Heuristic assessment of the economic effects of pandemic control

TL;DR

This paper develops a novel method to construct and control risk network dynamics based on empirical data, aiming to optimize pandemic control strategies and reduce economic costs.

Contribution

It introduces the first data-driven approach for modeling and controlling risk networks with complex interactions and multiple cost sources.

Findings

Identified seven key risks used in COVID-19 control.

Demonstrated existence of alternative risk sets with lower control costs.

Applied optimal control to empirically constructed risk networks.

Abstract

Data-driven risk networks describe many complex system dynamics arising in fields such as epidemiology and ecology. They lack explicit dynamics and have multiple sources of cost, both of which are beyond the current scope of traditional control theory. We construct the global risk network by combining the consensus of experts from the World Economic Forum with risk activation data to define its topology and interactions. Many of these risks, including extreme weather, pose significant economic costs when active. We introduce a method for converting network interaction data into continuous dynamics to which we apply optimal control. We contribute the first method for constructing and controlling risk network dynamics based on empirically collected data. We identify seven risks commonly used by governments to control COVID-19 spread and show that many alternative driver risk sets exist with potentially lower cost of control.