This is a quick post exploring estimates of the case fatality ratio for Tuberculosis (TB) from data published by the World Health Organisation (WHO). It makes use of getTBinR (which is now on CRAN), pacman for package management, hrbrthemes for plot themes, and pathwork for combining multiple plots into a storyboard. For an introduction to using getTBinR to explore the WHO TB data see this post.
It is estimated that in 2016 there was more than 10 million cases of active TB, with 1.
In November I attended Epidemics, which is a conference focused on modelling infectious diseases. There was a lot of great work and perhaps most excitingly a lot of work being offered as R packages.
I’ve recently begun wrapping all my analytical work in R packages, as it makes producing reproducible research a breeze! Unfortunately all of this work is still making it’s way towards publication and for a variety of reasons can’t be shared until it has passed this hurdle.
This interactive dashboard uses data on Tuberculosis incidence from 1913-1916 released by Public Health England and combines it with data on the interventions against Tuberculosis that have been discovered/implemented over the last century. The data was cleaned and imported into R using the tbinenglanddataclean R package, which also contains information on how to apply for additional data, scripts to clean data extracts and graphing functions to visualise them. The dashboard is a work in progress and additional interventions, new figures and increased interactivity will be added over time.
Previously I have looked at visualising the Property Partner portfolio using tableau, and explored their resale date from July 2017. In this post I will be exploring the August Open House resale data focussing on property premiums over both initial and latest valuation. The code for this post is available here.
Property Partner advertises returns by combining both projected capital and rental returns. Properties are held for 5 years at which point any capital gain can be realised.
As peer to peer lending matures platforms have begun to have increasingly divergent stances on sharing their data, making it increasingly important that their is external pressure on them to improve their data sharing. This blog series will focus on platforms sharing their data, exploring what their data is saying and suggesting possible changes to their releases that would make it easier for investors to gain insights on their own.
As a quick update to last weeks post this week we will be using the estimates of bad debt from last week to assess the true risk/rewards of loans at various lengths from the funding circle (FC) loan book. I realised recently that when picking loans my default criteria tends towards picking loans with the lowest percieved risk (i.e lowest risk band) with the highest interest rates possible. This strategy has some interesting consequences in that it preferentially selects long term loans that tend to have higher rates.
We are again looking at the peer to peer (P2P) loan book for funding circle (FC), with the focus being the variation in return based on portfolio composition and diversification. FC states that the average return on investment is 6.6%, with 93% of investors that invested in more than 100 companies, with a maximum exposure of 1% earning 5% or more. The original purpose of this blog was to look at various portfolio’s and estimate the risk that they carry for the investor.
This is the first in a series of blog posts looking at P2P lenders, which will be aimed at surfacing more details about the structures of their loan books. I hope to expand this series by looking at optimal strategies, predictive modelling, and interactive data science.
Since 2005, with the launch of Zopa P2P companies have provided a middle path between cash holdings, and investing in stocks and shares. Funding circle, which was launched in 2010, has currently facilitated over £1 Billion in loans to British businesses.