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Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy.
Gecili, Emrah; Ziady, Assem; Szczesniak, Rhonda D.
Afiliación
  • Gecili E; Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States of America.
  • Ziady A; Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States of America.
  • Szczesniak RD; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States of America.
PLoS One ; 16(1): e0244173, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33411744
ABSTRACT
The novel coronavirus (COVID-19) is an emergent disease that initially had no historical data to guide scientists on predicting/ forecasting its global or national impact over time. The ability to predict the progress of this pandemic has been crucial for decision making aimed at fighting this pandemic and controlling its spread. In this work we considered four different statistical/time series models that are readily available from the 'forecast' package in R. We performed novel applications with these models, forecasting the number of infected cases (confirmed cases and similarly the number of deaths and recovery) along with the corresponding 90% prediction interval to estimate uncertainty around pointwise forecasts. Since the future may not repeat the past for this pandemic, no prediction model is certain. However, any prediction tool with acceptable prediction performance (or prediction error) could still be very useful for public-health planning to handle spread of the pandemic, and could policy decision-making and facilitate transition to normality. These four models were applied to publicly available data of the COVID-19 pandemic for both the USA and Italy. We observed that all models reasonably predicted the future numbers of confirmed cases, deaths, and recoveries of COVID-19. However, for the majority of the analyses, the time series model with autoregressive integrated moving average (ARIMA) and cubic smoothing spline models both had smaller prediction errors and narrower prediction intervals, compared to the Holt and Trigonometric Exponential smoothing state space model with Box-Cox transformation (TBATS) models. Therefore, the former two models were preferable to the latter models. Given similarities in performance of the models in the USA and Italy, the corresponding prediction tools can be applied to other countries grappling with the COVID-19 pandemic, and to any pandemics that can occur in future.
Asunto(s)
Texto completo: Disponible Colección: Bases de datos internacionales Contexto en salud: Agenda de Salud Sostenible para las Américas / ODS3 - Salud y Bienestar Tema en salud: Objetivo 10: Enfermedades transmisibles / Meta 3.3: Poner fin a las enfermedades desatendidas y detener enfermedades transmisibles Base de datos: MEDLINE Asunto principal: Predicción / Modelos Biológicos Tipo de estudio: Estudio pronóstico Límite: Humanos País/Región como asunto: America del Norte / Europa Idioma: Inglés Revista: PLoS One Asunto de la revista: Ciencia / Medicina Año: 2021 Tipo del documento: Artículo País de afiliación: Estados Unidos

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Texto completo: Disponible Colección: Bases de datos internacionales Contexto en salud: Agenda de Salud Sostenible para las Américas / ODS3 - Salud y Bienestar Tema en salud: Objetivo 10: Enfermedades transmisibles / Meta 3.3: Poner fin a las enfermedades desatendidas y detener enfermedades transmisibles Base de datos: MEDLINE Asunto principal: Predicción / Modelos Biológicos Tipo de estudio: Estudio pronóstico Límite: Humanos País/Región como asunto: America del Norte / Europa Idioma: Inglés Revista: PLoS One Asunto de la revista: Ciencia / Medicina Año: 2021 Tipo del documento: Artículo País de afiliación: Estados Unidos