Modelling for prediction of the spread and severity of COVID-19 and its association with socioeconomic factors and virus types
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Abstract
We report the development of a Weibull based Long-Short-Term-Memory approach (W-LSTM) for the prediction of COVID-19 disease. The W-LSTM model developed in this study, performs better in terms of MSE, R2 and MAPE, as compared to the previously published models, including ARIMA, LSTM and their variations. Using W-LSTM model, we have predicted the beginning and end of the current cycle of COVID-19 in several countries. Performance of the model was validated as satisfactory in 82% of the 50 test countries, while asking for prediction for 10 days beyond the period of training. Accuracy of the above prediction with days beyond training was assessed in comparison with the MAPE that the model gave with cumulative global data. The model was applied to study correlation between the growth of infection and deaths, and a number of effectors that may influence the epidemic. The model identified age groups, trade with China, air traffic, country temperature and CoV-2 virus types as the likely effectors of infection and virulence leading to deaths. The predictors likely to promote or suppress the epidemic were identified. Some of the predictors had significant effect on the shape parameters of Weibull distribution. The model has been deployed on cloud, taking inputs in real time to handle large data country wise, at low costs and make predictions dynamically. Such predictions are highly valuable in guiding policy makers, administration and health. Interactive prediction curves generated from the W-LSTM model deployed on cloud platform can be seen at http://collaboration.coraltele.com/covid2/ (updated daily).
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SciScore for 10.1101/2020.06.18.20134874: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Identifying infected cases is central to such containment, but non availability and non-affordability of quality diagnostic kits is a serious limitation. Whole population screening is a utopian thought and the savaging character of CoV-2 to continue to be incubated in a large proportion of population without causing any symptoms, weakens all management strategies. Management of COVID-19 requires robust prediction of the size, duration and …
SciScore for 10.1101/2020.06.18.20134874: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Identifying infected cases is central to such containment, but non availability and non-affordability of quality diagnostic kits is a serious limitation. Whole population screening is a utopian thought and the savaging character of CoV-2 to continue to be incubated in a large proportion of population without causing any symptoms, weakens all management strategies. Management of COVID-19 requires robust prediction of the size, duration and dimensions of the ensuing epidemic. As discussed in the background, machine learning methods, backed by good statistical modelling provide a good opportunity to develop on line dynamic systems to prepare the society for managing such epidemic. Because the time series models are based on original numbers from a country, these models may be better predictions of the trend, rather than mathematical models based on epidemiological parameters whose values are often variable, do not account for a variety of socioeconomic, managerial and environmental interventions and involve certain assumptions. The hybrid model developed by us utilizes Weibull distribution as the best fit to the disease data on population and integrates the advantage of self-learning to smooth outliers and train itself in response to shifting trends and fluctuations in data. We have given error based analysis to establish that W-LSTM is superior to other Gaussian and Bayesian distributions used by others. Our model is also superior to other time series applications, including th...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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