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.

Excess deaths during the COVID-19 pandemic in Iran.

BACKGROUND: The actual number of deaths during the COVID-19 pandemic is expected to be higher than the reported deaths. We aimed to estimate the number of deaths in Iran during the COVID-19 pandemic from December 22, 2019 to March 20, 2022. METHODS: We compared the number of age- and sex-specific deaths reported by Iran's Bureau of Vital Statistics with the predicted deaths estimated using an improved Lee-Carter model. We estimated the number of all-cause excess deaths in three scenarios, including the baseline scenario (without any undercounting of deaths) and 4% and 8% undercounting of all-cause deaths. RESULTS: We estimated 282,378 (95% confidence intervals [CI]: 225,439; 341,951) excess deaths in the baseline model. This number was 303,148 (95% CI: 246,417; 357,823) and 308,486 (95% CI: 250,607; 364,417) in the 4% and 8% scenarios, respectively. During the same period, Iran reported 139,610 deaths as being directly related to COVID-19. The ratio of reported COVID-19 deaths to total excess deaths ranged from 45.2% to 49.4% in the various scenarios. Most excess deaths occurred in the baseline scenario in males (157,552 [95% CI: 125,142; 191,265]) and those aged ≥75 years (102,369 [95% CI: 93,894; 111,188]). CONCLUSIONS: The reported number of COVID-19 deaths was less than half of Iran's estimated number of excess deaths. The results of this study will be helpful for health policymakers' planning, and call for strengthening the timeliness and accuracy of Iran's death registration systems, planning for more accurate monitoring of epidemics, and planning to provide support services for survivors' families.

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).

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.

Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol.

INTRODUCTION: unCoVer-Unravelling data for rapid evidence-based response to COVID-19-is a Horizon 2020-funded network of 29 partners from 18 countries capable of collecting and using real-world data (RWD) derived from the response and provision of care to patients with COVID-19 by health systems across Europe and elsewhere. unCoVer aims to exploit the full potential of this information to rapidly address clinical and epidemiological research questions arising from the evolving pandemic. METHODS AND ANALYSIS: From the onset of the COVID-19 pandemic, partners are gathering RWD from electronic health records currently including information from over 22 000 hospitalised patients with COVID-19, and national surveillance and screening data, and registries with over 1 900 000 COVID-19 cases across Europe, with continuous updates. These heterogeneous datasets will be described, harmonised and integrated into a multi-user data repository operated through Opal-DataSHIELD, an interoperable open-source server application. Federated data analyses, without sharing or disclosing any individual-level data, will be performed with the objective to reveal patients' baseline characteristics, biomarkers, determinants of COVID-19 prognosis, safety and effectiveness of treatments, and potential strategies against COVID-19, as well as epidemiological patterns. These analyses will complement evidence from efficacy/safety clinical trials, where vulnerable, more complex/heterogeneous populations and those most at risk of severe COVID-19 are often excluded. ETHICS AND DISSEMINATION: After strict ethical considerations, databases will be available through a federated data analysis platform that allows processing of available COVID-19 RWD without disclosing identification information to analysts and limiting output to data aggregates. Dissemination of unCoVer's activities will be related to the access and use of dissimilar RWD, as well as the results generated by the pooled analyses. Dissemination will include training and educational activities, scientific publications and conference communications.

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.