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COVID-19 Regional Skipped Fits of Infected People

excluded from fitting by insufficient data

Jens Röder

2 minutes read

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For a fit we need a sufficient amount of good data points, otherwise the fit will not stabilize. If the number is below 13, the min13-filter will skip those data sets. Quite often we see some few cases and then a long period no new infections. We might meet the min 13 criteria, but the data are by shape of the curve insufficient to be fitted. Here the min7step-filter skips those who do not have 7 subsequent data steps. And also those are skipped, which`s sum of all infected numbers is less then 2.05 the last number, named max-sum-filter. Finally, there is a manual exclusion list for those data sets, that wont stabilize after manual intervention. During the automatic fitting most difficult data set are excluded and will be included, once sufficient more data are available.


Acualisation date: Thu Jul 9 04:23:34 UTC 2020

location ID, reason why fit was skipped,
Anguilla, United Kingdom 266 min13-filter
Bonaire - Sint Eustatius and Saba, Netherlands 273 min13-filter
British Virgin Islands, United Kingdom 267 min13-filter
Diamond Princess, Canada 249 min13-filter
Dominica 250 min7step-filter
Falkland Islands (Malvinas), United Kingdom 275 min7step-filter
Grand Princess, Canada 55 min7step-filter
Greenland, Denmark 111 min7step-filter
Holy See 145 min13-filter
Montserrat, United Kingdom 240 min13-filter
MS Zaandam 269 min13-filter
Nicaragua 189 manually skipped
Northwest Territories, Canada 262 min13-filter
Papua New Guinea 197 min13-filter
Saint Barthelemy, France 131 min13-filter
Saint Kitts and Nevis 261 min7step-filter
Saint Lucia 207 no-pre-fit-values-filter
Saint Pierre and Miquelon, France 276 min13-filter
Saint Vincent and the Grenadines 208 manually skipped
Seychelles 213 min7step-filter
Tibet, China 96 min13-filter
Turks and Caicos Islands, United Kingdom 268 manually skipped
Western Sahara 278 min13-filter
Yukon, Canada 263 min13-filter

For countries in this list, data are insufficiant for a good fit.

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