Continuing to delve into the Coronavirus statistics. 👾

As you can probably tell from the blog I’m an extraordinary consumer of news 📰 and statistics about Coronavirus. While it is a tragedy for many in the country I’m fascinated by the numbers 🔢 and math that make up the virus growth, predictions and modeling.

Most of the math behind the virus is relatively simple. 📊 With powerful computers being the norm it’s easy for a layman like myself to look at the numbers and make my own predictions based on the same data policy makers and scientists are using. While mine are more general it does give me insight and a way to pass the time.

Often my numbers are wrong. 〽Not because my theory is wrong but my simple mathematical models can’t possibly understand the complex scenarios as social distancing starves the virus and the virus starts to run up against heard immunity. 🐮 People aren’t perfect spreaders of viruses, thank God. But that’s the problem with models – they’re only accurate if all things remain consistent, which never happens in the real world. Asked me, and simple models show all of the world would have Coronavirus by mid April. Raw math suggested that. 🌍

What’s next, how bad is it going to be and what our future 🔮 holds is on everybody’s mind these days. Most of the news 📰 lately is just vamping for time as there is as many unknowns as definates in our future. While ultimately at the ordinary citizens level I lack the data of policy makers studying the data myself can be helpful at gleaning my own conclusions. 💭