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🌄 Please note: Daily generation of these dashboards was sunset on 2022.03.30.

"Real-Time" Covid19 County-Level & Choropleth Dashboards


  • A "real-time"1 county-level dashboard w/ a focus on estimated effective reproduction number (Rt)2, 2nd order growth rates and confirmed infection density for most US counties (counties w/ > 0.03% confirmed infection density and > 1000 cases)
  • State and national choropleths for exploring the geographic distribution of "real-time"1 county-level Rt2 along with other relevant epidemiological statistics. Due to resource constraints, the national choropleth represents exclusively Rt data while the state choropleths include additional county-level metrics. The national choropleth can currently be temporally evolved over a 14-day horizon.
  • Notebook for manual EDA of county-level hotspot data

Daily Onset Estimation

  • It's important to be clear that these county-level Rt estimates are "real-time" in the sense that the approach outlined in (Bettencourt & Ribeiro, 2008) is used while convolving the latest onset-confirmed latency distribution onto daily reported cases (then adjusting for right-censoring) to obtain the estimated daily onset values. The latency between case onset and confirmation/reporting means that significant changes in local conditions still require some time (days) to be fully reflected in the Rt estimates, but the estimate for a given point in time should improve with each passing day to a degree roughly correlated with the aforementioned onset-delay distribution.

Effective Reproduction Number Estimation

  • I've extended this great notebook to a county-level.
  • Importantly, it should be noted that (as of 2020.05.12) access to testing is continuing to increase and test positivity rates are therefore changing at a substantial rate. As the testing bias continues to evolve in the near-term, one should recognize that point Rt estimates will be biased to be higher than ground truth Rt. There are approaches that can mitigate this bias to a limited extent but fundamentally, we don't have sufficient data to eliminate the bias at this point so I've deprioritized making those model adjustments at the moment (I may make testing-related adjustments in the future though and PRs are welcome!). Fortunately, as testing access and bias stabilize at a level that increases validity of confirmed case counts, these Rt estimates should become increasingly accurate. I think we can expect hotspot monitoring tools such as this to have utility for a number of months, so this initial period of testing volatility does not nullify their value.
  • The most salient change I've made in the process of the extension is that rather than using a prior of gamma-distributed generation intervals to estimate R (which seems totally reasonable), I'm experimenting with incorporating more locally-relevant information by calculating an R0 using initial incidence data from each locality.
  • For execution environments that are compute-constrained, I've also provided (but left disabled) some performance enhancing functions that cut execution time by about 50% at the cost of ~5% accuracy.

"Real-Time" State Choropleth

"Real-Time" State Choropleth

"Real-Time" County-Level Dashboard

"Real-Time" County-Level Dashboard

"Real-Time" National Choropleth

"Real-Time" National Choropleth

Latest County-Level Grid Plots

  • Daily Estimated Effective Reproduction Number (Rt) (counties w/ highest total onset cases)

Daily Estimated Effective Reproduction Number (R<sub>t</sub>) (counties w/ highest total onset cases)

  • 2nd order case growth (disjoint 4-day windows)

County-level hotspots, 2nd order case growth (disjoint 4-day windows)

  • County-level hotspots: cumulative case growth (4-day MA)

County-level hotspots, cumulative case growth (4-day SMA)

  • County-level hotspots: Estimated Onset Cases

County-level hotspots, Estimated Onset Cases

SEIR Model Notes

  • At the time the SEIR model component of this notebook was written (2020.03.30) there remained significant uncertainty regarding some sars-cov-2 parameters. The data fit varied substantially by county so I used what I perceived (N.B.: w/ no personal epidemiological expertise!!) to be the consensus values, documented below:

Parameter Source Reference Value
Latent Period Lin et al., 2020 3
Latent Period Wu et al., 2020 3
Latent Period Li et al., 2020 2
Serial Interval Nishura et al. 2020 4.6
Serial Interval Li et al., 2020 7.5
Incubation Period Li et al., 2020 5.2
Infectious Period Li et al., 2020 2.3
Infectious Period Zhou et al., 2020 6
Infectious Period Bi et al., 2020 1.5
Infectious Period Kucharski et al., 2020 2.9
Time to Hospitalization Huang et al., 2020 8
Mean Hospitalization Period Wang et al., 2020 12
Hospitalization Rate Ferguson et al., 2020 (weighted by us demo by Covid Act Now) 0.073

Contributing

Thoughts or contributions welcome!

License

License