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CovidCurve: curve fitting modeling and early forecasting of the size and duration of Covid-19 outbreaks

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Abstract

The evolution of the cumulative number of infections, and the duration of Covid-19 outbreaks, are modeled and predicted, through a novel curve fitting approach overcoming the limitations of existing models, which on one hand focus on short-term forecasts, and on the other hand fail in estimating the duration of epidemic waves. The ingenuity of the CovidCurve model lies in the precise parameter estimation from the cumulative growth of infected individuals in the logarithmic scale. This approach enables the prediction of the size and duration of Covid-19 outbreaks in their beginning with minimal, macroscopic, public information, thus outperforming existing models in predictive power, speed, reliability, and simplicity. CovidCurve generates forecasts through forward extrapolation, and therefore it is able to predict the cumulative growth of confirmed Covid-19 cases throughout an epidemic wave, thus surpassing compartmental (SI, SIR, SEIR) epidemic models, timeseries analysis, and Machine Learning methods, which produce only short-term and mostly inaccurate predictions. The CovidCurve model is precise even during the exponential increase of Covid-19 cases, well before the peak of the infection rate, where existing exponential models fail, due to the inaccurate estimation of their exponent, and their failure to predict the duration of Covid-19 outbreaks. The validity and forecasting precision of the CovidCurve model are demonstrated for twelve countries, namely Greece, India, Argentina, Italy, Spain, Portugal, Denmark, Sweden, Norway, Israel, Romania, and Saudi Arabia

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Research data policy and data availability statements

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://coronavirus.jhu.edu/map.html

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Acknowledgements

The author wishes to thank the Editor-in-Chief, the Associate Editor, and the four anonymous Reviewers for their constructive and valuable comments, which substantially contributed to the improvement of the manuscript.

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  1. Department of Management Science and Technology, Athens University of Economics and Business, 47a Evelpidon Str., Athens, 11362, Greece

    Ilias N. Lymperopoulos

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  1. Ilias N. Lymperopoulos
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Correspondence to Ilias N. Lymperopoulos.

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Lymperopoulos, I.N. CovidCurve: curve fitting modeling and early forecasting of the size and duration of Covid-19 outbreaks. Appl Intell 53, 29043–29075 (2023). https://doi.org/10.1007/s10489-023-05011-7

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