Ecstatic Disregard (Page 2)

Ecstatic Disregard

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Using The R0 Package Correctly

I’ve just realised why my estimates of R do not tally with the established figures, especially after the peak. The reason is an absurdly simple case of user error, for which I can only apologise. The R0 estimate.R function accepts an epidemic curve as its primary data. An

Productivity During The Three Day Week

History shows that fewer hours worked does not mean lower productivity In 2019 Microsoft Japan reported that cutting the working week to four days improved productivity by 40%. The gains came partly from cost reductions. Less electricity was used and fewer documents were printed. The shorter week also demanded other,

Herd Immunity

Herd immunity occurs when enough of the population, by one means or another, are immune to a disease, such that chains of infection are unlikely to occur. Acquiring that immunity can be via a vaccine, or through immunity acquired by an earlier infection. We can define herd immunity in terms

Mapping COVID-19 In The UK With Leaflet And R

COVID-19 data for the UK has been provided in an up to date and tidy form by Tom White and others. I’ve used this source to see how easy it is to plot this data on a map. Plotting This data set makes it very easy to plot case,

Dockerising Shiny

Over the last couple of months, I’ve been running a Shiny app at shinyapps.io to model COVID-19 reproduction number. It has has a couple of hiccups recently. A couple of weeks ago it started running out of memory; the free tier of shinyapps.io limits apps to 1GB

Mapping Air Quality

Leaflet For R uses the JavaScript mapping library Leaflet to let turn data straight into nifty maps. We can use this to turn query results from the UK Air Quality API into maps of ozone levels from monitoring sites. All the data comes ultimately from the DEFRA Air Information Resource.

Predicting The Peak Automatically

In a previous post, I attempted a very rough estimate of when the peak for COVID-19 in the UK would be over. I reckoned it would be around April 22nd. I calculated this by doing a linear regression on the effective reproduction number as calculated using UK case data and

Predicting The Peak

A month or two ago I started modelling the reproduction number of COVID-19 using the R0 package. I then used this model to create a Shiny app to break down the data by country. Can we use this information to predict the peak in the UK? For a full detailed

Modelling Your Queues

Note this is adapted from an original article that goes into much greater detail. Please check it for the nitty-gritty R code in full. Using Simmer it’s possible to model the performance of a real-world software platform. Simmer is a process-oriented and trajectory-based Discrete-Event Simulation (DES) package for R

R0

I have been attempting to understand the nature of the “reproduction number” in epidemiology and to this end I’ve enlisted the help of the R0 package for R. For a good overview of what the reproduction number is and why it is so important please check out this article.