Daily COVID-19 Briefing: Thursday

Top news, reports and insights for today:

1. Daily headline summaries for Thursday:

• Scientists at Yale have found that viral RNA found in sewage sludge predicts COVID-19 cases and hospital burden. This paper, while not yet peer-reviewed, suggests the potential power of poop in biosurveillance (medrxiv)
• Asked why New York City was hit so hard by the first waves of COVID-19, experts point to 4 key factors: 1) large influx of travelers from Europe, 2) early reluctance to close subways and buses, 3) hesitation to issue stay-at-home orders by the Governor, 4) sending recovering patients back to nursing homes (Bloomberg)
• U.K. scientists honing in on the identification of blood markers for mysterious Kawasaki-like multisystem inflammatory syndrome in children (MISC-R) (NBC News)

2. A focus on growth factors by state: Hot spots in Kansas and the south
    As the epidemic has slowed in recent weeks, it makes sense to shift the time scale of how we track it. Daily ups and downs of cases and deaths at the national level have been choppy and unclear. We know that looking at numbers for the whole country can be misleading given that the outbreak has slowed in New York, Detroit and New Orleans, only to disseminate to smaller towns and rural areas. Let’s zoom out a bit and focus instead on longer term trends at the state and regional levels. The graphs below show 7-day week-over-week growth factors, which I have said previously can be a better metric to gauge the current situation compared to looking at the daily numbers. Growth factors are ratios, so looking at them gives a different perspective. In epidemiology, we get lots of practice with ratios. If we want to know the strength of a relationship between a risk factor and a disease, we can look at the absolute amount of disease attributed to the risk factor (also called Attributable risk) or the ratio of disease in those with and without the risk factor (also called Relative risk). There are definite plus-minus trade-offs in choosing which numbers to focus on, but in this case, we can improve the signal-to-noise ratio by looking at the ratio of cases/deaths last week to the cases/deaths the week before.
    The top graph shows the growth factors for new cases. States with values greater than 1 are more active, with positive growth in new cases week over week. What this shows is that all states with stable numbers in the northeast saw negative case growth except Maine. The midwest was a mixed bag with cases rising in 6 states and falling in 6. Cases grew 20% or more in Iowa, South Dakota and Wisconsin. Michigan is slowing fastest with just over half the cases last week as the week before. While little attention has been paid to the West, new cases rose in three states (California by 16%, Idaho by 24%, and Nevada by 23%). In my view, the biggest take-home message here is that cases grew last week substantially in the south, where 10 of 12 states saw cases rise. West Virginia more than doubled new cases adding 322 compared to the 141 the week before. Cases grew sizably also in Alabama (48%), Arkansas (66%) South Carolina (26%), Tennessee (28%) and Virginia (21%).
    The bottom graph covers growth in deaths. In the northeast, deaths rose in Maine (14%) and Rhode Island (54%). All western states saw deaths decrease last week. The fastest rise in new deaths occurred in Kansas where deaths nearly doubled week-over-week. In the south, new deaths increased in half the states, led by Alabama (24%), Arkansas (30%), Georgia (21%), South Carolina (31%) and Tennessee (22%).

3. The state of testing: which states are ready to reopen?
    Today’s Top Pick of the Day article from Vox is based on interviews and discussions with a number of infectious disease experts who were asked to list the top criteria that should be used to decide when it is safe for states to relax outbreak control measures and reopen. I thought I would take my own deeper look into two of those criteria related to testing. The two benchmarks suggested are:
    1) New daily tests of at least 150 per 100,000 population to insure adequate testing capacity for spotting new infections and tracing their contacts.
    2) A test positivity rate of less than 5% indicating that the testing is no longer selective and that a wide net is being cast.
    You can read the paper to see what they conclude about these numbers. I created two new graphs using numbers from the COVID Tracking Project (the same source they used) but looking slightly differently. The top graph below ranks 50 states and DC from highest to lowest on new daily tests per 100,000 residents, averaged over the last month. As was true before, the testing regimes in the northeast are considerably more mature than in most southern and midwestern states. Rhode Island is still in the lead, testing an average of 176 per 100,000 per day and is the only state clearly above the threshold of 150 per day. New York, Massachussetts, Louisiana and Utah are the other states that are over 100 per day. North Dakota is the only midwestern state testing more than 75 a day, which may explain why cases are growing in that state. Twenty six states are now testing less than a third of the 150 benchmark. Arizona continues to have the lowest testing rate of all states by any metric. States below average are very likely under-estimating their current case prevalences, some quite dramatically.
    The bottom graph shows the test positivity rate (or TPR) for the last month. Since lower TPR is “better” in this case, the ranking of states is reversed. A total of 21 states have a TPR in the last month that is below 5%, while 16 states still have a TPR that is high (greater than 10%). Maryland, Washington DC, Delaware and Virginia constitute a block of Atlantic seaboard states that have ramped up their testing capacity (top graph) but still are seeing a large fraction of tests come back positive.
    The bottom line: No states qualify to reopen just using these two benchmarks as proposed in the VOX analysis. Testing remains widely discrepant across states. This shows what happens when there is a complete absence of national leadership and standards for testing. We remain largely and unsettlingly in the dark about this epidemic in the vast majority of U.S. states.