A stochastic SIRD model with imperfect immunity for the evaluation of epidemics

Papageorgiou, Vasileios E.; Tsaklidis, George

doi:10.1016/j.apm.2023.08.011

Cited by 9 publications

(1 citation statement)

References 44 publications

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“…This approach handles the uncertainty inherent in pandemic phenomena by integrating particle filtering, which offers an alternative way to address the uncertainties present in both the equations defining the state and the observations of such phenomena. Moreover, delving into the disease's evolution using various stochastic methods like discrete or continuous time Markov chains holds significant promise aiming to examine interesting stochastic descriptors [32]. Finally, numerical methods for the computationally efficient solving of the ODE system can be investigated [33], as the establishment of methodologies of low complexity is always of interest in mathematical modelling [34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Asymptotic Behavior of an Extended SEIR Model with Vaccination for COVID-19

Papageorgiou,

Vasiliadis,

Tsaklidis

2023

Mathematics

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Several research papers have attempted to describe the dynamics of COVID-19 based on systems of differential equations. These systems have taken into account quarantined or isolated cases, vaccinations, control measures, and demographic parameters, presenting propositions regarding theoretical results that often investigate the asymptotic behavior of the system. In this paper, we discuss issues that concern the theoretical results proposed in the paper “An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter”. We propose detailed explanations regarding the resolution of these issues. Additionally, this paper focuses on extending the local stability analysis of the disease-free equilibrium, as presented in the aforementioned paper, while emphasizing the derivation of theorems that validate the global stability of both epidemic equilibria. Emphasis is placed on the basic reproduction number R0, which determines the asymptotic behavior of the system. This index represents the expected number of secondary infections that are generated from an already infected case in a population where almost all individuals are susceptible. The derived propositions can inform health authorities about the long-term behavior of the phenomenon, potentially leading to more precise and efficient public measures. Finally, it is worth noting that the examined paper still presents an interesting epidemiological scheme, and the utilization of the Kalman filtering approach remains one of the state-of-the-art methods for modeling epidemic phenomena.

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Section: Discussionmentioning

confidence: 99%

Analyzing the Asymptotic Behavior of an Extended SEIR Model with Vaccination for COVID-19

Papageorgiou,

Vasiliadis,

Tsaklidis

2023

Mathematics

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A stochastic particle extended SEIRS model with repeated vaccination: Application to real data of COVID‐19 in Italy

Papageorgiou,

Tsaklidis

2024

Math Methods in App Sciences

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The prediction of the evolution of epidemics plays an important role in limiting the transmissibility and the burdensome consequences of infectious diseases, which leads to the employment of mathematical modeling. In this paper, we propose a stochastic particle filtering extended SEIRS model with repeated vaccination and time‐dependent parameters, aiming to efficiently describe the demanding dynamics of time‐varying epidemics. The validity of our model is examined using daily records of COVID‐19 in Italy for a period of 525 days, revealing a notable capacity to uncover the hidden dynamics of the pandemic. The main findings include the estimation of asymptomatic cases, which is a well‐known feature of the current pandemic. Unlike other proposed models that employ extra compartments for asymptomatic cases, which force the estimation of this proportion and significantly increase the model's complexity, our approach leads to the evaluation of the hidden dynamics of COVID‐19 without additional computational burden. Other findings that confirm the model's appropriateness and robustness are its parameter evolution and the estimation of more ICU‐admitted cases compared to the official records during the most prevalent infection wave of January 2022, attributed to the intensified increase in admissions that may have led to full occupancy in ICUs. As the vast majority of datasets contain time series of total recovered and vaccinated cases, we propose a statistical algorithm to estimate the currently recovered and protected through vaccination cases. This necessity arises from the attenuation of antibodies after vaccination/infection and is necessary for long‐time interval predictions. Finally, we not only present a novel stochastic epidemiological model and test its efficiency but also investigate its mathematical properties, such as the existence and stability of epidemic equilibria, giving new insights to the literature. The latter provides additional details concerning the system's long‐term behavior, while the conclusions drawn from the index provide perspectives on the severity and future of the COVID‐19 pandemic.

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Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume

Georgakis,

Papageorgiou,

Gatziolis

et al. 2024

Oper. Res. Forum

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Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This study introduces an enhanced temporal-like bivariate Fay-Herriot model, improving upon its univariate counterpart. The model incorporates field data and auxiliary data, including canopy height metrics from WorldView stereo-imagery and past census data, sourced from the University Forest of Pertouli in Central Greece. The model aims to estimate the growing stock volume for 2008 and 2018, focusing on enhancing the precision of the 2018 estimates. The 2008 dependent variable is used as auxiliary information by the model for more reliable 2018 small area estimates. A novel preprocessing pipeline is also introduced, which includes outlier identification, cluster analysis, and variance smoothing. Compared to direct estimates and the standard univariate Fay-Herriot model, our bivariate approach shows a percentage variance reduction of 96.58% and 13.52%, respectively. The methodology not only offers more reliable estimates with reduced variance and bias but also contributes to more accurate decision-making for sustainable forest management.

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A stochastic SIRD model with imperfect immunity for the evaluation of epidemics

Cited by 9 publications

References 44 publications

Analyzing the Asymptotic Behavior of an Extended SEIR Model with Vaccination for COVID-19

Analyzing the Asymptotic Behavior of an Extended SEIR Model with Vaccination for COVID-19

A stochastic particle extended SEIRS model with repeated vaccination: Application to real data of COVID‐19 in Italy

Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume

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