Hey there! I’m Henry, and like many of you, I’ve been thinking a lot about epidemiology lately, especially with the ongoing COVID-19 pandemic. The daily news reports can be overwhelming, with numbers changing rapidly due to exponential growth, making it hard to grasp the situation. Exponential growth is a concept that can be tricky for our brains to understand because it happens so fast. My friend Grant Sanderson from 3Blue1Brown has an excellent video explaining this, which I highly recommend.
In the early stages, epidemics like COVID-19 grow exponentially. This means the number of cases increases rapidly, making it difficult to predict when it will slow down or stop. The end of exponential growth is crucial because it determines how many people will ultimately be affected. However, it’s challenging to know when this will happen, as the growth can seem endless.
To better understand the trajectory of COVID-19, my friend Oddish created a graph using real data to visualize the epidemic on a global scale. This graph highlights which countries have managed to control the spread and which are still experiencing exponential growth. It shows that even if a country currently has few cases, it might follow the same path unless effective measures are taken.
There are three main ideas behind this graph:
The graph reveals that COVID-19 spreads similarly across countries, with public health measures like testing, isolation, and social distancing playing a crucial role in controlling the spread. It provides a clearer picture of whether a country is still in the “rocket ship” phase of contagion or if it has managed to slow down the spread.
While the graph is a powerful tool, it has some limitations. It uses a logarithmic scale, which can distort the perception of numbers, making 10,000 cases look close to 1,000. Additionally, the graph doesn’t show the true number of cases, only the detected ones, which can be influenced by the number of tests conducted. The trends are also delayed by a few days, as they represent the average growth rate over the past week.
Understanding trends is essential to predicting the future of the pandemic. By focusing on the rate of change, we can better anticipate what lies ahead. This approach empowers us to see beyond the daily numbers and understand the broader picture.
In these uncertain times, tools like this graph provide valuable insights into the pandemic’s trajectory. A big thanks to Oddish Bhatia for creating this visualization and helping write the script. If you’re interested in learning more about exponential growth and other math and science topics, check out Brilliant.org. They offer interactive courses and resources that are perfect for anyone looking to deepen their understanding during this time.
Explore an interactive graph that uses real COVID-19 data. Analyze the graph to identify patterns of exponential growth and determine which countries have successfully flattened the curve. Discuss your findings with peers to deepen your understanding of how public health measures impact the spread of the virus.
Research a specific country’s response to the COVID-19 pandemic. Prepare a presentation that highlights the country’s strategies to control exponential growth, using data and graphs to support your analysis. Present your findings to the class and engage in a discussion about the effectiveness of different approaches.
Participate in a workshop where you will learn to create simple mathematical models to simulate exponential growth in epidemics. Use these models to predict future trends and discuss the limitations and assumptions involved in modeling real-world scenarios.
Engage in an activity that involves plotting data on both linear and logarithmic scales. Compare the two to understand how a logarithmic scale helps visualize exponential growth more effectively. Reflect on how this understanding can be applied to interpreting real-world data.
Join a group debate on the effectiveness of various public health measures in controlling exponential growth during epidemics. Use data and case studies to support your arguments. This activity will help you critically evaluate different strategies and their impact on public health.
Exponential – In mathematics, exponential refers to a function or equation in which a constant base is raised to a variable exponent, often leading to rapid increases or decreases. – The population of bacteria in the lab grew at an exponential rate, doubling every hour.
Growth – In science, growth refers to the increase in size, number, or importance of a particular entity or phenomenon over time. – The growth of the crystal structure was observed under the microscope, showing distinct layers forming over several days.
Graph – In mathematics, a graph is a diagram representing a set of data points or a function, often used to illustrate relationships between variables. – The students plotted the temperature data on a graph to analyze the cooling rate of the liquid.
Logarithmic – In mathematics, logarithmic refers to a type of function or scale that represents exponential relationships, where the logarithm of a number is the exponent to which a fixed base must be raised to produce that number. – The pH scale is logarithmic, meaning each whole number change represents a tenfold increase or decrease in acidity.
Cases – In science, cases often refer to specific instances or examples used to illustrate a concept or phenomenon, particularly in studies or experiments. – The study included several cases of rare genetic mutations to better understand their effects on human health.
Trends – In data analysis, trends refer to the general direction or pattern in which something is developing or changing over time. – The researchers identified trends in climate data that indicated a gradual increase in global temperatures.
Pandemic – In science, a pandemic refers to an outbreak of a disease that occurs on a global scale, affecting a large number of people across multiple countries or continents. – The mathematical model was used to predict the spread of the pandemic and assess the impact of different intervention strategies.
Visualize – In data science, to visualize means to create graphical representations of data or concepts to facilitate understanding and analysis. – The team used software to visualize the complex data set, making it easier to identify patterns and anomalies.
Rate – In mathematics and science, rate refers to the measure of change in one quantity relative to another, often expressed as a ratio or percentage. – The reaction rate was calculated to determine how quickly the reactants were converted into products.
Change – In mathematics and science, change refers to the variation or difference in a particular quantity or condition over time or between states. – The change in pressure was measured to study its effect on the boiling point of the liquid.
