May 16-31, 2020 summary:
One thing seems clear now: the April lockdown was successful in curtailing the high growth rate that the US was experiencing in March - but not in reducing the daily cases from one day to the next. And, as various regions started opening up in May - and with a 2 to 3 week lag for its effects - we can see that corona virus infections are on the rise again. In California, where the per capita infection rate has been quite low (relative to other states), there is a 50-80% rise in daily cases during the last week. This is not good news.

May 8-15, 2020 summary: California finally has its testing in order (>40K tests per day, ~ 1 in very 1000 residents, positivity rate is low (4-5%). US-wide daily cases are under control but a mix between states where they’re falling (e.g., NY, WA) and those where they’re still rising (such as TX, MN, FL, NC, IL).

May 1-7, 2020 summary:
New York has certainly produced a persistent improvement in spread of covid19 and in critical cases and deaths. California, not so much - but at least we’re “flat” and far less per capita than NY - and that, combined with states where daily cases are still climbing - mean that covid19 is still raging strong in the US. Which makes “reopening” a very complex gamble. (https://www.linkedin.com/pulse/how-manage-covid19recovery-hemant-bhargava/)

April 26-30, 2020 summary: California seems to be into a second wave of higher daily cases during the past week (relative to week before). It could be the result of increased testing, so the thing to look at would be the number of “critical” cases = those going into the hospitals.

April 21-25, 2020 summary:
Not a huge amount to write about this week. Big upward blips in daily infections, once in California then in Massachusetts, and these look like the effect of bulk test reporting (not just increased testing). But the point is that the downwards progress that was becoming apparent earlier appears to be stalled.

April 18-20, 2020 summary: Hoping the upward “blip” is only a blip, especially in California. New York’s flattening trend is quite solid, about two weeks long.My expectation from 10 days ago regarding California has been rendered irrelevant because of a big shift in test reporting. Will need to interpret and recalibrate in a few days.

April 14-17, 2020 summary: Ouch. There’s an uptick in the numbers, in US and California (and other states possibly). Partially because of a shift in counting methods from including only “known” infections to “presumptive” infections. We’ll have to wait out a few days to see the “true trend” again.

April 10-13, 2020 summary: The mild positive trend noted earlier is becoming more solid. I think it holds even after allowing for some possible “weekend underreporting” especially in California.

April 7-9, 2020 summary: Glad to see a continuing slowdown (in a 2nd order sense) - I hope this foretells a first-order slowdown in about 5-10 days (i.e., one day’s new cases fewer than the previous day’s). For California my guess is that in about 10 days (April 18) we should be at about 800 cases per day, with about 10,000 new cases during these next 10 days.

April 5-6, 2020 summary: Positive evidence about containment is growing. We got about 30,000 new cases in the last day, but remember that’s simply uncovering something that happened 5-10-15 days back. So, the slowdown can be attributed to the lockdown measures initiated 10-20 days. Since they became stricter later, we should expect the slowdown to intensify. But one mitigating (negative) effect will be growth in cases in states that started late and haevn’t had strong measures. Meanwhile, NY and WA did great, and California results are showing finally (data are becoming more steady state).

April 3-4, 2020 summary: The big news today is that California has finally addressed its testing backlog, and its paucity of testing. So, big jump in new infections, but not necessarily bad news. After I adjust for jump in old test results, it seems pretty positive, though we’ll have to wait out 2-3 days to see the steady state. I’m more worried about other states that started late (in terms of infections, but particularly in taking measures to stop the spread).

April 1-2, 2020 summary: Continuing positive news - in the sense that #days to double the #infections has gone from 2-3 days to about 7 (i.e., daily growth rate fallen from ~ 30% to ~ 10%), indicating that lockdown measures are showing results (see my note below about 10-15 day lag). This is true nationally, and there’s some improvement in key states CA, WA, NY). But I’m a bit hesitant about California due to testing and reporting issues.

March 31, 2020 summary: A larger jump in new infections, both nationally and in California. California data remains a mess with still a huge number of pending results and inconsistent reporting from counties and private labs.

March 29-30, 2020 summary: Is a positive trend building up over the last few days?

March 28, 2020 summary: Continuation of the mildly positive trend from yesterday - in terms of the lack of increase in daily growth rate, especially in New York.

March 27, 2020 summary: Am I right in seeing some hopeful signs from national data. Based on the last 3 days, daily infection curve is linear rather than exponential (though still very high), leading to about a 20% “daily growth rate for cumulative infections” and doubling period of 4 days (rather than 2 or 3).

March 26, 2020 summary: There is a big jump in cases (known infections, or positive test results) relative to a day ago. Some due to increase in testing capacity, but it is disturbing that even the % of positive results is higher during the last 3 days. We are still in a danger zone of the cumulative number of known infections doubling about every 3 days.

March 25, 2020 summary: NY is mixed (% daily infections was higher, but growth over cumulative was lower); MA and FL an TX worse. CA and WA (west coast) - don’t have good data for the day yet. National numbers (daily growth) flat over yesteday, which is not good but not bad.

Corona Virus Crisis: Where is it Heading?

For a few weeks now my ears, eyes and thoughts have been consumed with corona virus issues - status, policy making, crisis management. Early in March, my co-chairs and I pulled the plug on our TEIS academic workshop (but had a very successful event over Zoom, instead of the original Newport Beach location). Next, I conducted my final class of the quarter over Zoom rather than in-person. And then, as the picture started looking bleaker, my friends and I began discussing broader community- and state-level actions that were needed to confront the impending crisis. We put out a blog (Needed: Bold Decisions to Stop Covid-19) and a change.org petition on March 12, calling for suspension of in-person activities at schools, universities and other places of gathering. Around this time, I started laying my hands on daily data about the spread of corona virus in the US.

