4P Model for Dynamic Prediction of the Covid-19: A Statistical and Machine Learning Approach

Abstract

Introduction: Around the world, scientists are racing hard to understand how Covid-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications come to pass. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only.Methods: Hence, in this study, a 4P model has been proposed based on four probabilities (4P) which has been found to be true for all affected countries taking Bangladesh as a case. Efficiency scores have been estimated from survey analysis not only for governing authorities on managing the situation (P(G)) but also for the compliance of the citizens ((P(P)). Since the immune responses of all the people are not uniform to a specific pathogen, the probability of a person getting infected ((P(I)) after being exposed has also been estimated. And the vital one is the probability of Test Positivity ((P(T)) which is a strong indicator of how effectively the infected people are diagnosed and isolated from the rest of the group that affects the rate of growth.Results and Conclusion: All the four parameters have been fitted in a non-linear exponential model that partly updates itself periodically with everyday facts. Along with the model, all the four probabilistic parameters are engaged to train a recurrent neural network using Long-Short Term Memory neural network and the followed trial confirmed a ruling functionality of the 4Ps.

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

“…2021 Saudi Arabia Johns Hopkins University Center for Systems Science and Engineering dataset Daily confirmed cases 22 January to 24 July 2020 LSTM 1768.35 - - - [ 43 ] Qatar 735.21 Oman 730.53 Kuwait 456.90 UAE 446.44 Bahrain 320.79 Ghazaly, N., M., et al., 2020 Egypt, Saudi Arabia, Jordan, USA, Spain, Italy, France, Iran, Russia WHO situation reports Daily cases and deaths from 21 January to 2 April, 2020 NAR - - - 2.6521 [ 44 ] Guo, Q. and He, Z., 2021 Global Official WHO data Daily confirmed cases 21 January to 11 November 2020 ANN 3102.9 2090.6 0.9683 - [ 45 ] Hasan, K. T., et al. 2021 Bangladesh Official WHO data and the Institute of Epidemiology, Disease Control and Research of Bangladesh Daily confirmed cases till 3 August 2020 + Government control and people's compliance data + Information of how many people will be in contact with an infected person outside of their home when they move out LSTM 10,368.318 - 1 5.96 [ 46 ] Hazarika, B. B., et al., 2020 Brazil Our World in Data Website Cumulative number of confirmed cases from 11 April to 10 July, 2020 WCRVFL (Using sigmoid or ReLu activation functions) 0.00323 - 0.99975 - [ 47 ] India 0.00147 0.99996 Peru 0.00197 0.99986 Russia 0.0002...…”