The successful application of epidemic models hinges on our ability to estimate model
parameters from limited observations reliably. An often-overlooked step before estimating …

We propose a novel data-driven framework for assessing the a-priori epidemic risk of a
geographical area and for identifying high-risk areas within a country. Our risk index is …

Higher-order interactions play a key role for the operation and function of a complex system.
However, how to identify them is still an open problem. Here, we propose a method to fully …

Mathematical models of viral infection have been developed, fitted to data, and provide
insight into disease pathogenesis for multiple agents that cause chronic infection, including …

Epidemiological models can provide the dynamic evolution of a pandemic but they are
based on many assumptions and parameters that have to be adjusted over the time the …

Many approaches using compartmental models have been used to study the COVID-19
pandemic, with machine learning methods applied to these models having particularly …

One of the significant challenges when a new virus circulates in a host population is to
detect the outbreak as it arises in a timely fashion and implement the appropriate preventive …

Mathematical models are a valuable tool for studying and predicting the spread of infectious
agents. The accuracy of model simulations and predictions invariably depends on the …

Structural identifiability determines the possibility of estimating the parameters of a model by
observing its output in an ideal experiment. If a parameter is structurally locally identifiable …

The large proportion of asymptomatic patients is the major cause leading to the COVID-19
pandemic which is still a significant threat to the whole world. A six-dimensional ODE system …