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Register a new userAn Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM
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New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. By understanding the development trend of a regional epidemic, the epidemic can be controlled using the development policy. The common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compared the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. And this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data; 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.
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A novel coronavirus originated from Wuhan, China in late December 2019 has now affected almost all countries worldwide. Pakistan reported its first case in late February. The country went to lockdown after three weeks since the first case, when the total number of cases were over 880. Pakistan imposed a lockdown for more than a month which slowed the spread of COVID 19 effectively, however in late April relaxation in lockdown was allowed by the government in stages to lift the strain on the economy. In this study, the data has been analyzed from daily situation reports by the National Institute of Health Pakistan and the effects of initial strict lockdown and later smart lockdown have been studied. Our analysis showed a 13.14 Percentage increase in cases before lockdown which drops down to 6.55 percent during the lockdown. It proved the effectiveness of lockdown. However, the Percentage Increase in case grows up to 7.24 during a smart lockdown. If it continues to rise in this manner, Pakistan may need to enter again into a strict second lockdown.
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