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.

Principio de complejidad: apuntes y reflexiones para una ampliación epistemológica del concepto de paradigma en bioética / Principle of complexity: notes and reflections for an epistemological extension of the paradigm concept in bioethics

Se indaga en las premisas básicas de la teoría del Pensamiento Complejo elaborado por el sociólogo Edgar Morin, buscando establecer espacios de diálogo entre las nociones sistémicas del concepto de paradigma y los eventos presentes –discusión, reflexión, crítica, posicionamientos teóricos– en el debate de la bioética como nexo socio–comunicativo entre el contexto social y clínico. Se ubica como principal objetivo de este trabajo, la cimentación de una base epistemológica reflexiva de la noción de bioética que sirva como precedente al debate sobre las construcciones asociativas y configurativas del lenguaje en ésta, y la necesidad de una postura sistémica que posicione la bioética como un fenómeno complejo en constante transformación.