Logistic curve fitting in epidemiology: Difference between revisions
Logistic curve fitting in epidemiology (view source)
Revision as of 16:41, 12 May 2022
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{{out}}
<pre>r = 0.11230215850810021, R0 = 3.8482784871191575</pre>
=={{header|Vlang}}==
{{trans|wren}}
<lang vlang>import math
const (
k = 7800000000 // approx world population
n0 = 27 // number of cases at day 0
y = [
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
]
)
fn f(r f64) f64 {
mut sq := 0.0
for i in 0..y.len {
eri := math.exp(r * i)
dst := (n0 * eri) / (1 + n0 * (eri - 1) / k) -y[i]
sq = sq + dst * dst
}
return sq
}
fn solve(fu fn(f64)f64, g f64, epsilon int) f64 {
mut guess := g
mut f0 := fu(guess)
mut delta := guess
mut factor := 2.0
for delta > epsilon {
mut nf := fu(guess - delta)
if nf < f0 {
f0 = nf
guess -= delta
} else {
nf = fu(guess + delta)
if nf < f0 {
f0 = nf
guess += delta
} else {
factor = 0.5
}
}
delta *= factor
}
return guess
}
fn main() {
r := math.round(solve(f, 0.5, 0) * 1e10) / 1e10
r0 := math.round(math.exp(12 * r) * 1e8) / 1e8
println("r = $r, R0 = $r0")
}</lang>
{{out}}
<pre>r = 0.1123021757, R0 = 3.84827928</pre>
=={{header|Wren}}==
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