Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe
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Abstract
Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate a graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.
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SciScore for 10.1101/2020.04.20.20070938: (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: We detected the following sentences addressing limitations in the study:5.2 Limitation and future work: The generative graph of the model along with other factors of the model empirically has enhanced the predicted values for each country globally (based on trial and errors). However, it remains a challenge that countries with spread over a longer period are more likely to be predicted more accurate than …
SciScore for 10.1101/2020.04.20.20070938: (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: We detected the following sentences addressing limitations in the study:5.2 Limitation and future work: The generative graph of the model along with other factors of the model empirically has enhanced the predicted values for each country globally (based on trial and errors). However, it remains a challenge that countries with spread over a longer period are more likely to be predicted more accurate than countries with no prior cases. Re-training the model with more data in the future would yield better results at both; global and country levels. However, despite data improvement, there are three main domains that the model algorithms can be advanced in future works. First, finding more significant spatial or demographic factors that show significant associations with the spread may also enhance the forecast of the model Second, applying the same concept and goals of the model to other subjects of coronavirus, could lead to a better understanding of its future. For instance, estimating deaths or recovery, bearing in mind the health system capability and capacity, in addition to the governmental responses could be another assisting tool. Put all together, more data, more factors, different forecasting model could also lead to better long-term forecast (1-3 months) for each country based on the lesson learned from the global and country-level trends of spread.
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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