Bayesian SIR model with change points with application to the Omicron wave in Singapore

Gu, Jiaqi; Yin, Guosheng

doi:10.1038/s41598-022-25473-y

Cited by 3 publications

(7 citation statements)

References 20 publications

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“…Following the lead of other investigators [4,33,[36][37][38], we adopt a discrete-time approach. In our SIR epidemic model, the time axis is marked off in equally spaced intervals 𝑡 = 0,1, … , 𝑇, where the duration of each interval is sufficiently small as to adequately approximate the classical, continuous-time version [5,[47][48][49][50].…”

Section: Discrete-time Sir Modelmentioning

confidence: 99%

“…Perhaps the most traveled road to a solution has been the use of various parameter search algorithms [17,[21][22][23][24][25][26][27] which, when it comes down to it, offer only a marginal improvement over brute force search [28]. Bayesian estimation may be better able to integrate prior information into our search procedures [29][30][31][32][33][34],…”

Section: Introductionmentioning

confidence: 99%

“…Bayesian estimation may be better able to MedRxiv 2023.03. 13.23287177v3 26-Feb-2024 3 integrate prior information into our search procedures [31][32][33][34][35][36], but its computational burden is usually even greater. Last but not least, we can resort to trial and error combined with visual inspection [37].…”

Section: Introductionmentioning

confidence: 99%

“…Perhaps the most traveled road to a solution has been the use of various parameter search algorithms [18,[23][24][25][26][27][28][29] which, when it comes down to it, offer only a marginal improvement over brute force search [30]. Bayesian estimation may be better able to Version 4 22-May-2024 3 integrate prior information into our search procedures [31][32][33][34][35][36], but its computational burden is usually even greater. Last but not least, we can resort to trial and error combined with visual inspection [37].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tight Fit of the SIR Dynamic Epidemic Model to Daily Cases of COVID-19 Reported During the 2021-2022 Omicron Surge in New York City: A Novel Approach

Harris

2023

Preprint

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We describe a novel approach to recovering the underlying parameters of the SIR dynamic epidemic model from observed data on case incidence or deaths. We formulate a discrete-time approximation to the original continuous-time model and search for the parameter vector that minimizes the standard least squares criterion function. We show that the gradient vector and matrix of second-order derivatives of the criterion function with respect to the parameters adhere to their own systems of difference equations and thus can be exactly calculated iteratively. Applying our new approach, we estimate four-parameter SIR models on two types of datasets: (1) daily reported cases of COVID-19 during the SARS-CoV-2 Omicron/BA.1 surges of December 2021 - March 2022 in New York City and Los Angeles County; and (2) weekly deaths from a plague outbreak on the Isle of Bombay during December 1905 - July 1906, originally studied by Kermack and McKendrick in their now-classic 1927 paper. The estimated parameters from the COVID-19 data suggest a duration of persistent infectivity beyond that reported in small-scale clinical studies of mostly symptomatic subjects. The estimated parameters from the plague data suggest that the Bombay outbreak was in fact driven by pulmonic rather than bubonic plague.

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Section: Discrete-time Sir Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tight Fit of the SIR Dynamic Epidemic Model to Daily Cases of COVID-19 Reported During the 2021-2022 Omicron Surge in New York City: A Novel Approach

Harris

2023

Preprint

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show abstract

“…The dynamic method is based on the infection and transmission mechanism of COVID-19 to establish mathematical models to describe the transmission process of the virus in the population, and to evolve the dynamic transmission trend of infectious diseases ( Gu & Yin, 2022 ; Mamis & Farazmand, 2022 , p. 479; Metcalf et al., 2020 ; Vespignani et al., 2020 ). Dynamic modeling is widely used in human infectious diseases and animal infectious diseases ( Chang et al., 2022 ; Ma et al., 2022 ; Sun et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City, 2022 to 2023

Wang,

Xiao,

Zhang

et al. 2024

Infectious Disease Modelling

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A tight fit of the SIR dynamic epidemic model to daily cases of COVID-19 reported during the 2021–2022 Omicron surge in New York City: A novel approach

Harris

2024

Stat Methods Med Res

View full text Add to dashboard Cite

We describe a novel approach for recovering the underlying parameters of the SIR dynamic epidemic model from observed data on case incidence. We formulate a discrete-time approximation of the original continuous-time model and search for the parameter vector that minimizes the standard least squares criterion function. We show that the gradient vector and matrix of second-order derivatives of the criterion function with respect to the parameters adhere to their own systems of difference equations and thus can be exactly calculated iteratively. Applying our new approach, we estimated a four-parameter SIR model from daily reported cases of COVID-19 during the SARS-CoV-2 Omicron/BA.1 surge of December 2021–March 2022 in New York City. The estimated SIR model showed a tight fit to the observed data, but less so when we excluded residual cases attributable to the Delta variant during the initial upswing of the wave in December. Our analyses of both the real-world COVID-19 data and simulated case incidence data revealed an important problem of weak parameter identification. While our methods permitted for the separate estimation of the infection transmission parameter and the infection persistence parameter, only a linear combination of these two key parameters could be estimated with precision. The SIR model appears to be an adequate reduced-form description of the Omicron surge, but it is not necessarily the correct structural model. Prior information above and beyond case incidence data may be required to sharply identify the parameters and thus distinguish between alternative epidemic models.

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Bayesian SIR model with change points with application to the Omicron wave in Singapore

Cited by 3 publications

References 20 publications

Tight Fit of the SIR Dynamic Epidemic Model to Daily Cases of COVID-19 Reported During the 2021-2022 Omicron Surge in New York City: A Novel Approach

Tight Fit of the SIR Dynamic Epidemic Model to Daily Cases of COVID-19 Reported During the 2021-2022 Omicron Surge in New York City: A Novel Approach

Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City, 2022 to 2023

A tight fit of the SIR dynamic epidemic model to daily cases of COVID-19 reported during the 2021–2022 Omicron surge in New York City: A novel approach

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