ArticleSpatial statistics2022
Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions.
Article in Spatial statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 32 citations in OpenAlex.
- Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru.Entropy (Basel, Switzerland) · 2024Article
- Estimating actual SARS-CoV-2 infections from secondary data.Scientific reports · 2024Article
- Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.PloS one · 2024Article
- Monitoring the West Nile virus outbreaks in Italy using open access data.Scientific data · 2023Article
- Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models.Healthcare (Basel, Switzerland) · 2023Article
- Learning from the past: A short term forecast method for the COVID-19 incidence curve.PLoS computational biology · 2023Article
- Short-term forecasts of Monkeypox cases in multiple countries: keep calm and don't panic.Journal of medical virology · 2023Article
- Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy.Environmental research · 2023Article
- Two years of COVID-19 pandemic: The Italian experience of Statgroup-19.Environmetrics · 2022Article
- There exists the "smartest" movement rate to control the epidemic rather than "city lockdown".Applied mathematical modelling · 2022Article
- Covid-19 in Italy: Modelling, communications, and collaborations.Significance (Oxford, England) · 2022Article
- Estimating COVID-19-induced excess mortality in Lombardy, Italy.Aging clinical and experimental research · 2022Article
- Interval forecasts of weekly incident and cumulative COVID-19 mortality in the United States: A comparison of combining methods.PloS one · 2022Article
- A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy.Statistical methods & applications · 2022Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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 both the first and the second wave of COVID-19 in Italy, i.e. from February 2020 to July 2020 and from July 2020 to December 2020, respectively. We analyse data at the regional level and, interestingly enough, prove that substantial spatial and temporal dependence occurred in both waves, although strong restrictive measures were implemented during the first wave. Accurate predictions are obtained, improving those of the model where independence across regions is assumed.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.