of Italy. Contacts were monitored daily for symptoms and tested for SARS-CoV-2 infection, by either real-time reverse transcriptase-polymerase chain reaction using nasopharyngeal swabs or retrospectively via IgG serological assays. Close contacts of individuals with laboratory-confirmed COVID-19 were selected as those belonging to clusters (ie, groups of contacts associated with an index case) where all individuals were followed up for symptoms and tested for SARS-CoV-2 infection. Data were analyzed from February to June 2020.

Exposure

Close contact with individuals with confirmed COVID-19 cases as identified by contact tracing operations.

Main Outcomes and Measures

Age-specific estimates of the risk of developing respiratory symptoms or fever greater than or equal to 37.5 °C and of experiencing critical disease (defined as requiring intensive care or resulting in death) in SARS-CoV-2-infected case contacts.

Results

In total, 5484 case contacts (median [interquartile range] age, 50 [30-61] years; 3086 female contacts [56.3%]) were analyzed, 2824 of whom (51.5%) tested positive for SARS-CoV-2 (median [interquartile range] age, 53 [34-64] years; 1604 female contacts [56.8%]). The proportion of infected persons who developed symptoms ranged from 18.1% (95% CI, 13.9%-22.9%) among participants younger than 20 years to 64.6% (95% CI, 56.6%-72.0%) for those aged 80 years or older. Most infected contacts (1948 of 2824 individuals [69.0%]) did not develop respiratory symptoms or fever greater than or equal to 37.5 °C. Only 26.1% (95% CI, 24.1%-28.2%) of infected individuals younger than 60 years developed respiratory symptoms or fever greater than or equal to 37.5 °C; among infected participants older than 60 years, 6.6% (95% CI, 5.1%-8.3%) developed critical disease. Female patients were 52.7% (95% CI, 24.4%-70.7%) less likely than male patients to develop critical disease after SARS-CoV-2 infection.

Conclusions and Relevance

In this Italian cohort study of close contacts of patients with confirmed SARS-CoV-2 infection, more than one-half of individuals tested positive for the virus. However, most infected individuals did not develop respiratory symptoms or fever. The low proportion of children and young adults who developed symptoms highlights the possible challenges in readily identifying SARS-CoV-2 infections.

Dynamics of the seroprevalence of SARS-CoV-2 antibodies among healthcare workers at a COVID-19 referral hospital in Milan, Italy

OBJECTIVES

Healthcare workers (HCWs) are at high risk of developing SARS-CoV-2 infection. The aim of this single-centre prospective study was to evaluate the trend of SARS-CoV-2 seroprevalence in HCWs working at the primary referral centre for infectious diseases and bioemergencies (eg, COVID-19) in Northern Italy and investigate the factors associated with seroconversion.

METHODS

Six hundred and seventy-nine HCW volunteers were tested for anti-SARS-CoV-2 antibodies three times between 4 March and 27 May 2020 and completed a questionnaire covering COVID-19 exposure, symptoms and personal protective equipment (PPE) training and confidence at each time.

RESULTS

SARS-CoV-2 seroprevalence rose from 3/679 to 26/608 (adjusted prevalence: 0.5%, 95% CI 0.1 to 1.7% and 5.4%, 95% CI 3.6 to 7.9, respectively) between the first two time points and then stabilised, in line with the curve of the COVID-19 epidemic in Milan. From the first time point, 61.6% of the HCWs had received training in the use of PPE and 17 (61.5%) of those who proved to be seropositive reported symptoms compatible with SARS-CoV-2 infection. Contacts with ill relatives or friends and self-reported symptoms were independently associated with an increased likelihood of seroconversion (p<0.0001 for both), whereas there was no significant association with professional exposure.

CONCLUSION

The seroprevalence of SARS-CoV-2 among the HCWs at our COVID-19 referral hospital was low at the time of the peak of the epidemic. The seroconversions were mainly attributable to extrahospital contacts, probably because the hospital readily adopted effective infection control measures. The relatively high number of asymptomatic seropositive HCWs highlights the need to promptly identify and isolate potentially infectious HCWs.

