The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic.

This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.

Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19.

Safe and effective vaccines are crucial for the control of Covid-19 and to protect individuals at higher risk of severe disease. The test-negative design is a popular option for evaluating the effectiveness of Covid-19 vaccines. However, the findings could be biased by several factors, including imperfect sensitivity and/or specificity of the test used for diagnosing the SARS-Cov-2 infection. We propose a simple Bayesian modeling approach for estimating vaccine effectiveness that is robust even when the diagnostic test is imperfect. We use simulation studies to demonstrate the robustness of our method to misclassification bias and illustrate the utility of our approach using real-world examples.

Systematic review and meta-analysis of the effect of ABO blood group on the risk of SARS-CoV-2 infection.

We have been experiencing a global pandemic with baleful consequences for mankind, since the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first identified in Wuhan of China, in December 2019. So far, several potential risk factors for SARS-CoV-2 infection have been identified. Among them, the role of ABO blood group polymorphisms has been studied with results that are still unclear. The aim of this study was to collect and meta-analyze available studies on the relationship between SARS-CoV-2 infection and different blood groups, as well as Rhesus state. We performed a systematic search on PubMed/MEDLINE and Scopus databases for published articles and preprints. Twenty-two studies, after the removal of duplicates, met the inclusion criteria for meta-analysis with ten of them also including information on Rhesus factor. The odds ratios (OR) and 95% confidence intervals (CI) were calculated for the extracted data. Random-effects models were used to obtain the overall pooled ORs. Publication bias and sensitivity analysis were also performed. Our results indicate that blood groups A, B and AB have a higher risk for COVID-19 infection compared to blood group O, which appears to have a protective effect: (i) A group vs O (OR = 1.29, 95% Confidence Interval: 1.15 to 1.44), (ii) B vs O (OR = 1.15, 95% CI 1.06 to 1.25), and (iii) AB vs. O (OR = 1.32, 95% CI 1.10 to 1.57). An association between Rhesus state and COVID-19 infection could not be established (Rh+ vs Rh- OR = 0.97, 95% CI 0.83 to 1.13).

Temporal trends and differences of SARS-CoV-2-specific antibody responses in symptomatic and asymptomatic subjects: a longitudinal study from Umbria in Italy.

OBJECTIVES: Dynamics of antibody responses following SARS-CoV-2 infection are controversial in terms of immunity and persistence. We aimed to assess longitudinally the trend of antibody serological titres, their correlation with clinical severity as well as clinical reinfection during a follow-up. DESIGN: Longitudinal cohort, 12 months follow-up study. SETTING: USL Umbria 2. PARTICIPANTS: Consecutive subjects aged 15-75 who were discharged with the diagnosis of Sars-Cov-2 from the hospitals of the AUSL Umbria 2, or resulted positive to a PCR test for SARS-CoV-2 infection with or without symptoms were recruited. SARS-CoV-2 serological testing for antibodies targeting the Nucleocapside and Spike proteins were determined. RESULTS: Of 184 eligible subjects, 149 were available for evaluation: 17 were classified as oligo/asymptomatic, 107 as symptomatic, 25 as hospital admitted. Participants differed in terms of signs and symptoms as well as treatment. Overall there was a significant difference in terms of antibody titres between groups (anti-S: p<0.00; anti-N: p=0.019). Median anti-S titres in the symptomatic and hospital admitted participants were significantly higher compared with the oligo/asymptomatic participants. During follow-up, the median titre of anti-S antibodies did not show significant variations (p=0.500) and the difference within groups remained constant overtime. Subjects that showed an anti-S titre above the threshold of 12 U/mL were 88.7% at first visit and 88.2% at last follow-up. Anti-N values were higher in the hospital admitted participants compared with the other two groups. Anti-N titre reduced constantly overtime (p<0.001) and across the three groups of participants. The percentage of the subjects with serological titre above threshold (<1.4 U/mL) decreased from 74.5%% to 29.2% (p<0.001). None of the participants developed clinically evident reinfection. CONCLUSION: Anti-N and anti-S correlate well with clinical severity. While anti-N declines overtime, anti-S antibodies persist for at least 1 year.

The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic.

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

Stroke network performance during the first COVID-19 pandemic stage: A meta-analysis based on stroke network models.

