MarcvdSluys

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# general options

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stop:  1187001000

instruments: H1L1
min-instruments: 1

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MarcvdSluys

* 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     |

MarcvdSluys

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

MarcvdSluys

# 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:

MarcvdSluys

# 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)

MarcvdSluys

# 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$/