COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics

Naomie Salim; Weng Howe Chan; Shuhaimi Mansor; Nor Erne Nazira Bazin; Safiya Amaran; Ahmad Athif Mohd Faudzi; Anazida Zainal; Sharin Hazlin Huspi; Eric Jiun Hooi Khoo; Shaekh Mohammad Shithil

However, curve fitting models mentioned above expects data from a small portion of the behavior to predict the peak. If variations occur in the data, such as dramatic shifts in test coverage, the forecast might not be accurate. To solve this problem, other methods such as machine learning can be used to analyze information from a multitude of sources and track over a hundred infectious diseases (Forbes, 2020). For instance, in December 2019, Blue Dot predicted the COVID19 outbreak using machine learning and sent out a warning to its customers to avoid Wuhan, ahead of both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). Blue Dot also predicted where other Asian city outbreaks could be by analyzing traveler itineraries and flight paths. However insufficient amount of available data when an epidemic just started is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. Fong et al. proposed a methodology based on data augmentation to the existing little data, panel selection to pick the best forecasting model from several models and fine-tuning the parameters of an individual forecasting model for the highest possible accuracy [21] . They constructed a polynomial neural network with corrective feedback model to forecast the COVID-19 outbreak with low prediction error, which is useful for predicting disease outbreak when the samples are small.