From start to finish, the prevailing question during the development of this project was: How do we model the effects of policy changes on a virus that we still know so little about?

Working extensively with our content expert, Dr. Laura Kahn, we put together the foundation of our model. We selected Wuhan, China, South Korea and New York state in the U.S. as our three regions to compare. Each region responded to the spread of the virus differently, prioritizing different policies and responding on a different timeline. From there, we moved on to tackle the larger issue: How infectious is COVID-19?

The infectiousness of a virus is measured by a number known as the R₀ (pronounced “R naught”). This number represents the number of people that one infected individual might infect in a population with no previous exposure to the virus, and with no vaccinations. The global R₀ for COVID-19 depends on the precautions being taken. We used infection data from the World Health Organization and New York state to set the baseline trends and determine what we should use for R₀. Based on Dr. Kahn’s ongoing research, as well as the body of research available through Johns Hopkins University, we selected a global value of 2.7 for R₀.

We chose to demonstrate the impact of the specific policies in our interactive based on the data in the three regions we used, as well as Dr. Kahn’s recommendations. Because the changes in the interactive timelines are both simplified and hypothetical, the numbers that you generate with this model will not exactly match real-world data. Based on the data from the World Health Organization and Dr. Kahn’s research, we developed our model to show the general effects of each policy.

There is no quantifiable multiplier for any one policy that will communicate the reality of the effects of these policies — there are too many potential factors influencing our real-world experiences. Our simplified model uses values that would demonstrate the observed trend of these policies, and would allow us to extrapolate what the effect might have been had these policies been implemented at some other time.

Our goal for this tool is that you leave thinking about the dramatic effect that public policies can have on the global spread of a virus. How could things have gone differently? Based on the numbers we see, what could the world have done differently to prevent the outcome that we did see? We hope that you will come away from the experience thinking about it, talking about it and wanting to learn more.