The Data

This blog is based on looking at the data over the last few days, trying to get a sense of how bad things are, their trends, and whether institutional measures are having a mitigating effect. My analysis is based on daily data from https://covidtracking.com/; see the section on “Data Source” and “Data Limitations and Data Quality” for a discussion of data limitations and the need to interpret any analyses cautiously. The raw numbers are listed at the bottom of the blog.

What Metrics to Consider?

There’s no single metric that provides a comprehensive perspective, partly because the questions are many. How bad is the situation? In absolute terms? In relative terms? Is it getting worse? Even the “situation” has multiple aspects: infections, known vs potential infections, critical cases (hospitalized, on ventilator, in ICU), deaths, etc. Plus the issues of data reliability. As a result, we’ll look at multiple metrics, because collectively they can create an informed view.

  • Amount of tests conducted (daily, cumulative): absolute and per capita
  • Cases = positive test results: daily/cumulative, per capita
  • Infection rate (% of daily test results)
  • Growth rate in daily cases, adjusted for # test results
  • Doubling period (# days to double the cumulative infections)
  • Critical cases = Hospitalizations, ICU, ventilated patients
  • Daily and total cases, adjusted for population size
  • Daily deaths - adjust for known cases, population size

Testing and Spread of Infections

For many weeks after this crisis hit the US, the main story was the growing number of infections and the lack of testing. During March, testing capacity has increased rapidly - from about 500 tests per day on March 2 to 50,000 tests per day on March 22, and now 100,000 on March 26 - although some states still aren’t testing much. Because of the variation in testing volume, we need to look at both absolute number of (known) infections, but also how they map to number of overall tests.

[March 25, 2020:] We see a huge growth in infections (> 15,000 in the US each of the last few days), and part of it is due to increased testing. But, is the new infections curve becoming linear rather than exponential? If so, that would be some good news (although, even then, the “critical outcome” numbers will increase at a high rate for about 2 weeks). Still, before making any strong opinion on this, you might want to check the section “Data Limitations and Data Quality”.

Is the Lockdown Sensible?

The big question - apart from “what is happening” - is “what should we do about it?” We’ve seen several measures such as lockdowns at the state and federal level.

First, there is no question that significant restrictions - such as a lockdown - are needed. Despite some who argue that this is an overreaction, the reality is that the infection has spread undetected among the population, that many more people have the virus than we know, and these unknown asymptomatic individuals will keep propogating the spread. Slowing the spread down - which the restrictions do - will enable us to “flatten the curve” and make it less unmanageable for hospitals and other responders. There are even others who argue that current lockdown measures are too untargeted (and based on data which is highly unprecise) - and that a more precise targeting would still limit the spread but reduce the accompanying harm (see, e.g., https://www.dailywire.com/news/stanford-professor-data-indicates-were-overreacting-to-coronavirus). The criticism is that we’re using the big cannons to shoot a few ants. But that misses the point. Sure, if we could compute a precise targeting solution, we should. But we need good data for that, which is not available. Moreover, even if we could, what looks like a precise targeted multidimensional action plan to an expert will appear vague and ambiguous to the general public. A general lockdown, on the other hand, is more likely to be enforced and implemented.

Is the Lockdown Working, and How to Bend the Curve?

Some states that had early cases–Washington and California–also took early decisive action (early March). [This article provides a good description: https://www.nytimes.com/2020/04/08/nyregion/new-york-coronavirus-response-delays.html] New York was next in getting a spurt of cases, initially slow to place restrictive measures, but then took strong action (mid-March), well before such suggestions from the federal government. Did these measures work?

One metric everyone is interested in is “how quickly is the cumulative number of known infections growing each day?” For instance, if we had 100 known infections until yesterday, and then learnt about another 20 new infections in the last day, then the growth rate (over cumulative) is 20%. Is this growth rate slowing down? Let’s look at these numbers, but first note some important caveats.

  • Results will lag actions by about 10-15 days: the time before people get tested, the time for test results, and some extra days to account for people really changing their behavior.

Lesson: Don’t conclude that the measures are not working just because you don’t observe quick results.

  • Volume of past-infected people still in the population. If this number is high, then even if the spread slows down, the available mass of infected people will continue to produce more positive test results as they get tested. So, even longer time lag to see the effects of measures.

[March 30, 2020] There’s some “slowdown in the growth rate”, with daily growth rate moving from about 40% to 20%. An important aspect of looking at the data is that “signals” (measured infections) lag the “event” (someone getting infected) by 10-15 days, so it is notable that signs of slowdown are emerging about 2 weeks after many states (and later the US) adopted restrictions.

From the growth rate r we can quickly estimate the number of days d(r) for infections to double. The relationship is (1+r)^d = 2. In other words, Log(2) = d*log(1+r), so that d = log(2)/log(1+r). (Note: log = with base e.) We’re seeing very rapid doubling in some countries, e.g., in Spain it is about 2 days to double.

At a 30% growth rate (red line), the total number of infections doubles in less than 3 days - that’s a pace our systems simply can’t handle. If we can reduce the rate of growth from 30% to 15% (yellow line), the doubling-period goes up from fewer than 3 days to 5 days. Then reducing again from 15% to 10% to 5% (green line) takes us from 5 days to 7 days to 15 days! And at that point, the pace of daily recovered cases should exceed daily new cases.

New Cases and Rate of Positive Results

Another metric to look at is the percentage of positive test results each day. This number is huge, but look for the change over time. To get a sense for what this means, consider a simple model and visualization of the spread of infection. Suppose that at some day, the mix of infected and non-infected people looked like the picture below (center panel). The red dots are infected individuals, the orange ones are not infected but otherwise indistinguishable from them, and the green ones are healthy. The center panel is the picture at some time, and the right panel a few days later.

Suppose that the red-orange-green status and mix remained constant. That is, many people are already infected - but no more new infections! Now imagine a circular net thrown into the center panel, representing people we picked up to test one day. The number of positive results for the day (new cases) will depend on the size of the net, the method for picking whom to test (we’ll ignore this for a moment), and the mix of red and orange (which remains constant). Therefore, if we didn’t change the method for picking whom to test, then the number of positive test results should be somewhat proportional to the size of the net. The more you test, the more show up infected - but the proportion or percentage of people who show up positive should remain the same regardless of the size of the net. (Recall this is the case where the red-orange status remains static.)

