# DXY-COVID-19

2019新型冠状病毒疫情实时爬虫及API | COVID-19/2019-nCoV Realtime Infection Crawler and API

Repository: https://github.com/BlankerL/DXY-COVID-19-Crawler
Canonical: https://ross.abutalabs.com/products/dxy-covid-19
Homepage: https://lab.isaaclin.cn/nCoV/
Language: Python
License: MIT
License Family: permissive
Topics: 2019-ncov, crawler, realtime-api
Archived: true
Last push: 2025-06-01T15:47:46+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 24, release rhythm 35, longevity 100
- inputs: {"age_days": 2416, "days_push": 458, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1971, forks 391 (observed 2026-08-28T04:06:00.860943+00:00)

## What it is
A time-series data warehouse of COVID-19 (2019-nCoV) infection statistics for China, scraped from Dingxiangyuan (DXY) and published as CSV/JSON files via GitHub Releases. It includes a companion Python crawler and a public API, and is now in archived mode since upstream data sources stopped updating.

## Use cases
- download covid-19 time series data for china
- get historical covid case counts by province and city
- csv covid data for statistical analysis in R or SPSS
- scrape dxy pneumonia data with python
- epidemiological research dataset for 2019-ncov
- visualize covid-19 infection trends over time

## When to choose
- you need historical China COVID-19 statistics in ready-to-use CSV/JSON format
- you want data collected from Dingxiangyuan with daily snapshots preserved in releases
- you are doing retrospective research or teaching with pandemic time-series data

## When to avoid
- you need current, real-time COVID-19 data - the project is archived and no longer collects new data
- you need cleaned or validated data - known anomalies and duplicates are left in place and require manual cleaning
- you need global (non-China) COVID-19 statistics

## Facets
- artifact type: dataset
- maturity: maintenance
- function: data-science, etl, web-scraping, csv, analytics
- domain: data-science, healthcare, analytics, time-series, crawlers
- platform: python, cli, cross-platform
- tags: covid-19, epidemiology, time-series-data, china, dxy, public-health, archived-data, open-data

## Member repositories
- BlankerL/DXY-COVID-19-Crawler (main) score 10
- BlankerL/DXY-COVID-19-Data (infra) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:00.860943+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:49:00.974085+00:00, confidence not recorded.
  - readme: https://github.com/BlankerL/DXY-COVID-19-Crawler (fetched 2026-08-28T04:06:00.860943+00:00, sha fb14fadba219)
- Data as of 2026-08-30T08:39:29.467469+00:00.