The following chart shows the number of reported cases of Covid-19 cases per 100,000 population is US metropolitan areas with a population of 1 million or more as of March 29, 2020.  Metropolitan data are computed by aggregating county level data available from USAFacts.org.  Metropolitan areas are ranked highest to lowest according to the number of reported cases per capita.

New York, New Orleans, Seattle and Detroit have the highest number of cases per capita of US metro areas.  New York’s rate is currently 340 cases per 100,000.  New Orleans (192), Detroit (108) and Seattle (91) have the next highest rates of reported cases.  The median large metropolitan area has about 20.9 cases per 100,000 population.

Map of metro areas, reported Covid-19 cases per 100,000 population

The following map illustrates the relative number of reported Covid-19 cases per capita among large US metropolitan areas.  Darker red colors indicate metro areas with the highest reported incidence of cases.  Numbers on each metro area represent cases per 100,000 on March 28.

Growth rates in the number of cases

The key strategy in fighting the Covid-19 pandemic is reducing social distancing to slow the rate of transmission of the virus.  A key indicator of whether we are “flattening the curve” is whether the growth rate of the number of cases is increasing or decreasing.  The following chart shows the growth in the number of cases for selected metropolitan areas from March 13 through March 29.

The growth rates of the four cities with the highest rates of reported cases per capita paint divergent and interesting patterns of the pandemic.  For a time, Seattle had the highest rate of cases per capita of any US city.  That has changed in the past two weeks.  New York, New Orleans, and Detroit have surpassed Seattle.  On March 19, Seattle, New York and New Orleans all had nearly the same number of reported cases per 100,000 (about 30 per capita).  Since then, their growth paths have diverged:  New York has grown most rapidly, followed by New Orleans; Seattle’s growth has been subdued.  Meanwhile, over that same period of time, the growth of cases in Detroit has increased sharply:  On March 18, Detroit had just 1.4 reported cases per 100,000 population, essentially the same as the median of all large metro areas.  By March 29, that had increased to 108 cases per 100,000; the third highest rate among large US metro areas. (Please note that there seems to be a weekly anomaly with New Orleans reported cases:  the growth trend flattened on both Sunday March 22 and Sunday March 29:  this may reflect non-reporting on Sundays by some sources).

To put the spread of the pandemic in context, it is worth noting that just sixteen days ago, no large US metro had a prevalence of reported Covid-19 cases of more than 15 per 100,000 population (Seattle was 12.2).  Today, more than two-thirds (36 of 53) of the nation’s largest metro areas have a reported prevalence of more than 15 cases per 100,000.  Over the past few weeks,  it appears that there’s about two weeks difference between the worst afflicted metro, and the typical large metro.  Whether that continues to be the case depends on the efficacy of social distancing and other measures.

Prevalence versus Growth

Slowing or stopping the spread of the virus depends on steadily decreasing the growth rate in the number of cases.  This is especially important as the prevalence of the virus becomes more widespread.  Here we’ve plotted the current prevalence of reported cases in each metropolitan area (shown on the horizontal axis) against the growth rate of reported cases in the past week in that metropolitan area (on the vertical axis).  The number of cases in each metropolitan area is proportional to the size of the circle representing each metro area.  You can mouse-over individual circles on the chart to fully identify each metro area, and see the underlying data for numbers of cases, cases per 100,000 and the growth rate in cases over the last week.

We’ve used the means of the two variables (growth rate (22% daily) and number of cases per 100,000 (31), to divide the chart into four quadrants. These quadrants help sort out which metro areas are experiencing the crisis to a greater or lesser degree.  Metro areas in the upper right hand quadrant are clearly most afflicted:  they have both higher than average rates of cases per capita and are growing faster than the average metro area (in the past week).  The lower right hand quadrant identifies metro areas with relatively higher rtes of cases per capita, but slower rates of increase.  Ideally, one wants to be in the lower left hand quadrant (low number of cases per capita, low growth rate).  The upper left hand quadrant is uncertain, but with cause for concern:  these cities (so far) have lower rates of cases per capita, but are seeing the virus spread faster than in the average metro area.  Memphis and Providence both have high rates of increases in the number of cases in the past week, but on a very low base, so the weekly growth rate may not be representative of the longer term trend.  Over time, the strategy of flattening the curve should lead individual metropolitan areas to progress from the upper left hand quadrant (low rates and fast growth) to the lower right hand quadrant (higher rates but a slower rate of growth).

To make this picture a bit clearer, we’ve shortened the horizontal scale to exclude the two cities–New York and New Orleans–with the highest numbers of cases per capita.   This chart makes it clearer which cities are in which quadrants.

The table and map rely on published data from state health departments, aggregated by USAFActs.org. Please use caution in interpreting these data. It is likely that in some areas, the number of cases is under-reported due to the lack of available testing capacity, or pressing medical conditions.  There are widespread differences in the number of tests administered relative to the size of the population in each state, and tests are not given randomly, and may be restricted solely to persons with symptoms, likely exposure or high risk in some states.  As a result, the ratio of reported to unreported, undiagnosed cases may vary across geography.  Moreover, changes in reported numbers of cases from day to day or week to week may reflect changes in the availability or application of testing over time, rather than the true rate of growth in the number of persons affected.

Notes and revisions

This post updates and supersedes our earlier posts with data through March 28.