Uncertainty Reduction in Logistic Growth Regression Using Surrogate Systems Carrying Capacities: a COVID-19 Case Study

Logistic growth regressions present high uncertainties when data are not past their inflection points. In such conditions, the uncertainty in estimated carrying capacity K, for example, can be of order K. Here, we a method reduction logistic regression using from surrogate process. We illustrate Richards’ function to predict points COVID-19 first-wave accumulated causalities Brazilian cities. First waves epidemics known reasonably well modeled posteriori by Richard’s function. Yet, make predictions early that end before or around point. For goal, estimate K international cities where clearly The constraint stabilizes cities, reducing prediction parameters even is rough estimate. peaks agree with official data. may used other models and processes, areas as economics biology, populations systems identified.