Simple models of the impact of overlap in bucket rendering

1998 ◽  
Author(s):  
Milton Chen ◽  
Gordon Stoll ◽  
Homan Igehy ◽  
Kekoa Proudfoot ◽  
Pat Hanrahan
Keyword(s):  
The Impact ◽  
Simple Models

scholarly journals Top-down transformation choice

2018 ◽  
Vol 18 (3-4) ◽  
pp. 274-298 ◽  
Author(s):  
Torsten Hothorn
Keyword(s):  
Simple Model ◽  
Distribution Functions ◽  
Model Fit ◽  
Transformation Models ◽  
Constant Variance ◽  
Alternative Approach ◽  
Complex Models ◽  
Interpretable Models ◽  
The Impact ◽  
Simple Models

Simple models are preferred over complex models, but over-simplistic models could lead to erroneous interpretations. The classical approach is to start with a simple model, whose shortcomings are assessed in residual-based model diagnostics. Eventually, one increases the complexity of this initial overly simple model and obtains a better-fitting model. I illustrate how transformation analysis can be used as an alternative approach to model choice. Instead of adding complexity to simple models, step-wise complexity reduction is used to help identify simpler and better interpretable models. As an example, body mass index (BMI) distributions in Switzerland are modelled by means of transformation models to understand the impact of sex, age, smoking and other lifestyle factors on a person's BMI. In this process, I searched for a compromise between model fit and model interpretability. Special emphasis is given to the understanding of the connections between transformation models of increasing complexity. The models used in this analysis ranged from evergreens, such as the normal linear regression model with constant variance, to novel models with extremely flexible conditional distribution functions, such as transformation trees and transformation forests.

scholarly journals Did the Indian lockdown avert deaths?

2020 ◽  
Author(s):  
Suvrat Raju
Keyword(s):  
Early Stage ◽  
Compartmental Models ◽  
Epidemiological Models ◽  
Heuristic Argument ◽  
The Impact ◽  
Simple Models

Within the context of SEIR models, we consider a lockdown that is both imposed and lifted at an early stage of an epidemic. We show that, in these models, although such a lockdown may delay deaths, it eventually does not avert a significant number of fatalities. Therefore, in these models, the efficacy of a lockdown cannot be gauged by simply comparing figures for the deaths at the end of the lockdown with the projected figure for deaths by the same date without the lockdown. We provide a simple but robust heuristic argument to explain why this conclusion should generalize to more elaborate compartmental models. We qualitatively discuss some important effects of a lockdown, which go beyond the scope of simple models, but could cause it to increase or decrease an epidemic's final toll. Given the significance of these effects in India, and the limitations of currently available data, we conclude that simple epidemiological models cannot be used to reliably quantify the impact of the Indian lockdown on fatalities caused by the COVID-19 pandemic.

scholarly journals Did the Indian lockdown avert deaths?

2020 ◽  
Author(s):  
Suvrat Raju
Keyword(s):  
Early Stage ◽  
Compartmental Models ◽  
Epidemiological Models ◽  
Heuristic Argument ◽  
The Impact ◽  
Simple Models

Abstract Within the context of SEIR models, we consider a lockdown that is both imposed and lifted at an early stage of an epidemic. We show that, in these models, although such a lockdown may delay deaths, it eventually does not avert a significant number of fatalities. Therefore, in these models, the efficacy of a lockdown cannot be gauged by simply comparing figures for the deaths at the end of the lockdown with the projected figure for deaths by the same date without the lockdown. We provide a simple but robust heuristic argument to explain why this conclusion should generalize to more elaborate compartmental models. We qualitatively discuss some important effects of a lockdown, which go beyond the scope of simple models, but could cause it to increase or decrease an epidemic's final toll. Given the significance of these effects in India, and the limitations of currently available data, we conclude that simple epidemiological models cannot be used to reliably quantify the impact of the Indian lockdown on fatalities caused by the COVID-19 pandemic.

scholarly journals Targeting vaccination against novel infections: risk, age and spatial structure for pandemic influenza in Great Britain

2010 ◽  
Vol 8 (58) ◽  
pp. 661-670 ◽  
Author(s):  
Matt J. Keeling ◽  
Peter J. White
Keyword(s):  
Age Groups ◽  
Risk Groups ◽  
H1n1 Influenza ◽  
Primary Concern ◽  
H1n1 Influenza Virus ◽  
Optimal Deployment ◽  
Age Structured ◽  
Vaccination Campaigns ◽  
The Impact ◽  
Simple Models

The emergence of a novel strain of H1N1 influenza virus in Mexico in 2009, and its subsequent worldwide spread, has focused attention to the question of optimal deployment of mass vaccination campaigns. Here, we use three relatively simple models to address three issues of primary concern in the targeting of any vaccine. The advantages of such simple models are that the underlying assumptions and effects of individual parameters are relatively clear, and the impact of uncertainty in the parametrization can be readily assessed in the early stages of an outbreak. In particular, we examine whether targeting risk-groups, age-groups or spatial regions could be optimal in terms of reducing the predicted number of cases or severe effects; and how these targeted strategies vary as the epidemic progresses. We examine the conditions under which it is optimal to initially target vaccination towards those individuals within the population who are most at risk of severe effects of infection. Using age-structured mixing matrices, we show that targeting vaccination towards the more epidemiologically important age groups (5–14 year olds and then 15–24 year olds) leads to the greatest reduction in the epidemic growth and hence reduces the total number of cases. Finally, we consider how spatially targeting the vaccine towards regions of country worst affected could provide an advantage. We discuss how all three of these priorities change as both the speed at which vaccination can be deployed and the start of the vaccination programme is varied.

scholarly journals Did the Indian lockdown avert deaths?

2020 ◽  
Author(s):  
Suvrat Raju
Keyword(s):  
Early Stage ◽  
Compartmental Models ◽  
Epidemiological Models ◽  
Heuristic Argument ◽  
The Impact ◽  
Simple Models

Within the context of SEIR models, we consider a lockdown that is both imposed and lifted at an early stage of an epidemic. We show that, in these models, although such a lockdown may delay deaths, it eventually does not avert a significant number of fatalities. Therefore, in these models, the efficacy of a lockdown cannot be gauged by simply comparing figures for the deaths at the end of the lockdown with the projected figure for deaths by the same date without the lockdown. We provide a simple but robust heuristic argument to explain why this conclusion should generalize to more elaborate compartmental models. We qualitatively discuss some important effects of a lockdown, which go beyond the scope of simple models, but could cause it to increase or decrease an epidemic's final toll. Given the significance of these effects in India, and the limitations of currently available data, we conclude that simple epidemiological models cannot be used to reliably quantify the impact of the Indian lockdown on fatalities caused by the COVID-19 pandemic.

scholarly journals The Impact of Realistic Age Structure in Simple Models of Tuberculosis Transmission

PLoS ONE ◽  
2010 ◽  
Vol 5 (1) ◽  
pp. e8479 ◽  
Author(s):  
Ellen Brooks-Pollock ◽  
Ted Cohen ◽  
Megan Murray
Keyword(s):  
Age Structure ◽  
Tuberculosis Transmission ◽  
The Impact ◽  
Simple Models
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