Prediction of Active Cases of COVID-19 Based on small-scale-KNN-LSTM

Anomalies in vaccine data are often encountered when projecting the active cases in Africa. This issue could severely impact the accuracy of expected active case, thereby affecting future government intervention protection policies etc. In this paper, a new method to accurately predict active cases considering anomalies is presented. Firstly, an autoencoder is used to detect the vaccine anomalies, Then, in order to impute missing values in the complete African vaccination data a small-scale k nearest neighbor (ssKNN) with upper and lower bounds is developed. As an extension of K-nearest neighbor interpolation, ssKNN could guarantee the interpolation quality and efficiency by limiting the filling range. To be specific, the upper and lower bounds of missing values are found based on the proposed method, and then the missing values could be filled according to the upper and lower bounds as well as the Euclidean distance. Then, the transmission rate β is estimated using long short-term memory (LSTM), which is an essential parameter in a classical mechanistic model for projecting the number of future active cases. Finally, the effectiveness of proposed method is verified on dataset of 15 worst-affected countries by COVID-19 epidemic in Africa demonstrating its efficacy in prediction of active cases of COVID-19 with vaccine anomalies.