Nonlinear Compartmental Models

1983 ◽  
pp. 22-29
Author(s):  
David H. Anderson
Keyword(s):  
Compartmental Models

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 Reliable compartmental models for double-pipe heat exchangers: An analytical study

2007 ◽  
Vol 31 (9) ◽  
pp. 1739-1752 ◽  
Author(s):  
A. Zavala-Río ◽  
R. Santiesteban-Cos
Keyword(s):  
Heat Exchangers ◽  
Analytical Study ◽  
Compartmental Models ◽  
Double Pipe ◽  
Pipe Heat

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.

scholarly journals Lumpability in compartmental models

1990 ◽  
Vol 146 (2) ◽  
pp. 306-317 ◽  
Author(s):  
Octavian Iordache ◽  
Irina Bucurescu ◽  
Anca Pascu
Keyword(s):  
Compartmental Models

scholarly journals Simple Inclusion of Complex Diagnostic Algorithms in Infectious Disease Models for Economic Evaluation

2018 ◽  
Vol 38 (8) ◽  
pp. 930-941
Author(s):  
Peter J. Dodd ◽  
Jeff J. Pennington ◽  
Liza Bronner Murrison ◽  
David W. Dowdy
Keyword(s):  
Infectious Disease ◽  
Cost Effectiveness ◽  
Decision Trees ◽  
Disease Transmission ◽  
Time Scale ◽  
Compartmental Models ◽  
Diagnostic Process ◽  
Design Strategies ◽  
Diagnostic Algorithms ◽  
Transmission Models

Introduction. Cost-effectiveness models for infectious disease interventions often require transmission models that capture the indirect benefits from averted subsequent infections. Compartmental models based on ordinary differential equations are commonly used in this context. Decision trees are frequently used in cost-effectiveness modeling and are well suited to describing diagnostic algorithms. However, complex decision trees are laborious to specify as compartmental models and cumbersome to adapt, limiting the detail of algorithms typically included in transmission models. Methods. We consider an approximation replacing a decision tree with a single holding state for systems where the time scale of the diagnostic algorithm is shorter than time scales associated with disease progression or transmission. We describe recursive algorithms for calculating the outcomes and mean costs and delays associated with decision trees, as well as design strategies for computational implementation. We assess the performance of the approximation in a simple model of transmission/diagnosis and its role in simplifying a model of tuberculosis diagnostics. Results. When diagnostic delays were short relative to recovery rates, our approximation provided a good account of infection dynamics and the cumulative costs of diagnosis and treatment. Proportional errors were below 5% so long as the longest delay in our 2-step algorithm was under 20% of the recovery time scale. Specifying new diagnostic algorithms in our tuberculosis model was reduced from several tens to just a few lines of code. Discussion. For conditions characterized by a diagnostic process that is neither instantaneous nor protracted (relative to transmission dynamics), this novel approach retains the advantages of decision trees while embedding them in more complex models of disease transmission. Concise specification and code reuse increase transparency and reduce potential for error.

Sign in / Sign up

Export Citation Format

Share Document