Based on the analysis of the empirical data for the number of infections in more than 20 countries we propose here a hitherto unknown universal model for the spreading of the COVID-19 pandemic that depends not on time, but on the number of infections itself. This change of the independent variable overcomes the crucial issue of analyzing very different countries worldwide within one mathematical framework with similar parameters. This was previously impossible leading to individual description for every country. Our model allows describing the pandemicincluding its endpoint surprisingly good and giving a figure of merit for the success of the measuresto fight the pandemic .
The ongoing COVID-19 pandemic is challenging every part of society. From a scientific point of view the first major task is to predict the dynamics of the pandemic, allowing governments to allocate proper resources and measures to fight it, as well as gauging the success of these measures by comparison with the predictions in hindsight. The vast majority of pandemic models are based on extensive models with large numbers of fit parameters, leading to individual descriptions for every hot spot on the world. This makes predictions and comparisons cumbersome, if not impossible. We here propose a different approach, by moving away from a description over time, and instead choosing the total number of infected people in an enclosed area as the independent variable . Analyzing a few hot spots data, we derive an empirical formula for the dynamics, dependent only on three variables. The final number of infections is strictly connected to one fit parameter we call mitigation factor, which in turn is mostly dependent only on the enclosed population. Despite its simpleness, this description applies to every of the around 50 countries we have analyzed, allows to separate different waves of the pandemic, provides a figure of merit for the overall usefulness of government measures, and shows when a pandemic is ending. Our model is robust against undetected cases, and allows all nations, in particular those with fewer resources, to reasonably predict the outcome of the pandemic in their country .
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Based on the analysis of
The ongoing COVID-19 pandemic is challenging every part of society. From a scientific point of view the first major task is to predict the dynamics of
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empirical data for the number of infections in more than 20 countries we propose here a hitherto unknown universal model for the spreading of
pandemic, allowing governments to allocate proper resources and measures to fight it, as well as gauging the success of these measures by comparison with the predictions in hindsight. The vast majority of pandemic models are based on extensive models with large numbers of fit parameters, leading to individual descriptions for every hot spot on the world. This makes predictions and comparisons cumbersome, if not impossible. We here propose a different approach, by moving away from a description over time, and instead choosing the total number of infected people in an enclosed area as
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COVID-19 pandemic that depends not on time, but on the number of infections itself. This change of the
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overcomes the crucial issue of analyzing very different countries worldwide within one mathematical framework with similar parameters. This was previously impossible leading to individual description for every country. Our model allows describing the pandemicincluding its endpoint surprisingly good and giving
. Analyzing a few hot spots data, we derive an empirical formula for the dynamics, dependent only on three variables. The final number of infections is strictly connected to one fit parameter we call mitigation factor, which in turn is mostly dependent only on the enclosed population. Despite its simpleness, this description applies to every of the around 50 countries we have analyzed, allows to separate different waves of the pandemic, provides
R
success of the measuresto fight the pandemic
overall usefulness of government measures, and shows when a pandemic is ending. Our model is robust against undetected cases, and allows all nations, in particular those with fewer resources, to reasonably predict the outcome of the pandemic in their country