Agent-Based Modelling Laboratory, York University, Toronto, ON, Canada (MN, ML, SMM).
Unit of PharmacoTherapy, Epidemiology & Economics (PTEE), Department of Pharmacy, University of Groningen, Groningen, The Netherlands (PTdB).
Med Decis Making. 2017 Nov;37(8):871-881. doi: 10.1177/0272989X17708564. Epub 2017 May 24.
Nosocomial influenza poses a serious risk among residents of long-term care facilities (LTCFs).
We sought to evaluate the effect of resident and staff movements and contact patterns on the outcomes of various intervention strategies for influenza control in an LTCF.
We collected contact frequency data in Canada's largest veterans' LTCF by enroling residents and staff into a study that tracked their movements through wireless tags and signal receivers. We analyzed and fitted the data to an agent-based simulation model of influenza infection, and performed Monte-Carlo simulations to evaluate the benefit of antiviral prophylaxis and patient isolation added to standard (baseline) infection control practice (i.e., vaccination of residents and staff, plus antiviral treatment of residents with symptomatic infection).
We calibrated the model to attack rates of 20%, 40%, and 60% for the baseline scenario. For data-driven movements, we found that the largest reduction in attack rates (12.5% to 27%; ANOVA P < 0.001) was achieved when the baseline strategy was combined with antiviral prophylaxis for all residents for the duration of the outbreak. Isolation of residents with symptomatic infection resulted in little or no effect on the attack rates (2.3% to 4.2%; ANOVA P > 0.2) among residents. In contrast, parameterizing the model with random movements yielded different results, suggesting that the highest benefit was achieved through patient isolation (69.6% to 79.6%; ANOVA P < 0.001) while the additional benefit of prophylaxis was negligible in reducing the cumulative number of infections.
Our study revealed a highly structured contact and movement patterns within the LTCF. Accounting for this structure-instead of assuming randomness-in decision analytic methods can result in substantially different predictions.
医院内流感对长期护理机构（LTCF）的居民构成严重威胁。
我们旨在评估居民和员工流动及接触模式对 LTCF 中流感控制各种干预策略结果的影响。
我们通过招募居民和员工参与一项研究来收集加拿大最大的退伍军人 LTCF 的接触频率数据，该研究通过无线标签和信号接收器跟踪他们的活动轨迹。我们对基于代理的流感感染模拟模型进行分析和拟合，并进行蒙特卡罗模拟，以评估在标准（基线）感染控制实践（即居民和员工接种疫苗以及对有症状感染的居民使用抗病毒药物治疗）基础上，添加抗病毒预防和患者隔离对流感控制的益处。
我们根据基线方案的发病率为 20%、40%和 60%对模型进行了校准。对于基于数据的活动，我们发现，当将基线策略与所有居民在疫情期间的抗病毒预防相结合时，发病率的降幅最大（12.5%至 27%；方差分析 P < 0.001）。对有症状感染的居民进行隔离对居民的发病率几乎没有影响（2.3%至 4.2%；方差分析 P > 0.2）。相比之下，使用随机活动参数化模型得出了不同的结果，表明通过患者隔离可以获得最大的收益（69.6%至 79.6%；方差分析 P < 0.001），而预防措施的额外益处在减少累积感染数量方面微不足道。
我们的研究揭示了 LTCF 内高度结构化的接触和活动模式。在决策分析方法中考虑这种结构而不是假设随机性，可以产生大不相同的预测结果。
