Estimación del intervalo serial y número reproductivo básico para los casos importados de COVID-19

Estrada-Álvarez, Jorge Mario; Ospina-Ramírez, Juan J.; Hincapié-Acuña, Maryluz; González, María del Pilar Gómez

doi:10.15446/rsap.v22n2.87492

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Since 2020, it has resulted in numerous cases and fatalities [ 1 ]. Its transmission primarily occurs from person to person, exhibiting high transmissibility and a variable, unpredictable course [ 2 ]. Consequently, in March 2020, the World Health Organization (WHO) classified COVID-19 as a pandemic due to its global spread.…”

Section: Introductionmentioning

confidence: 99%

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

Espinosa,

Mora,

Sanabria

et al. 2024

Syst Rev

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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.

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Section: Introductionmentioning

confidence: 99%