Hi Aloke,
I understand that you want to fit a 5-ODE model to real data and then forecast infection trends. Here’s how you can achieve this:
- First, load your Excel data into MATLAB using “readtable”.
- Then, define your 5-ODE system in a MATLAB function (e.g., “odeModel”) that takes parameters and calculates how the infected group changes over time.
- Next, use “lsqcurvefit” to fit your model to the confirmed cases data. Start with an initial guess for your parameters (“initialParams”) and let “lsqcurvefit” optimize them based on the real data (e.g., “totalConfirmed”). For your time data, create a vector “tData” representing the days of your observations. Here’s a small snippet to guide you:
fittedParams = lsqcurvefit(@yourObjectiveFunction, initialParams, tData, totalConfirmed);
- Once you have the fitted parameters (“fittedParams”), simulate your model using ODE solver, like “ode45”. Define your time span for the simulation with “tSpan”, which can be a range of future days you want to forecast. Here’s how you can do it:
[tSol, ySol] = ode45(@(t, y) odeModel(t, y, fittedParams), tSpan, initialConditions);
- At the end, analyse the simulation results. Use “max(ySol(:, 3))” to find the peak of infected cases and check when infections drop below a certain threshold (for example, by comparing “ySol(:, 3)” to a small value) to determine when the infection ends.
Using this approach will help you identify the peak infection time and the end of the epidemic.
To know more about “lsqcurvefit” and ODE solvers, you can refer to the following documentation links:
- lsqcurvefit: https://www.mathworks.com/help/optim/ug/lsqcurvefit.html
- ode45: https://www.mathworks.com/help/matlab/ref/ode45.html
Hope this helps.
