https://github.com/speediedan/covid19
A set of "real-time" covid19 county-level dashboards w/ national and state choropleths for monitoring localized infection resurgences as distancing, testing and tracing measures evolve.
https://github.com/speediedan/covid19
bayesian-methods bokeh choropleth coronavirus-real-time county-level covid-19 covid19 covid19-data dashboard differential-equations effective-reproductive-number health-policy infectious-disease-models jupyter jupyter-notebook r0 real-time rt sars-cov-2 seir
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A set of "real-time" covid19 county-level dashboards w/ national and state choropleths for monitoring localized infection resurgences as distancing, testing and tracing measures evolve.
- Host: GitHub
- URL: https://github.com/speediedan/covid19
- Owner: speediedan
- Created: 2020-03-27T02:46:25.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-31T04:01:08.000Z (over 2 years ago)
- Last Synced: 2023-03-05T06:23:38.201Z (about 2 years ago)
- Topics: bayesian-methods, bokeh, choropleth, coronavirus-real-time, county-level, covid-19, covid19, covid19-data, dashboard, differential-equations, effective-reproductive-number, health-policy, infectious-disease-models, jupyter, jupyter-notebook, r0, real-time, rt, sars-cov-2, seir
- Language: Python
- Homepage: https://speediedan.github.io/covid19/
- Size: 43 MB
- Stars: 10
- Watchers: 2
- Forks: 1
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
> :sunrise_over_mountains: **Please note**: Daily generation of these dashboards was sunset on 2022.03.30.
# "Real-Time" Covid19 [County-Level](https://speediedan.github.io/covid19/county_covid_explorer.html) & [Choropleth](https://speediedan.github.io/covid19/choropleth_covid_county_explorer.html) Dashboards
---
> ### [The "Real-Time" County-Level Dashboard](county_covid_explorer.html):
> * **A "real-time"[1](#daily-onset-estimation) county-level dashboard w/ a focus on estimated effective reproduction number (Rt)[2](#effective-reproduction-number-estimation), 2nd order growth rates and confirmed infection density for most US counties (counties w/ > 0.03% confirmed infection density and > 1000 cases)**
> ### [The "Real-Time" Choropleth Dashboard](choropleth_covid_county_explorer.html):
> * **State and national choropleths for exploring the geographic distribution of "real-time"[1](#daily-onset-estimation) county-level Rt[2](#effective-reproduction-number-estimation) 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.**
> ### [County-Level EDA Notebook](https://github.com/speediedan/covid19/blob/master/covid19_county_level_EDA.ipynb)
> * **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)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002185) is used while convolving the latest [onset-confirmed latency distribution](https://github.com/beoutbreakprepared/nCoV2019/tree/master/latest_data) 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](https://github.com/k-sys/covid-19/blob/master/Realtime%20R0.ipynb) 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](https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html). 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](http://freerangestats.info/blog/2020/05/09/covid-population-incidence) 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" County-Level Dashboard
### "Real-Time" National Choropleth
### Latest County-Level Grid Plots
* #### Daily Estimated Effective Reproduction Number (Rt) (counties w/ highest total onset cases)
.jpg)
* #### 2nd order case growth (disjoint 4-day windows)
* #### County-level hotspots: cumulative case growth (4-day MA)
.jpg)
* #### 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](https://www.ijidonline.com/article/S1201-9712(20)30117-X/fulltext) | 3 |
| Latent Period | [Wu et al., 2020](https://www.sciencedirect.com/science/article/pii/S0140673620302609) | 3 |
| Latent Period | [Li et al., 2020](https://www.medrxiv.org/content/10.1101/2020.03.06.20031880v1.full.pdf) | 2 |
| Serial Interval | [Nishura et al. 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/pdf) | 4.6 |
| Serial Interval | [Li et al., 2020](https://www.nejm.org/doi/pdf/10.1056/NEJMoa2001316?articleTools=true) | 7.5 |
| Incubation Period | [Li et al., 2020](https://www.nejm.org/doi/pdf/10.1056/NEJMoa2001316?articleTools=true) | 5.2 |
| Infectious Period | [Li et al., 2020](https://www.nejm.org/doi/pdf/10.1056/NEJMoa2001316?articleTools=true) | 2.3 |
| Infectious Period | [Zhou et al., 2020](https://www.medrxiv.org/content/10.1101/2020.02.24.20026773v1.full.pdf) | 6 |
| Infectious Period | [Bi et al., 2020](https://www.medrxiv.org/content/10.1101/2020.03.03.20028423v3) | 1.5
| Infectious Period | [Kucharski et al., 2020](https://cmmid.github.io/topics/covid19/current-patterns-transmission/wuhan-early-dynamics.html) | 2.9
| Time to Hospitalization | [Huang et al., 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext) | 8
| Mean Hospitalization Period | [Wang et al., 2020](https://jamanetwork.com/journals/jama/fullarticle/2761044?guestAccessKey=f61bd430-07d8-4b86-a749-bec05bfffb65) | 12
| Hospitalization Rate | [Ferguson et al., 2020](https://spiral.imperial.ac.uk/bitstream/10044/1/77482/5/Imperial%20College%20COVID19%20NPI%20modelling%2016-03-2020.pdf) (weighted by us demo by [Covid Act Now](https://covidactnow.org/model)) | 0.073
## Contributing
> Thoughts or contributions welcome!
## License
[](http://badges.mit-license.org)
- **[MIT license](http://opensource.org/licenses/mit-license.php)**