able on the Center for Systems Science and Engineering at Johns Hopkins University [6] , the available data analyzed is considered between January 21 th 2020 and April 39 12 th 2020, included, with a feedback process in a neural network applied; it allows 40 to examined the information in real time in each state, at Fig. 1 • . With COVID-19, the spatial representation of the disease by using GIS plat-73 form allows to verified the "material, population and social psychology at three 74 scales: individual,group and regional" [9] ; in this case, with GIS technology is 75 necessary to implement big data techniques, for cross validations and analysis. 
• Associated to GPR the idea is to "utilize nonlinear diffusion map coordinates 79 and formulate a deterministic dynamical system on the system manifold" [12] .
A interesting approach has a reduced-space data-driven dynamical system with 81 an evaluation, with efficiency with low intrinsic dimensionality.
• An "advantage of employing GPR to reconstruct the reduced-order dynamics 83 is the simultaneous estimation of the dynamics and the associated uncertaint" with an input xn ∈ R and "noisy scalar output yn" [11] . For infection population, 
Where: 
Where: 119
θ 1 : Is a hyper-parameter with maximum covariance in chaotic systems. 132
In Eq. (9), Bayes rule is written with a normalized process to find (f, f * ) in Eq. (10).
In Eq. (11), associates to "conditioning the joint Gaussian prior distribution on 134 the observations, resulting in the closed-form Gaussian distribution" [12] . With Eq.
(12), f * , the mean and covariance should be directly added to obtain Eq. (13).
136
Finally, in Eq.(14) makes feasible to use up to more than twelve thousands of training x ∈ R
In the Eq. (16), individually node in the hidden layer has "linear combination" [18] 152 with a chain events. The hidden layers are composed as following: 
In Eq. (18) describes a matrix of weights, it is analogous to regression coefficient in
Where:
172 h: it is a description of the hidden layer, due to restriction of the "linearly trans-173 formed and passed to the output layer". However, China has implemented robust strategies for distancing control and quar-206 antine around this country, therefore, the recovery rate is 90%. Far away from New 207 York, with recovery rate of 9.42% and on the other hand, cities as Texas is 13.09%.
Besides, Hawaii has different rate of 56.79% for the restrictions initiated with more 209 influence in their population.
Combining a mathematical model with multiple datasets, Fig. 6 and Fig. 7 , we 211 found that the median daily Rt of COVID-19 in USA probably varied between 2.6 212 and 5.6 in April, 2020, before 3 weeks of travel restrictions were introduced. We In traditional methods, we detect mistakes in the forecast associated to old pan-269 demic researches, and as we can see, the COVID-19 has behaved in unpredictable 270 and changing ways, according to the factors used in its treatment. Therefore, we 271 think that the pandemic behaves as a dynamic-chaotic system, which sets new start-272 ing points that do not intersect and changes its behavior according to the way it 273 is treated. Consequently, this study could be a real contribution for understanding 274 this problem with a good tool of processing a systematic review. As we can see 275 in the Fig. 9 , the example started with three countries, with three initial states. 276 Hence they have been evolving together until some days, but after a month, they 
Grants/financial support.
None.
Credit authorship contribution statement. Conflict of interest.
There is no conflict of interest in this work. 