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

Insight diagram
The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.
SEIR - COVID-19 (v.1)
Insight diagram
Revisited Conceptual map (Directed Cyclic Graph) of Applied Category Theory as practised by Osgood, Rosiak, Spivak and Baez, based on my Apr 2026 Gemini interaction using Gene Bellinger's AI prompts
Synthesis of Applied Category Theory Sheafs and Model Practice
3 weeks ago
Insight diagram
Examples of reinforcing and balancing loops enhanced with images to maybe them more visually engaging.
@LinkedInTwitterYouTube
Causal Loop Structures
Insight diagram

Dynamic simulation modelers are particularly interested in understanding and being able to distinguish between the behavior of stocks and flows that result from internal interactions and those that result from external forces acting on a system.  For some time modelers have been particularly interested in internal interactions that result in stable oscillations in the absence of any external forces acting on a system.  The model in this last scenario was independently developed by Alfred Lotka (1924) and Vito Volterra (1926).  Lotka was interested in understanding internal dynamics that might explain oscillations in moth and butterfly populations and the parasitoids that attack them.  Volterra was interested in explaining an increase in coastal populations of predatory fish and a decrease in their prey that was observed during World War I when human fishing pressures on the predator species declined.  Both discovered that a relatively simple model is capable of producing the cyclical behaviors they observed.  Since that time, several researchers have been able to reproduce the modeling dynamics in simple experimental systems consisting of only predators and prey.  It is now generally recognized that the model world that Lotka and Volterra produced is too simple to explain the complexity of most and predator-prey dynamics in nature.  And yet, the model significantly advanced our understanding of the critical role of feedback in predator-prey interactions and in feeding relationships that result in community dynamics.The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


ECM-Training - Predator-Prey Model ("Lotka'Volterra")
57 5 months ago
Insight diagram
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
Insight diagram
💡 Insight sobre dinâmica de crescimento dependente e independente da densidade

O objetivo deste insight é mostrar que diferentes modelos populacionais não são independentes entre si, mas sim construídos progressivamente a partir de um modelo base: o crescimento exponencial.

No modelo exponencial, assumimos que a taxa de crescimento não depende da densidade populacional. Ou seja, cada indivíduo contribui igualmente para o crescimento, independentemente do tamanho da população. A partir desse modelo base, novos modelos surgem pela incorporação de termos que representam processos biológicos adicionais.

  • Ao incluir a limitação por recursos, introduzimos a dependência da densidade, chegando ao modelo logístico. Nesse caso, o crescimento passa a ser reduzido por interações entre indivíduos da própria espécie.
  • Ao adicionar uma segunda população, mantemos a mesma estrutura básica e os mesmos parâmetros das espécies já modeladas, mas introduzimos um novo elemento: a interação entre espécies.

Esse novo termo representa o efeito que indivíduos de uma população exercem sobre a outra, alterando sua taxa de crescimento.

🔁 Extensão para múltiplas espécies

Nos modelos com mais de uma espécie, a lógica permanece a mesma:

  • o crescimento continua sendo descrito a partir do modelo exponencial
  • a limitação intraespecífica continua sendo representada por termos dependentes da densidade (efeitos em nascimentos e mortes a medida que a população cresce)
  • e novos termos são adicionados para capturar interações entre espécies

Essas interações são geralmente modeladas como proporcionais ao produto das abundâncias das espécies envolvidas, refletindo a ideia de encontros entre indivíduos.

🎯 Objetivo pedagógico

Os estudantes devem ser capazes de identificar o tipo de interação ecológica representada em cada modelo a partir de sua estrutura matemática. Mais especificamente, espera-se que eles consigam:

  • reconhecer quando o crescimento é dependente ou independente da densidade
  • distinguir efeitos intraespecíficos de interespecíficos
  • interpretar o sinal e a forma dos termos de interação
  • inferir o tipo de interação (competição, mutualismo, etc.) com base em como as populações afetam umas às outras
🧠 Mensagem central

Todos os modelos aqui são variações de uma mesma ideia fundamental: o crescimento exponencial modificado pela adição de processos biológicos que introduzem restrições ou interações.


Crescimentos denso independente, dependente e com mais de uma espécie
13 3 days ago