In the constant fight against illnesses, respiratory infectious diseases significantly affect human society bringing about a high medical burden and severe economic loss. This is especially highlighted by the COVID-19 pandemic, which has had devastating health and economic effects. There is often regularity to respiratory epidemics, with peaks commonly during the fall and winter months.
Generally, hospitals, especially tertiary ones, face these respiratory infection challenges since they house critically ill patients highly susceptible to infections. Furthermore, the wide-ranging medical procedures, particularly in outpatient and emergency departments, inadvertently increase the risk of catching respiratory infections.
To counter this, the authors of this study sought to develop an innovative dynamic prevention and control method for such infections. This strategy leverages the autoregressive integrated moving average model (ARIMA) to forecast the onset and endpoints of epidemic peaks. Surveillance data like influenza-like case counts and search frequency of respiratory tract infection keywords, among others, are used in this model. Additionally, a Delphi process was used to build consensus on hierarchical infection control measures.
The ARIMA model was highly accurate in its estimates, indicating its potential usefulness in guiding infection control measures. The proposed infection control measures encompassed three categories with nine critical points, including a risk-period judgment matrix designed to link surveillance data and control measures.
Findings demonstrated that dynamic prevention and control of respiratory infections in outpatients could be constructed based on the daily medical service monitoring data of hospitals. This could potentially lead to accurate and cost-effective infection prevention and control outcomes. Thus, this proposed method can be seen as an effective tool for healthcare providers in managing and controlling respiratory nosocomial infections.