COVID-19: R0 is lower where outbreak is larger

We use daily data from Lombardy, the Italian region most affected by the COVID-19 outbreak, to calibrate a SIR model on each municipality. Municipalities with a higher initial number of cases feature a lower rate of diffusion, not attributable to herd immunity: there is a robust and strongly significant negative correlation between the estimated basic reproduction number (R0) and the initial outbreak size. This represents novel evidence of the prevalence-response elasticity in a cross-sectional setting, characterized by a same health system and homogeneous social distancing regulations. By ruling out alternative explanations, we conclude that a higher number of cases causes changes of behavior, such as a more strict adoption of social distancing measures among the population, that reduce the spread. This finding calls for the distribution of detailed epidemiological data to populations affected by COVID-19 outbreaks.

Range of reproduction number estimates for COVID-19 spread

To monitor local and global COVID-19 outbreaks, and to plan containment measures, accessible and comprehensible decision-making tools need to be based on the growth rates of new confirmed infections, hospitalization or case fatality rates. Growth rates of new cases form the empirical basis for estimates of a variety of reproduction numbers, dimensionless numbers whose value, when larger than unity, describes surging infections and generally worsening epidemiological conditions. Typically, these determinations rely on noisy or incomplete data gained over limited periods of time, and on many parameters to estimate. This paper examines how estimates from data and models of time-evolving reproduction numbers of national COVID-19 infection spread change by using different techniques and assumptions. Given the importance acquired by reproduction numbers as diagnostic tools, assessing their range of possible variations obtainable from the same epidemiological data is relevant. We compute control reproduction numbers from Swiss and Italian COVID-19 time series adopting both data convolution (renewal equation) and a SEIR-type model. Within these two paradigms we run a comparative analysis of the possible inferences obtained through approximations of the distributions typically used to describe serial intervals, generation, latency and incubation times, and the delays between onset of symptoms and notification. Our results suggest that estimates of reproduction numbers under these different assumptions may show significant temporal differences, while the actual variability range of computed values is rather small.

Controlling COVID-19 Outbreaks with Financial Incentives

In this paper, we consider controlling coronavirus disease 2019 (COVID-19) outbreaks with financial incentives. We use the recently developed susceptible-unidentified infected-confirmed (SUC) epidemic model. The unidentified infected population is defined as the infected people who are not yet identified and isolated and can spread the disease to susceptible individuals. It is important to quickly identify and isolate infected people among the unidentified infected population to prevent the infectious disease from spreading. Considering financial incentives as a strategy to control the spread of disease, we predict the effect of the strategy through a mathematical model. Although incentive costs are required, the duration of the disease can be shortened. First, we estimate the unidentified infected cases of COVID-19 in South Korea using the SUC model, and compute two parameters such as the disease transmission rate and the inverse of the average time for confirming infected individuals. We assume that when financial incentives are provided, there are changes in the proportion of confirmed patients out of unidentified infected people in the SUC model. We evaluate the numbers of confirmed and unidentified infected cases with respect to one parameter while fixing the other estimated parameters. We investigate the effect of the incentives on the termination time of the spread of the disease. The larger the incentive budget is, the faster the epidemic will end. Therefore, financial incentives can have the advantage of reducing the total cost required to prevent the spread of the disease, treat confirmed patients, and recover overall economic losses.

Telemedicine in rheumatology: a reliable approach beyond the pandemic

Objectives

The SARS-CoV-2 outbreak has imposed considerable restrictions on people’s mobility, which affects the referral of chronically ill patients to health care structures. The emerging need for alternative ways to follow these patients up is leading to a wide adoption of telemedicine. We aimed to evaluate the feasibility of this approach for our cohort of patients with CTDs, investigating their attitude to adopting telemedicine, even after the pandemic.

Methods

We conducted a telephonic survey among consecutive patients referred to our CTD outpatients’ clinic, evaluating their capability and propensity for adopting telemedicine and whether they would prefer it over face-to-face evaluation. Demographical and occupational factors were also collected, and their influence on the answers has been evaluated by a multivariate analysis.

