Predicting US Covid-19 Infection Rate using Time-delay Recurrence Simulation

Alexander Harrison¹

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.5 , pp.1-5, Oct-2020

Online published on Oct 31, 2020

Copyright © Alexander Harrison . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
A recursion model is developed to describe the growth of Covid-19 cases in the USA. An essential requirement of any model is confidence in its predictive capability. Published growth rates for Covid-19 in the USA are shown to correlate well with recursive time-delay simulations. Data for six months after March 2020 is compared to predictions from known logistic equations and modified time-delay relations. Simple logistic equations do not show a correlated trajectory with case numbers, whereas a time-delay recurrence equation can be calibrated to follow actual data. Modelling predicts over 10 million infections by January 2021. Growth curves projected to mid-2022 are examined and discussed. Infection totals in the USA is predicted to approach 35 million cases by the end of 2021 without human control.

Key-Words / Index Term :
US Covid-19, Simulations, Prediction, Growth Rates, Recurrence Model

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