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Agent-Based Modeling Suggests We Can Modulate COVID-19 Spread By Encouraging Localized Social Interactions — Part Two
Written by Deborah Duong and Ben Goertzel
This post elaborates on the points made in the prequel post, showing some of the detailed simulation results that led us to the conclusions presented there.
Our recent experiments with Agent-Based Modeling of COVID-19 have led us to some interesting hypotheses regarding the relationship between a population’s social interaction network and the spread of the virus through that population. For instance, we have found in our simulations that if the “clumpiness” of the social network of a certain population is above a certain threshold, then it becomes much easier to control the spread of COVID-19 in that population via lockdowns and other mechanisms. It becomes much easier to keep the maximum infection rate under a level consistent with the limits of the healthcare system, and also makes it easier to achieve herd immunity with a lower percentage of the population immune.
We will show here some of the specific Agent-Based Simulation results that led us to these tentative conclusions. The basic gist of the results presented here was covered in my talk in the Medical Applications stream of the D.OS (Decentralized OS Summit) event that SingularityNET and Cardano co-organized on November 9, 2020.
The post definitely digs fairly deep into the weeds, and is offered in the vein of “open science.” The matters we’re addressing here are critically important for all of us, the results we’re presenting and discussing are highly intriguing yet also in many ways rough and preliminary — so we consider it especially important to be explicit and transparent about everything involved.
For those who want to dig yet further, iPython notebooks containing the results summarized here may be found at this link. Further analysis based on these ...
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SingularityNET - Medium