Kalman filter based short term prediction model for COVID-19 spread

Abstract

Corona Virus Disease 2019 (COVID19) has emerged as a global medical emergency in the contemporary time. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all data is integrated and passed into different Machine Learning Models in order to check its appropriateness. Ensemble Learning Technique, Random Forest, gives a good evaluation score on the tested data. Through this technique, various important factors are recognized and their contribution to the spread is analyzed. Also, linear relationships between various features are plotted through the heat map of Pearson Correlation matrix. Finally, Kalman Filter is used to estimate future spread of SARS-Cov-2, which shows good results on the tested data. The inferences from the Random Forest feature importance and Pearson Correlation gives many similarities and few dissimilarities, and these techniques successfully identify the different contributing factors. The Kalman Filter gives a satisfying result for short term estimation, but not so good performance for long term forecasting. Overall, the analysis, plots, inferences and forecast are satisfying and can help a lot in fighting the spread of the virus.

A New Random Forest Algorithm Based on Learning Automata

“…Identifying the key risk factors associated with acute rejection in organ transplantation is the main propose of [ 40 ]. In Singh et al [ 41 ], RF has been used as one of the classifiers to classify the covid-19 spread. Na et al [ 42 ] propose an automatic walking mode change of the above-knee prosthesis.…”

“…At each instant n, the action probability vector pi(n) is updated by the linear learning algorithm given in equation ( 13) if the chosen action ai(k) is rewarded by the environment, and it is updated according to equation ( 14) if the chosen action is penalized [104]. [11], [12], [13] Global problem [26], [27], [28] Healthcare [32], [33], [34], [35], [36], [41], [98], [37], [39], [40], [42], [43], [45], [46], [47], [48], [49], [50], [51], Industrial [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62] Network [63], [67], [68], [69], [99], [100] Physics [71], [72] Text processing …”

Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification

“…The model created a relationship between the countries and predicted the spread of the virus, behavior, and geographic region cases. Koushlendra Kumar Singh et al in [50] have reported a Kalman filter-based shortterm prediction model for forecasting COVID-19 using the popular machine learning techniques such as Random Forest and Pearson Correlation. However, the proposed approach is not used to predict the geographic region cases; instead, it is used to classify CXR images.…”

SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

“…It estimates the number of cases in worst and best-case scenarios. Mathematical models are also helpful in understanding the scenario for spreading the disease[52-54].…”