CovidCountries

CovidCountries

Mathematical modelling of COVID-19

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We predict the development of COVID-19 based on purely mathematical modelling of the virus' growth rate. You can normalise the predictions by population and compare them across countries. Our goal is to estimate how much longer this crisis will take.
CovidCountries gallery image
CovidCountries gallery image
CovidCountries gallery image
Launch tags:Medical
Launch Team
Anima - OnBrand Vibe Coding
Design-aware AI for modern product teams.
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What do you think? …

Finn Bauer
Hey Product Hunt! πŸ‘‹ We are a team of developers and physicists and wanted to share with you our coronavirus prediction tool. πŸ“° Situation Coronavirus (COVID-19) is spreading fast and our governments must react decisively in order to prevent a humanitarian catastrophe around the globe. As a consequence almost all affected countries are locked down. πŸ˜• Complication We need to predict the spread of the virus going forward to make reasonable decisions today. This is a difficult challenge and there are many different approaches. πŸ’‘ Solution - How does it work ? Our approach is purely mathematical. Predictions are made by fitting the available data to a function for logistic growth using non-linear regression. The specific regression used is a trust-region-reflective algorithm. This is a purely mathematical model based on the availabe data points. In human terms this means that the growth rate of the coronavirus seems to have the same shape as a logistic function. Hence we can look at the growth rates for a country and fit a logistic function to the past observations. Then we can use this function to give a rough estimate of future values. The estimates change daily. Please take this with a grain of salt. We are not epidemiologist or virus experts we can only do math. Stay safe folks! Finn
Lasse
Very nice idea. Which function for logistic growth exactly is the prediction based on?
Finn Bauer
@lasse_hertle Glad you like it. Our predictions are based on the richards model for logistic growth with 5 paramterers that can be fitted.
Chris Di An
Cool dashboard! I love that it’s a time-series graphic. What about the second page Analysis though?