Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA.
Med Decis Making. 2021 Aug;41(6):623-640. doi: 10.1177/0272989X211006025. Epub 2021 Apr 24.
Analyses of the effectiveness of infectious disease control interventions often rely on dynamic transmission models to simulate intervention effects. We aim to understand how the choice of network or compartmental model can influence estimates of intervention effectiveness in the short and long term for an endemic disease with susceptible and infected states in which infection, once contracted, is lifelong.
We consider 4 disease models with different permutations of socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk. The models have susceptible and infected populations calibrated to the same long-term equilibrium disease prevalence. We consider a simple intervention with varying levels of coverage and efficacy that reduces transmission probabilities. We measure the rate of prevalence decline over the first 365 d after the intervention, long-term equilibrium prevalence, and long-term effective reproduction ratio at equilibrium.
Prevalence declined up to 10% faster in homogeneous risk models than heterogeneous risk models. When the disease was not eradicated, the long-term equilibrium disease prevalence was higher in mass-action mixing models than in network models by 40% or more. This difference in long-term equilibrium prevalence between network versus mass-action mixing models was greater than that of heterogeneous versus homogeneous risk models (less than 30%); network models tended to have higher effective reproduction ratios than mass-action mixing models for given combinations of intervention coverage and efficacy.
For interventions with high efficacy and coverage, mass-action mixing models could provide a sufficient estimate of effectiveness, whereas for interventions with low efficacy and coverage, or interventions in which outcomes are measured over short time horizons, predictions from network and mass-action models diverge, highlighting the importance of sensitivity analyses on model structure.
• We calibrate 4 models-socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk-to 10% preintervention disease prevalence.• We measure the short- and long-term intervention effectiveness of all models using the rate of prevalence decline, long-term equilibrium disease prevalence, and effective reproduction ratio.• Generally, in the short term, prevalence declined faster in the homogeneous risk models than in the heterogeneous risk models.• Generally, in the long term, equilibrium disease prevalence was higher in the mass-action mixing models than in the network models, and the effective reproduction ratio was higher in network models than in the mass-action mixing models.
传染病控制干预措施的效果分析通常依赖于动态传播模型来模拟干预效果。我们旨在了解在具有易感染和感染状态的地方性疾病中，选择网络或房室模型如何影响短期和长期干预效果，在这种疾病中，一旦感染，感染就是终身的。
我们考虑了 4 种具有不同的社会关联网络与非结构化接触（质量作用混合）模型以及异质与同质疾病风险组合的疾病模型。这些模型具有易感染和感染人群，其长期平衡疾病流行率是经过校准的。我们考虑了一种简单的干预措施，其覆盖范围和效果不同，可降低传播概率。我们衡量了干预后 365 天内的流行率下降速度、长期平衡流行率以及平衡时的长期有效繁殖率。
同质风险模型中流行率的下降速度比异质风险模型快了高达 10%。当疾病未被根除时，在质量作用混合模型中，长期平衡疾病流行率比网络模型高 40%或更多。网络模型与质量作用混合模型之间的长期平衡流行率差异大于异质风险与同质风险模型之间的差异（小于 30%）；对于给定的干预覆盖范围和效果组合，网络模型的有效繁殖率往往高于质量作用混合模型。
对于具有高效果和高覆盖范围的干预措施，质量作用混合模型可以提供足够的有效性估计，而对于效果和覆盖范围较低的干预措施，或者在短时间内测量结果的干预措施，网络和质量作用模型的预测结果会有所不同，这凸显了对模型结构进行敏感性分析的重要性。
