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##-------------------------------- | |
# general options | |
start: 1187000000 | |
stop: 1187001000 | |
instruments: H1L1 | |
min-instruments: 1 | |
##-------------------------------- |
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* Results | |
+ Original: VK assumption that vaccinated carry the virus as often as non-vaccinated outside the event. | |
+ Updated: vaccinated carry the virus 25% as often as non-vaccinated outside the event (like inside). | |
|----------+------------+--------------------+------------------+-------------------------| | |
| Model | Var. | Infections | Hospitalisations | Comments | | |
|----------+------------+--------------------+------------------+-------------------------| | |
| Original | No testing | 833 + 1110 = 1943 | 1 + 20 = 21 | Mine, no VK equivalent | | |
| Original | 3G | 531 + 708 = 1239 | 0 + 13 = 14 | Perfect match | | |
| Original | 2G | 807 + 0 = 807 | 1 + 0 = 1 | ~ match ('800') | | |
| Original | Asian VK | 175 + 155 = 330 | 0 + 4 = 4 | Numbers (~) from VK | |
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Reconstruction of model of infections at a large event in the Netherlands, quoted in the Volkskrant on Fri 2021-11-12. | |
https://www.volkskrant.nl/nieuws-achtergrond/waarom-de-coronapas-geen-redding-bracht-en-of-2g-wel-gaat-werken~bb63b508/ | |
Point of interest: the VK seems to implicitly assume that vaccinated people get infected 75% less often than | |
non-vaccinated people at the event, but not outside. See what happens when using 25% for both (assumption 2). | |
Assumptions: | |
- number of people at the event: 300000.0 | |
- if vaccinated, relative probability of infection at contact (NL): [1. 0.25] | |
- if vaccinated, relative probability of infection at contact (event): 0.25 |
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# Read file: | |
df = pd.read_csv(inFile, header=0, sep=r'\s*,\s*', engine='python') | |
# Make period_end a datetime object (with tz=UT) and convert the dates to CET: | |
cet = tz.timezone('Europe/Amsterdam') | |
df.date = pd.to_datetime(df.date, utc=True) # utc=True needed because midnight timestamps have no time and no tz! | |
df.date = df.date.dt.tz_convert(cet) # Works when already converted to UT by to_datetime! | |
# Make a plot of DNI and DHI: |
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# MvdS: use a loop: | |
year = [-103,-102,-101,-100, -99, -98, -97, -96, -94, -93, -92] | |
month = [ 5, 6, 7, 8, 9,10,11,12, 1, 2, 3] | |
day = [ 2, 7,13,18,24,28,27,27,26,26,30] | |
Mod = np.zeros(len(year)) # Create array with zeros to collect modern positions | |
for iDat in range(len(year)): | |
JD = julianDay(year[iDat],month[iDat],day[iDat]) | |
Lpl,Bpl,Rpl = computeLBR(JUP,JD) | |
Lea,Bea,Rea = computeLBR(EAR,JD) | |
l,b,r = hc2gc(Lpl,Bpl,Rpl,Lea,Bea,Rea) |
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# Define Qt class: match files *.cpp, except moc_*.cpp, *.h except ui_*.h, and *.ui: | |
--type-set=qt:match:/^(?!moc_).*\.cpp$/ | |
--type-add=qt:match:/^(?!ui_).*\.h$/ | |
--type-add=qt:match:/.*\.ui$/ |