Ratti Matteo, Concina Diego, Rinaldi Maurizio, Salinelli Ernesto, Di Brisco Agnese Maria, Ferrante Daniela, Volpe Alessandro, Panella Massimiliano
Department of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy.
Department of Pharmaceutical Science (DSF), Università del Piemonte Orientale, 28100 Novara, Italy.
Vaccines (Basel). 2022 Dec 23;11(1):32. doi: 10.3390/vaccines11010032.
seasonal influenza in nursing homes is a major public health concern, since in EU 43,000 long term care (LTC) facilities host an estimated 2.9 million elderly residents. Despite specific vaccination campaigns, many outbreaks in such institutions are occasionally reported. We explored the dynamics of seasonal influenza starting from real data collected from a nursing home located in Italy and a mathematical model. Our aim was to identify the best vaccination strategy to minimize cases (and subsequent complications) among the guests.
after producing the contact matrices with surveys of both the health care workers (HCW) and the guests, we developed a mathematical model of the disease. The model consists of a classical SEIR part describing the spreading of the influenza in the general population and a stochastic agent based model that formalizes the dynamics of the disease inside the institution. After a model fit of a baseline scenario, we explored the impact of varying the HCW and guests parameters (vaccine uptake and vaccine efficacy) on the guest attack rates (AR) of the nursing home.
the aggregate AR of influenza like illness in the nursing home was 36.4% (ward1 = 56%, ward2 = 33.3%, ward3 = 31.7%, ward4 = 34.5%). The model fit to data returned a probability of infection of the causal contact of 0.3 and of the shift change contact of 0.2. We noticed no decreasing or increasing AR trend when varying the HCW vaccine uptake and efficacy parameters, whereas the increase in both guests vaccine efficacy and uptake parameter was accompanied by a slight decrease in AR of all the wards of the LTC facility.
from our findings we can conclude that a nursing home is still an environment at high risk of influenza transmission but the shift change room and the handover situation carry no higher relative risk. Therefore, additional preventive measures in this circumstance may be unnecessary. In a closed environment such as a LTC facility, the vaccination of guests, rather than HCWs, may still represent the cornerstone of an effective preventive strategy. Finally, we think that the extensive inclusion of real life data into mathematical models is promising and may represent a starting point for further applications of this methodology.
养老院中的季节性流感是一个重大的公共卫生问题，因为在欧盟，43000家长期护理（LTC）机构中估计有290万老年居民。尽管开展了特定的疫苗接种活动，但此类机构中仍偶尔报告有许多疫情爆发。我们从意大利一家养老院收集的真实数据和一个数学模型出发，探索了季节性流感的动态变化。我们的目的是确定最佳的疫苗接种策略，以尽量减少住客中的病例（及随后的并发症）。
在通过对医护人员（HCW）和住客进行调查生成接触矩阵后，我们开发了该疾病的数学模型。该模型由一个描述流感在普通人群中传播的经典SEIR部分和一个基于随机主体的模型组成，该模型将机构内疾病的动态变化形式化。在对基线情景进行模型拟合后，我们探讨了改变医护人员和住客参数（疫苗接种率和疫苗效力）对养老院住客感染率（AR）的影响。
养老院中流感样疾病的总体感染率为36.4%（1号病房 = 56%，2号病房 = 33.3%，3号病房 = 31.7%，4号病房 = 34.5%）。与数据拟合的模型得出因果接触感染的概率为0.3，轮班交接接触感染的概率为0.2。当改变医护人员的疫苗接种率和效力参数时，我们未发现感染率有下降或上升趋势，而住客疫苗效力和接种率参数的增加伴随着长期护理机构所有病房感染率的轻微下降。
从我们的研究结果可以得出结论，养老院仍然是流感传播的高风险环境，但轮班交接室和交接情况并不具有更高的相对风险。因此，在这种情况下可能无需采取额外的预防措施。在诸如长期护理机构这样的封闭环境中，住客而非医护人员的疫苗接种可能仍然是有效预防策略的基石。最后，我们认为将实际生活数据广泛纳入数学模型很有前景，可能代表了该方法进一步应用的起点。
