Effective algorithms for solving statistical problems posed by COVID-19 pandemic
Dmitriy Klyushin, Taras Shevchenko National University of Kyiv, Ukraine, Kyiv
Abstract
The COVID-19 pandemic has exposed an urgent problem of forecasting the epidemic curves. Now, the most popular tools for outbreak forecasting are SIR and SEIR models. The main drawback of these models is that they use uncertain parameters that may be invalid. We can solve this problem using machine learning models trained on real data. Every such model has its own accuracy. Yet, a comparison of the accuracy of these models is quit a difficult task due to their statistical nature. Two models may have different but statistically equivalent errors. Thus, we may reduce the ...
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