The University of Texas at Dallas, Richardson, Texas.
University of Texas Southwestern Medical Center, Dallas, Texas.
AMIA Annu Symp Proc. 2022 Feb 21;2021:1009-1018. eCollection 2021.
The rapidly changing situation characterized by the COVID-19 pandemic highlighted a need for new epidemic modeling strategies. Due to an absence of computationally efficient models robust to paucity of reliable data, we developed NetworkSIR, a model capable of making predictions when only the approximate population density is known. We then extend NetworkSIR to capture the effect of indirect disease spread on the progression of an epidemic (EnvironmentalSIR).
由 COVID-19 大流行所呈现的快速变化局势凸显了对新的传染病建模策略的需求。由于缺乏对可靠数据不足具有稳健性的计算效率模型，我们开发了 NetworkSIR，这是一种仅在已知大致人口密度时就能进行预测的模型。然后，我们将 NetworkSIR 扩展到捕获间接疾病传播对传染病进展的影响（EnvironmentalSIR）。
