Logistic curve fitting in epidemiology: Difference between revisions
Logistic curve fitting in epidemiology (view source)
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{{task|
The least-squares method (see references below) in statistics is used to fit data
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{{out}}
<pre>r = 0.1123022, R0 = 3.8482793</pre>
=={{header|Arturo}}==
<syntaxhighlight lang="arturo">K: 7.8e9
N0: 27
Actual: [
27.0, 27.0, 27.0, 44.0, 44.0, 59.0, 59.0,
59.0, 59.0, 59.0, 59.0, 59.0, 59.0, 60.0,
60.0, 61.0, 61.0, 66.0, 83.0, 219.0, 239.0,
392.0, 534.0, 631.0, 897.0, 1350.0, 2023.0, 2820.0,
4587.0, 6067.0, 7823.0, 9826.0, 11946.0, 14554.0, 17372.0,
20615.0, 24522.0, 28273.0, 31491.0, 34933.0, 37552.0, 40540.0,
43105.0, 45177.0, 60328.0, 64543.0, 67103.0, 69265.0, 71332.0,
73327.0, 75191.0, 75723.0, 76719.0, 77804.0, 78812.0, 79339.0,
80132.0, 80995.0, 82101.0, 83365.0, 85203.0, 87024.0, 89068.0,
90664.0, 93077.0, 95316.0, 98172.0, 102133.0, 105824.0, 109695.0,
114232.0, 118610.0, 125497.0, 133852.0, 143227.0, 151367.0, 167418.0,
180096.0, 194836.0, 213150.0, 242364.0, 271106.0, 305117.0, 338133.0,
377918.0, 416845.0, 468049.0, 527767.0, 591704.0, 656866.0, 715353.0,
777796.0, 851308.0, 928436.0, 1000249.0, 1082054.0, 1174652.0
]
f: function [r][
result: 0
loop 0..dec size Actual 'i [
eri: exp r * to :floating i
guess: (N0 * eri) // (1 + N0 * (eri - 1) // K)
diff: guess - Actual\[i]
result: result + diff * diff
]
return result
]
solve: function [fn][
guess: 0.5
eps: 0.0
result: guess
delta: guess
f0: call fn @[result]
factor: 2.0
while [and? -> delta > eps
-> result <> result - delta][
nf: call fn @[result-delta]
if? nf < f0 [
f0: nf
result: result - delta
]
else [
nf: call fn @[result+delta]
if? nf < f0 [
f0: nf
result: result + delta
]
else [
factor: 0.5
]
]
delta: delta * factor
]
return result
]
r: solve 'f
r0: exp 12 * r
print ["r =" r]
print ["R0 =" r0]</syntaxhighlight>
{{out}}
<pre>r = 0.11230217569755
R0 = 3.848279280916719</pre>
=={{header|C}}==
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<pre>r = 0.11230215850810021, R0 = 3.8482784871191575</pre>
=={{header|V (Vlang)}}==
{{trans|wren}}
<syntaxhighlight lang="v (vlang)">import math
const (
k = 7800000000 // approx world population
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=={{header|Wren}}==
{{trans|Phix}}
<syntaxhighlight lang="
var n0 = 27 // number of cases at day 0
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<pre>
r = 0.1123021757, R0 = 3.84827928
</pre>
=={{header|XPL0}}==
{{trans|C}}
<syntaxhighlight lang "XPL0">def K = 7.8e9;
def N0 = 27.;
real Actual;
int ActualSize;
func real Func(R);
real R;
real Sq, Eri, Guess, Diff;
int I;
[Sq:= 0.;
for I:= 0 to ActualSize-1 do
[Eri:= Exp(R * float(I));
Guess:= N0 * Eri / (1. + N0 * (Eri-1.) / K);
Diff:= Guess - Actual(I);
Sq:= Sq + Diff*Diff;
];
return Sq;
];
func real Solve(Guess, Epsilon);
real Guess, Epsilon;
real Delta, F0, Factor, NF;
[Delta:= if Guess # 0. then Guess else 1.;
F0:= Func(Guess);
Factor:= 2.;
while Delta > Epsilon and Guess # Guess-Delta do
[NF:= Func(Guess - Delta);
if NF < F0 then
[F0:= NF;
Guess:= Guess - Delta;
]
else
[NF:= Func(Guess + Delta);
if NF < F0 then
[F0:= NF;
Guess:= Guess + Delta;
]
else Factor:= 0.5;
];
Delta:= Delta * Factor;
];
return Guess;
];
real R, R0;
[Actual:= [
27., 27., 27., 44., 44., 59., 59., 59., 59., 59., 59., 59., 59., 60., 60.,
61., 61., 66., 83., 219., 239., 392., 534., 631., 897., 1350., 2023., 2820.,
4587., 6067., 7823., 9826., 11946., 14554., 17372., 20615., 24522., 28273.,
31491., 34933., 37552., 40540., 43105., 45177., 60328., 64543., 67103.,
69265., 71332., 73327., 75191., 75723., 76719., 77804., 78812., 79339.,
80132., 80995., 82101., 83365., 85203., 87024., 89068., 90664., 93077.,
95316., 98172., 102133., 105824., 109695., 114232., 118610., 125497.,
133852., 143227., 151367., 167418., 180096., 194836., 213150., 242364.,
271106., 305117., 338133., 377918., 416845., 468049., 527767., 591704.,
656866., 715353., 777796., 851308., 928436., 1000249., 1082054., 1174652.];
ActualSize:= 97;
R:= Solve(0.5, 0.0);
R0:= Exp(12.*R);
Text(0, "R = "); RlOut(0, R); CrLf(0);
Text(0, "R0 = "); RlOut(0, R0); CrLf(0);
]</syntaxhighlight>
{{out}}
<pre>
R = 0.11230
R0 = 3.84828
</pre>
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