Results

A total of 175 patients answered our survey (M/F = 28/147), with a median age of 62.5 years [interquartile range (IQR) 53–73]. About 80% of patients owned a device allowing video-calls, and 86% would be able to perform a tele-visit, either alone (50%) or with the help of a relative (36%). Telemedicine was considered acceptable by 78% of patients and 61% would prefer it. Distance from the hospital and patient’s educational level were the strongest predictive factors for the acceptance of telemedicine (P < 0.05), whereas age only affected the mastering of required skills (P < 0.001).

Conclusion

Telemedicine is a viable approach to be considered for routine follow-up of chronic patients, even beyond the pandemic. Our data showed that older patients would be willing to use this approach, although a proper guide for them would be required.

Impact of a Nationwide Lockdown on SARS-CoV-2 Transmissibility, Italy

On March 11, 2020, Italy imposed a national lockdown to curtail the spread of severe acute respiratory syndrome coronavirus 2. We estimate that, 14 days after lockdown, the net reproduction number had dropped below 1 and remained stable at »0.76 (95% CI 0.67-0.85) in all regions for >3 of the following weeks.

Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020

BackgroundOn 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak.AimOur aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures.MethodsWe analysed all RT-PCR-confirmed COVID-19 cases reported to the national integrated surveillance system until 31 March 2020. We provide a descriptive epidemiological summary and estimate the basic and net reproductive numbers by region.ResultsOf the 98,716 cases of COVID-19 analysed, 9,512 were healthcare workers. Of the 10,943 reported COVID-19-associated deaths (crude case fatality ratio: 11.1%) 49.5% occurred in cases older than 80 years. Male sex and age were independent risk factors for COVID-19 death. Estimates of R₀ varied between 2.50 (95% confidence interval (CI): 2.18-2.83) in Tuscany and 3.00 (95% CI: 2.68-3.33) in Lazio. The net reproduction number R_t in northern regions started decreasing immediately after the first detection.ConclusionThe COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R₀ at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, R_t in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures.

Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020

BackgroundOn 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak.AimOur aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures.MethodsWe analysed all RT-PCR-confirmed COVID-19 cases reported to the national integrated surveillance system until 31 March 2020. We provide a descriptive epidemiological summary and estimate the basic and net reproductive numbers by region.ResultsOf the 98,716 cases of COVID-19 analysed, 9,512 were healthcare workers. Of the 10,943 reported COVID-19-associated deaths (crude case fatality ratio: 11.1%) 49.5% occurred in cases older than 80 years. Male sex and age were independent risk factors for COVID-19 death. Estimates of R0 varied between 2.50 (95% confidence interval (CI): 2.18-2.83) in Tuscany and 3.00 (95% CI: 2.68-3.33) in Lazio. The net reproduction number Rt in northern regions started decreasing immediately after the first detection.ConclusionThe COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R0 at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, Rt in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures.

Beyond the peak: A deterministic compartment model for exploring the Covid-19 evolution in Italy

Novel Covid-19 has had a huge impact on the world's population since December 2019. The very rapid spreading of the virus worldwide, with its heavy toll of death and overload of the healthcare systems, induced the scientific community to focus on understanding, monitoring and foreseeing the epidemic evolution, weighing up the impact of different containment measures. An immense literature was produced in few months. Many papers were focused on predicting the peak features through a variety of different models. In the present paper, combining the surveillance data-set with data on mobility and testing, we develop a deterministic compartment model aimed at performing a retrospective analysis to understand the main modifications occurred to the characteristic parameters that regulate the epidemic spreading. We find that, besides self-protective behaviors, a reduction of susceptibility should have occurred in order to explain the fast descent of the epidemic after the peak. A sensitivity analysis of the basic reproduction number, in response to variations of the epidemiological parameters that can be influenced by policy-makers, shows the primary importance of a rigid isolation procedure for the diagnosed cases, combined with an intensive effort in performing extended testing campaigns. Future scenarios depend on the ability to protect the population from the injection of new cases from abroad, and to pursue in applying rigid self-protective measures.

High rates of 30-day mortality in patients with cirrhosis and COVID-19

BACKGROUND & AIMS

Coronavirus disease 2019 (COVID-19) poses a major health threat to healthy individuals and those with comorbidities, but its impact on patients with cirrhosis is currently unknown. Herein, we aimed to evaluate the impact of COVID-19 on the clinical outcome of patients with cirrhosis.

