Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model

MERIEM ALLALI; PATRICK PORTECOP; MICHEL CARLES; DOMINIQUE GIBERT

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.12.20063008 doi: medRxiv preprint In our case, this multiplicative noise may be explained by the growing Brownian divergence of some random walks in the network. Practically, this conducts to the appearance of some outlier simulations and justifies the use of the median. We now address another important characteristic of the epidemic process through the random variations occurring at the very beginning of the process. The features we want to discuss are illustrated in Figure 7 where the plots have been obtained by running the model with a different number on initial infected Z I . In the case of rather small values of Z I (i.e. 1, 10 or 20 in Fig. 7A ,B,C), random fluctuations perturb the beginning of the curves, with a longer persistence for the N s curve. For larger values of Z I (i.e. 40, 60 or 80 in Fig. 7D ,E,F), the random fluctuations almost disappear while the starting sequence becomes steeper. Consequently, a careful observation of the starting sequence may provide some information about the number Z I of initial infectious persons. Let us remark that these features can only be obtained with a stochastic model as the one developed in the present study. . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.