0000000000136214

AUTHOR

Giovanna Jona Lasinio

showing 4 related works from this author

Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data

2013

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{presence}. In this work we present a Bayesian approach to the problem of presence-only data based on a two levels scheme. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to the censoring and one due to the sampling. We propose a new formalization for the logistic model with presence-only data that allows further insight into inferential issues related to the model. We concentrate on the case of the linear logistic regression and, in order to make inference on…

FOS: Computer and information sciencesStatistics - Other StatisticsOther Statistics (stat.OT)Statistics - ComputationComputation (stat.CO)
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Nowcasting COVID‐19 incidence indicators during the Italian first outbreak

2020

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 forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replica…

FOS: Computer and information sciencesStatistics and ProbabilityNowcastingEpidemiologyComputer scienceCOVID-19 growth curves Richards’ equation SARS-CoV-2COVID-19; growth curves; Richards' equation; SARS-CoV-2; Disease Outbreaks; Humans; Incidence; Italy; SARS-CoV-2; COVID-19growth curvesStatistics - Applications01 natural sciencesSARS‐CoV‐2Disease Outbreaks010104 statistics & probability03 medical and health sciences0302 clinical medicineCOVID‐19StatisticsHumansApplications (stat.AP)030212 general & internal medicine0101 mathematicsResearch ArticlesParametric statisticsrichards' equationExternal variableDisease OutbreakSARS-CoV-2Estimation theorycovid-19; richards' equation; sars-cov-2; growth curvesIncidenceIncidence (epidemiology)COVID-19OutbreakRegression analysisReplicatesars-cov-2Richards' equationItalycovid-19Settore SECS-S/01Settore SECS-S/01 - StatisticaResearch Articlegrowth curveHuman
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Spatio-temporal modelling of COVID-19 incident cases using Richards’ curve: An application to the Italian regions

2021

Abstract We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of bot…

Statistics and ProbabilityCoronavirus disease 2019 (COVID-19)Computer scienceNetwork structureGeographic proximityCOVID-19COVID-19; conditional auto-regressive; Stan; generalised logistic growthManagement Monitoring Policy and LawConditional Auto-RegressiveCOVID-19 Conditional Auto-Regressive Stan generalised logistic growthStanEconometricsIndependence (mathematical logic)Bayesian frameworkComputers in Earth SciencesLogistic functionProbabilistic programming languageSettore SECS-S/01 - StatisticaSettore SECS-S/01generalised logistic growth
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Spatial Bayesian Modeling of Presence-only Data

2011

Settore ING-IND/09 - Sistemi per l'Energia e L'AmbienteData augmentationMCMCPresence-only dataBayesian modelSpatial distributionBayesian model Data augmentation MCMC Presence-only data Spatial distribution.Bayesian model; Data augmentation; MCMC; Presence-only data; Spatial distribution.
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