METHODS

In this multicentre retrospective study, patients with cirrhosis and a confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection were enrolled between 1^st and 31^th March 2020. Clinical and biochemical data at diagnosis of COVID-19 and at the last outpatient visit were obtained through review of medical records.

RESULTS

Fifty patients with cirrhosis and confirmed SARS-CoV-2 infection were enrolled (age 67 years, 70% men, 38% virus-related, 52% previously compensated cirrhosis). At diagnosis, 64% of patients presented fever, 42% shortness of breath/polypnea, 22% encephalopathy, 96% needed hospitalization or a prolonged stay if already in hospital. Respiratory support was necessary in 71%, 52% received antivirals, 80% heparin. Serum albumin significantly decreased, while bilirubin, creatinine and prothrombin time significantly increased at COVID-19 diagnosis compared to last available data. The proportion of patients with a model for end-stage liver disease (MELD) score ≥15 increased from 13% to 26% (p = 0.037), acute-on-chronic liver failure and de novo acute liver injury occurred in 14 (28%) and 10 patients, respectively. Seventeen patients died after a median of 10 (4-13) days from COVID-19 diagnosis, with a 30-day-mortality rate of 34%. The severity of lung and liver (according to CLIF-C, CLIF-OF and MELD scores) diseases independently predicted mortality. In patients with cirrhosis, mortality was significantly higher in those with COVID-19 than in those hospitalized for bacterial infections.

CONCLUSION

COVID-19 is associated with liver function deterioration and elevated mortality in patients with cirrhosis.

LAY SUMMARY

Coronavirus disease 2019 (COVID-19) poses a major health threat to healthy individuals and those with comorbidities. Herein, we assessed its impact on patients with cirrhosis. Infection with COVID-19 was associated with liver function deterioration and elevated mortality in patients with cirrhosis.

Mapping Spatiotemporal Diffusion of COVID-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities

The epidemic of coronavirus-disease-2019 (COVID-19) started in Italy with the first official diagnosis on 21 February 2020; hence, it is now known how many cases were already present in earlier days and weeks, thus limiting the possibilities of conducting any retrospective analysis. We hypothesized that an unbiased representation of COVID-19 diffusion in these early phases could be inferred by the georeferenced calls to the emergency number relevant to respiratory problems and by the following emergency medical services (EMS) interventions. Accordingly, the aim of this study was to identify the beginning of anomalous trends (change in the data morphology) in emergency calls and EMS ambulances dispatches and reconstruct COVID-19 spatiotemporal evolution on the territory of Lombardy region. Accordingly, a signal processing method, previously used to find morphological features on the electrocardiographic signal, was applied on a time series representative of territorial clusters of about 100,000 citizens. Both emergency calls and age- and gender-weighted ambulance dispatches resulted strongly correlated to COVID-19 casualties on a provincial level, and the identified local starting days anticipated the official diagnoses and casualties, thus demonstrating how these parameters could be effectively used as early indicators for the spatiotemporal evolution of the epidemic on a certain territory.

Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City

As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted "salutary sheltering" by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.

COVID-19 Disease Severity Risk Factors for Pediatric Patients in Italy

OBJECTIVES

To describe the epidemiological and clinical characteristics of coronavirus disease (COVID-19) pediatric patients aged <18 years in Italy.

METHODS

Data from the national case-based surveillance system of confirmed COVID-19 infections until May 8, 2020, were analyzed. Demographic and clinical characteristics of subjects were summarized by age groups (0-1, 2-6, 7-12, 13-18 years), and risk factors for disease severity were evaluated by using a multilevel (clustered by region) multivariable logistic regression model. Furthermore, a comparison among children, adults, and elderly was performed.

RESULTS

Pediatric patients (3836) accounted for 1.8% of total infections (216 305); the median age was 11 years, 51.4% were male, 13.3% were hospitalized, and 5.4% presented underlying medical conditions. The disease was mild in 32.4% of cases and severe in 4.3%, particularly in children ≤6 years old (10.8%); among 511 hospitalized patients, 3.5% were admitted in ICU, and 4 deaths occurred. Lower risk of disease severity was associated with increasing age and calendar time, whereas a higher risk was associated with preexisting underlying medical conditions (odds ratio = 2.80, 95% confidence interval = 1.74-4.48). Hospitalization rate, admission in ICU, disease severity, and days from symptoms onset to recovery significantly increased with age among children, adults and elderly.

