Robust inference for non‐linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy

Girardi, Paolo; Greco, Luca; Mameli, Valentina; Monachini, Monica; Racugno, Walter; Ruli, Erlis; Ventura, Laura

doi:10.1002/sta4.309

Cited by 16 publications

(13 citation statements)

References 14 publications

(28 reference statements)

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“…This allows us to automatically fit more pandemic waves by the same model. On the other hand more common parametric models, typically based on logistic growth curves(e.g., Cabras, 2020 , Girardi et al, 2020 , Alaimo DiLoro et al, 2020 ), are restricted to modeling only a single wave at a time, and often involve arbitrarily setting an initial and final date for the specific wave. For model estimation we adopt a Markov chain Monte Carlo (MCMC) algorithm that extends the proposal of Bartolucci and Farcomeni (2021) to our more general model class.…”

Section: Introductionmentioning

confidence: 99%

A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence

Bartolucci

Farcomeni

2022

Spatial Statistics

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We propose a model based on discrete latent variables, which are spatially associated and time specific, for the analysis of incident cases of SARS-CoV-2 infections. We assume that for each area the sequence of latent variables across time follows a Markov chain with initial and transition probabilities that also depend on latent variables in neighboring areas. The model is estimated by a Markov chain Monte Carlo algorithm based on a data augmented scheme, in which the latent states are drawn together with the model parameters for each area and time. As an illustration we analyze incident cases of SARS-CoV-2 collected in Italy at regional level for the period from February 24, 2020, to January 17, 2021, corresponding to 48 weeks, where we use number of swabs as an offset. Our model identifies a common trend and, for every week, assigns each region to one among five distinct risk groups.

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Section: Introductionmentioning

confidence: 99%

A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence

Bartolucci

Farcomeni

2022

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“…Its quick spread required a global response to prepare health systems worldwide. In its present form, COVID-19 seems to have very challenging characteristics (see, e.g., Del Sole et al, 2020;Girardi et al, 2020;Peeri et al, 2020): it is highly infectious and, despite having a benign course in the vast majority of patients, it requires hospital admission and even intensive care for a far from negligible proportion of infected. In Italy, particularly in the two regions of Lombardia and Veneto, the COVID-19 infection emerged in February 2020 with a basic reproductive number 0 between 3 and 4 (Flaxman et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An ensemble approach to short‐term forecast of COVID‐19 intensive care occupancy in Italian regions

et al. 2020

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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 simple strategy for short‐term prediction of COVID‐19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area‐specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave‐last‐out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.

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“…More generally, for interesting works applying spatial and spatio-temporal model to COVID-19 data, we can refer to Aràndiga et al, 2020 , Bertuzzo et al, 2020 , Giuliani et al, 2020 , Mollalo et al, 2020 , Kang et al, 2020 , Bartolucci and Farcomeni, 2021 , Lee et al, 2021 , Sahu and Böhning, 2021 , Vitale et al, 2021 . For the application of a robust non linear regression model to the same data, see Girardi et al (2020) .…”

Section: Introductionmentioning

confidence: 99%

A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy

D’Urso¹,

Giovanni²,

Vitale³

2022

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The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.

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Robust inference for non‐linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy

Cited by 16 publications

References 14 publications

A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence

A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence

An ensemble approach to short‐term forecast of COVID‐19 intensive care occupancy in Italian regions

A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy

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