  • At one extreme it could be that a large-enough segment of the population was infected some days ago - and there are no new infections since then - but as we’re testing more people we’re catching more infected ones.

  • At the other extreme, it is that more people are getting infected each day - and we’re seeing exponentially more - and that is why they show up in the results when we test more people.

Any Conclusions?

A few things.

  • Testing capacity (though still quite low in an absolute sense) is increasing rapidly. That’s good!

  • Each day, we’re seeing roughly the same percentage of positive results (among all tests) - which is partially good news.

  • Each day we’re getting many more known infections (20-40% new ones, relative to the cumulative until then) - which is scary - but a lot of this is due to increased testing. However, it does suggest that a large number of untested people are infected.

  • [March 18, 2020:] Drawing sharp conclusions from national-level data is too dicey at the moment, because the national numbers hide a lot of across-state heterogeneity in how tests are done, who’s tested, how they’re counting various things, levels of lockdown and other measures etc. But the lesson from that is be cautious until you know solidly that there’s no reason to be concerned

Next, we’ll want to look at state level data. We do this below - but with a huge caveat: state-level numbers are still too sketchy and finicky, so any insights are probably even more faulty.

State-Level Analysis

Growth Rates in States

States with Early Hits but Early Decisive Action

California, Washington, New York

California experienced the earliest known covid19 cases in the US, starting in January-February, in both the Bay Area and in Davis/Sacramento. Early in March, Bay Area counties imposed movement restrictions, and California Governor Gavin Newsom displayed great leadership in taking stock of the situation quickly and taking decisive action. Statewide lockdowns came into effect in mid-March, and many universities and schools had already announced suspension of normal activity before that.

The reported number of infections grew in the state during March, but both absolute numbers and growth rates have been below that of, say, New York. California lagged on testing - too few, lengthy delays, and inconsistent reporting from counties and private labs. As of the end of March, only about 25,000 total test results were reported since the start, with almost 60,000 tests pending! Finally, testing backlog cleared on April 4.

Washington was an early state in terms of covid19 infections, especially in King County. But the state has seemed to done exceptionally well in managing the situation since then, not least because of early and decisive action and good testing.

[April 7, 2020:] New York has been the epicenter in the US, lots of infections (and deaths) - but the growth rate of infections is finally slowing down, and hopefully the death rate will too in about 10-15 days. New York has done well in testing.

Massachusetts was also hit early, with the Biogen spread, but somewhat late to introduce stringent measures.

For California, it becomes meaningful to look at additional metrics - number of critical cases, those in the hospital and those in ICU. We have daily “new hospitalizations” and “in ICU (total)” both of which provide a somewhat different perspective. Note that there is a time lag between having a positive test and ending up in the hospital, then there is some time lag for going to ICU.

States with Delayed Spread and Less Decisive Action

Data Source (US numbers)

The first question is where to get the data, whose data to believe? For instance, on March 18 morning, the New York Times’ updated number (March 18, 10 am, based on Johns Hopkins University) was 5,879, but the Johns Hopkins page itself showed 6,519 confirmed cases. The CDC puts out numbers daily (updated at 4pm ET) but this appears to be a substantial undercount relative to others: 4,226. This site shows a history of daily numbers (in US at 4pm EDT): https://covidtracking.com/, with a March 17 update of 5,723 cases.

I’ll use the covidtracking site in this analysis, mainly because it provides a running table of data vs. just current reports. Here’s a look at the data. n.states is the number of states with known infections (including Puerto Rico and other territories). The next 3 columns are about known test results (positive, negative, total) and pending results.

Data

Limitations and Data Quality

  • Test data involves a lag of several days, perhaps 5-15. Reason: testing-and-reporting itself takes a few days, plus most people who get tested probably were infected several days prior to the test.

  • These are country level numbers for the US, aggregated acrosss cities, counties, states – with lot of data mixing across highly heterogeneous reporting sources. Even, for instance, some locations report only positive tests while others report both positive and negative.

  • The rate of infection varies across states, so does the rate of testing, and the nature of testing - who is tested and how much of a lag there is in test results.

  • Lower-level reports arrive at different times of day, so “4pm ET” doesn’t really mean that.

  • Very few tests are being performed in the US - and you might think this means that those being tested are precisely the ones most likely to have an infection. If so, that should show up as very high ratios of positive to negative results each day.

