Task: Logistic curve fitting in epidemiology


C++: #include <cmath>
#include <functional>
#include <iostream>

constexpr double K = 7.8e9;
constexpr int n0 = 27;
constexpr double 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
};

double f(double r) {
    double sq = 0;
    constexpr size_t len = std::size(actual);
    for (size_t i = 0; i < len; ++i) {
        double eri = std::exp(r * i);
        double guess = (n0 * eri)/(1 + n0 * (eri - 1)/K);
        double diff = guess - actual[i];
        sq += diff * diff;
    }
    return sq;
}

double solve(std::function<double(double)> fn, double guess=0.5, double epsilon=0) {
    for (double delta = guess ? guess : 1, f0 = fn(guess), factor = 2;
             delta > epsilon && guess != guess - delta;
             delta *= factor) {
        double nf = fn(guess - delta);
        if (nf < f0) {
            f0 = nf;
            guess -= delta;
        } else {
            nf = fn(guess + delta);
            if (nf < f0) {
                f0 = nf;
                guess += delta;
            } else
                factor = 0.5;
        }
    }
    return guess;
}

int main() {
    double r = solve(f);
    double R0 = std::exp(12 * r);
    std::cout << "r = " << r << ", R0 = " << R0 << '\n';
    return 0;
}



Go: package main

import (
    "fmt"
    "github.com/maorshutman/lm"
    "log"
    "math"
)

const (
    K  = 7_800_000_000 
    n0 = 27            
)

var y = []float64{
    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,
}

func f(dst, p []float64) {
    for i := 0; i < len(y); i++ {
        t := float64(i)
        dst[i] = (n0*math.Exp(p[0]*t))/(1+n0*(math.Exp(p[0]*t)-1)/K) - y[i]
    }
}

func main() {
    j := lm.NumJac{Func: f}
    prob := lm.LMProblem{
        Dim:        1,
        Size:       len(y),
        Func:       f,
        Jac:        j.Jac,
        InitParams: []float64{0.5},
        Tau:        1e-6,
        Eps1:       1e-8,
        Eps2:       1e-8,
    }
    res, err := lm.LM(prob, &lm.Settings{Iterations: 100, ObjectiveTol: 1e-16})
    if err != nil {
        log.Fatal(err)
    }
    r := res.X[0]
    fmt.Printf("The logistic curve r for the world data is %.8f\n", r)
    fmt.Printf("R0 is then approximately equal to %.7f\n", math.Exp(12*r))
}