ABSTRACT
We analyze the evolution of the COVID19 infections in the first months of the pandemics and show that the basic compartmental SIR model cannot explain the data, some characteristic time series being by more than an order of magnitude different from the fit function over significant parts of the documented time interval. To correct this large discrepancy, we amend the SIR model by assuming that there is a relatively large population that is infected but was not tested and confirmed. This assumption qualitatively changes the fitting possibilities of the model and, despite its simplicity, in most cases the time series can be well reproduced. The observed dynamic is only due to the transitions between two infected compartments, which are the unconfirmed infected and the confirmed infected, and the rate of closing the cases (by recovery or death) in the confirmed infected compartment. We also discuss some relevant extensions of this model, to improve the interpretation and the fitting of the data. These findings qualitatively and quantitatively evidences the “iceberg phenomenon” in epistemology. © 2021 Institute of Physics Publishing. All rights reserved.