Vasilis Z Marmarelis1. 1. Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089 USA.
Abstract
There are currently intensified efforts by the scientific community world-wide to analyze the dynamics of the Covid-19 pandemic in order to predict key epidemiological effects and assist the proper planning for its clinical management, as well as guide sociopolitical decision-making regarding proper mitigation measures. Most efforts follow variants of the established SIR methodological framework that divides a population into "Susceptible", "Infectious" and "Recovered/Removed" fractions and defines their dynamic inter-relationships with first-order differential equations. GOAL: This paper proposes a novel approach based on data-guided detection and concatenation of infection waves - each of them described by a Riccati equation with adaptively estimated parameters. METHODS: This approach was applied to Covid-19 daily time-series data of US confirmed cases, resulting in the decomposition of the epidemic time-course into five "Riccati modules" representing major infection waves to date (June 18th). RESULTS: Four waves have passed the time-point of peak infection rate, with the fifth expected to peak on July 20th. The obtained parameter estimates indicate gradual reduction of infectivity rate, although the latest wave is expected to be the largest. CONCLUSIONS: This analysis suggests that, if no new waves of infection emerge, the Covid-19 epidemic will be controlled in the US (<5000 new daily cases) by September 26th, and the maximum of confirmed cases will reach 4,160,000. Importantly, this approach can be used to detect (via rigorous statistical methods) the emergence of possible new waves of infections in the future. Analysis of data from individual states or countries may quantify the distinct effects of different mitigation measures.
There are currently intensified efforts by the scientific community world-wide to analyze the dynamics of the Covid-19 pandemic in order to predict key epidemiological effects and assist the proper planning for its clinical management, as well as guide sociopolitical decision-making regarding proper mitigation measures. Most efforts follow variants of the established SIR methodological framework that divides a population into "Susceptible", "Infectious" and "Recovered/Removed" fractions and defines their dynamic inter-relationships with first-order differential equations. GOAL: This paper proposes a novel approach based on data-guided detection and concatenation of infection waves - each of them described by a Riccati equation with adaptively estimated parameters. METHODS: This approach was applied to Covid-19 daily time-series data of US confirmed cases, resulting in the decomposition of the epidemic time-course into five "Riccati modules" representing major infection waves to date (June 18th). RESULTS: Four waves have passed the time-point of peak infection rate, with the fifth expected to peak on July 20th. The obtained parameter estimates indicate gradual reduction of infectivity rate, although the latest wave is expected to be the largest. CONCLUSIONS: This analysis suggests that, if no new waves of infection emerge, the Covid-19 epidemic will be controlled in the US (<5000 new daily cases) by September 26th, and the maximum of confirmed cases will reach 4,160,000. Importantly, this approach can be used to detect (via rigorous statistical methods) the emergence of possible new waves of infections in the future. Analysis of data from individual states or countries may quantify the distinct effects of different mitigation measures.
Entities:
Keywords:
Adaptive modeling of Covid-19 time-series data; epidemiological predictive modeling; riccati-based phase-space modeling; statistical detection of Covid-19 infection waves
Authors: Catherine P Adans-Dester; Stacy Bamberg; Francesco P Bertacchi; Brian Caulfield; Kara Chappie; Danilo Demarchi; M Kelley Erb; Juan Estrada; Eric E Fabara; Michael Freni; Karl E Friedl; Roozbeh Ghaffari; Geoffrey Gill; Mark S Greenberg; Reed W Hoyt; Emil Jovanov; Christoph M Kanzler; Dina Katabi; Meredith Kernan; Colleen Kigin; Sunghoon I Lee; Steffen Leonhardt; Nigel H Lovell; Jose Mantilla; Thomas H McCoy; Nell Meosky Luo; Glenn A Miller; John Moore; Derek O'Keeffe; Jeffrey Palmer; Federico Parisi; Shyamal Patel; Jack Po; Benito L Pugliese; Thomas Quatieri; Tauhidur Rahman; Nathan Ramasarma; John A Rogers; Guillermo U Ruiz-Esparza; Stefano Sapienza; Gregory Schiurring; Lee Schwamm; Hadi Shafiee; Sara Kelly Silacci; Nathaniel M Sims; Tanya Talkar; William J Tharion; James A Toombs; Christopher Uschnig; Gloria P Vergara-Diaz; Paul Wacnik; May D Wang; James Welch; Lina Williamson; Ross Zafonte; Adrian Zai; Yuan-Ting Zhang; Guillermo J Tearney; Rushdy Ahmad; David R Walt; Paolo Bonato Journal: IEEE Open J Eng Med Biol Date: 2020-08-07