I have been doing a COVID-19 related project. Here is the question:
- N = vector of daily new infected cases
- D = vector of daily deaths
- E[D] = estimation of daily deaths
N is a n-dimensional vector, n is around 60. E[D] is another n-dimensional vector. Under certain assumptions, each entry of E[D] can be calculated as a linear combination of the entries of N.
We want to find the vector N such that the E[D] derived from N has least mean squared error when compared to actual D data. I think a gradient descent algorithm is needed here. However, I am not very familiar with gradient descent.
This seems to be a basic data science problem, but I am kind of lost right now. Does anyone has any idea about which algorithm should I dig into?