Real Time COVID-19 Tracking

Abe Stanway
Mar 14 · 4 min read

EpiQuery is a realtime “influenza-like illness” (ILI) tracker. It’s updated on a daily basis. The system was set up in 2016 to track emergency room visits with chief complaints that mention flu, fever, and sore throat. These are not confirmed nor denied to be influenza, nor any other disease. Meanwhile, the US government has severely bungled the COVID-19 test rollout, and we’ve only tested ~15k people total so far. In the absence of widespread testing, we need to rely in EpiQuery (and ILINet, the federal CDC version covering all 50 states) to understand the likely growth of the COVID-19 outbreak.

Seasonal peaks highlighted in influenza-like illness in ER visits in NYC from 2016 to present
Seasonal peaks highlighted in influenza-like illness in ER visits in NYC from 2016 to present
Daily ER ILI visits since 2016, seasonal peaks highlighted in pink.

The chart above shows daily ILI ER visits in NYC. The seasonal peaks are highlighted in pink. We see a very seasonal pattern in the data — every year, there’s generally one major peak in December or January, followed by a gradual decrease in ILI cases. This is the annual flu season, visualized.

Zoomed in ILI ER visit data, with first confirmed NYC COVID-19 highlighted

This is the same data, but zoomed in on 2020. We see our normal seasonal peak on January 29th, and then we see a marked anomaly starting at around March 1st. The anomaly displays a peak of equal magnitude to the regular seasonal peak.

A double peak flu season appears to be exceedingly unlikely, as it has never occurred in any historical flu season since the start of this data (at least in NYC), nor has it ever occurred with a slope of this magnitude. Therefore, I believe a large percentage of this peak indicates COVID-19 ER visits in NYC, and not nominal flu visits.

The fact that the peak starts at around March 1st, and the fact that this was also the date first confirmed case of COVID-19 in NYC, lends further evidence to support that this spike represents COVID-19 cases.

The data above represents daily ER visits. This means that since March 1st, there have been 8,000 cases of ILI-based ER visits in NYC. Subtracting the nominal flu season data (~3,800 cases over this period, assuming a late season R0 of .95), that means there are likely a minimum of 4,200 COVID-19 cases in NYC as of March 12th.

Hand drawn best-fit lines for a normal flu season (pink) and current COVID-19 outbreak (red)

This analysis should be considered a napkin sketch — a more detailed study could estimate the precise start date in NYC, knowing the R0 of COVID-19 is estimated to be 2.2¹ and working backwards to infer when when Patient 0 actually arrived based this parameter and the current curve in red.

The true number of COVID-19 cases in NYC is likely several times higher (given the fact that not all cases present to an ER, and ER cases that are not admitted are sent home without any proper quarantine protocols — aka they are sending people home in Ubers or subways), but I will refrain from speculating on an exact number until I find more data. However, assuming the exponential curve holds, the current case count as of March 14th is around 6,300. Despite the napkin math, this data indicates that NYC is currently adding around 1,000 ER admissions of COVID-19 per day and growing fast.

BONUS (or, this is where it gets weird):

Below is a breakdown of the cases by neighborhood.

The epicenter appears to be somewhere in Queens.

This is a neighborhood called Corona. You just can’t make this shit up. Edit: yes, I’m fully aware Corona is *always* the highest density of ILI symptoms. This is likely due to the concentration of hospitals in the area. Regardless, this is a joke, and if you take it seriously, you should get out of the house more (once your isolation period is over, of course!)

¹https://www.ncbi.nlm.nih.gov/pubmed/32097725

All data are available for analysis here. Additional data nationwide can be found on ILINet FluView. Thank you to Ben Hunt for discovering this trove of data, and to Dr. Alfred Illoreta of Mount Sinai and Dr. Ydo Wexler of Amperon for reviewing drafts of this post.

Abe Stanway

Written by

Data scientist/engineer. Founder and CTO of Amperon. Forbes 30 Under 30, Energy. Ex-McKinsey, Etsy, Planet Labs.

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