C. Gaetan | Searcharxiv

arXiv2021

A SEIR model with time-varying coefficients for analysing the SARS-CoV-2 epidemic

In this study, we propose a time-dependent Susceptible-Exposed-Infected-Recovered (SEIR) model for the analysis of the SARS-CoV-2 epidemic outbreak in three different countries, the United States of America, Italy and Iceland using public data inherent the numbers of the epidemic wave. Since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. The parameters of interest for the SEIR model were estimated employing a composite likelihood approach. Moreover, standard errors have been corrected for temporal dependence. The adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases.

q-bio.PE↗

arXiv2020

On modelling positive continuous data with spatio-temporal dependence

In this paper we concentrate on an alternative modeling strategy for positive data that exhibit spatial or spatio-temporal dependence. Specifically we propose to consider stochastic processes obtained trough a monotone transformation of scaled version of $χ^2$ random processes. The latter are well known in the specialized literature and originates by summing independent copies of a squared Gaussian process. However their use as stochastic models and related inference have not been much considered. Motivated by a spatio-temporal analysis of wind speed data from a network of meteorological stations in the Netherlands, we exemplify our modeling strategy by means of a non-stationary process with Weibull marginal distributions. For the proposed Weibull process we study the second-order and geometrical properties and we provide analytic expressions for the bivariate distribution. Since the likelihood is intractable, even for relatively small data-set, we suggest to adopt the pairwise likelihood as a tool for the inference. Moreover we tackle the prediction problem and we propose a linear prediction. The effectiveness of our modeling strategy is illustrated through the analysis of the aforementioned Netherland wind speed data that we supplement with a simulation study.

stat.ME↗