  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.
