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Article summary:
1. This article used big data analytics to explore the relationship between government response policies, human mobility trends and numbers of coronavirus disease 2019 (COVID-19) cases comparatively in Poland, Turkey and South Korea.
2. The study found that while the stringency index of day t is significantly related to mobility data of the same day, mobility data of day t is associated with number of cases of day t + 30.
3. The study proposed implications for policy makers on deciding the targeted levels of mobility to maintain numbers of cases in a manageable range based on the results of created scenarios.
Article analysis:
The article “Big Data Analytics and COVID-19: Investigating the Relationship Between Government Policies and Cases in Poland, Turkey and South Korea” is a well-researched piece that provides an analysis into how governmental response policies affect human mobility trends and how this can be used to model the spread of COVID-19 in terms of numbers of cases. The authors have used reliable sources such as Google Mobility Data, Oxford COVID-19 Government Response Tracker, and OWID database for their research. Furthermore, they have implemented big data analytics techniques such as multilayer perceptron (MLP) neural networks for modelling numbers of cases per million with the mobility data.
However, there are some potential biases in this article that should be noted. Firstly, it only focuses on three countries – Poland, Turkey and South Korea – which may not be representative enough to draw general conclusions about other countries or regions. Secondly, it does not consider other factors that could influence human mobility trends such as weather conditions or economic factors. Thirdly, it does not provide any evidence for its claims regarding the effectiveness or accuracy of its MLP model for predicting COVID-19 spread indicators. Finally, it does not present both sides equally; instead it focuses mainly on how governments can use big data analytics to manage uncertain environments created by outbreak situations without considering any potential risks or drawbacks associated with this approach.
In conclusion, while this article provides an interesting analysis into how governmental response policies affect human mobility trends and how this can be used to model the spread of COVID-19 in terms of numbers of cases using big data analytics techniques, there are some potential biases that should be taken into consideration when assessing its trustworthiness and reliability.