Predicting US Covid-19 Infection Rate using Time-delay Recurrence Simulation - Harrison, Alexander (International Journal of Scientific Research in Biological Sciences, 2020)
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.