Public health interventions in the face of pandemics: Network structure, social distancing, and heterogeneity

Complexity, resulting from interactions among many components, is a characterizing feature of healthcare systems and related decisions. It scales up in the face of pandemics that give rise to multiple sources of uncertainty and where various contextual factors interact with each other and with policy parameters that combine to yield outcome distributions. This paper proposes a unified agent-based modeling framework to derive qualitative insights that assist and inform policy decisions related to pandemics. The general framework comprises a contagion model that explicates exogenous policy-relevant variables, as well as their links with features of the environment in which the policy decisions will be implemented. Furthermore, the framework identifies sources of uncertainty at different system layers. The characterization of the macro level, for example, as manifested in the network structure, encompasses two constitutive factors. These two factors, in turn, capture much of the stochasticity that results from the network's inherent randomness. By synthesizing the model components further into a broader agent-based model, the current framework also accounts for heterogeneous micro-level attributes that collectively yield macro level outcomes. Several stylized examples help establish insights into the overall tendency of complex systems to produce multidimensional outputs. A comprehensive, controlled, computational experiment offers further evidence across a range of scenarios and various policy conditions.

Automated summary

This paper proposes a unified agent-based modeling framework for policy decisions related to pandemics. The framework considers sources of uncertainty in the system and combines macro and micro-level attributes to generate comprehensive results.