BACKGROUND: The effect of the COVID pandemic on stroke network performance is unclear, particularly with consideration of drip&ship vs. mothership models. AIMS: We systematically reviewed and meta-analyzed variations in stroke admissions, rate and timing of reperfusion treatments during the first wave COVID pandemic vs. the pre-pandemic timeframe depending on stroke network model adopted. SUMMARY OF FINDINGS: The systematic review followed registered protocol (PROSPERO-CRD42020211535), PRISMA and MOOSE guidelines. We searched MEDLINE, EMBASE, and CENTRAL until 9 October 2020 for studies reporting variations in ischemic stroke admissions, treatment rates, and timing in COVID (first wave) vs. control-period. Primary outcome was the weekly admission incidence rate ratio (IRR = admissions during COVID-period/admissions during control-period). Secondary outcomes were (i) changes in rate of reperfusion treatments and (ii) time metrics for pre- and in-hospital phase. Data were pooled using random-effects models, comparing mothership vs. drip&ship model. Overall, 29 studies were included in quantitative synthesis (n = 212,960). COVID-period was associated with a significant reduction in stroke admission rates (IRR = 0.69, 95%CI = 0.61-0.79), with higher relative presentation of large vessel occlusion (risk ratio (RR) = 1.62, 95% confidence interval (CI) = 1.24-2.12). Proportions of patients treated with endovascular treatment increased (RR = 1.14, 95%CI = 1.02-1.28). Intravenous thrombolysis decreased overall (IRR = 0.72, 95%CI = 0.54-0.96) but not in the mothership model (IRR = 0.81, 95%CI = 0.43-1.52). Onset-to-door time was longer for the drip&ship in COVID-period compared to the control-period (+32 min, 95%CI = 0-64). Door-to-scan was longer in COVID-period (+5 min, 95%CI = 2-7). Door-to-needle and door-to-groin were similar in COVID-period and control-period. CONCLUSIONS: Despite a 35% drop in stroke admissions during the first pandemic wave, proportions of patients receiving reperfusion and time-metrics were not inferior to control-period. Mothership preserved the weekly rate of intravenous thrombolysis and the onset-to-door timing to pre-pandemic standards.

Diagnostic Accuracy Estimates for COVID-19 Real-Time Polymerase Chain Reaction and Lateral Flow Immunoassay Tests With Bayesian Latent-Class Models.

Our objective was to estimate the diagnostic accuracy of real-time polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA) tests for coronavirus disease 2019 (COVID-19), depending on the time after symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent-class models, which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (immunoglobulin G (IgG) and/or immunoglobulin M (IgM)) assays using RT-PCR as the reference method. The sensitivity of RT-PCR was 0.68 (95% probability interval (PrI): 0.63, 0.73). IgG/M sensitivity was 0.32 (95% PrI :0.23; 0.41) for the first week and increased steadily. It was 0.75 (95% PrI: 0.67; 0.83) and 0.93 (95% PrI: 0.88; 0.97) for the second and third weeks after symptom onset, respectively. Both tests had a high to absolute specificity, with higher point median estimates for RT-PCR specificity and narrower probability intervals. The specificity of RT-PCR was 0.99 (95% PrI: 0.98; 1.00). and the specificity of IgG/IgM was 0.97 (95% PrI: 0.92, 1.00), 0.98 (95% PrI: 0.95, 1.00) and 0.98 (95% PrI: 0.94, 1.00) for the first, second, and third weeks after symptom onset. The diagnostic accuracy of LFIA varies with time after symptom onset. Bayesian latent-class models provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and different risk profiles.

Reduced Admissions for Cerebrovascular Events During COVID-19 Outbreak in Italy.

BACKGROUND AND PURPOSE: We aimed to investigate the rate of hospital admissions for cerebrovascular events and of revascularization treatments for acute ischemic stroke in Italy during the coronavirus disease 2019 (COVID-19) outbreak. METHODS: The Italian Stroke Organization performed a multicenter study involving 93 Italian Stroke Units. We collected information on hospital admissions for cerebrovascular events from March 1 to March 31, 2020 (study period), and from March 1 to March 31, 2019 (control period). RESULTS: Ischemic strokes decreased from 2399 in 2019 to 1810 in 2020, with a corresponding hospitalization rate ratio (RR) of 0.75 ([95% CI, 0.71-0.80] P<0.001); intracerebral hemorrhages decreased from 400 to 322 (hospitalization RR, 0.81 [95% CI, 0.69-0.93]; P=0.004), and transient ischemic attacks decreased from 322 to 196 (hospitalization RR, 0.61 [95% CI, 0.51-0.73]; P<0.001). Hospitalizations decreased in Northern, Central, and Southern Italy. Intravenous thrombolyses decreased from 531 (22.1%) in 2019 to 345 in 2020 (19.1%; RR, 0.86 [95% CI, 0.75-0.99]; P=0.032), while primary endovascular procedures increased in Northern Italy (RR, 1.61 [95% CI, 1.13-2.32]; P=0.008). We found no correlation (P=0.517) between the hospitalization RRs for all strokes or transient ischemic attack and COVID-19 incidence in the different areas. CONCLUSIONS: Hospitalizations for stroke or transient ischemic attacks across Italy were reduced during the worst period of the COVID-19 outbreak. Intravenous thrombolytic treatments also decreased, while endovascular treatments remained unchanged and even increased in the area of maximum expression of the outbreak. Limited hospitalization of the less severe patients and delays in hospital admission, due to overcharge of the emergency system by COVID-19 patients, may explain these data.