Addressing Misclassification Bias in Vaccine Effectiveness Studies with an Application to Covid-19 (preprint)

Safe and effective vaccines are crucial to control Covid-19 and to protect persons who are at high risk of complications or death. Test-negative design is a popular option for evaluating the effectiveness of Covid-19 vaccines, but the findings could be biased by several factors, including imperfect sensitivity and/or specificity of the test used for 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 this robustness to misclassification bias for estimating Covid-19 vaccine effectiveness, 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 COVID-19 infection (preprint)

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. An association between Rhesus state and COVID-19 infection could not be estabished.

Seroprevalence of SARS CoV-2 infection in Low and Middle-Income Countries and risk classification in the context of rolling out vaccine candidate across these countries : Protocol for a systematic review and meta-analysis (preprint)

Background Seroprevalence of SARS Cov-2 provides a good indication of the extent of exposure and spread in the population, as well as those likely to benefit from a vaccine candidate. To date, there is no published or ongoing systematic review on the seroprevalence of COVID-19 in Low- and Middle-Income Countries (LMICs). This systematic review and meta-analysis will estimate SARS Cov-2 seroprevalence and the risk factors for SARS Cov-2 infection in LMICs.Methods We will search PubMed, EMBASE, WHO COVID-19 Global research database, Google Scholar, the African Journals Online, LILAC, HINARI, medRxiv, bioRxiv and Cochrane Library for potentially useful studies on seroprevalence of COVID-19 in LMICs from December 2019 to December 2020 without language restriction. Two authors will independently screen all the articles, select studies based on pre-specified eligibility criteria and extract data using a pre-tested data extraction form. Any disagreements will be resolved through discussion between the authors. The pooled seroprevalence of SARS CoV-2 for people from LMICs will be calculated. Random effects model will be used in case of substantial heterogeneity in the included studies, otherwise fixed-effect model will be used. A planned subgroup, sensitivity and meta-regression analyses will be performed. For comparative studies, the analyses will be performed using Review Manager v 5.4; otherwise, STATA 16 will be used. All effect estimates will be presented with their confidence intervals.Discussion The study will explore and systematically review empirical evidence on SARS Cov-2 seroprevalence in LMICs, and to assess the risk factors for SARS Cov-2 infection in Low Middle Income Countries in the context of rolling out vaccines in these countries. Finally, explore risk classifications to help with the rolling out of vaccines in LMICs.Systematic review registration: The protocol for this review has been registered in PROSPERO (CRD422020221548).

The Epidemic Volatility Index: an early warning tool for epidemics (preprint)

Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming 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 fast and optimize containment of outbreaks.

Diagnostic accuracy estimates for COVID-19 RT-PCR and Lateral flow immunoassay tests with Bayesian latent class models (preprint)

The objective of this work was to estimate the diagnostic accuracy of RT-PCR and Lateral flow immunoassay tests (LFIA) for COVID-19, depending on the time post symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent class models (BLCMs), which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (IgG and/or IgM) assays using RT-PCR as the reference method. The cross-classified results of LFIA and RT-PCR were analysed separately for the first, second and third week post symptom onset. The Se RT-PCR was 0.695 (95% probability intervals: 0.563; 0.837) for the first week and remained similar for the second and the third week. The Se IgG/M was 0.318 (0.229; 0.416) for the first week and increased steadily. It was 0.755 (0.673; 0.829) and 0.927 (0.881; 0.965) for the second and third week, respectively. Both tests had a high to absolute Sp , with point median estimates for Sp RT-PCR being consistently higher. Sp RT-PCR was 0.990 (0.980; 0.998) for the first week. The corresponding value for Sp IgG/M was 0.962 (0.905; 0.998). Further, Sp estimates for each test did not differ between weeks. BLCMs provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and for different risk profiles.