scholarly journals Compartmental Models with Application to Pharmacokinetics

2021 ◽  
Vol 187 ◽  
pp. 60-70
Author(s):  
Bing Chen ◽  
Adnan OM Abuassba
Keyword(s):  
Compartmental Models

scholarly journals Fractional kinetics for relaxation and superdiffusion in a magnetic field

2002 ◽  
Vol 9 (1) ◽  
pp. 78-88 ◽  
Author(s):  
A. V. Chechkin ◽  
V. Yu. Gonchar ◽  
M. Szydl/owski
Keyword(s):  
Magnetic Field ◽  
Fractional Kinetics

COVID-19 IN KERALA: A COMPARATIVE STUDY OF COMPARTMENTAL MODELS AND SOCIAL NETWORK-BASED ANALYSIS AND THE CURRENT PERFORMANCE OF THE STATE

2021 ◽  
Vol 10 (5) ◽  
pp. 2549-2559
Author(s):  
K.R. Kumar ◽  
E.N. Satheesh ◽  
V.R. Pravitha
Keyword(s):  
Social Network ◽  
Comparative Study ◽  
Poor Performance ◽  
Compartmental Models ◽  
The State ◽  
Southern State ◽  
Prediction And Control ◽  
Kerala State ◽  
Control And Management ◽  
And Control

Kerala, a southern state of India, has shown better performance in the initial months of the spread of the disease. But in the last few months, the spread of the disease in the state has grown breaking all controls and the control and management system has shown very poor performance. Mathematical modeling of the spread of the disease is effectively is being used in the prediction and control of the disease world over. In this paper, we make a comparative study of the research conducted on this subject based on the compartmental models and social network analysis based models giving special emphasis to Kerala state. We also point out the drawbacks of the current studies in comparison with the intensity of the actual spread of disease.

scholarly journals Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models

2018 ◽  
Vol 28 (12) ◽  
pp. 3591-3608 ◽  
Author(s):  
Christoph Zimmer ◽  
Sequoia I Leuba ◽  
Ted Cohen ◽  
Reza Yaesoubi
Keyword(s):  
Stochastic Systems ◽  
Dynamic Models ◽  
Calibration Method ◽  
Compartmental Models ◽  
Prediction Intervals ◽  
Multiple Shooting ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Calibration Methods ◽  
Google Search

Stochastic transmission dynamic models are needed to quantify the uncertainty in estimates and predictions during outbreaks of infectious diseases. We previously developed a calibration method for stochastic epidemic compartmental models, called Multiple Shooting for Stochastic Systems (MSS), and demonstrated its competitive performance against a number of existing state-of-the-art calibration methods. The existing MSS method, however, lacks a mechanism against filter degeneracy, a phenomenon that results in parameter posterior distributions that are weighted heavily around a single value. As such, when filter degeneracy occurs, the posterior distributions of parameter estimates will not yield reliable credible or prediction intervals for parameter estimates and predictions. In this work, we extend the MSS method by evaluating and incorporating two resampling techniques to detect and resolve filter degeneracy. Using simulation experiments, we demonstrate that an extended MSS method produces credible and prediction intervals with desired coverage in estimating key epidemic parameters (e.g. mean duration of infectiousness and R0) and short- and long-term predictions (e.g. one and three-week forecasts, timing and number of cases at the epidemic peak, and final epidemic size). Applying the extended MSS approach to a humidity-based stochastic compartmental influenza model, we were able to accurately predict influenza-like illness activity reported by U.S. Centers for Disease Control and Prevention from 10 regions as well as city-level influenza activity using real-time, city-specific Google search query data from 119 U.S. cities between 2003 and 2014.

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