| layout | single |
|---|
The rapid spread of SARS-CoV-2 has led many countries and regions to enact various interventions, such as social distancing, school closures, and border control, in order to mitigate the growth of infection. Understanding the effects of these interventions is particularly important since each strategy comes with its side effects. We wanted to understand the impact of intervention strategies and their combinations on the disease spread. After collecting data at the country and state levels for certain types of interventions, we overlaid them on the disease growth curves, shown below. {% comment %} For example, in addition to the economic impact to businesses, there are negative mental health implications to self-isolation and quarantine. {% endcomment %}
As a first step towards understanding the impact of interventions, the visualization above shows the logarithm of the number of confirmed cases over time, for the 20 countries with the most confirmed cases of SARS-CoV-2. Selecting the World: Deaths tab will lead to a similar visualization for deaths in countries, while selecting the USA tabs show the confirmed cases or deaths across US states. We overlaid our trends with various countermeasures taken by the governing entities. We invite the reader to reveal such measures on a per-region basis in the chart by clicking the legend (recommended), the dropdown below the chart, or the chart itself.
For countries, a full lockdown is one where there a nation-wide declaration of a lockdown. On the other hand, a partial lockdown means that some but not all regions within the country that have declared a lockdown, such as in case of the United States. Likewise, when visualizing US states, a full lockdown is one where there is a state-wide declaration of a stay at home-type order.
To visualize the impact of these lockdowns, we also plot a projection line for the original trajectory of the trend before the lockdown date. The projection extrapolates the growth based on the slope computed up until the lockdown date. This projection is based on the simple assumption that the growth rate stays fixed throughout the entire period of time, which is not always a valid assumption for a number of reasons. For example, as the number of infected individuals increases, the growth will likely slow down due to the increasing number of recovered people with immunity. Nevertheless, this serves as one comparison point that we can use to understand the effects of interventions in slowing the infection rates.
- What are the drawbacks of our visualization dashboards?
There is danger in extrapolating too much from limited historical data, especially since many of the case numbers are subject to other confounding variables, such as the amount and availability of tests. We will be keeping the dashboard up-to-date with the latest data to see how these trends unfold.
Another drawback is that our extrapolation (labeled as original trajectory in the visualization) is easy-to-understand but simplistic: other more sophisticated models exist. That said, our intent is not prediction, but rather provide a visual cue to study the differences before and after the intervention.
Finally, we must mention that aggregate patterns and trends often obscure individual datapoints and outliers. Visualizing data on a logarithmic scale, while making it easier to visualize exponential growth, often gives us a false sense of linear behavior.
- How was the original trajectory computed?
The trajectory was computed by drawing a straight line from the start of the visualization to the point of the intervention, and then extending that post the intervention.
- Why build yet another COVID-19 visualization?
While there are many COVID-19 visualization dashboards, including those that employ helpful log-linear extrapolation to understand the trends in various regions, we haven't found any dashboards that try to visualize the overlaid visual impact of various intervention measures, apart from anecdotal reports of the curve being flattened thanks to interventions. If there are any visualization dashboards that we should be aware of and can link to, please share them with us at covidvis@berkeley.edu.
- What's next for the project?
The dashboards above simply scratch the surface of what can be and what needs to be done for this project. Apart from keeping our dashboards up-to-date on a regular basis, we're in the process of collecting, studying, and visualizing the impact of fine-grained interventions (specifically, the impact of various combinations of interventions, such as school closures, banning of gatherings, non-essential business closures, and so on). Beyond Wikipedia, we are aware of other data-gathering efforts on this front, such as those from Keystone and Stanford. We hope to leverage these datasets as well as others we manually collect to perform these analyses.
- How can we reproduce the charts above?
Our Jupyter notebooks and processing scripts are online on GitHub. We also plan to make our intervention data available soon.
- How can I contribute?
Please write to us at covidvis@berkeley.edu
- Data derived from John Hopkins CSSE [link] {% endcomment %}
We draw on data regarding COVID-19 cases and deaths from JHU Coronavirus Resource Center as well as the New York Times US dataset. We draw on data regarding national and regional interventions from Wikpedia as well as the New York Times.
There are many visualizations of COVID-19 growth curves online that we draw on for inspiration. We are fans of visualizations from John Burn-Murdoch, Financial Times, such as this one, as well as the New York Times, such as this, this, this, and this. We drew on data preprocessing scripts from Wade Fagen's excellent "Flip the script on COVID-19" dashboard.
Our visualization dashboard employs many popular open-source packages, including Altair and Vega-Lite, for visualization, Pandas for data processing, and Jupyter for sharing code and visualizations.
Covidvis is a collaborative effort across computational epidemiology, public health, and visualization researchers at UC Berkeley (EECS, School of Information, and School of Public Health), University of Illinois (Computer Science), and Georgia Tech (Computational Science and Engineering).
From the visualization side, the team includes Doris Jung-Lin Lee (UC Berkeley School of Information); Stephen Macke (University of Illinois Computer Science and UC Berkeley EECS); Ti-Chung Cheng, Tana Wattanawaroon, and Pingjing Yang (University of Illinois Computer Science); and Aditya Parameswaran (UC Berkeley School of Information and EECS).
From the public health and epidemiology side, the team includes Ziad Obermeyer (UC Berkeley School of Public Health) and B Aditya Prakash (Georgia Tech Computational Science and Engineering).