Updated April 8th 2020
I’ve spent a disturbing amount of time trying to come up with a decent model for the CoVID-19 pandemic. The big challenge is how little good data there is. Pretty much all available data is riddled with confounding variables and bias. There is a long list of challenges but some I find most daunting are that:
Continue reading “CoVID-19 Projections using Kernel SVR and Death Rate Analysis”
Note: The data in this chart is taken from the National Center for Health Statistics on CDC’s website.
The following charts compare the number of deaths resulting from the CoVID-19 pandemic in various countries as of March 20th 2020. The daily totals are normalized by the population in each country to produce per capita numbers. Per capita numbers allow for more easy comparison between countries.
Figure 1: CoVID-19 Deaths by Country
The curves are aligned by the date of the first reported death in each country, allowing for more ready comparison of the growth rates.
Figure 2: Projection of CoVID-19 Deaths
In Figure 2, the smoothed daily growth rates from one country are projected onto the numbers of another country to predict forward in time. By comparing different growth rates for the same country, a range of potential outcomes for the country is produced.
The following plots explore the influence of two economic factors during recessions on the S&P 500 index: unemployment and gross domestic product (GDP). A linear model is constructed to predict the low of the S&P 500 index during a given recession using two transformed variables derived from the maximum unemployment and GDP differentials for that recession.
Figure 1: Multiple Regression Analysis of Past Recessions
Predictions for the decrease in GDP and unemployment levels are taken to be 25% and 20% respectively. These are taken from Goldman Sachs predictions and estimates from Mnuchin respectively. A ridge regression model is fit with regularization weight chosen using grid search and leave one out validation.
Past recessions with more similarity both temporally and in terms of the current estimates are given more weight. By so doing, the model focuses on past recessions that are more similar in nature to the current situation. In the above plots, the weight of each recession is indicated by the size of the scatter plot marker.
Note: This post is for informational purposes only and does not constitute financial, professional, or any other form of advice.
The follow plots show the amount of daylight, sunrise time, and sunset time for four US cities throughout the year of 2019.
Figure 1: Hours of Daylight, Sunrise Time, and Sunset Time by City
The rise and set times are shown in local time without adjustment for daylight saving times (DST). Data obtained from the U.S. Naval Observatory Astronomical Applications Department.
Maps are a fundamental data structure. Their prevalence is a testament to their importance. Indeed, many search problems can be reduced to the construction of an appropriate map. However, a search problem occasionally arises that is difficult to solve, at least directly, with a map. The interval query problem is one such problem.
Continue reading “RangeMap: A Simple Interval Query Datastructure”
With the rise of globalization, countries increasingly trade food products internationally. Acting in their own economic interests, countries buy and sell food where profitable, much like any other product. Import and export records provide a fascinating window into this complex world of international food trade.
Continue reading “Visualizing International Trade of Food Products”