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Explore What Others Are Building

Here is a sample of public Insights made by Insight Maker users. This list is auto-generated and updated daily.

The interaction of a population of Moose and Wolves. Video @ LinkedIn ,  Twitter ,  YouTube
BCTD AIAC
The interaction of a population of Moose and Wolves.
Moose and Wolves
Gene Bellinger
74
From Fig.1 Communication for Social Change: An Integrated Model for Measuring the Process and Its Outcomes/Maria Elena Figueroa et al (2002)  paper (may need free registration)
Social Health Care Communication Dialogue Systems Science Organization

From Fig.1 Communication for Social Change: An Integrated Model for Measuring the Process and Its Outcomes/Maria Elena Figueroa et al (2002)  paper (may need free registration)

Convergence Model of Communication
Geoff McDonnell
4 11 months ago
SARS-CoV-19 spread  in different countries - please  adjust variables accordingly Italy elderly population (>65): 0.228 estimated undetected cases factor: 4-11 starting population size: 60 000 000 high blood pressure: 0.32 (gbe-bund) heart disease: 0.04 (statista) free intensive
Disease Coronavirus Infection Corona Virus COVID-19 SARS-CoV-19 Pandemic
SARS-CoV-19 spread in different countries
- please adjust variables accordingly

Italy
  • elderly population (>65): 0.228
  • estimated undetected cases factor: 4-11
  • starting population size: 60 000 000
  • high blood pressure: 0.32 (gbe-bund)
  • heart disease: 0.04 (statista)
  • free intensive care units: 3 100

Germany
  • elderly population (>65): 0.195 (bpb)
  • estimated undetected cases factor: 2-3 (deutschlandfunk)
  • starting population size: 83 000 000
  • high blood pressure: 0.26 (gbe-bund)
  • heart disease: 0.2-0.28 (herzstiftung)
  • free intensive care units: 5 880

France
  • elderly population (>65): 0.183 (statista)
  • estimated undetected cases factor: 3-5
  • starting population size: 67 000 000
  • high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
  • heart disease: 0.1-0.2 (oecd)
  • free intensive care units: 3 000

As you wish
  • numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
  • practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
  • government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
  • Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key
  • Healthy: People are not infected with SARS-CoV-19 but could still get it
  • Infected: People have been infected and developed the disease COVID-19
  • Recovered: People just have recovered from COVID-19 and can't get it again in this stage
  • Dead: People died because of COVID-19
  • Immune: People got immune and can't get the disease again
  • Critical recovery percentage: Chance of survival with no special medical treatment
SARS-CoV-19 model
Lucia Vega Resto
70
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover Assumptions Govt policy reduces infection and
Burnie Economy BMA708 Tasmania COVID-19
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Assumptions
Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy.  This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates trigger more rapid government intervention, which reduces infectious cases.  The impact on the economy, though, of higher detected cases is negative. 




Burnie COVID-19 outbreak demo model version 2
Steven D'Alessandro
39 3 months ago
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process. By adjusting the sliders below you can  observe the work process without any work in process limitations ( WIP Limits ),  with process step specific WIP Limits* (
TOC Flow Tameflow Theory Of Constraints DBR Agile Kanban Drum-Buffer-Rope
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process.

By adjusting the sliders below you can 
  • observe the work process without any work in process limitations (WIP Limits), 
  • with process step specific WIP Limits* (work state WIP limits), 
  • or you may want to see the impact of the Tameflow approach with Kanban Token and Replenishment Token 
  • or see the impact of the Drum-Buffer-Rope** method. 
* Well know in (agile) Kanban
** Known in the physical world of factory production

The "Tameflow approach" using Kanban Token and Replenishment Token as well as the Drum-Buffer-Rope method take oth the Constraint (the weakest link of the work process) into consideration when pulling in new work items into the delivery "system". 

You can also simulate the effects of PUSH instead of PULL. 

Feel free to play around and recognize the different effects of work scheduling methods. 

If you have questions or feedback get in touch via twitter @swilluda

The work flow itself
Look at the simulation as if you would look on a kanban board. 

The simulation mimics a "typical" software delivery process. 