CONCLUSIONS

Data suggest that pediatric cases of COVID-19 are less severe than adults; however, age ≤1 year and the presence of underlying conditions represent severity risk factors. A better understanding of the infection in children may give important insights into disease pathogenesis, health care practices, and public health policies.

Endoscopic Findings in Patients Infected With 2019 Novel Coronavirus in Lombardy, Italy

Coronavirus disease 2019 (COVID-19) is a major worldwide threat caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly spreading to a global pandemic. As of May 11, 2020, 4,176,346 cases have been reported worldwide, 219,814 in Italy, and of them, 81,871 occurred in the Lombardy region.¹ Although the respiratory manifestations of COVID-19 have been widely described, the impact on the gastrointestinal (GI) system remains less clear. The reported prevalence of digestive symptoms ranges from 3% to 79%, depending on the setting,^2-5 but data on GI endoscopic and histologic findings in COVID-19 patients are lacking. Therefore, the aim of this study is to describe the GI endoscopic and histologic findings in COVID-19 patients.

The geography of COVID-19 spread in Italy and implications for the relaxation of confinement measures

The pressing need to restart socioeconomic activities locked-down to control the spread of SARS-CoV-2 in Italy must be coupled with effective methodologies to selectively relax containment measures. Here we employ a spatially explicit model, properly attentive to the role of inapparent infections, capable of: estimating the expected unfolding of the outbreak under continuous lockdown (baseline trajectory); assessing deviations from the baseline, should lockdown relaxations result in increased disease transmission; calculating the isolation effort required to prevent a resurgence of the outbreak. A 40% increase in effective transmission would yield a rebound of infections. A control effort capable of isolating daily  ~5.5% of the exposed and highly infectious individuals proves necessary to maintain the epidemic curve onto the decreasing baseline trajectory. We finally provide an ex-post assessment based on the epidemiological data that became available after the initial analysis and estimate the actual disease transmission that occurred after weakening the lockdown.

COVID-19, Renin-Angiotensin System and Endothelial Dysfunction

The newly emergent novel coronavirus disease 2019 (COVID-19) outbreak, which is caused by SARS-CoV-2 virus, has posed a serious threat to global public health and caused worldwide social and economic breakdown. Angiotensin-converting enzyme 2 (ACE2) is expressed in human vascular endothelium, respiratory epithelium, and other cell types, and is thought to be a primary mechanism of SARS-CoV-2 entry and infection. In physiological condition, ACE2 via its carboxypeptidase activity generates angiotensin fragments (Ang 1-9 and Ang 1-7), and plays an essential role in the renin-angiotensin system (RAS), which is a critical regulator of cardiovascular homeostasis. SARS-CoV-2 via its surface spike glycoprotein interacts with ACE2 and invades the host cells. Once inside the host cells, SARS-CoV-2 induces acute respiratory distress syndrome (ARDS), stimulates immune response (i.e., cytokine storm) and vascular damage. SARS-CoV-2 induced endothelial cell injury could exacerbate endothelial dysfunction, which is a hallmark of aging, hypertension, and obesity, leading to further complications. The pathophysiology of endothelial dysfunction and injury offers insights into COVID-19 associated mortality. Here we reviewed the molecular basis of SARS-CoV-2 infection, the roles of ACE2, RAS signaling, and a possible link between the pre-existing endothelial dysfunction and SARS-CoV-2 induced endothelial injury in COVID-19 associated mortality. We also surveyed the roles of cell adhesion molecules (CAMs), including CD209L/L-SIGN and CD209/DC-SIGN in SARS-CoV-2 infection and other related viruses. Understanding the molecular mechanisms of infection, the vascular damage caused by SARS-CoV-2 and pathways involved in the regulation of endothelial dysfunction could lead to new therapeutic strategies against COVID-19.

Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study

BACKGROUND

In the early months of 2020, a novel coronavirus disease (COVID-19) spread rapidly from China across multiple countries worldwide. As of March 17, 2020, COVID-19 was officially declared a pandemic by the World Health Organization. We collected data on COVID-19 cases outside China during the early phase of the pandemic and used them to predict trends in importations and quantify the proportion of undetected imported cases.

