It’s hard to get a good sense of what’s happening with the pandemic, whether locally, nationally, or globally. Whether you’re interested because you’re trying to figure out whether it’s a good time to plan riskier activities, or because you have family in other countries, or just prefer data and visualization to narrative, the following sources will help you understand the bigger picture.
Note: You’ll see mention of something called a 7-day rolling average (or sometimes 3-day or 5-day). Rather than graphing today’s raw number of new cases, for example, it’s today’s raw number plus those of the last 6 days.
The 7-day rolling average makes graphs more accurate as a general trend, and less prone to number variations that are caused by data reporting patterns (for example, labs don’t report test results on weekends, so Mondays often have 3 days’ worth of results). You can also see the raw data as a bar graph under the 7-day rolling average.
The SF Chronicle’s graphs use a 7-day rolling average, which is ideal.
Best things about this tracker:
- color-coded map of the state’s situation at a glance
- county-level information includes breakdown of cases by specific cities (not weighted by population)
- graphs lots of different variables over time
- at-a-glance comparison of trends between different counties, and percentage change in new cases (see image below)
Feeling lost with all these graphs and numbers? The Chronicle also has narrative that helps you make sense of what you’re seeing and summarizes the trends, and they include links to articles about significant COVID-19 related developments in the Bay Area and state as a whole.
The state has its own data tracker that covers some of the same information as the SF Chronicle one discussed above.
Here’s what’s different:
- county-level information on number of tests reported daily (per 1000 residents) and what percentage came back positive
- includes information about reopening status for all industries
- percentage of ICU beds and ventilators are still available, overall and by county
Unfortunately, the state website doesn’t graph test results received over time. But the testing data included here is important because if you keep returning to the site, you can compare the increase in testing capacity to the rise of new cases. If the testing capacity is staying the same or rising slowly, and the new cases are shooting upwards, this can mean that those new cases of COVID-19 can’t be solely attributed to the fact that counties are testing more people.
The main page gives an at-a-glance snapshot of the whole country. Make sure to switch to the tabs that are weighted by population to get an accurate sense of how different states have been affected.
They have a valuable Demographics section that allows you to see how COVID-19 cases and deaths break down along other factors such as race, age, and sex.
Covid Tracking Project
In addition to lots of other variables, The Covid Tracking Project has a graph of U.S. daily cases, using the 7-day rolling average discussed above, as well as graphs of daily cases by state in a useful comparison format (select which states you want to see displayed from the dropdown on the right).
Furthermore, The Covid Tracking Project includes a Racial Data Tracker which shows disparities between different racial groups as a percentage of the population, versus their percentage of total COVID-19 cases. The Racial Data Dashboard explores this data by state.
Integrating case data and policy, Johns Hopkins’s Timeline of Cases, Policies, and Deaths allows you to select a state and see when policies such as shelter-in-place and mask ordinances went into effect, when they were relaxed, and how they might be interacting with the number of new cases and deaths in the state
Johns Hopkins has its own maps of COVID-19 data, both for the U.S. and globally, including:
- testing rate
- cumulative cases
- active cases
- lots of links to other sources of information
But arguably the most interesting and valuable section is Critical Trends, which poses a variety of questions and visualizes data that might provide an answer. Here are a few examples:
Mortality Analyses: Visual and numerical representation of how mortality differs across countries
New Cases of COVID-19 in World Countries: Visual representation of outbreak evolution for the 10 current most affected countries