Variante del modelo SEIRDQ con confinamiento imperfecto, donde el 75% de los susceptibles son confinados al superar un umbral de infectados, pero conservan un 1% de su susceptibilidad original, permitiendo una transmisión residual del virus.
Extensión del modelo SEIRD que introduce un mecanismo de confinamiento perfecto: cuando los infectados superan un umbral predefinido, se confina automáticamente al 75% de la población susceptible, eliminando completamente su exposición al virus.
Extensión del modelo SEIRD que introduce un mecanismo de confinamiento perfecto: cuando los infectados superan un umbral predefinido, se confina automáticamente al 75% de la población susceptible, eliminando completamente su exposición al virus.
Simple bathtub model to show the difference between Stock and Flow. Run the model with various values for filling and draining to see the implications.
Modelo básico SEIRD que simula la propagación de una enfermedad infecciosa dividiendo la población en cinco compartimentos: Susceptibles, Expuestos, Infectados, Recuperados y Fallecidos.
Modelo básico SEIRD que simula la propagación de una enfermedad infecciosa dividiendo la población en cinco compartimentos: Susceptibles, Expuestos, Infectados, Recuperados y Fallecidos.
Before addressing the specific case of the smallpox epidemic among the Aztecs, it is important to introduce the basic epidemiological model known as SIR (Susceptible – Infected – Recovered). This model helps explain how an infection spreads through a population over time by classifying people into three main categories:
Susceptible (S): Individuals who have not yet been infected but are at risk.
Infected (I): Individuals who have contracted the disease and can transmit it.
Recovered (R): Individuals who are no longer contagious, either because they have developed immunity or have died.
One of the most powerful principles of systems thinking is that the structure of a system determines the patterns of behavior we observe over time. Trying to change behavior without altering structure is like treating symptoms without curing the cause.
The simple savings account is used to demonstrate the nature of a reinforcing loop. Change the initial amount and interest rate and run the model to see the implications of changing these values.
This is a high level system dynamics model which is built to
determine the dynamic relationships of the FSA and Followups capacity.
Therefore, it can help clinicians to find out the optimistic method in order to
reduce the waiting list. At past clinicians were seeing more FSA patients,
however, after few months, the followups patients overwhelmed the clinics.
Therefore waiting list has been built up again. By running this model,
clinicians can find out the balanced leverage point(s). New Model has been developed, this is a very draft model.
One of the most powerful principles of systems Este artículo explora el principio fundamental del pensamiento sistémico que establece que la estructura de un sistema determina su comportamiento. A través del caso del programa estadounidense TANF (Asistencia Temporal para Familias Necesitadas) —que ofrece apoyos económicos, alimentos básicos y servicios asociados— se ilustran tres configuraciones estructurales distintas que generan comportamientos radicalmente diferentes. Este análisis está orientado a propósitos educativos y muestra cómo una comprensión profunda de las estructuras permite intervenir de manera más efectiva tanto en sistemas sociales como organizacionales.
This is a high level system dynamics model which is built to
determine the dynamic relationships of the FSA and Followups capacity.
Therefore, it can help clinicians to find out the optimistic method in order to
reduce the waiting list. At past clinicians were seeing more FSA patients,
however, after few months, the followups patients overwhelmed the clinics.
Therefore waiting list has been built up again. By running this model,
clinicians can find out the balanced leverage point(s). New Model has been developed, this is a very draft model.
From Excel to Insight Maker: Visualizing System Dynamics with AI
Spreadsheets revolutionized how we work with data - they made computation accessible and practical for everyone. But tables full of numbers often hide the bigger picture: the systemic relationships between variables.
What if you could see your spreadsheet not just as numbers, but as an interactive diagram that reveals how everything connects?
That’s exactly what this project demonstrates. Using ChatGPT 5.2, I transformed a traditional Excel model into a stock and flow diagram ready for simulation in Insight Maker.
Here’s how it worked. I prompted ChatGPT to:
"Convert the attached spreadsheet into a stock and flow diagram to be imported as a JSON file into Insight Maker. Parameters (M11, M12, etc.) should be adjustable via sliders, time unit is MONTHS, and use Euler integration."
After a few refinements in the chat, I imported the generated JSON directly - no manual tweaks. The resulting diagram is shown here exactly as created by AI. It’s admittedly a bit rough, but intentionally so: it shows the raw potential of AI-assisted modeling.
The best part? The simulation output matched the original spreadsheet values exactly - validated in the "Table from Insight Maker" and "Validation" tabs.
This isn’t just a technical experiment. It’s a step toward making System Dynamics more intuitive and spreadsheets more insightful. By visualizing data flows and feedback loops, we can communicate complex models more clearly and make better decisions.
I hope this inspires you to explore similar integrations between spreadsheets, AI, and simulation tools. Have you tried something like this? I’d love to hear about your experiments!
Spreadsheets revolutionized how we work with data - they made computation accessible and practical for everyone. But tables full of numbers often hide the bigger picture: the systemic relationships between variables.
What if you could see your spreadsheet not just as numbers, but as an interactive diagram that reveals 𝗵𝗼𝘄 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝘀?
That’s exactly what this project demonstrates. Using 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝟱.𝟮, I transformed a traditional Excel model into a 𝘀𝘁𝗼𝗰𝗸 𝗮𝗻𝗱 𝗳𝗹𝗼𝘄 𝗱𝗶𝗮𝗴𝗿𝗮𝗺 ready for simulation in 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗠𝗮𝗸𝗲𝗿.
After a few refinements in the chat, I imported the generated JSON directly - 𝗻𝗼 𝗺𝗮𝗻𝘂𝗮𝗹 𝘁𝘄𝗲𝗮𝗸𝘀. The resulting diagram is shown here exactly as created by AI. It’s admittedly a bit rough, but intentionally so: it shows the raw potential of AI-assisted modeling.
The best part? The simulation output matched the original spreadsheet values exactly - validated in the "𝘛𝘢𝘣𝘭𝘦 𝘧𝘳𝘰𝘮 𝘐𝘯𝘴𝘪𝘨𝘩𝘵 𝘔𝘢𝘬𝘦𝘳" and "𝘝𝘢𝘭𝘪𝘥𝘢𝘵𝘪𝘰𝘯" tabs.
This isn’t just a technical experiment. It’s a step toward making 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝘆𝗻𝗮𝗺𝗶𝗰𝘀 𝗺𝗼𝗿𝗲 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲 and 𝘀𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝗺𝗼𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝗳𝘂𝗹. By visualizing data flows and feedback loops, we can communicate complex models more clearly and make better decisions.
I hope this inspires you to explore similar integrations between spreadsheets, AI, and simulation tools. Have you tried something like this? I’d love to hear about your experiments!
Let’s push how we model, visualize, and communicate systems. If you find this useful, feel free to share and connect.
