Warning: This dashboard was not built by an epidemiologist.

Note: Click a country name to open a search results page for that country’s COVID-19 news.

Based on data up to: 2022-11-10. Compared to (10 days before): 2022-10-31

Transmission rate:

Note: "transmission rate" here is a measure of speed of spread of infection, and means how much of the susceptible population each infected person is infecting per day (if everyone is susceptible). E.g. 10% means that 100 infected patients will infect 10 new people per day. Related to R0. See Methodology for details of calculation.

⭕ Bad news: new waves 🇧🇷 🇲🇽 🇲🇳 🇲🇺 🇰🇲 🇨🇩 🇮🇸 🇳🇴 🇰🇼 🇮🇷 🇨🇿 🇫🇷 🇮🇪 🇩🇴 🇲🇦 🇮🇳 🇷🇴 🇵🇹 🇯🇵

Large increase in transmission rate vs. 10 days ago, that might mean a relapse, new wave, worsening outbreak.

  • Countries are sorted by size of change in transmission rate.
  • Includes only countries that were previously active (more than 100 estimated new cases).
  • "Large increase" = at least +2% change.
  Current:
Estimated
daily
transmission
rate		 10 days ago:
Estimated
daily
transmission
rate		 Estimated
recent cases
in last 5 days		 Estimated
current
ICU need
per 100k
population		 Estimated
total
affected
population
percentage
🇧🇷 Brazil 702.9% 388.3% 99,422 0.22 99.1%
🇲🇽 Mexico 139.7% 89.3% 8,424 0.03 96.3%
🇲🇳 Mongolia 16.8% 8.1% 1,016 0.06 50.8%
🇲🇺 Mauritius 15.4% 7.4% 2,078 0.67 34.4%
🇰🇲 Comoros 10.3% 2.5% 1,846 0.25 16.1%
🇨🇩 Congo (Kinshasa) 8.6% 1.4% 165 0.00 0.8%
🇮🇸 Iceland 18.2% 11.5% 232 0.48 61.0%
🇳🇴 Norway 9.1% 2.4% 682 0.32 29.1%
🇰🇼 Kuwait 9.2% 3.2% 286 0.01 42.1%
🇮🇷 Iran 16.0% 10.4% 4,182 0.03 78.2%
🇨🇿 Czechia 5.8% 0.7% 2,339 0.60 61.1%
🇫🇷 France 13.6% 8.7% 113,752 3.93 73.6%
🇮🇪 Ireland 9.0% 5.0% 882 0.31 46.1%
🇩🇴 Dominican Republic 3.9% 0.5% 129 0.01 16.2%
🇲🇦 Morocco 8.7% 5.4% 352 0.00 15.9%
🇮🇳 India 7.4% 4.2% 16,732 0.01 19.0%
🇷🇴 Romania 8.4% 5.5% 1,803 0.19 55.5%
🇵🇹 Portugal 9.9% 7.5% 2,906 0.73 62.2%
🇯🇵 Japan 7.5% 5.3% 279,010 4.54 18.6%

Tip: Click country name in legend to switch countries. Uze mouse wheel to zoom Y axis. 
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  for col_name, dtype in df.dtypes.iteritems():