A paper by Dr. Cioffi-Revilla was released on Research Gate a little over a year ago. At thirty-seven pages it is a succinct description of the Computational Social Science (CSS) field. The paper is titled, "The Scope of Computational Social Science."

New computer programming languages are created every year so from time to time I update the database of influences. In this update, languages Ballerina, Bosque, Microsoft Excel Functions, Microsoft Excel Macros, Microsoft Power Fx, Q#, Reason, Wolfram Mathematica, and Zig have been added or changed. On the original post you will find a link to the network visualization.

I presented a paper titled, "Non-Pharmaceutical Herd Immunity using Homemade Masks" at the 2020 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS) on 20 October 2020.

On 8 October 2020 I presented a paper to the Computational Social Science Society of the Americas annual conference CSS 2020. I am the lead author of the paper, "Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning" with my co-author, Dr. Andrew Crooks.

Despite homemade mask use in a number of areas of the world there has not been a lot of research. As I continued my research into the efficiency of these types of masks, I found some recent work that may expand your knowledge of a variety of materials.

While conducting research for a new agent-based model I came across a 2013 paper titled, "Testing the efficacy of homemade masks: would they protect in an influenza pandemic?" It details how well different materials fared when faced, no pun intended, with the task of stopping a virus.

Another useful bit of information that helps to maintain perspective is to know the impact of seasonal influenza...

Since 1900 there have been at least five viral epidemics or pandemics in the United States. The death toll varies and in some cases is disputed. To place these deaths in context it is important to know the population at the time and show deaths as a percentage of the population.

In my previous blog, I provided a model of COVID-19 based on the U.S. population. Running that model demonstrates how social distancing can flatten the curve by reducing the load on the U.S. health system. Here I provide a few data graphics from an analysis of the model results.

I have created a simulation of the COVID-19 virus (SARS-CoV-2) in the United States population. It demonstrates how social distancing "flattens the curve." The model is written in NetLogo and will run from this website in your browser.