US-level Numbers

date n.states n.cases n.neg n.pend n.death n.result new.result new.cases new.hosp new.death pct.inf
2020-06-13 56 2063789 20976515 1698 109249 23040304 523042 26259 1139 695 5.0
2020-06-12 56 2037530 20479732 1783 108554 22517262 583961 23752 1298 751 4.1
2020-06-11 56 2013778 19919523 1816 107803 21933301 446765 22251 1381 936 5.0
2020-06-10 56 1991527 19495009 1737 106867 21486536 419468 20762 1513 878 5.0
2020-06-09 56 1970765 19096303 1661 105989 21067068 413766 17035 1508 902 4.1
2020-06-08 56 1953730 18699572 1606 105087 20653302 402117 17013 675 655 4.2
2020-06-07 56 1936717 18314468 1733 104432 20251185 446651 19557 647 460 4.4
2020-06-06 56 1917160 17887374 1796 103972 19804534 478436 22711 1007 717 4.8
2020-06-05 56 1894449 17431649 1749 103255 19326098 539874 23299 1440 848 4.3
2020-06-04 56 1871150 16915074 3474 102407 18786224 459468 20568 -2828 876 4.5
2020-06-03 56 1850582 16476174 3556 101531 18326756 458890 20038 2258 969 4.4
2020-06-02 56 1830544 16037322 4054 100562 17867866 426517 23601 1678 1157 5.5
2020-06-01 56 1806943 15634406 3455 99405 17441349 405245 16194 2621 478 4.0
2020-05-31 56 1790749 15245355 3270 98927 17036104 399446 22085 949 655 5.5
2020-05-30 56 1768664 14867994 1668 98272 16636658 428008 24203 1440 937 5.7
2020-05-29 56 1744461 14464189 2978 97335 16208650 490646 23495 1720 1178 4.8
2020-05-28 56 1720966 13997038 1906 96157 15718004 418530 22379 2396 1272 5.3
2020-05-27 56 1698587 13600887 3132 94885 15299474 304717 19124 1706 1271 6.3
2020-05-26 56 1679463 13315294 1549 93614 14994757 306549 16432 17302 635 5.4
2020-05-25 56 1663031 13025177 3368 92979 14688208 421110 19008 967 562 4.5
2020-05-24 56 1644023 12623075 3860 92417 14267098 384633 20473 894 678 5.3
2020-05-23 56 1623550 12258915 4084 91739 13882465 398386 22102 1388 1048 5.5
2020-05-22 56 1601448 11882631 3709 90691 13484079 402794 24465 4037 1287 6.1
2020-05-21 56 1576983 11504302 3641 89404 13081285 421075 26047 4619 1403 6.2
2020-05-20 56 1550936 11109274 2973 88001 12660210 408844 21343 2006 1387 5.2
2020-05-19 56 1529593 10721773 2944 86614 12251366 409544 21575 1665 1363 5.3
2020-05-18 56 1508018 10333804 3596 85251 11841822 348516 19606 1039 794 5.6
2020-05-17 56 1488412 10004894 3449 84457 11493306 374816 20644 1185 882 5.5
2020-05-16 56 1467768 9650722 3788 83575 11118490 364429 25014 1873 1266 6.9
2020-05-15 56 1442754 9311307 2457 82309 10754061 357592 24606 1305 1498 6.9
2020-05-14 56 1418148 8978321 2673 80811 10396469 366634 26326 3200 1878 7.2
2020-05-13 56 1391822 8638013 1983 78933 10029835 315376 20876 1796 1691 6.6
2020-05-12 56 1370946 8343513 1803 77242 9714459 311172 22618 1564 1475 7.3
2020-05-11 56 1348328 8054959 1989 75767 9403287 383393 17900 1206 872 4.7
2020-05-10 56 1330428 7689466 3095 74895 9019894 267531 21834 1034 1016 8.2
2020-05-09 56 1308594 7443769 3054 73879 8752363 291909 25129 1704 1440 8.6
2020-05-08 56 1283465 7176989 3307 72439 8460454 298946 27595 6867 1778 9.2
2020-05-07 56 1255870 6905638 3171 70661 8161508 302837 27468 3713 2733 9.1
2020-05-06 56 1228402 6630269 2742 67928 7858671 248127 25044 2100 1930 10.1
2020-05-05 56 1203358 6407186 2633 65998 7610544 271856 22374 1966 2438 8.2
2020-05-04 56 1180984 6157704 2791 63560 7338688 232483 22108 1614 1009 9.5
2020-05-03 56 1158876 5947329 2812 62551 7106205 237198 25864 1916 1210 10.9
2020-05-02 56 1133012 5735995 1578 61341 6869007 249745 29461 2120 1544 11.8
2020-05-01 56 1103551 5515711 1639 59797 6619262 297636 33017 9774 1746 11.1
2020-04-30 56 1070534 5251092 2775 58051 6321626 234628 29425 2275 2114 12.5
2020-04-29 56 1041109 5045889 4832 55937 6086998 236735 26923 3391 2692 11.4
2020-04-28 56 1014186 4836077 4206 53245 5850263 207084 24842 2071 2050 12.0
2020-04-27 56 989344 4653835 4077 51195 5643179 198298 21555 3034 1219 10.9
