Effects of Covid-19 in Peru: A System Dynamics Approach – Part 1 [Coronavirus]

What if I told you that humanity has already created powerful tools for addressing highly complex problems*?

Would you believe me?

In this article, we will use a causal diagram to represent the problem of Covid-19 or coronavirus in my country, Peru.

Interested?

Let's get started! => If you want to skip to the quick explanation, click here to watch

UPDATE April 2020: WE SECURED FUNDING, SUPPORT AND PROMOTION FOR THIS INITIATIVE FROM THE PERUVIAN GOVERNMENT THROUGH CONCYTEC PERU:

Read the update here

Introduction: Covid-19

Since an artificial intelligence system began to detect anomalous patterns in the news in late December, humanity was beginning to realize that a disruptive phenomenon was about to emerge. What was unclear was whether this phenomenon might affect just one region of the world or become global. As of the publication date of this article—March 29, 2020—there is unanimous consensus that this disease is already having effects in every corner of the world, even though there are still authorities who deny this.

The disease originated in a region in central China, the province of Hubei, specifically in its capital: Wuhan. But you are probably already asking the million-dollar question: how did this disease, Covid-19, originate? The answer is still under investigation. Initial studies exist, but there is no conclusive result yet. What is known is that Covid-19 is a coronavirus, a type of virus with small crown-like structures on its exterior. If you want to better understand the protagonist of this story, here is a mind map to help:

There is still much discussion about the origin of the disease, whether it came from eating wild animals. Or even the wildest theories suggesting it was created by humans: an educational video explains that it clearly was not, here.

About System Dynamics

The system dynamics method models and simulates complex problems. It was created at MIT by the legendary professor Jay Forrester at its business school, Sloan School of Management. Its principles rest on the idea that any problem, however complex, can be expressed through differential equations using 3 types of variables: stocks, flows and auxiliaries.

If you pause to wonder why an approach or methodology like this for tackling complex problems originated at MIT, I can tell you from my own experience. During my studies in MIT GCLOG in 2018, while staying in Massachusetts, I gained a clear insight into this top university: MIT applies an engineering approach to every situation. Under this approach, any phenomenon can be analyzed objectively. An analysis like this has many advantages, but the main one is that it helps us understand that regardless of culture or context, we can optimize and solve a problem. I cannot imagine MIT publishing purely qualitative tools. Numbers tell us about reality, and if you are not “mathematizing” you are not reaching the full potential of your creations.

How can we understand system dynamics in simple terms?

As an instructor of the System Dynamics Workshop at UNI – FIIS, the most practical way I have to explain this methodology is as follows:

In a complex environment, every phenomenon affects or is affected by another in one way or another. In mathematics, one way to express this is through a differential equation—where the change in one variable over time affects another. System dynamics makes full use of this, and therein lies its very definition.

• But there is something magical about system dynamics…

It can be used not only to analyze typical engineering problems—such as numerical optimization, structural design or modeling an expert system—but also to model social or cultural problems, or problems from other scientific fields, such as biology, geotechnical engineering, psychology, management, medicine… and many more.

• In other words, system dynamics can be applied to almost any situation

It can even be used to model a poem or a historical event (I hope to present an example soon).

You may wonder why. The reason is that before reaching a mathematical model (also called a Forrester model), we first create a causal diagram…

About causal diagrams

Causal diagrams or Causal Loop Diagrams help us interpret any phenomenon qualitatively, but this is not just any kind of interpretation: it uses a causal loop diagram. For humanity to reach this level of understanding of reality, we needed outstanding researchers such as Donella Meadows who in this video at Dartmouth College explains the principles of this way of understanding the world. I will summarize it in 3 key points:

1. Every cause produces an effect
2. Every effect eventually affects its own cause through another effect
3. This happens after some time; it is not immediate

Example:

1. If I study more, then I get better grades
2. If I get better grades, I am motivated to keep devoting more time to my studies
3. But if I indiscriminately devote more time to studying, I will have less time to socialize (family, friends, etc.)
4. If I have less time to socialize, my level of stress will increase
5. If my level of stress increases, my ability to study will decrease
6. And I will get lower grades

In this example, the initial variable is “study more” and its immediate effect is “better grades”, but the latter increases a variable called “stress from studying too much”, which ultimately affects the initial cause by reducing it.

