The epidemiologic parameters for COVID-19: A Systematic Review and Meta-Analysis

Izadi, Neda; Taherpour, Niloufar; Mokhayeri, Yaser; Ghorbani, Sahar Sotoodeh; Rahmani, Khaled; Nazari, Seyed Saeed Hashemi

doi:10.1101/2020.05.02.20088385

Cited by 6 publications

(5 citation statements)

References 79 publications

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“…In this paper we summarised more epidemiological parameters than other available multi-parameter reviews on COVID-19 [18][19][20][21][22][23][24] . However, most of them use more studies for most of the parameters than us, mainly due to our language and geographic restrictions, as well as the application of risk of bias analysis and preprints exclusion.…”

Section: Discussionmentioning

confidence: 99%

Scoping review and meta-analysis of COVID-19 epidemiological parameters for modeling from early Asian studies

Fonfría

Vigo

García-García

et al. 2022

Preprint

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Retrospective epidemiological models are powerful tools to understand its transmission dynamics and to assess the efficacy of different control measures. This study summarises key epidemiological parameters of COVID-19 for retrospective mathematical and clinical modeling. A review of scientific papers and preprints published in English between 1 January and 15 April 2020 in PubMed, MedRxiv and BioRxiv was performed to obtain epidemiological parameters of the initial stage of COVID-19 pandemic in Asia. After excluding articles with unacceptable risks of bias and those that remained as preprints as of 15 November 2021, meta-analyses were performed to derive summary effect estimates from the data collected using the statistical software R. Out of 4,893 articles identified, 88 provided data for 22 parameters for the overall population and 7 specifically for children. Meta-analyses were conducted considering time period as a categorical moderator when it was statistically significant. The results obtained are essential for building more reliable models to help clinicians and policymakers improve their knowledge on COVID-19 and apply it in future decisions.

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Section: Discussionmentioning

confidence: 99%

Scoping review and meta-analysis of COVID-19 epidemiological parameters for modeling from early Asian studies

Fonfría

Vigo

García-García

et al. 2022

Preprint

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“…Para pi se consideró una distribución Gama con un retraso promedio d = 14 días (22,23). Estimamos el número reproductivo efectivo (Rt) con el método de Cori et al (24) y con el método RKI (25,26), con un intervalo serial de 5 días (27)(28)(29). Se estimó además el número reproductivo (R0) con R0 = 1 + e0/E, donde e0 es la esperanza de vida al nacer y E la edad media de infección (30,31).…”

Section: Materials Y Métodosunclassified

Resumen analítico de la experiencia chilena de la pandemia COVID-19, 2020-2022

Canals¹,

Canals²

2022

CMS

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Objetivo. Realizar un análisis crítico de la evolución de 2 años y medio de la pandemia en Chile, evaluando su relación con aspectos socioculturales propios de este país y con las estrategias usadas para mitigar la pandemia. Métodos. Analizamos datos oficiales reportados por el Ministerio de Salud de Chile: cas os nuevos con- firmados diarios, los casos que requirieron admisión en unidades de cuidados intensivos (UCI), las muertes atribuidas a COVID-19 y positividad de las pruebas (RT-PCR) para SARS CoV-2. Estimamos el número reproductivo y la letalidad a lo largo de la pandemia. Resultados. Encontramos cuatro períodos epidémicos: 1) Una primera epidemia concentrada en la Región Metropolitana en 2020; 2) Una gran epidemia en todo el país en 2021, 3) Un brote de corta dura- ción en noviembre-diciembre 2021) y 4) Un gran brote epidémico asociado a la variante O. La pandemia ha producido alta letalidad y mortalidad y gran impacto en sistema de salud. La dinámica ha sido fuertemente influenciada por los movimientos sociales típicos de Chile, como períodos de vacaciones, festividades y el hacinamiento. Las variantes virales de SARS CoV-2 han jugado un rol importante en la transmisión, do- minando la variante original (WIV04/2019), y la variante α (UK; B.1.1.7) en la Epidemia RM, las variantes γ (P.1) y la variante andina (λ; C.37) en la Epidemia 20/21 y las variantes δ y O en los últimos dos brotes. Conclusiones. A pesar de los esfuerzos de vacunación y la intervención con medidas no farmacológicas, la pandemia en Chile ha causado un gran impacto poblacional y hospitalario. La estrategia fue afectada en 2020 y 2021 por aspectos socioculturales y por el levantamiento rápido de las restricciones bajo el supuesto que la inmunidad poblacional a consecuencia de la vacunación permitiría una incidencia tolerable por los sistemas de salud. En 2022 ha sido fuertemente dominada por la variante Omicrón, de alta transmisión y baja letalidad, semejante a la de las epidemias de Influenza.

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“…Calculamos Rt utilizando el método desarrollado por Cori y colaboradores 34 . Consideramos las últimas 2 semanas (14 días) y un intervalo serial τ = 5 días con variabilidad entre 2 y 8 días (basado en [35][36][37] ). El subregistro de casos se estimó según el método propuesto por Russell y colaboradores 38,39 adaptado a la situación chilena 33 .…”

Section: Métodosunclassified

“…Donde f corresponde a un factor de corrección como consecuencia de la distribución de probabilidad del intervalo serial. Para una carga máxima, el factor de corrección se estima como el máximo p i (i = 0 a 13) para una distribución γ con una media de 5 y una desviación estándar de 4,3 días (basado en [35][36][37] ); el factor de corrección es f ≈0,8. La ecuación (2) es similar a una serie de Fibonacci y requiere dos condiciones iniciales (t1 y t2).…”

Section: Modelo De Carga Potencial Máximaunclassified

COVID-19 in Chile: The usefulness of simple epidemic models in practice

Canals¹,

Cuadrado²,

Canals³

2021

Medwave

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Objectives The purpose of this article is to describe and develop the predictive value of three models during the COVID-19 epidemic in Chile, providing knowledge for decision-making in health. Methods We developed three models during the epidemic: a discrete model to predict the maximum burden on the health system in a short time frame—a basic SEIR (susceptible-exposed-infected-removed) model with discrete equations; a stochastic SEIR model with the Monte Carlo method; and a Gompertz-type model for metropolitan city of Santiago. Results The maximum potential burden model has been useful throughout the monitoring of the epidemic, providing an upper bound for the number of cases, intensive care unit occupancy, and deaths. Deterministic and stochastic SEIR models were very useful in predicting the rise of cases and the peak and onset of case decline; however, they lost utility in the current situation due to the asynchronous recruitment of cases in the regions and the persistence of a strong endemic. The Gompertz model had a better fit in the decline since it best captures the epidemic curve’s asymmetry in Santiago. Conclusions The models have shown great utility in monitoring the epidemic in Chile, with different objectives in different epidemic stages. They have complemented empirical indicators such as reported cases, fatality, deaths, and others, making it possible to predict situations of interest and visualization of the short and long-term local behavior of this pandemic.

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The epidemiologic parameters for COVID-19: A Systematic Review and Meta-Analysis

Cited by 6 publications

References 79 publications

Scoping review and meta-analysis of COVID-19 epidemiological parameters for modeling from early Asian studies

Scoping review and meta-analysis of COVID-19 epidemiological parameters for modeling from early Asian studies

Resumen analítico de la experiencia chilena de la pandemia COVID-19, 2020-2022

COVID-19 in Chile: The usefulness of simple epidemic models in practice

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