2020-04-26 56 967789 4477092 4445 49976 5444881 206418 27158 2163 1179 13.2
2020-04-25 56 940631 4297832 5315 48797 5238463 278304 35923 2448 1712 12.9
2020-04-24 56 904708 4055451 4396 47085 4960159 236229 33874 3268 1859 14.3
2020-04-23 56 870834 3853096 4258 45226 4723930 194059 31458 2758 1761 16.2
2020-04-22 56 839376 3690495 4191 43465 4529871 324009 28720 3043 2060 8.9
2020-04-21 56 810656 3395206 3956 41405 4205862 152958 26113 2652 2426 17.1
2020-04-20 56 784543 3268361 4037 38979 4052904 146786 25649 2144 1756 17.5
2020-04-19 56 758894 3147224 11324 37223 3906118 153903 27404 2193 1735 17.8
2020-04-18 56 731490 3020725 9906 35488 3752215 145945 27984 3511 1830 19.2
2020-04-17 56 703506 2902764 10889 33658 3606270 159466 31956 3374 2081 20.0
2020-04-16 56 671550 2775254 16927 31577 3446804 163430 30751 3026 2142 18.8
2020-04-15 56 640799 2642575 16901 29435 3283374 137357 30182 4392 2486 22.0
2020-04-14 56 610617 2535400 16615 26949 3146017 151750 25492 3233 2284 16.8
2020-04-13 56 585125 2409142 17159 24665 2994267 133626 25105 4237 1566 18.8
2020-04-12 56 560020 2300621 16419 23099 2860641 140136 27990 3251 1632 20.0
2020-04-11 56 532030 2188475 16593 21467 2720505 138764 30620 2783 1996 22.1
2020-04-10 56 501410 2080331 17435 19471 2581741 157308 34193 4889 2049 21.7
2020-04-09 56 467217 1957216 17622 17422 2424433 169383 34367 3879 1977 20.3
2020-04-08 56 432850 1822200 17219 15445 2255050 147474 30425 4258 1943 20.6
2020-04-07 56 402425 1705151 16548 13502 2107576 153855 30543 2947 1954 19.9
2020-04-06 56 371882 1581839 17283 11548 1953721 151657 28774 2887 1246 19.0
2020-04-05 56 343108 1458956 17303 10302 1802064 119079 25482 3854 1277 21.4
2020-04-04 56 317626 1365359 15569 9025 1682985 228785 33233 5042 1432 14.5
2020-04-03 56 284393 1169807 61976 7593 1454200 133016 31836 4503 1257 23.9
2020-04-02 56 252557 1068627 62097 6336 1321184 118213 27950 4141 1153 23.6
2020-04-01 56 224607 978364 59665 5183 1202971 108565 25714 4180 989 23.7
2020-03-31 56 198893 895513 59518 4194 1094406 111763 24631 3850 861 22.0
2020-03-30 56 174262 808381 65369 3333 982643 118736 21862 2485 573 18.4
2020-03-29 56 152400 711507 65545 2760 863907 87850 19609 2734 519 22.3
2020-03-28 56 132791 643266 65709 2241 776057 105111 19674 2382 512 18.7
2020-03-27 56 113117 557829 60091 1729 670946 103452 19001 2523 396 18.4
2020-03-26 56 94116 473378 60251 1333 567494 101614 17625 2460 311 17.4
2020-03-25 56 76491 389389 51235 1022 465880 84210 12805 1859 233 15.2
2020-03-24 56 63686 317984 14433 789 381670 68932 10588 1150 225 15.4
2020-03-23 56 53098 259640 14571 564 312738 58214 11462 870 98 19.7
2020-03-22 56 41636 212888 2842 466 254524 45275 9253 986 141 20.4
2020-03-21 56 32383 176866 3468 325 209249 45267 6876 NA 60 15.2
2020-03-20 56 25507 138475 3330 265 163982 36617 6250 NA 68 17.1
2020-03-19 56 19257 108108 3016 197 127365 27931 4654 NA 45 16.7
2020-03-18 56 14603 84831 2526 152 99434 25081 3160 NA 30 12.6
2020-03-17 56 11443 62910 1687 122 74353 14236 682 NA 22 4.8
2020-03-16 56 10761 49356 1691 100 60117 18707 2216 NA 22 11.8
2020-03-15 51 8545 32865 2242 78 41410 8069 1684 NA 14 20.9
2020-03-14 51 6861 26480 1236 64 33341 4920 1263 NA 9 25.7
2020-03-13 51 5598 22823 1130 55 28421 9456 1368 NA 4 14.5
2020-03-12 51 4230 14735 673 51 18965 5527 931 NA 8 16.8
2020-03-11 50 3299 10139 563 43 13438 4006 716 NA 6 17.9
2020-03-10 50 2583 6849 469 37 9432 2575 587 NA 2 22.8
2020-03-09 50 1996 4861 313 35 6857 1886 486 NA 4 25.8
2020-03-08 50 1510 3461 347 31 4971 924 205 NA 4 22.2
2020-03-07 50 1305 2742 602 27 4047 782 286 NA 1 36.6
2020-03-06 36 1019 2246 458 26 3265 992 385 NA 6 38.8
2020-03-05 24 634 1639 197 20 2273 607 140 NA 4 23.1
2020-03-04 15 494 1172 103 16 1666 1104 200 NA 2 18.1
2020-03-03 3 294 268 NA 14 562 247 101 NA 3 40.9
2020-03-02 3 193 122 NA 11 315 189 81 NA 3 42.9
2020-03-01 3 112 14 NA 8 126 85 88 NA 3 103.5

