Successive Pandemic Waves with Different Virulent Strains and the Effects of Vaccination for SARS-CoV-2

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

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.

A Clinical Update on SARS-CoV-2: Pathology and Development of Potential Inhibitors

SARS-CoV-2 (severe acute respiratory syndrome) is highly infectious and causes severe acute respiratory distress syndrome (SARD), immune suppression, and multi-organ failure. For SARS-CoV-2, only supportive treatment options are available, such as oxygen supportive therapy, ventilator support, antibiotics for secondary infections, mineral and fluid treatment, and a significant subset of repurposed effective drugs. Viral targeted inhibitors are the most suitable molecules, such as ACE2 (angiotensin-converting enzyme-2) and RBD (receptor-binding domain) protein-based inhibitors, inhibitors of host proteases, inhibitors of viral proteases 3CLpro (3C-like proteinase) and PLpro (papain-like protease), inhibitors of replicative enzymes, inhibitors of viral attachment of SARS-CoV-2 to the ACE2 receptor and TMPRSS2 (transmembrane serine proteinase 2), inhibitors of HR1 (Heptad Repeat 1)-HR2 (Heptad Repeat 2) interaction at the S2 protein of the coronavirus, etc. Targeting the cathepsin L proteinase, peptide analogues, monoclonal antibodies, and protein chimaeras as RBD inhibitors interferes with the spike protein's ability to fuse to the membrane. Targeting the cathepsin L proteinase, peptide analogues, monoclonal antibodies, and protein chimaeras as RBD inhibitors interferes with the spike protein's ability to fuse to the membrane. Even with the tremendous progress made, creating effective drugs remains difficult. To develop COVID-19 treatment alternatives, clinical studies are examining a variety of therapy categories, including antibodies, antivirals, cell-based therapy, repurposed diagnostic medicines, and more. In this article, we discuss recent clinical updates on SARS-CoV-2 infection, clinical characteristics, diagnosis, immunopathology, the new emergence of variant, SARS-CoV-2, various approaches to drug development and treatment options. The development of therapies has been complicated by the global occurrence of many SARS-CoV-2 mutations. Discussion of this manuscript will provide new insight into drug pathophysiology and drug development.

A Scoping Review of Three Dimensions for Long-Term COVID-19 Vaccination Models: Hybrid Immunity, Individual Drivers of Vaccinal Choice, and Human Errors

The virus that causes COVID-19 changes over time, occasionally leading to Variants of Interest (VOIs) and Variants of Concern (VOCs) that can behave differently with respect to detection kits, treatments, or vaccines. For instance, two vaccination doses were 61% effective against the BA.1 predominant variant, but only 24% effective when BA.2 became predominant. While doses still confer protection against severe disease outcomes, the BA.5 variant demonstrates the possibility that individuals who have received a few doses built for previous variants can still be infected with newer variants. As previous vaccines become less effective, new ones will be released to target specific variants and the whole process of vaccinating the population will restart. While previous models have detailed logistical aspects and disease progression, there are three additional key elements to model COVID-19 vaccination coverage in the long term. First, the willingness of the population to participate in regular vaccination campaigns is essential for long-term effective COVID-19 vaccination coverage. Previous research has shown that several categories of variables drive vaccination status: sociodemographic, health-related, psychological, and information-related constructs. However, the inclusion of these categories in future models raises questions about the identification of specific factors (e.g., which sociodemographic aspects?) and their operationalization (e.g., how to initialize agents with a plausible combination of factors?). While previous models separately accounted for natural- and vaccine-induced immunity, the reality is that a significant fraction of individuals will be both vaccinated and infected over the coming years. Modeling the decay in immunity with respect to new VOCs will thus need to account for hybrid immunity. Finally, models rarely assume that individuals make mistakes, even though this over-reliance on perfectly rational individuals can miss essential dynamics. Using the U.S. as a guiding example, our scoping review summarizes these aspects (vaccinal choice, immunity, and errors) through ten recommendations to support the modeling community in developing long-term COVID-19 vaccination models.