Abstract
Non-Pharmaceutical Interventions (NPIs) are essential measures that reduce and control a severe outbreak or a pandemic, especially in the absence of drug treatments. However, estimating and evaluating their impact on society remains challenging, considering the numerous and closely tied aspects to examine. This article proposes a fine-grain modeling methodology for NPIs, based on high-order relationships between people and environments, mimicking direct and indirect contagion pathways over time. After assessing the ability of each intervention in controlling an epidemic propagation, we devise a multi-objective optimization framework, which, based on the epidemiological data, calculates the NPI combination that should be implemented to minimize the spread of an epidemic as well as the damage due to the intervention. Each intervention is thus evaluated through an agent-based simulation, considering not only the reduction in the fraction of infected but also to what extent its application damages the daily life of the population. We run experiments on three data sets, and the results illustrate how the application of NPIs should be tailored to the specific epidemic situation. They further highlight the critical importance of correctly implementing personal protective (e.g., using face masks) and sanitization measures to slow down a pathogen spreading, especially in crowded places.
Highlights
From ancient times different populations have adopted varying strategies to prevent and contain diseases, from the isolation of sick individuals to the establishment of a time limit to the manifestation of symptoms to magical practices [29]
Current development of the recent pandemic highlighted to what extent the increase of human mobility and goods exchange made Non-Pharmaceutical Interventions (NPIs), such as lockdown and border closure, more challenging to apply to past cases in history, mainly because of their negative impact on the worldwide economy [24, 9] as well as on the psychological wellness of society [33, 68, 56, 69]
We evaluated and compared the effect of direct and indirect contacts on a disease propagation, studying the impact of accurately modeling the time interval within which the two contagion pathways may happen in hastening the epidemic diffusion
Summary
From ancient times different populations have adopted varying strategies to prevent and contain diseases, from the isolation of sick individuals to the establishment of a time limit to the manifestation of symptoms to magical practices [29]. Aleta et al [3] build a multilayer synthetic population that models the socio-demographic features of the Boston metropolitan area using high-resolution data describing the movements and potential interactions of people in the city to investigate the impact of different reopening scenarios Their results suggest how a proactive policy of testing, contact tracing, and household quarantine could gradually reopen economic activities and workplaces with a low impact on the healthcare system. Alike (temporal) graphs, TVHs abstract and formalize contact among agents simulated with an ABM, but adding information about where the contact is happening Such structures allow to formally analyze diffusion mechanisms while accounting for group interactions and indirect contagion processes via contaminated locations. HIGH-ORDER TEMPORAL EPIDEMIC DYNAMICS This section describes some useful concepts related to the framework presented in [6], formally introducing TVHs and the high-order diffusion process to simulate human-to-human (direct) and human-to-environment (indirect) infection propagation
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