One could look at state-level data (as I do below, just as an exercise to see how a few specific states are doing), but the same occurs there because state data is just an aggregate of county-level reporting, and so on. I’d love to get a hold of every individual-level (or transactional) data.

Current data are very sketchy - data-driven analysis is still needed - but tread cautiously.

With that said, let’s see what we have, see what clues are contained in the data … but, at the end, interpret all results cautiously.

State-Level Numbers

date state n.cases n.neg n.pend n.death n.result new.result new.cases new.hosp new.death pct.inf
2020-06-13 CA 145643 2578750 NA 4989 2724393 62135 3660 NA 46 5.89
2020-06-12 CA 141983 2520275 NA 4943 2662258 64611 2702 NA 62 4.18
2020-06-11 CA 139281 2458366 NA 4881 2597647 56849 3090 NA 105 5.44
2020-06-10 CA 136191 2404607 NA 4776 2540798 54553 2702 NA 79 4.95
2020-06-09 CA 133489 2352756 NA 4697 2486245 55055 2170 NA 44 3.94
2020-06-08 CA 131319 2299871 NA 4653 2431190 68972 2507 NA 27 3.63
2020-06-07 CA 128812 2233406 NA 4626 2362218 53918 2796 NA 67 5.19
2020-06-06 CA 126016 2182284 NA 4559 2308300 69837 3115 NA 74 4.46
2020-06-05 CA 122901 2115562 NA 4485 2238463 55792 3094 NA 63 5.55
2020-06-04 CA 119807 2062864 NA 4422 2182671 51377 2120 NA 61 4.13
2020-06-03 CA 117687 2013607 NA 4361 2131294 59703 2377 NA 75 3.98
2020-06-02 CA 115310 1956281 NA 4286 2071591 59008 2304 NA 35 3.90
2020-06-01 CA 113006 1899577 NA 4251 2012583 67735 2423 NA 38 3.58
2020-05-31 CA 110583 1834265 NA 4213 1944848 56253 3705 NA 57 6.59
2020-05-30 CA 106878 1781717 NA 4156 1888595 53117 2992 NA 88 5.63
2020-05-29 CA 103886 1731592 NA 4068 1835478 44919 2189 NA 95 4.87
2020-05-28 CA 101697 1688862 NA 3973 1790559 53665 2717 NA 89 5.06
2020-05-27 CA 98980 1637914 NA 3884 1736894 40498 2247 NA 70 5.55
2020-05-26 CA 96733 1599663 NA 3814 1696396 52294 2175 NA 19 4.16
2020-05-25 CA 94558 1549544 NA 3795 1644102 61357 1848 NA 21 3.01
2020-05-24 CA 92710 1490035 NA 3774 1582745 67439 2079 NA 66 3.08
2020-05-23 CA 90631 1424675 NA 3708 1515306 48533 2187 NA 78 4.51
2020-05-22 CA 88444 1378329 NA 3630 1466773 45646 2247 NA 88 4.92
2020-05-21 CA 86197 1334930 NA 3542 1421127 41007 2140 NA 106 5.22
2020-05-20 CA 84057 1296063 NA 3436 1380120 40804 2262 NA 102 5.54
2020-05-19 CA 81795 1257521 NA 3334 1339316 46644 1365 NA 32 2.93
2020-05-18 CA 80430 1212242 NA 3302 1292672 57429 1591 NA 41 2.77
2020-05-17 CA 78839 1156404 NA 3261 1235243 56117 2046 NA 57 3.65
2020-05-16 CA 76793 1102333 NA 3204 1179126 45220 1857 NA 96 4.11
2020-05-15 CA 74936 1058970 NA 3108 1133906 29255 1772 NA 76 6.06
2020-05-14 CA 73164 1031487 NA 3032 1104651 39059 2023 NA 98 5.18
2020-05-13 CA 71141 994451 NA 2934 1065592 32222 1759 NA 87 5.46
2020-05-12 CA 69382 963988 NA 2847 1033370 41473 1443 NA 77 3.48
2020-05-11 CA 67939 923958 NA 2770 991897 36233 1259 NA 25 3.47
2020-05-10 CA 66680 888984 NA 2745 955664 43094 2119 NA 67 4.92
2020-05-09 CA 64561 848009 NA 2678 912570 37298 2049 NA 93 5.49
2020-05-08 CA 62512 812760 NA 2585 875272 32398 1898 NA 81 5.86
2020-05-07 CA 60614 782260 NA 2504 842874 33838 1799 NA 92 5.32
2020-05-06 CA 58815 750221 NA 2412 809036 29134 2603 NA 95 8.93
2020-05-05 CA 56212 723690 NA 2317 779902 32028 1275 NA 63 3.98
2020-05-04 CA 54937 692937 NA 2254 747874 32123 1321 NA 39 4.11
2020-05-03 CA 53616 662135 NA 2215 715751 28948 1419 NA 44 4.90
2020-05-02 CA 52197 634606 NA 2171 686803 31818 1755 NA 98 5.52
2020-05-01 CA 50442 604543 NA 2073 654985 29648 1525 NA 91 5.14
2020-04-30 CA 48917 576420 NA 1982 625337 22198 2417 NA 95 10.89
2020-04-29 CA 46500 556639 NA 1887 603139 25531 1469 NA 78 5.75
2020-04-28 CA 45031 532577 NA 1809 577608 24199 1567 NA 54 6.48
2020-04-27 CA 43464 509945 NA 1755 553409 27325 1300 NA 45 4.76
2020-04-26 CA 42164 483920 NA 1710 526084 20049 1027 NA 59 5.12
2020-04-25 CA 41137 464898 NA 1651 506035 11862 1883 NA 89 15.87
2020-04-24 CA 39254 454919 NA 1562 494173 12076 1885 NA 93 15.61
2020-04-23 CA 37369 444728 NA 1469 482097 16770 1973 NA 115 11.77
2020-04-22 CA 35396 429931 NA 1354 465327 165227 2135 NA 86 1.29
2020-04-21 CA 33261 266839 NA 1268 300100 9600 2283 NA 60 23.78
2020-04-20 CA 30978 259522 NA 1208 290500 9600 645 NA 42 6.72
2020-04-19 CA 30333 250567 7200 1166 280900 21234 1370 NA 94 6.45
2020-04-18 CA 28963 230703 7200 1072 259666 8052 1435 NA 87 17.82
2020-04-17 CA 27528 224086 7200 985 251614 5214 1346 NA 95 25.82
2020-04-16 CA 26182 220218 13200 890 246400 29914 1758 NA 69 5.88
