Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

Abstract

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Uncertainty quantification in Covid-19 spread: Lockdown effects

“…1 . The situation is quite different from the german case, in which mild measures were implemented very early to curb the spread [8] , the italian case, where strongth spatiotemporal differences between regions occurred [6] , [17] , and from studies of initial stages [11] , [19] . Nevertheless, our methods apply to data for diagnosed, dead and recovered individuals from any other country.…”

“…An increasing number of mathematical studies assess the efficacy of different policies [4] , [5] , [6] , [7] , [8] , [9] . Moreover, mathematical models and data analysis are employed to estimate relevant epidemiological parameters [4] , [10] , [11] , [12] , [13] , [14] and to try to forecast the evolution [15] , [16] , [17] , [18] , [19] , [20] , [21] . While some of this research is based on direct data analysis [4] , [13] , machine learning techniques [15] , [21] or empirical laws for different populations [9] , the use of balance equations to predict population dynamics is a common approach.…”

“…The specific structure of the selected models depends on the available information and on assumptions about the epidemic spread [24] . Basic SIR models involve populations of susceptible , infected , and recovered individuals, expecting immunity of the latter [8] , [14] , [17] , [25] . SEIR variants distinguish also the individuals exposed to the virus , which may become infective [11] , [19] .…”

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

“…Pinter et al [106] Multi-layered perceptron Predictions of mortality rate and infected cases Aminu et al [107] Deep neural networks Detection of people with COVID-19 Magar et al [108] Ensemble techniques Virus-antibody sequence analysis and patients' Identification Zeng et al [109] Extreme Gradient Boosting (XGBoost) Forecasting of patient survival probability Ashraf et al [110] Machine & deep learning models Predict the severity of disease or chances of death Shah et al [111] Convolutional neural network (CNN) COVID-19 detection from X-ray images Prakash et al [112] Autoregressive Integrated Moving Average Impact analysis of various policies Rathod et al [113] AI Prediction models Effective crisis preparedness and management Ullah et al [114] Logistic Regression and Support Vector Machine Classification of patients with/without COVID-19 Rathod et al [115] SVM, RProp, and Decision tree Detection of abnormal data for effective analysis Hu et al [116] Spectral Clustering (SC) algorithm Feasible analysis model for the treatment & diagnosis Rashed et al [117] Long short-term memory (LSTM) network Provides public awareness about the risks of COVID-19 Singh et al [118] ResNet152V2 and VGG16 CNN Reduce the high false-negative results of the RT-PCR Saverino et al [119] Digital and artificial intelligence platform (DAIP) Changes implementation in rehabilitation services Peddinti et al [120] Convolutional Neural Network (CNN) Detection of COVID-19 cases in public places Malla et al [121] Ensemble deep learning model Real-time sentiment analysis of COVID-19 data Lella et al [122] Convolutional Neural Network (CNN) model Respiratory sound classification for patient identification Haleem et al [123] Artificial neuronal networks (ANN) Predictions of survival of COVID-19 patients Hashimi et al [124] Deep learning models Tracking and identifying potential virus spreaders Amaral et al [125] Artificial neuronal networks (ANN) forecasting and monitoring the progress of Covid-19 Zgheib et al [126] Collection of ensemble learning methods Detecting COVID-19 virus based on patient's demographics Ferrari et al [127] Bayesian framework Predictions about the behavior of the COVID-19 epidemic Almalki et al [128] COVID Inception-ResNet model (CoVIRNet) Automatic diagnosis of the COVID-19 patients Umair et al …”

Long-term prediction of the sporadic COVID-19 epidemics induced by $$\delta$$-virus in China based on a novel non-autonomous delayed SIR model

“…In [ 12 , 13 ], accurate prediction of NACPs and NADPs of COVID-19 in different countries were made. Alos [ 14 ], Lounis [ 15 ], Ferrari [ 16 ], and Sedaghat [ 17 ] used SIRD model to predict the spread trend of COVID-19 in some countries. Long-term forecast of COVID-19 was made in some regions at home and abroad in [ 18 , 19 ].…”