Modelling New Cases of Covid-19 in the Philippines using Polynomial and MLP Regression

Covid-19 has been a serious issue in the Philippines for the past two years. Its spread has taken a toll on the country’s economy and society. Furthermore, the populous has been suffering throughout the pandemic as new cases and deaths are increasing. These massive problems warrant research on modelling and predicting this pandemic. Although there are numerous research with regards to using statistical modelling, Machine learning, deep learning, and artificial intelligence to model and understand the pandemic throughout the world, few pieces of researches focus on the Philippines alone. In addition to that, simple models are seen to fit the Covid-19 data more than complex ones. With these in mind, the authors fit and modelled Philippine new cases of Covid-19 using Sklearn Polynomial and MLP regressors. It was found out that Polynomial models fit the entire dataset from January 2020 to September 2021, but MLP model fits the recent September 2021 data better. Further research using different countries as case studies or different models is recommended.