Background: Despite specific vaccination campaigns, many outbreaks of seasonal influenza like ilness (ILI) in long term care (LTC) facilities are occasionally reported. We explored the dynamics of 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 among the guests. Materials and Methods: 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. Results: The aggregate AR of ILI 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 guest vaccine efficacy and uptake parameter was accompanied by a slight decrease in AR of all the wards of the LTC facility. Conclusions: We can conclude that a nursing home is still an environment at high risk of influenza transmission but the handover situation carry no higher relative risk. Therefore, additional preventive measures in this circumstance may be unnecessary. Finally, 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.

Vaccination strategies against seasonal Influenza in long term care setting: lessons from a Mathematical Modelling study

Matteo Ratti
Primo
Writing – Original Draft Preparation
;
Diego Concina
Investigation
;
Maurizio Rinaldi
Formal Analysis
;
Ernesto Salinelli
Formal Analysis
;
Agnese Di Brisco
Formal Analysis
;
Daniela Ferrante
Data Curation
;
Alessandro Volpe
Supervision
;
Massimiliano Panella
Ultimo
Conceptualization
2023-01-01

Abstract

Background: Despite specific vaccination campaigns, many outbreaks of seasonal influenza like ilness (ILI) in long term care (LTC) facilities are occasionally reported. We explored the dynamics of 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 among the guests. Materials and Methods: 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. Results: The aggregate AR of ILI 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 guest vaccine efficacy and uptake parameter was accompanied by a slight decrease in AR of all the wards of the LTC facility. Conclusions: We can conclude that a nursing home is still an environment at high risk of influenza transmission but the handover situation carry no higher relative risk. Therefore, additional preventive measures in this circumstance may be unnecessary. Finally, 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11579/209744
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