Seeing as I am still stuck in Quarantine:
I created a Matlab model of Covid-19 growth that engineers can use to help determine the growth pattern that we can expect.

Note: I used the SEIR model for differential equations, in addition I used Eulers method as I can effectively make day to day changes in how the interventions and treatment plans can change the actual treatment rate vs the model.



%% Matlab Code
%Differential equation function
function [dQ, dE, dI, dS, dC, dR, died] = COVID_Euler(Q, E, I, S, C, Pop, Ro, Natural_Immune)

% Q = Susceptible
% E = Incubating disease
% I = Infectious
% S = Symptomatic
% C = Critical
% R = Recovery

%Parameters all in days
Incubation_time = 1;
Infectious_time = 3;
Population_Increase = (1001734/365.0);
Symptom_Time = 5;
Crit_Time = 10;
prob_Crit = 0.04; %Probability someone with symptoms becomes cr
prob_Death = 0.25;


r = Ro * (Q/(Pop * (1-Natural_Immune)))
New_Incubate = I*r / Infectious_time;
New_Infectious = E/Incubation_time;
New_Symptomatic = I/Infectious_time;
S_out = S/Symptom_Time;
Cout = C/Crit_Time;
New_Crit = S_out * prob_Crit;
died = Cout * prob_Death;
New_Recovery = (S_out + Cout) - (New_Crit + died);


dQ = Population_Increase - New_Incubate;
dE = New_Incubate - New_Infectious;
dI = New_Infectious - New_Symptomatic;
dS = New_Symptomatic - S_out;
dC = New_Crit - Cout;
dR = New_Recovery;



%============================================================
% Main file using assumption of no policy or government intervention
%============================================================
clear all; clc;

%% Parameters
R_0 = 2.5; % A single infected person will infect about Ro others in a novel population
N = 327.23e6; % Population of USA (2020)
Natural_Immunity = 0.35; %Natural immuninity in the general population, This is to include rural areas with reduced spread and infection.
total_Time = 200;
Pre_Recovered = 10000000; %Assume 10 million people not tested already recovered


Q = zeros(1,total_Time); % Q = Susceptible
E = zeros(1,total_Time); % E = Incubating disease
I = zeros(1,total_Time); % I = Infectious
S = zeros(1,total_Time); % S = Symptomatic
C = zeros(1,total_Time); % C = Critical
R = zeros(1,total_Time); % R = Recovery
t = 1:total_Time;
Dead = zeros(1,total_Time);

%Stats on March 27 2020

Q(1) = N*(1 - Natural_Immunity);
E(1) = 500000;
I(1) = 500000;
S(1) = 20000;
C(1) = 2463;
R(1) = 2522 + Pre_Recovered;
Dead(1) = 1696;


%% Differential equations

for i = 1:(total_Time-1)
[dQ,dE,dI,dS,dC,dR,Dying] = COVID_Euler(Q(i), E(i), I(i), S(i), C(i), N, R_0, Natural_Immunity);

k = (i+1);
Q(k) = Q(i) + dQ;
E(k) = E(i) + dE;
I(k) = I(i) + dI;
S(k) = S(i) + dS;
C(k) = C(i) + dC;
R(k) = R(i) + dR;
Dead(k) = Dying;

end

Height = (1.2 * max(I));
HeightCrit = (1.2* max(C));
Hospitalized = 0.15*S;
Total_Dead = sum(Dead)



%% plot
figure(1)
plot(t,E, 'c', t,I, 'g', t,Hospitalized,'y', t,C,'r')
legend('Pre-Infectious','Infectious','Hospitalized', 'Critical', 'Location', 'Best')
axis([0 total_Time 0 Height]);
xlabel('Days after March 26, 2020')
ylabel('Population')
title('USA Predicted Spread of COVID-19');
grid on;
grid minor;
set(gca, 'FontSize', 26)


figure(2)
plot(t,C,'r', t,Dead,'k')
legend('Critical', 'Death', 'Location', 'Best')
axis([0 total_Time 0 HeightCrit/10]);
xlabel('Days after March 26, 2020')
ylabel('Population')
title('USA Predicted Spread of COVID-19')
grid on;
grid minor;
set(gca, 'FontSize', 26)