Pandemics, notably the recent COVID-19 outbreak, have impacted both public
health and the global economy. A profound understanding of disease progression
and efficient response strategies is thus needed to prepare for potential
future outbreaks. In this paper, we emphasize the potential of Agent-Based
Models (ABM) in capturing complex infection dynamics and understanding the
impact of interventions. We simulate realistic pharmaceutical, behavioral, and
digital interventions that mirror challenges in real-world policy adoption and
suggest a holistic combination of these interventions for pandemic response.
Using these simulations, we study the trends of emergent behavior on a
large-scale population based on real-world socio-demographic and geo-census
data from Kings County in Washington. Our analysis reveals the pivotal role of
the initial 100 days in dictating a pandemic's course, emphasizing the
importance of quick decision-making and efficient policy development. Further,
we highlight that investing in behavioral and digital interventions can reduce
the burden on pharmaceutical interventions by reducing the total number of
infections and hospitalizations, and by delaying the pandemic's peak. We also
infer that allocating the same amount of dollars towards extensive testing with
contact tracing and self-quarantine offers greater cost efficiency compared to
spending the entire budget on vaccinations.

利用基于代理的模型 (ABM) 对复杂的感染动态和干预影响进行模拟，研究了疫情流行的趋势以及干预措施的综合应用，发现在前 100 天内做出迅速决策和高效政策制定对疫情发展至关重要，同时揭示了行为和数字干预可以减少药物干预的负担，延迟疫情高峰，并指出将预算相同的资源投入广泛的测试、接触追踪和自我隔离相比，更具成本效益。