Which lockdowns are the best lockdowns? Optimal interventions and the trolley problem in heterogeneous populations
Kollepara, P. K.; Chisholm, R. H.; Kiss, I. Z.; Miller, J. C.
Show abstract
Interventions to mitigate the spread of infectious diseases, while succeeding in their goal, have economic and social costs associated with them. These limit the duration and intensity of the interventions. We study a class of interventions which reduce the reproduction number and find the optimal strength of the intervention which minimises the final epidemic size for an immunity inducing infection. The intervention works by eliminating the overshoot part of an epidemic, and avoids a second-wave of infections. We extend the framework by considering a heterogeneous population and find that the optimal intervention can pose an ethical dilemma for decision and policy makers. This ethical dilemma is shown to be analogous to the trolley problem. We apply this optimisation strategy to real world contact data and case fatality rates from three pandemics to underline the importance of this ethical dilemma in real world scenarios.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada 98%
- The trade-off between mobility and vaccination for COVID-19 control: a metapopulation modeling approach 97%
- Modelling COVID-19 mutant dynamics: understanding the interplay between viral evolution and disease transmission dynamics 97%
Similar papers in this journal
- Modelling, prediction and design of national COVID-19 lockdowns by stringency and duration 98%
- The effect of the definition of ‘pandemic’ on quantitative assessments of infectious disease outbreak risk 97%
- Ranking the Effectiveness of Non-Pharmaceutical Interventions to Counter COVID-19 in UK Universities with Vaccinated Population 97%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.