Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic
Date
2020Author
Contreras, Sebastián
Biron-Lattes, Juan Pablo
Villavicencio, H. Andrés
Medina-Ortiz, David
Llanovarced-Kawles, Nyna
Olivera-Nappa, Álvaro
Metadata
Show full item record
Documentos PDF
Imagenes y Videos
Abstract
COVID-19 pandemic has reshaped our world in a timescale much shorter than what we can understand.
Particularities of SARS-CoV-2, such as its persistence in surfaces and the lack of a curative treatment or
vaccine against COVID-19, have pushed authorities to apply restrictive policies to control its spreading. As
data drove most of the decisions made in this global contingency, their quality is a critical variable for
decision-making actors, and therefore should be carefully curated. In this work, we analyze the sources of
error in typically reported epidemiological variables and usual tests used for diagnosis, and their impact
on our understanding of COVID-19 spreading dynamics. We address the existence of different delays in
the report of new cases, induced by the incubation time of the virus and testing-diagnosis time gaps, and
other error sources related to the sensitivity/specificity of the tests used to diagnose COVID-19. Using a
statistically-based algorithm, we perform a temporal reclassification of cases to avoid delay-induced errors, building up new epidemiologic curves centered in the day where the contagion effectively occurred.
We also statistically enhance the robustness behind the discharge/recovery clinical criteria in the absence
of a direct test, which is typically the case of non-first world countries, where the limited testing capabilities are fully dedicated to the evaluation of new cases. Finally, we applied our methodology to assess
the evolution of the pandemic in Chile through the Effective Reproduction Number Rt, identifying different moments in which data was misleading governmental actions. In doing so, we aim to raise public
awareness of the need for proper data reporting and processing protocols for epidemiological modelling
and predictions.
Palabras clave
COVID-19; SARS-CoV-2; Public health; Statistics; ARIMA Models; Data analysisLink to resource
https://doi.org/10.1016/j.chaos.2020.110087Collections
Estadísticas Google Analytics
Comments
Respuesta Comentario Repositorio Expeditio
Gracias por tomarse el tiempo para darnos su opinión.