CaltechAUTHORS
  A Caltech Library Service

Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic

Contreras, Sebastián and Biron-Lattes, Juan Pablo and Villavicencio, H. Andrés and Medina-Ortiz, David and Llanovarced-Kawles, Nyna and Olivera-Nappa, Álvaro (2020) Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic. Chaos, Solitons, and Fractals, 139 . Art. No. 110087. ISSN 0960-0779. PMCID PMC7341964. https://resolver.caltech.edu/CaltechAUTHORS:20200706-145145631

[img] PDF - Published Version
See Usage Policy.

851Kb
[img] PDF (Free COVID-19 resource) - Accepted Version
See Usage Policy.

1816Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200706-145145631

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 R_t, 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.chaos.2020.110087DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341964PubMed CentralArticle
ORCID:
AuthorORCID
Contreras, Sebastián0000-0001-8909-774X
Medina-Ortiz, David0000-0002-8369-5746
Olivera-Nappa, Álvaro0000-0001-6053-7019
Additional Information:© 2020 Elsevier Ltd. Received 26 May 2020, Revised 19 June 2020, Accepted 2 July 2020, Available online 3 July 2020. The authors gratefully acknowledge support from the Chilean National Agency for Research and development through ANID PIA Grant AFB180004, and the Centre for Biotechnology and Bioengineering - CeBiB (PIA project FB0001, Conicyt, Chile). DM-O gratefully acknowledges Conicyt, Chile, for PhD fellowship 21181435. CRediT authorship contribution statement: Sebastián Contreras: Conceptualization, Methodology, Investigation, Writing - original draft, Visualization, Writing - review & editing, Supervision. Juan Pablo Biron-Lattes: Validation, Investigation, Writing - original draft, Writing - review & editing. H. Andrés Villavicencio: Conceptualization, Investigation, Writing - original draft, Supervision. David Medina-Ortiz: Validation, Writing - review & editing. Nyna Llanovarced-Kawles: Investigation, Writing - original draft. Álvaro Olivera-Nappa: Validation, Writing - review & editing, Supervision, Project administration, Funding acquisition. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funders:
Funding AgencyGrant Number
Comisión Nacional de Investigación Científica y Tecnológica (CONICYT)AFB180004
Comisión Nacional de Investigación Científica y Tecnológica (CONICYT)FB0001
Comisión Nacional de Investigación Científica y Tecnológica (CONICYT)21181435
Subject Keywords:COVID-19; SARS-CoV-2; Public health; Statistics; ARIMA models; Data analysis
PubMed Central ID:PMC7341964
Record Number:CaltechAUTHORS:20200706-145145631
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200706-145145631
Official Citation:Sebastián Contreras, Juan Pablo Biron-Lattes, H. Andrés Villavicencio, David Medina-Ortiz, Nyna Llanovarced-Kawles, Álvaro Olivera-Nappa, Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic, Chaos, Solitons & Fractals, Volume 139, 2020, 110087, ISSN 0960-0779, https://doi.org/10.1016/j.chaos.2020.110087. (http://www.sciencedirect.com/science/article/pii/S0960077920304847)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104231
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:06 Jul 2020 21:58
Last Modified:14 Jul 2020 22:04

Repository Staff Only: item control page