Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony | PNAS

The preference for simple explanations, known as the parsimony principle, has long guided the development of scientific theories, hypotheses, and m...

pnas.org

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Selçuk Korkmaz

Selçuk Korkmaz @selcukorkmaz

1 Sep 2023

The intricacies of compartmental models (SIR, SEIR) and their role in understanding and predicting disease dynamics: 1/ Understanding disease dynamics is crucial for predicting outbreaks and implementing effective public health measures. Compartmental models like SIR and SEIR are fundamental tools used by epidemiologists for this purpose. Let's delve deeper into how they work. #Epidemiology 2/ The SIR model divides the population into three compartments: Susceptible (S), Infected (I), and Recovered (R). It assumes that everyone in the population belongs to one of these compartments. #SIRmodel #DiseaseDynamics 3/ The SEIR model adds another compartment, Exposed (E), to the SIR model. Individuals in the Exposed compartment have been infected but are not yet infectious. This accounts for the incubation period of the disease. #SEIRmodel #IncubationPeriod 4/ The transition of individuals between compartments is represented by parameters: beta (infection rate), gamma (recovery rate), and in the case of the SEIR model, sigma (rate of progression from exposed to infectious). #Parameters #InfectionRate #RecoveryRate 5/ These parameters are crucial because they determine the dynamics of the disease. For example, 'R0', the basic reproduction number, is calculated as beta/gamma in the SIR model. It represents the average number of people an infected person will infect. But this is a simplification, and actual calculations may vary based on the disease and model specifics. #R0 #ReproductionNumber 6/ The parameters (beta, gamma, sigma) may vary over time and between different populations. More advanced models can incorporate this variability to provide more accurate predictions. 7/ The models can be further refined by adding more compartments, such as a compartment for deceased individuals, or by dividing the population into different age groups or geographical locations. #ModelRefinement #PopulationDynamics 8/ Compartmental models are usually fitted to real-world data to estimate the parameters and make predictions. This involves using statistical methods to find the values of the parameters that best explain the observed data. #ModelFitting #Predictions 9/ It is important to note that these models make several assumptions, such as homogeneous mixing of the population and constant parameters over time. In reality, these assumptions may not hold, and more complex models may be needed. #ModelAssumptions #ComplexModels 10/ There are several R packages available for fitting and analyzing compartmental models. Some popular ones include 'EpiModel', 'epimdr ' and 'deSolve'. #Rpackages #DataAnalysis 11/ Understanding the intricacies of compartmental models is key to interpreting their results and using them to inform public health decisions. Compartmental models like SIR and SEIR can be used to estimate the impact of interventions like social distancing or vaccination. This helps policymakers make informed decisions to control the spread of the disease. #PublicHealth #ModelInterpretation 12/ While SIR and SEIR models are valuable tools for understanding and predicting disease dynamics, they are simplifications of reality. They should be used with caution and in conjunction with other forms of analysis and expert opinion. #epidemiology #publichealth #DataScience

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Methods in Ecology and Evolution

Methods in Ecology and Evolution @MethodsEcolEvol

25 May 2021

⏰DEADLINE EXTENDED! We are still welcoming submissions for our Special Feature on Realising the Promise of #LargeData and #ComplexModels. Submit by 1 September. Find out about the scope of the Special Feature here: bit.ly/2RepXAB

Methods in Ecology and Evolution

Methods in Ecology and Evolution @MethodsEcolEvol

30 Apr 2021

Call for submissions! We are welcoming submissions for a new special feature on methods for realising the promise of #LargeData and #ComplexModels Find out more here: bit.ly/3riK4u9

Analytics Steps

Analytics Steps @AnalyticsSteps

1 Mar 2021

Regularization is the most applied technique for penalizing #complexmodels, and is deployed for reducing overfitting by putting network weights small. Learn about L2 and L1 techniques for reducing #overfitting in #machinelearningmodels. analyticssteps.com/blogs/l2-…

The OR Society

The OR Society @TheORSociety

4 Sep 2020

The Science of Data Visualisation course, hosted by Ian Taylor on 24th September will help you become more fluent in the language of #data for effective communication of #complexmodels, ideas and #solutions: bit.ly/3b2yOuC #ThisisOR #ORMS

Sree Varahhas Technologies

Sree Varahhas Technologies @SreeVarahhas

25 Jun 2020

Created in #NX... Won the #NationalAward in #SIEMENSSolidModelContest.. @SreeVarahhas team has the ability to create such #ComplexModels. #SREEVARAHHAS #G4SLTOOLKIT #CAD #CAM #CAE Services, WWW.SREEVARAHHAS.COM #UGNX #UNIGRAPHICS #NX #2DCADDRAWING #CADDRAWING #NXOPEN

IXXI

IXXI @I_X_X_I_

12 Jul 2018

Félicitations à Pierre Borgnat, lauréat du Scientific Breakthrough Program (SBP #IDEXLYON), pour son projet ACADEMICS (Machine Learning & Data Science for Complex and Dynamical Models) @I_X_X_I_ @ENSdeLyon #ACADEMICS #MachineLearning #DataScience #ComplexModels #DynamicalModels