2020-04-15 CA 24424 192062 13200 821 216486 14278 1086 NA 63 7.61
2020-04-14 CA 23338 178870 13200 758 202208 11326 990 NA 71 8.74
2020-04-13 CA 22348 168534 13200 687 190882 554 554 NA 36 100.00
2020-04-12 CA 21794 168534 13200 651 190328 17109 1179 NA 42 6.89
2020-04-11 CA 20615 162371 13200 591 173219 8356 1143 NA 68 13.68
2020-04-10 CA 19472 145391 13900 541 164863 1363 1163 NA 49 85.33
2020-04-09 CA 18309 145191 14100 492 163500 19236 1352 NA 50 7.03
2020-04-08 CA 16957 127307 14600 442 144264 13035 1092 NA 68 8.38
2020-04-07 CA 15865 115364 14100 374 131229 13798 1529 NA 31 11.08
2020-04-06 CA 14336 103095 15000 343 117431 898 898 NA 24 100.00
2020-04-05 CA 13438 103095 15000 319 116533 2833 1412 NA 43 49.84
2020-04-04 CA 12026 101674 13000 276 113700 78400 1325 NA 39 1.69
2020-04-03 CA 10701 24599 59500 237 35300 2300 1510 NA 34 65.65
2020-04-02 CA 9191 23809 59500 203 33000 3073 1036 NA 32 33.71
2020-04-01 CA 8155 21772 57400 171 29927 673 673 NA 18 100.00
2020-03-31 CA 7482 21772 57400 153 29254 2258 1035 NA 20 45.84
2020-03-30 CA 6447 20549 64400 133 26996 739 739 NA 10 100.00
2020-03-29 CA 5708 20549 64400 123 26257 1065 1065 NA 22 100.00
2020-03-28 CA 4643 20549 64400 101 25192 3933 764 NA 23 19.43
2020-03-27 CA 3879 17380 57400 78 21259 873 873 NA 13 100.00
2020-03-26 CA 3006 17380 57400 65 20386 2110 651 NA 12 30.85
2020-03-25 CA 2355 15921 48600 53 18276 2722 253 NA 13 9.29
2020-03-24 CA 2102 13452 12100 40 15554 1254 369 NA 13 29.43
2020-03-23 CA 1733 12567 12100 27 14300 1460 197 NA 0 13.49
2020-03-22 CA 1536 11304 NA 27 12840 312 257 NA 3 82.37
2020-03-21 CA 1279 11249 NA 24 12528 1041 216 NA 4 20.75
2020-03-20 CA 1063 10424 NA 20 11487 1776 139 NA 2 7.83
2020-03-19 CA 924 8787 NA 18 9711 1119 313 NA 5 27.97
2020-03-18 CA 611 7981 NA 13 8592 128 128 NA 2 100.00
2020-03-17 CA 483 7981 NA 11 8464 148 148 NA 5 100.00
2020-03-16 CA 335 7981 NA 6 8316 7107 42 NA 1 0.59
2020-03-15 CA 293 916 NA 5 1209 41 41 NA 0 100.00
2020-03-14 CA 252 916 NA 5 1168 50 50 NA 1 100.00
2020-03-12 CA 202 916 NA 4 1118 45 45 NA NA 100.00
2020-03-11 CA 157 916 NA NA 1073 250 24 NA NA 9.60
2020-03-10 CA 133 690 NA NA 823 19 19 NA NA 100.00
2020-03-09 CA 114 690 NA NA 804 254 26 NA NA 10.24
2020-03-08 CA 88 462 NA NA 550 19 19 NA NA 100.00
2020-03-07 CA 69 462 NA NA 531 9 9 NA NA 100.00
2020-03-06 CA 60 462 NA NA 522 7 7 NA NA 100.00
date state n.cases n.neg n.pend n.death n.result new.result new.cases new.hosp new.death pct.inf
2020-06-13 NY 382630 2489610 NA 24527 2872240 70840 916 0 32 1.3
2020-06-12 NY 381714 2419686 NA 24495 2801400 72395 822 0 53 1.1
2020-06-11 NY 380892 2348113 NA 24442 2729005 60839 736 0 38 1.2
2020-06-10 NY 380156 2288010 NA 24404 2668166 62297 674 0 56 1.1
2020-06-09 NY 379482 2226387 NA 24348 2605869 49973 683 0 49 1.4
2020-06-08 NY 378799 2177097 NA 24299 2555896 58054 702 0 40 1.2
2020-06-07 NY 378097 2119745 NA 24259 2497842 60435 781 0 47 1.3
2020-06-06 NY 377316 2060091 NA 24212 2437407 77895 1108 0 37 1.4
2020-06-05 NY 376208 1983304 NA 24175 2359512 66480 1075 0 42 1.6
2020-06-04 NY 375133 1917899 NA 24133 2293032 63559 1048 0 54 1.6
2020-06-03 NY 374085 1855388 NA 24079 2229473 61642 1045 134 56 1.7
2020-06-02 NY 373040 1794791 NA 24023 2167831 54054 1329 158 64 2.5
2020-06-01 NY 371711 1742066 NA 23959 2113777 49952 941 113 54 1.9
2020-05-31 NY 370770 1693055 NA 23905 2063825 58444 1110 190 57 1.9
2020-05-30 NY 369660 1635721 NA 23848 2005381 61251 1376 206 68 2.2
2020-05-29 NY 368284 1575846 NA 23780 1944130 67341 1551 152 58 2.3
2020-05-28 NY 366733 1510056 NA 23722 1876789 65245 1768 176 79 2.7
2020-05-27 NY 364965 1446579 NA 23643 1811544 37416 1129 180 79 3.0
2020-05-26 NY 363836 1410292 NA 23564 1774128 34679 1072 132 76 3.1
2020-05-25 NY 362764 1376685 NA 23488 1739449 39623 1249 230 97 3.1
2020-05-24 NY 361515 1338311 NA 23391 1699826 47765 1589 241 109 3.3
2020-05-23 NY 359926 1292135 NA 23282 1652061 51268 1772 240 87 3.5
2020-05-22 NY 358154 1242639 NA 23195 1600793 45738 1696 205 112 3.7
2020-05-21 NY 356458 1198597 NA 23083 1555055 49219 2088 179 107 4.2
2020-05-20 NY 354370 1151466 NA 22976 1505836 38097 1525 290 133 4.0
2020-05-19 NY 352845 1114894 NA 22843 1467739 28182 1474 268 114 5.2
2020-05-18 NY 351371 1088186 NA 22729 1439557 26161 1250 326 110 4.8
2020-05-17 NY 350121 1063275 NA 22619 1413396 34679 1889 368 141 5.5
2020-05-16 NY 348232 1030485 NA 22478 1378717 40669 2419 425 174 6.0
2020-05-15 NY 345813 992235 NA 22304 1338048 39291 2762 328 134 7.0