From left to right you find the following ten process steps. 
  1. Input Queue (Backlog)
  2. Selected for work (waiting for analysis or work break down)
  3. Analyse, break down and understand
  4. Waiting for development
  5. In development
  6. Waiting for review
  7. In review
  8. Waiting for deployment
  9. In deployment
  10. Done
Kanban Board Simulation - WIP Limit, Tameflow Kanban Token and Drum-Buffer-Rope
Stefan Willuda
17
Ce modèle simule la dynamique de deux populations en interaction : une population de proies (X) et une population de prédateurs (Y) . Il est inspiré des travaux fondateurs de Lotka et Volterra et permet de comprendre l'origine des cycles de population. Ce modèle s'inscrit dans la suite de notre
Population Dynamics

Ce modèle simule la dynamique de deux populations en interaction : une population de proies (X) et une population de prédateurs (Y). Il est inspiré des travaux fondateurs de Lotka et Volterra et permet de comprendre l'origine des cycles de population.

Ce modèle s'inscrit dans la suite de notre cours sur la dynamique des populations. Après avoir étudié la dynamique d'une seule population (exponentielle, logistique), ce modèle introduit la dynamique des communautés en couplant le destin de deux espèces.

Contrairement aux modèles précédents centrés uniquement sur le nombre d’individus (N), ce modèle explore comment les interactions trophiques (le fait de "manger" et d'"être mangé") créent des comportements émergents complexes, tels que les oscillations décalées et la stabilité du système.

Chaque population n'est pas isolée ; son taux de croissance ou de déclin dépend directement de l'abondance de l'autre.

Les Composants du Modèle :

Variables d’état (Stocks) :

  • X (Proie) : Abondance de la population de proies.

  • Y (Prédateur) : Abondance de la population de prédateurs.

Flux (représentant dX/dt et dY/dt) :

  • Prey Births : Taux de croissance intrinsèque de la proie (rX).

  • Prey Deaths : Mortalité de la proie, due à l'auto-limitation (bX2), à la prédation (cXY) et à la chasse (HX).

  • Predator Births : Croissance du prédateur, qui dépend de sa capacité à convertir les proies mangées en nouveaux prédateurs (c′XY).

  • Predator Deaths : Mortalité du prédateur, due à sa mort naturelle (mY) et à la chasse (HY).

Paramètres modifiables (Curseurs) :

  • X (Proie) : Abondance initiale des proies.

    • Valeur initiale : 50

  • Y (Prédateur) : Abondance initiale des prédateurs.

    • Valeur initiale : 15

  • r (Taux de croissance des proies) : Taux de reproduction intrinsèque des proies.

    • Valeur initiale : 0.5

  • b (Auto-limitation des proies) : Force de la compétition intraspécifique (l'effet logistique K).

    • Valeur initiale : 0

  • m (Mortalité des prédateurs) : Taux de mortalité naturel (intrinsèque) des prédateurs.

    • Valeur initiale : 0.3

  • c (Taux de prédation) : Efficacité de la chasse du prédateur sur la proie.

    • Valeur initiale : 0.02

  • c_prime (Efficacité de conversion) : Capacité du prédateur à convertir une proie mangée.

    • Valeur initiale : 0.01

  • H (Effort de Chasse) : Taux de mortalité externe (chasse, pêche) s'appliquant aux deux espèces.

    • Valeur initiale : 0

Indicateurs produits :

  • Graphique temporel : Montre les oscillations et le décalage caractéristique entre le pic des proies et celui des prédateurs.

  • Diagramme de phase : Montre la trajectoire du système (cercle, spirale) et révèle sa stabilité (neutre ou amortie).

  • Abondance moyenne : Le niveau d'équilibre autour duquel les populations oscillent.

Votre Mission d'Exploration :

Votre objectif est de vous mettre dans la peau d'un écologue théoricien pour tester les fondements du modèle Lotka-Volterra et résoudre l'énigme de D'Ancona.

  1. Validez les briques de base : Isolez les populations (Mission 1) pour vérifier la croissance logistique et le déclin exponentiel.

  2. Recréez le "pendule" : Simulez le modèle original de Volterra (b=0) et explorez la stabilité neutre (Mission 2).

  3. Testez la stabilité moderne : Ajoutez de l'auto-limitation (b>0) et observez la convergence vers un équilibre stable (Mission 3).

  4. Explorez la physiologie : Testez l'effet d'un prédateur au "métabolisme lent" (Mission 4).

  5. Résolvez l'énigme : Utilisez le modèle (b=0) et le curseur H (Chasse) pour recréer le "Paradoxe de la Chasse" (Mission 5).

Cliquez sur "SIMULATE" et explorez la dynamique fondamentale qui régit les interactions prédateurs-proies !

Modèle Proie-Prédateur (Lotka-Volterra)
Florian Orgeret
2 weeks ago