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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.

covid19 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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> :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" State Choropleth](https://raw.githubusercontent.com/speediedan/covid19/master/docs/assets/state_choropleth.gif)

### "Real-Time" County-Level Dashboard
!["Real-Time" County-Level Dashboard](https://raw.githubusercontent.com/speediedan/covid19/master/docs/assets/rt_explorer.gif)

### "Real-Time" National Choropleth
!["Real-Time" National Choropleth](https://raw.githubusercontent.com/speediedan/covid19/master/docs/assets/national_choropleth.gif)

### Latest County-Level Grid Plots

* #### Daily Estimated Effective Reproduction Number (Rt) (counties w/ highest total onset cases)
![Daily Estimated Effective Reproduction Number (Rt) (counties w/ highest total onset cases)](https://raw.githubusercontent.com/speediedan/covid19/master/docs/assets/Rt%20(Top%20Total%20Estimated%20Cases).jpg)
* #### 2nd order case growth (disjoint 4-day windows)
![County-level hotspots, 2nd order case growth (disjoint 4-day windows)](https://raw.githubusercontent.com/speediedan/covid19/master/docs/assets/2nd%20Order%20Growth.jpg)
* #### County-level hotspots: cumulative case growth (4-day MA)
![County-level hotspots, cumulative case growth (4-day SMA)](https://raw.githubusercontent.com/speediedan/covid19/master/docs/assets/Cumulative%20Case%20Growth%20(4-Day%20MA).jpg)
* #### County-level hotspots: Estimated Onset Cases
![County-level hotspots, Estimated Onset Cases](https://raw.githubusercontent.com/speediedan/covid19/master/docs/assets/Estimated%20Onset%20Cases.jpg)

### 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:

## Contributing

>  Thoughts or contributions welcome!

## License
[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://badges.mit-license.org)
- **[MIT license](http://opensource.org/licenses/mit-license.php)**

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