Endemic–epidemic models to understand COVID-19 spatio-temporal evolution

Spatial Statistics - Tập 49 - Trang 100528 - 2022
Alessandro Celani1, Paolo Giudici2
1Dipartimento di Scienze Economiche e Sociali, Polytechnic University of Marche, Piazzale Raffaele Martelli 8, 60121 Ancona, Italy
2Dipartimento di Scienze Economiche e Aziendali, University of Pavia, Via San Felice al Monastero 5, 27100 Pavia, Italy

Tài liệu tham khảo

Agosto, 2021, A statistical model to monitor COVID-19 contagion growth, Stat. Med., 10.1002/sim.9020 Agosto, 2020, A Poisson autoregressive model to understand Covid-19 contagion dynamics, Risks, 8, 77, 10.3390/risks8030077 Bartolucci, 2021, A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence, Spatial Stat. Berlemann, 2020 Bracher, 2020, Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction, Int. J. Forecast., 69 Cameron, 1996, R-squared measures for count data regression models with applications to health-care utilization, J. Bus. Econom. Statist., 14, 209 Ceylan, 2020, Estimation of Covid-19 prelevance in Italy, Spain and France, Scie. Total Environ., 729 Cornelia, 2020 Czado, 2009, Predictive model assessment for count data, Biometrics, 65, 1254, 10.1111/j.1541-0420.2009.01191.x Dong, 2020, An interactive web-base dashboard to track COVID-19 in real time, Lancet Inf Dis., 20, 533, 10.1016/S1473-3099(20)30120-1 Farcomeni, 2021, An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian regions, Biom. J., 63, 503, 10.1002/bimj.202000189 Ferland, 2006, Integer-valued GARCH processes, J. Time Series Anal., 27, 923, 10.1111/j.1467-9892.2006.00496.x Fronterre, 2020 Girandi, 2020 Giudici, 2000, Modelling categorical covariates in bayesian disease mapping by partition structures, Stat Med, 17-18, 2579, 10.1002/1097-0258(20000915/30)19:17/18<2579::AID-SIM589>3.0.CO;2-G Giudici, 2003, Mixtures of products of dirichlet processes for variable selection in survivavl analysis, Journ. Stat. Plan. Inf., 111, 101, 10.1016/S0378-3758(02)00291-4 Giuliani, 2020, Modelling and predicting the spatio-temporal spread of Covid-19 in Italy, BMC Infect. Dis., 20 Held, 2005, A statistical framework for the analysis of multivariate infectious disease surveillance counts, Stat. Model., 5, 187, 10.1191/1471082X05st098oa Held, 2012, Modeling seasonality in space-time infectious disease surveillance data, Biom. J., 54, 824, 10.1002/bimj.201200037 Hermanowicz, 2020 Loro, 2021, Nowcasting COVID-19 incidence indicators during the italian first outbreak, Stat. Med., 1 Meyer, 2014, Power-law models for infectious disease spread, Ann. Appl. Stat., 8, 1612 Meyer, 2017, Spatio-temporal analysis od epidemic phenomena using the R package surveillance, J. Stat. Softw., 77, 1, 10.18637/jss.v077.i11 Nishiura, 2020, Serial interval of novel coronavirus (COVID-19) infections, Int. J. Infect. Dis., 93, 284, 10.1016/j.ijid.2020.02.060 Paul, 2011, Predictive assessment of a non-linear effects for multivariate time series of infectious disease counts, Stat. Med., 30, 1118, 10.1002/sim.4177 Paul, 2008, Multivariate modelling of infectious disease surveillance data, Stat. Med., 27, 6250, 10.1002/sim.3440 Perone, 2020 2020 Silva, 2020 Ssentongo, 2020 Wu, 2020, 1561