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 This paper, while not yet peer-reviewed, suggests the potential power of poop in biosurveillance (medrxiv)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.

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Top pick of the day

Their States Are in Crisis. They’re Declaring Victory Anyway.

Article in The Atlantic, Politics by Elaine Godfrey, May 8, 2020

Five republican governors have claimed victory over the virus. In four of those states, cases are rising. The growing disconnect between what some officials are claiming and what the data shows.

Today’s bite-sized, handpicked selection of important news, information or science for all who want to know where this epidemic is going and what we should do.

Daily COVID-19 Briefing: 4/25/20

Top news, reports and insights for today:

1. NEW FEATURE: Top headlines for today:

• Older persons have different, atypical COVID-19 symptoms (Kaiser Health News)
• FDA warns against anti-malaria drugs for COVID-19 promoted by President Trump (Business Times)
• San Francisco thought they had 1918-19 “Spanish” Flu under control. Then it exploded after restrictions were lifted (NBC News)
• Substance use disorders may be another high risk group for COVID-19 (Scientific American)
• World Health Organizations warns that re-infection cannot be ruled out (Bloomberg)
• New study shows promising anti-viral drug remdesivir is not effective against COVID-19 (Stat)
• New study shows that as the case definition for COVID-19 changed in China, more cases were detected, highlighting importance of broadening the clinical indicators that should be considered (Lancet)
• Experts emphasize that COVID-19 will be with us for months (Axios)

2. Low agreement on U.S. Death totals
   Where do you look for information on the number of COVID-19 deaths in the U.S. and elsewhere? It should be straight forward to count deaths, even as we find increasingly that determining the fraction of Americans who are infected is a genuine hornet’s nest. According to WORLDOMETER, the U.S. has now experienced more than 52,843 deaths from COVID-19 as of noon today. Are we done? Not so fast. As the numbers grow, we increasingly see variation in the total death toll depending on where you look. The table below summarizes that variability as of noon on Saturday.
   What does this mean? This far into the epidemic, one might think that these discrepancies would be shrinking as we gain more experience, but that is not what we see. The average for these estimates is 47,500, but the standard error is a whopping 8,200 deaths! The high and low estimates disagree by 24,000. This means a couple of things: a) different reporting systems are not all using the same sources, b) CDC sites that use death certificates are so severely lagged that we shouldn’t be paying attention to them; c) whether or not a given reporting platform includes ‘probable’ deaths explains a big part of this discrepancy. It’s often difficult to tell what each site is doing with probable deaths. This is not a small issue. The New York Times data excludes over 5,000 probable deaths, and would be much more in alignment with the Johns Hopkins and Worldometer estimates if they were added. I believe that probable deaths should be included, because most probable deaths are people who died in hospitals (or outside hospitals) that had COVID-19 but weren’t tested or the test results were not available at time of death. Given the inadequacy of testing, particularly in hard-hit areas, it’s likely that the vast majority of “probable” deaths were COVID-19 infections. Add to this a false-negative testing rate of up to 30%, and the case for inclusion gets stronger. From a surveillance point of view, I am much more worried about undercounting deaths than over counting them.

Source:	U.S. Deaths	Comments:
Worldometer	52,843	Highest estimate
World Health Organization	44,053	Only “confirmed” cases; source not clear
Wikipedia	46,102	State reporting, “probable” deaths not included
Johns Hopkins CSSE Dashboard	52,782	Confirmed & probable, CDC guidelines
CDC/National Vital Statistics System	24,555	Based on death certificates, severely lagged
CDC/Case updates	48,816	Includes some probable deaths + 4 US territories
Our world in data	51,017	ECDPC data
New York Times	46,254	Excludes 5,100 “probable” deaths in New York
European Centre for Disease Prevention and Control	51,017	Definitions not clear
COVID Tracking Project	45,786	Data from State public health authorities

Comparison of death totals across data sources as of 4/25/20

3. Is COVID-19 worse than the seasonal flu? And other mass casualty events?
   Many people are still wrestling with the question of whether this disease is worth the hit our economy is taking. Isn’t this just like the flu? As states move toward re-opening, big questions emerge about whether we are over-reacting. All of this is understandable. But as Michael Osterholm recently said, we are in the 2nd inning of a baseball game. Take a good look at this new graph I spent the day making. I think it’s pretty impressive. This puts the COVID-19 death toll into a broader perspective. It says that by March 2, we had passed total U.S. deaths for Ebola. We passed SARS deaths around March 7. By March 28, we had exceeded the average deaths from influenza in March and April over the last 5 years. By April 6, we had more deaths than for all flu deaths in March and April for 5 years. By April 7, more people had died of COVID-19 in 5 weeks than all who died of 2009 Swine flu. And by April 21, COVID-19 had killed more Americans than died of influenza over the last 5 years combined. For further context, more us us died by April 10 than died in Ebola, SARS, Los Vegas shootings, the Gulf War, Hurricanes Andrew and Katrina, Swine flu, and the September 11 attacks. Combined.
   Bottom line: This is not like the flu