Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19

Marinov, Tchavdar; Marinova, Rossitza S.

doi:10.1038/s41598-022-20276-7

Cited by 24 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The standard SIR model and its variations of increasing complexity(e.g. SIRV, SEIR, SEIRS) have already been applied at different geographic scales, from region 14 , to country 15 , 16 and continent level 17 , 18 in order to develop a thorough understanding on the risk of transmission of COVID-19. To this end, risk estimation of COVID-19 spread has been studied from the perspective of time-varying SIR models 19 .…”

Section: Introductionmentioning

confidence: 99%

Social and economic variables explain COVID-19 diffusion in European regions

Cancedda,

Cappellato,

Maninchedda

et al. 2024

Sci Rep

View full text Add to dashboard Cite

At the beginning of 2020, Italy was the country with the highest number of COVID-19 cases, not only in Europe, but also in the rest of the world, and Lombardy was the most heavily hit region of Italy. The objective of this research is to understand which variables have determined the prevalence of cases in Lombardy and in other highly-affected European regions. We consider the first and second waves of the COVID-19 pandemic, using a set of 22 variables related to economy, population, healthcare and education. Regions with a high prevalence of cases are extracted by means of binary classifiers, then the most relevant variables for the classification are determined, and the robustness of the analysis is assessed. Our results show that the most meaningful features to identify high-prevalence regions include high number of hours spent in work environments, high life expectancy, and low number of people leaving from education and neither employed nor educated or trained.

show abstract

Section: Introductionmentioning

confidence: 99%

Social and economic variables explain COVID-19 diffusion in European regions

Cancedda,

Cappellato,

Maninchedda

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The model does not consider the dynamics of external factors, so the solutions obtained using the model are difficult to update to a changing situation. The paper [21] proposes an extension of the classical SIR model, the adaptive susceptibleinfected-removed-vaccinated model with time-dependent transmission and removal rates. The authors propose a numerical solution to the inverse problem using the variational embedding method, which reduces the inverse problem to the problem of minimizing a well-formed functional to obtain the desired values.…”

Section: Current Research Analysismentioning

confidence: 99%

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Chumachenko,

Dudkina,

Yakovlev

et al. 2023

International Journal of Telemedicine and Applications

View full text Add to dashboard Cite

This study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, adapting, and interpreting these models. The research constructs three statistical machine learning models to predict the spread of COVID-19 in specific regions and evaluates their performance using real COVID-19 incidence data. The paper presents short-term (3, 7, 14, 21, and 30 days) forecasts of COVID-19 morbidity and mortality for Germany, Japan, South Korea, and Ukraine. The precision of each model was scrutinized based on the type of input data used. Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. The initial findings suggest the need for the comprehensive utilization of all available data, favoring cumulative data during holiday-rich periods and daily data otherwise. To minimize the absolute error, databases should be compiled using daily morbidity and mortality rates.

show abstract

“…A scale-free model can be used in conjunction with a small-world model 20 – 23 . When these network models are incorporated into mathematical epidemic frameworks such as Susceptible, Infected, Recovered (SIR) 24 , Susceptible, Exposed, Infected, Recovered (SEIR) 25 , and Adaptive SIR 26 , they have capabilities of representing the propagation of pathogens more realistically. This integration results in more accurate predictions of viral transmission 27 .…”

Section: Introductionmentioning

confidence: 99%

Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19

Fujimoto,

Kuo,

Stott

et al. 2023

Sci Rep

View full text Add to dashboard Cite

This study evaluates the scale-free network assumption commonly used in COVID-19 epidemiology, using empirical social network data from SARS-CoV-2 Delta variant molecular local clusters in Houston, Texas. We constructed genome-informed social networks from contact and co-residence data, tested them for scale-free power-law distributions that imply highly connected hubs, and compared them to alternative models (exponential, log-normal, power-law with exponential cutoff, and Weibull) that suggest more evenly distributed network connections. Although the power-law model failed the goodness of fit test, after incorporating social network ties, the power-law model was at least as good as, if not better than, the alternatives, implying the presence of both hub and non-hub mechanisms in local SARS-CoV-2 transmission. These findings enhance our understanding of the complex social interactions that drive SARS-CoV-2 transmission, thereby informing more effective public health interventions.

show abstract

Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19

Cited by 24 publications

References 37 publications

Social and economic variables explain COVID-19 diffusion in European regions

Social and economic variables explain COVID-19 diffusion in European regions

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19

Contact Info

Product

Resources

About