Underdetection in a stochastic SIR model for the analysis of the COVID-19 Italian epidemic

Bodini, Alessandro; Pasquali, Sara; Pievatolo, Antonio; Ruggeri, Fabrizio

doi:10.1007/s00477-021-02081-2

Cited by 9 publications

(4 citation statements)

References 70 publications

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“…Considering the randomness of the spread of infectious diseases and the ubiquitous environmental white noise in life, the deterministic COVID-19 infectious disease model is inevitably affected by environmental noise, which makes the parameters involved in the model tend to vary with the surrounding environment (see ( Khan et al, 2021b ; Niu et al, 2021 ; Pasquali et al, 2022 ; Zhu et al, 2021 )). The fluctuation of the environment fluctuates randomly around some average value, therefore, it is necessary to introduce random fluctuation in the deterministic COVID-19 infectious disease model (2.1).…”

Section: Model Description and Preliminariesmentioning

confidence: 99%

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Analysis and simulation of a stochastic COVID-19 model with large-scale nucleic acid detection and isolation measures: A case study of the outbreak in Urumqi, China in August 2022

Zeng¹,

Teng²,

Rifhat³

et al. 2023

Infectious Disease Modelling

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Section: Model Description and Preliminariesmentioning

confidence: 99%

“…The proof proof of Lemma 2.2 is similar to Reference ( Pasquali et al, 2022 ) by means of the following assistant function

So, we omit it here.…”

Section: Model Description and Preliminariesmentioning

confidence: 99%

Analysis and simulation of a stochastic COVID-19 model with large-scale nucleic acid detection and isolation measures: A case study of the outbreak in Urumqi, China in August 2022

Zeng¹,

Teng²,

Rifhat³

et al. 2023

Infectious Disease Modelling

View full text Add to dashboard Cite

“…where I is the identity matrix. With proper tuning of the regularisation parameter η, WLS will be more robust to errors in (14).…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Some works involve multiple measurement data. [14], [15] incorporate state augmentation method to estimate the model parameters. [16], [17] treat state and parameter separately and solve them iteratively.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of States and Parameters in Stochastic SIR Model

Liu

Hendeby

Gustafsson

2022

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramér Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach.

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Limits of epidemic prediction using SIR models

et al. 2022

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The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In practice, it is of substantial interest to estimate the model parameters based on noisy observations early in the outbreak, well before the epidemic reaches its peak. This allows prediction of the subsequent course of the epidemic and design of appropriate interventions. However, accurately inferring SIR model parameters in such scenarios is problematic. This article provides novel, theoretical insight on this issue of practical identifiability of the SIR model. Our theory provides new understanding of the inferential limits of routinely used epidemic models and provides a valuable addition to current simulate-and-check methods. We illustrate some practical implications through application to a real-world epidemic data set.

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Underdetection in a stochastic SIR model for the analysis of the COVID-19 Italian epidemic

Cited by 9 publications

References 70 publications

Analysis and simulation of a stochastic COVID-19 model with large-scale nucleic acid detection and isolation measures: A case study of the outbreak in Urumqi, China in August 2022

Analysis and simulation of a stochastic COVID-19 model with large-scale nucleic acid detection and isolation measures: A case study of the outbreak in Urumqi, China in August 2022

Joint Estimation of States and Parameters in Stochastic SIR Model

Limits of epidemic prediction using SIR models

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