  This study aims at modeling the universal failure in preventing the outbreak
of COVID-19 via real-world data from the perspective of complexity and network
science. Through formalizing information heterogeneity and government
intervention in the coupled dynamics of epidemic and infodemic spreading;
first, we find that information heterogeneity and its induced variation in
human responses significantly increase the complexity of the government
intervention decision. The complexity results in a dilemma between the socially
optimal intervention that is risky for the government and the privately optimal
intervention that is safer for the government but harmful to the social
welfare. Second, via counterfactual analysis against the COVID-19 crisis in
Wuhan, 2020, we find that the intervention dilemma becomes even worse if the
initial decision time and the decision horizon vary. In the short horizon, both
socially and privately optimal interventions agree with each other and require
blocking the spread of all COVID-19-related information, leading to a
negligible infection ratio 30 days after the initial reporting time. However,
if the time horizon is prolonged to 180 days, only the privately optimal
intervention requires information blocking, which would induce a
catastrophically higher infection ratio than that in the counter-factual world
where the socially optimal intervention encourages early-stage information
spread. These findings contribute to the literature by revealing the complexity
incurred by the coupled infodemic-epidemic dynamics and information
heterogeneity to the governmental intervention decision, which also sheds
insight into the design of an effective early warning system against the
epidemic crisis in the future.
