Estimating the basic reproduction number from noisy daily data.

In this paper, we propose an easy to implement generalized linear models (GLM) methodology for estimating the basic reproduction number, R, a major epidemic parameter for assessing the transmissibility of an infection. Our approach rests on well known qualitative properties of the classical SIR and SEIR systems for large populations. Moreover, we assume that information at the individual network level is not available.

A statistical tool for comparing seasonal ILI surveillance data.

In this paper, we consider the yearly influenza epidemic, as reflected in the seasonal surveillance data compiled by the CDC (Center for Disease Control and Prevention, USA) and we explore a new methodology for comparing specific features of these data. In particular, we focus on the ten HHS (Health and Human Services) regions, and how the incidence data evolves in these regions. In order to perform the comparisons, we consider the relative distribution of weekly new cases over one season and replace the crude data with predicted values.

A new method for estimating the demographic history from DNA sequences: an importance sampling approach.

The effective population size over time (demographic history) can be retraced from a sample of contemporary DNA sequences. In this paper, we propose a novel methodology based on importance sampling (IS) for exploring such demographic histories. Our starting point is the generalized skyline plot with the main difference being that our procedure, skywis plot, uses a large number of genealogies.

Estimating the basic reproduction number from surveillance data on past epidemics.

In this paper, we consider the basic reproduction number, R0, a parameter that characterizes the transmission potential of an epidemic, and explore a novel way for estimating it. We introduce a stochastic process which takes as starting points the classical SIR (susceptibles-infected-removed) models, deterministic and stochastic. The estimation method rests on an extremum property of the deterministic SIR model, and could be applied to past surveillance data on epidemic outbreaks, data gathered at different locations or in different years.

Prediction of predator-prey populations modelled by perturbed ODEs.

In this paper we explore a stochastic model in continuous time for predator-prey interactions, which accounts for the periodical behaviour observed in many animal populations. More precisely, we consider a solution to the classical Lotka-Volterra system of equations, but we view the actual population sizes as random perturbations of the solutions to this ODE system. Namely, we assume that the perturbations follow correlated Ornstein-Uhlenbeck processes; this approach generalizes the one of Froda and Colavita [Aust N Z J Stat 2:235-254, 2005] who considered only i.