3 citations · 3 across the 6 of their papers we have counts for
6 papers
Spatial modelling of COVID-19 incident cases using Richards' curve: an application to the Italian regions
Marco Mingione, Pierfrancesco Alaimo Di Loro, Alessio Farcomeni +4
We introduce an extended generalised logistic growth model for discrete outcomes, in which a network structure can be specified to deal with spatial dependence and time dependence…
Nowcasting COVID-19 incidence indicators during the Italian first outbreak
Pierfrancesco Alaimo Di Loro, Fabio Divino, Alessio Farcomeni +4
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecas…
An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian Regions
Alessio Farcomeni, Antonello Maruotti, Fabio Divino +2
The availability of intensive care beds during the Covid-19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simpl…
Bayesian Modelling of Lexis Mortality Data
Fabio Divino, Denekew Bitew Belay, Nico Keilman +1
In this work we present a spatial approach to model and investigate mortality data referenced over a Lexis structure. We decompose the force of mortality into two interpretable com…
Empirical Bayes to assess ecological diversity and similarity with overdispersion in multivariate counts
Fabio Divino, Johanna Ärje, Antti Penttinen +2
The assessment of diversity and similarity is relevant in monitoring the status of ecosystems. The respective indicators are based on the taxonomic composition of biological commun…
Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data
Fabio Divino, Natalia Golini, Giovanna Jona Lasinio +1
Presence-only data are referred to situations in which, given a censoring mechanism, a binary response can be observed only with respect to on outcome, usually called \textit{prese…