There are of course many data sources for SARS CoV-2 and COVID-19. I think it is great that so much epidemiological data is collected in near-real time on such a large scale. Of course, as with many data sets, there can be inaccuracies/discrepancies/idiosyncrasies (hopefully all small). Using a grain of salt is always recommended.
Source of data that I regularly peruse include:
- A World view from Max Roser’s Our World in Data website
- CDC’s COVIDView, together with an updated estimate of cases in the U.S.A., now ~83M, dramatically revised in the last two months (see the page on 12/07/20). In particular it seems to imply that more than 30% of the people being vaccinated in the near future already had COVID, which, together with this paper, Lasting immunity found after recovery from COVID-19 (link to original paper in the journal Science), would imply that the current prioritization of vaccines is suboptimal.
- OurWorldInData’s excess mortality in the U.S., and EuroMOMO for Europe
- Pages with data on COVID-19 impact as a function of age
- CDC’s COVID-19 pandemic planning scenarios, with estimates on key parameters for modeling COVID-19 spread
- Explanation of test positivity rates
Estimation of actual number of infections
- Several interactive charts, mostly from OutWorldInData’s pages on COVID-19
- Data on COVID-19 at schools, U.S.-wide.
- A rather thorough vaccine tracking page from Bloomberg, with granular data from the U.S.A., as well as world-wide data, albeit with glaring mistakes, conceptual and beyond, in parts of the reporting, for example in calculating number of vaccinations needed to reach herd immunity, which dismisses ~80M cases (as estimated by the CDC).
- 2021 CDC seasonal flu tracker, and a page by M. Roser on flu pandemics
- Tracking cases on campus at Johns Hopkins and at Duke University
- Local: Johns Hopkins Medicine allocates thousands of vaccines to Baltimore public schools starting 1/18/21; notwithstanding prioritization in the Maryland vaccination plan, cases and deaths in Maryland long-term care facilities continue to grow, e.g. more than 2000 since 1/18/21, but hopefully will abate soon (in March?); JHU COVID testing dashboard with test results updated daily; from the Maryland vaccine data page and Maryland population data it seems that as of beginning of March, a majority of people over 65 has received at least the first dose of vaccine.
Max Roser’s Our World in Data has a great page on data on coronavirus. Here are two snapshots of the world’s situation:
- the CDC’s COVIDView, with lots of interesting view on current U.S.A. data (albeit the lag in data might be a bit larger than for other sites, it may be a bit cleaner data), including excess mortality, stratification of data by age, sex, race/ethnicity, and co-tracking of influenza, which might become more and more relevant during Fall and Winter ’20-’21.
The CDC’s page on Excess Deaths Associated with COVID-19 is also useful in similar respects, with similar caveats (estimates are mortality, and excess thereof, are…estimates; data lags by about 1-3 weeks, etc…); a summary here to the right.
Also, EuroMOMO tracks excess deaths from 24 European countries, both across all age groups, and stratified. It shows rather clearly the dramatic difference across age groups. Unfortunately the graph on the side here is not an interactive embedding of their graph, so it may not be the most up-to-date info, for which please use the link at the beginning of this bullet point.
In the plot below there are confirmed cases per unit of population (1,000,000 people here), with aligned outbreaks, for many different countries (you may interactively change that selection). Moreover, the trajectory of confirmed cases is colored, at each time point, by the positive rate. A high positive rate may be caused by constraints on testing capacity, possibly indicating there is a significant amount of non-confirmed cases; however this is only a possibility – as discussed in the link above explanation of test positivity rates. Note you may switch to log scale in the vertical axis, which may be helpful depending on the countries selected (yes, even quantities normalized by population size have differences of several orders of magnitude!). The very latest available data is at OurWorldInData
|Here is another interactive chart, with deaths, and non-aligned outbreaks, different countries, and log scale. Again, many options (which countries to show, log vs. natural scale, etc…) can be changed interactively. If you move your pointer near the graphs you can see actual values at any point in time.|
This measures how skewed towards the older (and, likely, with more co-morbidities) part of population the number of critical cases and deaths has been. However grim the numbers are, this can perhaps be interpreted as good news, as efforts focused to protect this relatively smaller high-risk part of the population, while not easy, would lead to outsized rewards. [This is of course similar to other diseases – e.g. the flu, albeit for the flu smaller kids are also at higher risk of severe complications compared to the rest of the population (which is why vaccination rates are higher for higher-risk groups).] This page on Wikipedia on COVID in Sweden reports that 3.6% of all fatalities are for people less than 59yrs old, which constitutes 70% of the population (I did find the source of data referenced therein, which also contains rather detailed co-morbity statistics, but not processed it myself in order to confirm this).
OurWorldInData’s statement at the web page above: “What we want to know isn’t the case fatality rate: it’s the infection fatality rate“, which is the ratio between deaths and total infected people (whether or not confirmed by a test). This denominator is not known, the degree of uncertainty with which it is unknown depends on many factors, and is subject to much work (and perhaps too little measuring so far, see point above).
CDC flu tracker for the U.S., which, rather predictably (at least, according to my lower-school daughter), is behaving in line with what happened in the southern hemisphere. I have no idea how Norio Sugaya, a WHO member on the influenza committee, can say “This is an extremely puzzling phenomenon. We’re in a historic, unbelievable situation,” according to this WSJ article. See the full CDC page on FLU tracking here, whence this image was taken: