TY - JOUR

T1 - Monitoring and Forecasting COVID-19

T2 - Heuristic Regression, Susceptible-Infected-Removed Model and, Spatial Stochastic

AU - de Andres, P. L.

AU - de Andres-Bragado, L.

AU - Hoessly, L.

N1 - Funding Information: LH is supported by the Swiss National Science Foundations Early Postdoc. Mobility grant (P2FRP2_188023). This work has been financed by the Spanish MINECO (MAT2017-85089-C2-1-R) and the European Research Council under contract (ERC-2013-SYG-610256 NANOCOSMOS). Computing resources have been provided by CTI-CSIC. Open access is partly funded by CSIC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. Publisher Copyright: © Copyright © 2021 de Andres, de Andres-Bragado and Hoessly.

PY - 2021/5/21

Y1 - 2021/5/21

N2 - The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. The dynamics of such public-health threats can often be efficiently analyzed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derive the three free parameters for both models in several cases and test them against the amount of data needed to bring accuracy in predictions. The SHR model is (Formula presented.) accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar accuracy a fortnight before. All the analyzed cases assert the utility of SHR and SIR approximants as a valuable tool to forecast the disease’s evolution. Finally, we have studied simulated stochastic individual-based SIR dynamics, which yields a detailed spatial and temporal view of the disease that cannot be given by SIR or SHR methods.

AB - The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. The dynamics of such public-health threats can often be efficiently analyzed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derive the three free parameters for both models in several cases and test them against the amount of data needed to bring accuracy in predictions. The SHR model is (Formula presented.) accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar accuracy a fortnight before. All the analyzed cases assert the utility of SHR and SIR approximants as a valuable tool to forecast the disease’s evolution. Finally, we have studied simulated stochastic individual-based SIR dynamics, which yields a detailed spatial and temporal view of the disease that cannot be given by SIR or SHR methods.

KW - COVID-19

KW - Monte-Carlo

KW - SARS-CoV-2

KW - spatial stochastic

KW - statistical heuristic regression

KW - susceptible-infected-removed model

U2 - 10.3389/fams.2021.650716

DO - 10.3389/fams.2021.650716

M3 - Journal article

C2 - 34336986

AN - SCOPUS:85107343976

VL - 7

JO - Frontiers in Applied Mathematics and Statistics

JF - Frontiers in Applied Mathematics and Statistics

SN - 2297-4687

M1 - 650716

ER -