RESUMEN

Epidemiological models usually contain a set of parameters that must be adjusted based on available observations. Once a model has been calibrated, it can be used as a forecasting tool to make predictions and to evaluate contingency plans. It is customary to employ only point estimators of model parameters for such predictions. However, some models may fit the same data reasonably well for a broad range of parameter values, and this flexibility means that predictions stemming from them will vary widely, depending on the particular values employed within the range that gives a good fit. When data are poor or incomplete, model uncertainty widens further. A way to circumvent this problem is to use Bayesian statistics to incorporate observations and use the full range of parameter estimates contained in the posterior distribution to adjust for uncertainties in model predictions. Specifically, given an epidemiological model and a probability distribution for observations, we use the posterior distribution of model parameters to generate all possible epidemic curves, whose information is encapsulated in posterior predictive distributions. From these, one can extract the worst-case scenario and study the impact of implementing contingency plans according to this assessment. We apply this approach to the evolution of COVID-19 in Mexico City and assess whether contingency plans are being successful and whether the epidemiological curve has flattened.

Asunto(s)

Betacoronavirus , Infecciones por Coronavirus/epidemiología , Epidemias , Neumonía Viral/epidemiología , Teorema de Bayes , COVID-19 , Infecciones por Coronavirus/mortalidad , Bases de Datos Factuales , Epidemias/estadística & datos numéricos , Humanos , Conceptos Matemáticos , México/epidemiología , Modelos Biológicos , Modelos Estadísticos , Pandemias , Neumonía Viral/mortalidad , Probabilidad , SARS-CoV-2 , Factores de Tiempo , Incertidumbre

RESUMEN

The coherent transport of n fermions in disordered networks of l single-particle states connected by k-body interactions is studied. These networks are modeled by embedded Gaussian random matrix ensemble (EGE). The conductance bandwidth and the ensemble-averaged total current attain their maximal values if the system is highly filled n∼l-1 and k∼n/2. For the cases k=1 and k=n the bandwidth is minimal. We show that for all parameters the transport is enhanced significantly whenever centrosymmetric embedded Gaussian ensemble (csEGE) are considered. In this case the transmission shows numerous resonances of perfect transport. Analyzing the transmission by spectral decomposition, we find that centrosymmetry induces strong correlations and enhances the extrema of the distributions. This suppresses destructive interference effects in the system and thus causes backscattering-free transmission resonances that enhance the overall transport. The distribution of the total current for the csEGE has a very large dominating peak for n=l-1, close to the highest observed currents.

RESUMEN

We study the nearest-neighbor distributions of the k -body embedded ensembles of random matrices for n bosons distributed over two-degenerate single-particle states. This ensemble, as a function of k , displays a transition from harmonic-oscillator behavior (k=1) to random-matrix-type behavior (k=n) . We show that a large and robust quasidegeneracy is present for a wide interval of values of k when the ensemble is time-reversal invariant. These quasidegenerate levels are Shnirelman doublets which appear due to the integrability and time-reversal invariance of the underlying classical systems. We present results related to the frequency in the spectrum of these degenerate levels in terms of k and discuss the statistical properties of the splittings of these doublets.