Simulate population

simulate-china-us-pop.py

import numpy as np


# FROM 
# http://worldpopulationreview.com/countries/china-population/

pyramid0china = np.array(
    [16446861., 16821572., 17097250., 17284418., 17390933., 17423080.,
       17403550., 17341743., 17247061., 17129365., 16998517., 16861620.,
       16727156., 16605907., 16498945., 16404588., 16387661., 16480191.,
       16658788., 16890336., 17208221., 17504667., 17731393., 17957759.,
       18174382., 18230709., 18872166., 20419719., 22447682., 24344339.,
       26393956., 27309041., 26438650., 24441637., 22631937., 20751729.,
       19327966., 18757173., 18781901., 18672355., 18483518., 18750087.,
       19636105., 20905854., 22146113., 23423377., 24430011., 24965493.,
       25141072., 25319552., 25489605., 25252984., 24467257., 23312424.,
       22134346., 20891958., 19709826., 18694187., 17813256., 16857969.,
       15784894., 15117366., 15067733., 15376031., 15629092., 15980514.,
       15802845., 14764696., 13185556., 11711864., 10209011.,  8923817.,
        8042567.,  7433678.,  6768742.,  6100779.,  5519507.,  5032721.,
        4617640.,  4246309.,  3927467.,  3590802.,  3199414.,  2782278.,
        2406162.,  2058786.,  1738256.,  1450657.,  1193422.,   929524.,
         713716.,   579453.,   446889.,   313169.,   221644.,   177575.,
         142819.,   104928.,    63902.,    37710.,    67839.])

# There were 14.6m births in 2019
fr0china = 2*14.6e6/pyramid0china [20:40].mean()

# http://worldpopulationreview.com/countries/united-states-population/
pyramid0us = np.array([3931967., 3919500., 3919461., 3930158., 3903010., 3955644.,
       4008192., 4059364., 4107872., 4156677., 4208742., 4241520.,
       4245220., 4231306., 4220681., 4208740., 4210781., 4236404.,
       4278618., 4316059., 4347272., 4397310., 4474657., 4565701.,
       4651027., 4737732., 4788205., 4782769., 4739004., 4695388.,
       4645691., 4592419., 4541165., 4490237., 4433909., 4375200.,
       4315098., 4254149., 4194587., 4137614., 4082405., 4040406.,
       4017264., 4008404., 4003094., 4002870., 4009404., 4022256.,
       4040872., 4061465., 4080383., 4112964., 4165027., 4226569.,
       4281521., 4332795., 4362769., 4360922., 4333852., 4300884.,
       4260806., 4197638., 4106208., 3993650., 3871350., 3735929.,
       3602786., 3480392., 3361570., 3234769., 3107225., 2956039.,
       2770249., 2564001., 2361344., 2156197., 1973453., 1827535.,
       1707062., 1586129., 1469802., 1357365., 1245835., 1137042.,
       1035221.,  938832.,  849231.,  767290.,  691462.,  616131.,
        563171.,  502660.,  421119.,  320109.,  246668.,  212578.,
        178289.,  135554.,   84374.,   52727.,   89949.])
fr0us = 2*pyramid0us[0]/pyramid0us[20:40].mean()

def simul(pyramid0, fr, emigration = 0.0, change_fr = False, fr_target=1.2):
    totals = []
    pyramid = pyramid0.copy()
    
    for _ in range(131):
        totals.append(pyramid.sum())
        
    
        pyramid[1:] = pyramid[:-1].copy()
        pyramid[0] = pyramid[20:40].mean() * fr /2.
        pyramid[25] *= 1 + emigration
        # Simulate deaths
        pyramid *= .999 # 1/1000 overall
        pyramid[70:] *= .97
        pyramid[80:] *= .95
        pyramid[90:] *= .80
        if change_fr and fr > fr_target:
            fr *= .98
        if change_fr and fr < fr_target:
            fr *= 1.02
    return np.array(totals)/1e6


print('''
# CHINA

SCENARIO      2050   2100   2120   2150
------------  ----  -----  -----  -----
FR  = 1.4     {sc1[30]}    {sc1[80]}    {sc1[100]}    {sc1[130]}
FR -> 1.2     {sc2[30]}    {sc2[80]}    {sc2[100]}    {sc2[130]}
FR -> 1.0     {sc3[30]}    {sc3[80]}    {sc3[100]}    {sc3[130]}
FR -> 0.9     {sc4[30]}    {sc4[80]}    {sc4[100]}    {sc4[130]}

'''.format(
    sc1=simul(pyramid0china, fr0china).astype(int),
    sc2=simul(pyramid0china, fr0china, change_fr=True, fr_target=1.2).astype(int),
    sc3=simul(pyramid0china, fr0china, change_fr=True, fr_target=1.0).astype(int),
    sc4=simul(pyramid0china, fr0china, change_fr=True, fr_target=0.9).astype(int),
    ))


print('''
# US

SCENARIO      2050   2100   2120   2150
------------  ----  -----  -----  -----
FR  = 1.8     {sc1[30]}    {sc1[80]}    {sc1[100]}     {sc1[130]}
FR -> 1.9     {sc2[30]}    {sc2[80]}    {sc2[100]}     {sc2[130]}
FR -> 2.0     {sc3[30]}    {sc3[80]}    {sc3[100]}     {sc3[130]}
FR -> 2.2     {sc4[30]}    {sc4[80]}    {sc4[100]}     {sc4[130]}
'''.format(
    sc1=simul(pyramid0us, fr0us, emigration=.14).astype(int),
    sc2=simul(pyramid0us, fr0us, emigration=.14, change_fr=True, fr_target=1.9).astype(int),
    sc3=simul(pyramid0us, fr0us, emigration=.14, change_fr=True, fr_target=2.0).astype(int),
    sc4=simul(pyramid0us, fr0us, emigration=.14, change_fr=True, fr_target=2.2).astype(int),
    ))