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Intervirology 2019The John Cunningham virus (JCV) is the causative agent of progressive multifocal leukoencephalopathy. Anti-JCV antibody seropositivity is an important consideration in... (Meta-Analysis)
Meta-Analysis
BACKGROUND
The John Cunningham virus (JCV) is the causative agent of progressive multifocal leukoencephalopathy. Anti-JCV antibody seropositivity is an important consideration in patients with multiple sclerosis (MS). The reported prevalence of JCV in MS patients has been conflicting.
OBJECTIVE
We aimed to conduct a systematic review and meta-analysis to estimate the pooled prevalence of anti-JCV antibody seropositivity in cases with MS.
METHODS
We searched PubMed, Scopus, EMBASE, CINAHL, Web of Science, Ovid, ProQuest, Google Scholar, and gray literature including reference of included studies, and conference abstracts which were published up to April 2019. Two independent researchers independently assessed the articles.
RESULTS
The literature search found 181 articles. After eliminating duplicates, reviews, case reports, and trials, 15 articles remained. Finally, 8 articles were included for the final analysis (from Asia, Europe, the USA, and Canada). In total, 16,041 MS cases were analyzed. The prevalence of anti-JCV antibody seropositivity varied between 40 and 80%, and the pooled estimate was calculated as 60% (95% CI: 56-64%), though with significant heterogeneity (I2 = 95%, p = 0.01).
CONCLUSION
The prevalence of anti-JCV antibody seropositivity is variable among MS patients in different countries, and the pooled estimate showed that this is 60% overall.
PubMed: 32623436
DOI: 10.1159/000507367 -
Journal of Travel Medicine Apr 2021
Topics: Basic Reproduction Number; Humans; Lassa Fever; Lassa virus; Travel
PubMed: 33690795
DOI: 10.1093/jtm/taab029 -
Systematic Reviews Jan 2024The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by...
BACKGROUND
The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).
METHODOLOGY
PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.
FINDINGS
In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.
INTERPRETATION
The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
Topics: Humans; United States; SARS-CoV-2; COVID-19; Pandemics; Vaccination; Outcome Assessment, Health Care
PubMed: 38229123
DOI: 10.1186/s13643-023-02411-1