Interpreting Sars-Cov-2 Fatality Rate Estimates — A Case for Introducing Standardized Reporting to Improve Communication

Cavataio, Joseph P.; Schnell, Santiago

doi:10.2139/ssrn.3695733

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Data-driven Numerical Forecasts1

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2020

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“…One of the main reason for this is the reporting delay (cf. [ 49 , 50 ]). As explained in [ 50 ], “there will be two main sources of delay in monitoring trends.…”

Section: Data-driven Numerical Forecastsmentioning

confidence: 99%

Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19

Dat

Bem²,

Hang³

et al. 2020

Biology

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We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.

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“…One of the main reason for this is the reporting delay (cf. [ 49 , 50 ]). As explained in [ 50 ], “there will be two main sources of delay in monitoring trends.…”

Section: Data-driven Numerical Forecastsmentioning

confidence: 99%