2020-05-14 NY 343051 955706 NA 22170 1298757 39850 2390 446 157 6.0
2020-05-13 NY 340661 918246 NA 22013 1258907 33794 2176 519 168 6.4
2020-05-12 NY 338485 886628 NA 21845 1225113 20462 1430 295 205 7.0
2020-05-11 NY 337055 867596 NA 21640 1204651 21653 1660 433 162 7.7
2020-05-10 NY 335395 847603 NA 21478 1182998 29230 2273 476 207 7.8
2020-05-09 NY 333122 820646 NA 21271 1153768 32225 2715 555 226 8.4
2020-05-08 NY 330407 791136 NA 21045 1121543 31627 2758 533 217 8.7
2020-05-07 NY 327649 762267 NA 20828 1089916 33995 3671 627 951 10.8
2020-05-06 NY 323978 731943 NA 19877 1055921 27022 2786 652 232 10.3
2020-05-05 NY 321192 707707 NA 19645 1028899 21589 2239 542 230 10.4
2020-05-04 NY 318953 688357 NA 19415 1007310 21399 2538 608 226 11.9
2020-05-03 NY 316415 669496 NA 19189 985911 26840 3438 826 280 12.8
2020-05-02 NY 312977 646094 NA 18909 959071 31633 4663 718 299 14.7
2020-05-01 NY 308314 619124 NA 18610 927438 26802 3942 824 289 14.7
2020-04-30 NY 304372 596264 NA 18321 900636 28155 4681 950 306 16.6
2020-04-29 NY 299691 572790 NA 18015 872481 27487 4585 1088 377 16.7
2020-04-28 NY 295106 549888 NA 17638 844994 18899 3110 760 335 16.5
2020-04-27 NY 291996 534099 NA 17303 826095 20745 3951 1062 337 19.1
2020-04-26 NY 288045 517305 NA 16966 805350 27782 5902 1096 367 21.2
2020-04-25 NY 282143 495425 NA 16599 777568 46912 10553 1071 437 22.5
2020-04-24 NY 271590 459066 NA 16162 730656 34736 8130 1191 422 23.4
2020-04-23 NY 263460 432460 NA 15740 695920 25938 6244 1409 438 24.1
2020-04-22 NY 257216 412766 NA 15302 669982 20657 5526 1502 474 26.8
2020-04-21 NY 251690 397635 NA 14828 649325 15464 4178 1302 481 27.0
2020-04-20 NY 247512 386349 NA 14347 633861 16306 4726 1419 478 29.0
2020-04-19 NY 242786 374769 NA 13869 617555 21023 6054 1447 507 28.8
2020-04-18 NY 236732 359800 NA 13362 596532 23309 7090 1983 540 30.4
2020-04-17 NY 229642 343581 NA 12822 573223 22644 7358 2068 630 32.5
2020-04-16 NY 222284 328295 NA 12192 550579 24567 8505 2084 606 34.6
2020-04-15 NY 213779 312233 NA 11586 526012 26869 11571 2315 752 43.1
2020-04-14 NY 202208 296935 NA 10834 499143 20786 7177 1717 778 34.5
2020-04-13 NY 195031 283326 NA 10056 478357 16756 6337 2017 671 37.8
2020-04-12 NY 188694 272907 NA 9385 461601 20621 8236 2622 758 39.9
2020-04-11 NY 180458 260522 NA 8627 440980 23095 9946 2529 783 43.1
2020-04-10 NY 170512 247373 NA 7844 417885 26336 10575 2916 777 40.1
2020-04-09 NY 159937 231612 NA 7067 391549 26396 10621 2870 799 40.2
2020-04-08 NY 149316 215837 NA 6268 365153 25095 10453 3050 779 41.6
2020-04-07 NY 138863 201195 NA 5489 340058 19247 8174 2555 731 42.5
2020-04-06 NY 130689 190122 NA 4758 320811 18531 8658 2084 599 46.7
2020-04-05 NY 122031 180249 NA 4159 302280 18659 8327 2822 594 44.6
2020-04-04 NY 113704 169917 NA 3565 283621 23101 10841 3261 630 46.9
2020-04-03 NY 102863 157657 NA 2935 260520 21555 10482 3424 562 48.6
2020-04-02 NY 92381 146584 NA 2373 238965 18085 8669 2857 432 47.9
2020-04-01 NY 83712 137168 NA 1941 220880 15694 7917 2844 391 50.5
2020-03-31 NY 75795 129391 NA 1550 205186 18718 9298 2507 332 49.7
2020-03-30 NY 66497 119971 NA 1218 186468 14108 6984 1883 253 49.5
2020-03-29 NY 59513 112847 NA 965 172360 16426 7195 2241 237 43.8
2020-03-28 NY 52318 103616 NA 728 155934 10181 7683 1722 209 75.5
2020-03-27 NY 44635 101118 NA 519 145753 23649 7377 1811 134 31.2
2020-03-26 NY 37258 84846 NA 385 122104 18625 6447 1795 100 34.6
2020-03-25 NY 30811 72668 NA 285 103479 12209 5146 1085 75 42.1
2020-03-24 NY 25665 65605 NA 210 91270 12981 4790 916 96 36.9
2020-03-23 NY 20875 57414 NA 114 78289 16888 5707 771 0 33.8
2020-03-22 NY 15168 46233 NA 114 61401 15964 4812 823 70 30.1
2020-03-21 NY 10356 35081 NA 44 45437 13010 3254 NA 9 25.0
2020-03-20 NY 7102 25325 NA 35 32427 10143 2950 NA 23 29.1
2020-03-19 NY 4152 18132 NA 12 22284 7687 1770 NA 0 23.0
2020-03-18 NY 2382 12215 NA 12 14597 7391 682 NA 5 9.2
2020-03-17 NY 1700 5506 NA 7 7206 1713 750 NA 0 43.8
2020-03-16 NY 950 4543 NA 7 5493 221 221 NA 4 100.0
2020-03-15 NY 729 4543 NA 3 5272 1969 205 NA NA 10.4
2020-03-14 NY 524 2779 NA NA 3303 103 103 NA NA 100.0
2020-03-13 NY 421 2779 NA NA 3200 2892 205 NA NA 7.1
2020-03-11 NY 216 92 NA NA 308 43 43 NA NA 100.0
2020-03-10 NY 173 92 NA NA 265 31 31 NA NA 100.0
2020-03-09 NY 142 92 NA NA 234 37 37 NA NA 100.0
2020-03-08 NY 105 92 NA NA 197 29 29 NA NA 100.0
2020-03-07 NY 76 92 236 NA 168 43 43 NA NA 100.0
2020-03-06 NY 33 92 236 NA 125 27 11 NA NA 40.7
2020-03-05 NY 22 76 24 NA 98 44 16 NA NA 36.4