After the failures in the COVID-19 response, future pandemic preparedness will require to identify general control principles relying on the two main pillars emerged during the pandemic: the tension between suppression and mitigation and the centrality of social distancing as the critical control measure. We aim at comparing “abstract” optimal interventions where planners rely on steady population’s adherence to the proposed measures (“behaviour-free” case) with the alternative case where adherence is also affected by individuals’ behaviour (“behavioural” case). We use open-loop optimal control of a worst-case transmission model for COVID-19 to identify best social distancing policies balancing the direct epidemiological costs of the epidemic with its societal costs, depending on the three key policy factors, namely the (i) prioritization of direct costs (PDC), (ii) adherence to interventions, (iii) timeliness of interventions. In the behaviour-free case we assume a steadily constant adherence, while in the behavioural case adherence is fully endogenous depending on individuals’ risk perceptions of both direct and indirect costs. In the behaviour free case, combinations of decreasing values of PDC, adherence and timeliness force the optimal policy to switch from suppression to “effective” mitigation and eventually to palliative mitigation. Inadequate adherence and timeliness inevitably leave mitigation as the only accessible option even when PDC is high. The behavioural case yields a wealth of results, ranging from the case where policy-resistant behaviour worsens both adherence and timeliness thereby forcing palliative mitigation to be the only policy option even when planners’ PDC is high, up to the case where behaviour can enhance mitigation in the presence of a planner’s low prioritization to direct costs. Although the complexity of pandemic events is hardly captured by simple models, optimal control analyses with behavioral dimensions offer important insight for future preparedness.
Pandemic preparedness, suppression and mitigation: how does individual behaviour perturb optimal social distancing?
Pisaneschi Giulio;Tarani Matteo;Landi Alberto;Laurino Marco;Manfredi P
2024-01-01
Abstract
After the failures in the COVID-19 response, future pandemic preparedness will require to identify general control principles relying on the two main pillars emerged during the pandemic: the tension between suppression and mitigation and the centrality of social distancing as the critical control measure. We aim at comparing “abstract” optimal interventions where planners rely on steady population’s adherence to the proposed measures (“behaviour-free” case) with the alternative case where adherence is also affected by individuals’ behaviour (“behavioural” case). We use open-loop optimal control of a worst-case transmission model for COVID-19 to identify best social distancing policies balancing the direct epidemiological costs of the epidemic with its societal costs, depending on the three key policy factors, namely the (i) prioritization of direct costs (PDC), (ii) adherence to interventions, (iii) timeliness of interventions. In the behaviour-free case we assume a steadily constant adherence, while in the behavioural case adherence is fully endogenous depending on individuals’ risk perceptions of both direct and indirect costs. In the behaviour free case, combinations of decreasing values of PDC, adherence and timeliness force the optimal policy to switch from suppression to “effective” mitigation and eventually to palliative mitigation. Inadequate adherence and timeliness inevitably leave mitigation as the only accessible option even when PDC is high. The behavioural case yields a wealth of results, ranging from the case where policy-resistant behaviour worsens both adherence and timeliness thereby forcing palliative mitigation to be the only policy option even when planners’ PDC is high, up to the case where behaviour can enhance mitigation in the presence of a planner’s low prioritization to direct costs. Although the complexity of pandemic events is hardly captured by simple models, optimal control analyses with behavioral dimensions offer important insight for future preparedness.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
