Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown

TL;DR

This paper presents a genetic programming-based AI method to optimize visitation schedules during a pandemic, reducing infection risks and hospitalizations compared to uninformed scheduling, demonstrated through simulations involving diverse participants.

Contribution

It introduces a novel AI approach using genetic programming to minimize infection risks in lockdown scenarios, incorporating a new infection model based on age-related infection levels.

Findings

Significant reduction in infections, hospitalizations, and deaths in simulations.

Effective scheduling solutions outperform round robin methods.

Potential for real-world application with more accurate infection models.

Abstract

A computational methodology is introduced to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic, as is the 2020 COVID-19 pandemic. Persons use their mobile phone or computational device to request trips to places of their need or interest indicating a rough time of day: `morning', `afternoon', `night' or `any time' when they would like to undertake these outings as well as the desired place to visit. An artificial intelligence methodology which is a variant of Genetic Programming studies all requests and responds with specific time allocations for such visits that minimize the overall risks of infection, hospitalization and death of people. A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. The computations indicate vast improvements with far fewer dead and hospitalized. These auger well for a more realistic study using accurate infection models with the view to test deployment in the real world. The input that drives the infection model is the degree of infection by taxonomic class, such as the information that may arise from population testing for COVID-19 or, alternatively, any contamination model. The taxonomy class assumed in the computations is the likely level of infection by age group.