METHODS AND FINDINGS

Two hundred and eighty-eight cases have been confirmed out of China from January 3 to February 13, 2020. We collected and synthesized all available information on these cases from official sources and media. We analyzed importations that were successfully isolated and those leading to onward transmission. We modeled their number over time, in relation to the origin of travel (Hubei province, other Chinese provinces, other countries) and interventions. We characterized the importation timeline to assess the rapidity of isolation and epidemiologically linked clusters to estimate the rate of detection. We found a rapid exponential growth of importations from Hubei, corresponding to a doubling time of 2.8 days, combined with a slower growth from the other areas. We predicted a rebound of importations from South East Asia in the successive weeks. Time from travel to detection has considerably decreased since first importation, from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020. However, we estimated 36% of detection of imported cases. This study is restricted to the early phase of the pandemic, when China was the only large epicenter and foreign countries had not discovered extensive local transmission yet. Missing information in case history was accounted for through modeling and imputation.

CONCLUSIONS

Our findings indicate that travel bans and containment strategies adopted in China were effective in reducing the exportation growth rate. However, the risk of importation was estimated to increase again from other sources in South East Asia. Surveillance and management of traveling cases represented a priority in the early phase of the epidemic. With the majority of imported cases going undetected (6 out of 10), countries experienced several undetected clusters of chains of local transmissions, fueling silent epidemics in the community. These findings become again critical to prevent second waves, now that countries have reduced their epidemic activity and progressively phase out lockdown.

Evidence for mutations in SARS-CoV-2 Italian isolates potentially affecting virus transmission

Italy is the first western country suffering heavy severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) transmission and disease impact after coronavirus disease‐2019 pandemia started in China. Even though the presence of mutations on spike glycoprotein and nucleocapsid in Italian isolates has been reported, the potential impact of these mutations on viral transmission has not been evaluated. We have compared SARS‐CoV‐2 genome sequences from Italian patients with virus sequences from Chinese patients. We focussed upon three nonsynonymous mutations of genes coding for S(one) and N (two) viral proteins present in Italian isolates and absent in Chinese ones, using various bioinformatics tools. Amino acid analysis and changes in three‐dimensional protein structure suggests the mutations reduce protein stability and, particularly for S1 mutation, the enhanced torsional ability of the molecule could favor virus binding to cell receptor(s). This theoretical interpretation awaits experimental and clinical confirmation.

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two highly prevalent mutation have been developed by SARS‐CoV‐2, one on the Spike glycoprotein and one on the Nucleocapsid protein

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The enhanced torsional ability of Spike protein could favor virus binding to human cell receptors

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mutations on the Nucleocapsid region could influence the modulation of a number of virus properties, including cell signaling

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the study of SARS‐CoV‐2 mutations could be important for vaccines developmen

Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020

The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible-Exposed-Infectious-Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July-11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures

The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible-Exposed-Infected-Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number ([Formula: see text] = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.

Stop playing with data: there is no sound evidence that Bacille Calmette-Guérin may avoid SARS-CoV-2 infection (for now)

Since the beginning of the COVID-19 epidemic, a possible explanation for the high heterogeneity of infection/mortality rates across involved countries was hinted in the prevalence of tuberculosis vaccination with Bacille Calmette-Guérin (BCG). A systematic review was therefore performed on May 2, 2020. A total of 13 articles were ultimately retrieved, 12 of them as preprint papers. All articles were ecological studies of low quality. Most of them did not include main confounding factors (i.e. demographic of the assessed countries, share of people residing in urban settings, etc.), and simply assessed the differences among incidence/mortality of COVID-19 with vaccination rates or by having vs. having not any vaccination policy for BCG. Even though all studies shared the very same information sources (i.e. international registries for BCG vaccination rates and open source data for COVID-19 epidemics), results were conflicting, with later studies apparently denying any true correlation between COVID-19 occurrence and BCG vaccination rates and/or policies. As a consequence, there is no sound evidence to recommend BCG vaccination for the prevention of COVID-19. (www.actabiomedica.it)