As you can see, there are two types of influence: one that increases and one that decreases. Another important point is that a loop is created. That is the foundation of this paradigm.

All causes produce an effect that eventually, through another variable, affects the initial cause again

Personally, I consider it a significant advance for humanity to understand the interrelationships among the world's phenomena in such a “synthetic” way. It helps us understand that every action we take does not end with its effect; in other words, the world is not linear, because it will eventually have an effect on our own actions. The reason this principle is so difficult to understand is that we have a short-term view of the things around us. Understanding this paradigm requires strong critical analysis to reach high levels of abstraction, understand the space-time context and recognize that there is a connection between visible and invisible objects in the world.

What a causal loop diagram is NOT

I am briefly including this subsection because I have seen causal loop diagrams confused with other causal diagrams on more than one occasion:

• A causal diagram is NOT a fishbone or Ishikawa diagram, widely used in industrial engineering. Reason: it only shows linear effects
• A causal diagram is NOT a problem tree. Reason: a tree is an open graph with no loops; in other words, it is purely linear

It follows from the points above that, for example, in the Logical Framework methodology used by the World Bank to formulate innovation projects, a problem is analyzed solely through a problem tree with causes that have multiple effects, but feedback from an effect to its own cause is not observed. In other words, it remains a limited analysis. I hope they will soon continue to improve this methodology, which currently governs various public policy plans around the world.

The second part of the system dynamics methodology is the Forrester diagram, but we will cover that in the second part of this article.

Effects of Covid-19 in Peru: An Approach Using Causal Diagrams

This is the heart of the article. All the points mentioned above have served to make the core of this article easier to understand.

The latest version of the causal diagram is as follows:

To explain this section, I have two videos: a long, detailed explanation and a short, quick version

The long video is more academic in nature. It was made together with a colleague whom I greatly admire, engineer and master's degree holder Carlos Cárdenas or Unimauro, as he is also known (we both come from the same faculty at UNI, so you can imagine why he has that nickname).

Extended Explanation of the Effects of Covid-19 in Peru

Brief Explanation of the Effects of Covid-19 in Peru

Now comes the next video, which was made with great care and honestly took me quite a while to finish: the short version. If you want to quickly understand the problematic situation surrounding Coronavirus Covid-19 in Peru, I recommend watching this video. The explanation takes less than 10 minutes.

What is coming in the next installments?

In the following parts of this Covid-19 article series, we will see new versions of the Causal Diagram, presenting the loops in detail and adding views for each dimension of the problem (economic and social implications, the hospital system, the country's paradigms, etc.). Next, we will look at the Forrester model, and just as I explained system dynamics and causal diagrams in detail in this article, I will do the same in the next article. If you want to follow this series, I recommend subscribing to my newsletter; there is a form at the bottom of this article.

If you found this interesting, please share it so we can gather more feedback on this work!

Authors of this research:

• Engineer and master's degree holder Erwin Salas
• Engineer José Fiestas
• Distinguished Professor, PhD Ricardo Rodríguez Ulloa

(*) The aim of this work is to develop a model that serves as a tool for evaluating policies to contain the effects of this pandemic in Peru and around the world.

Update 1

Colleagues and brilliant students at UNI have created an interactive, responsive online dashboard to monitor coronavirus cases in Peru => https://covid19.orcebot.com/

Update 2

This research published in FIIS News, with an explanation in Spanish. https://erwinsalas.com/dinamica-del-nuevo-coronavirus-en-el-peru-publicacion-en-fiis-news-uni-peru/

Recommended Books

Here are some recommended books that will be very useful for learning System Dynamics thoroughly

A good book in Spanish:

BUY

The best book you will find for applying system dynamics to business

BUY