Using Machine Learning along with Data Science algorithms to pre-process and forecast COVID-19 Cases and Deaths
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Abstract
The Covid-19 pandemic has taken a major toll on the health and state of our global population. With tough decisions for allocating resources(i.e. vaccines)[1] are being made, forecasting through machine learning has become more important than ever. Moreover, as vaccines are being brought to the public and cases are going down, it is time that we reflect on where the pandemic has taken the most toll:for the purpose of future reform. This research illustrates two different models and algorithms for COVID-19 forecasting: Auto Regressive models and Recurrent Neural Networks(RNNs). The results show the true potential of RNNs to work with sequential and time-series data to forecast future cases and deaths in different states. As the paper utilizes the tanh activation function and multiple LSTM layers, the research will show the importance of machine learning and its ability to help politicians make decisions when it comes to helping states during the pandemic and future reform. The data will also pre-process the time-series data, using rolling statistics and will clean the data for the auto-regressive model and RNN layers. Thus, we show that along with Recurrent Neural Network layers, activation functions also play a crucial role in the accuracy of the forecast.
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SciScore for 10.1101/2021.03.15.21253571: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2021.03.15.21253571: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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