Objectives: We study partial unlock or reopening interaction with seasonal effects in a managed epidemic to quantify overshoot effects on small and large unlock steps and discover robust strategies for reducing overshoot. Methods: We simulate partial unlock of social distancing for epidemics...

This paper proposes to use a semi parametric regression method, named Sliced Inverse Regression (SIR hereafter), to analyse ambulatory blood pressure monitoring data.

We consider a class of stochastic mathematical programs with complementarity constraints, in which both the objective and the constraints involve limit functions or expectations that need to be estimated or approximated.Such programs can be used for modeling average or steady-state behavior of...

In this paper we introduce a parameter dependent class of Krylov-based methods, namely CD, for the solution of symmetric linear systems. We give evidence that in our proposal we generate sequences of conjugate directions, extending some properties of the standard Conjugate Gradient (CG) method,...

We develop an integrated epidemiological-macroeconomic model to analyze the interplay between the COVID-19 outbreak and economic activity, as a tool for capacity building purposes. We illustrate a workhorse framework that combines a rich epidemiological model with an economic block to shed light...

Epidemic analysis by dynamical modelling is a reliable and insightful way to analyse epidemiological data in order to extract key indicators about the outbreak and to make predictions on its future course. We develop a generalised SEIR model based on Peng et al. 2020 and estimate it on a...

COVID-19 has caused an immense worldwide impact, indicating the unpreparedness of several governments in handling contagious virus outbreaks. This research uses a time-dependent model analysing the sum of the epidemiological cost, relating to the direct cost from the loss of lives, ICU...