Extrapolation of Infection Data for the CoVid-19 Virus in 21 Countries and States and Estimate of the Efficiency of Lock Down
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Abstract
Predictions about the further development of the Corona pandemic are of great public interest but many approaches demand a large number of country specific parameters and are not easily transferable. A special interest of simulations on the pandemic is to trace the effect of politics for reducing the virus spread, since these measures have had an enormous impact on economy and daily life.
Here a simple yet powerful algorithm is introduced for fitting the infection numbers by simple analytic functions. This way, the increase of the case numbers in periods with different regulations can be distinguished, and by extrapolating the fit functions, a forecast for the maximum numbers and time scales are possible. The effect of the restraints such as lock down are demonstrated by comparing the resulting infection history with the likely unconstrained virus spread, and it is shown that a delay of 1-4 weeks before imposing measures aiming at social distancing could have led to a complete infection of the respective populations.
The approach is simply transferable to many different states. Here data from six E.U. countries, the UK, Russia, two Asian countries, the USA and ten states inside the USA with significant case numbers are analyzed, and striking qualitative similarities are found.
Keywords: Covid-19, forecast, analytic fit, France, Germany, Italy, Spain South Korea, New York, Washington, Florida, Michigan, Poland, Sweden, USA, Pennsylvania, China, Russia, UK, California, Illinois, Indiana, Maryland, North Carolina.
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SciScore for 10.1101/2020.06.17.20134254: (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
Software and Algorithms Sentences Resources For the fit of equation (1) to the data points, the standard solver in Microsoft Excel 2016 was applied to the logarithm of the number N(t) of confirmed infections as saved in steps of one day, and the least squares error with respect to equation (1) was minimized by varying the three parameters N(t = 0), N(t → ∞), and τ. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)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: An explicit …SciScore for 10.1101/2020.06.17.20134254: (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
Software and Algorithms Sentences Resources For the fit of equation (1) to the data points, the standard solver in Microsoft Excel 2016 was applied to the logarithm of the number N(t) of confirmed infections as saved in steps of one day, and the least squares error with respect to equation (1) was minimized by varying the three parameters N(t = 0), N(t → ∞), and τ. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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