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Innovations in Infection Risk Management: Applying ARIMA Model in COVID-19 Control

The global struggle against the COVID-19 pandemic has seen a reliance on subjective judgment when applying previous knowledge and management measures. This dependence has led to uneven qualities of preventative control. This article presents a research study centered on the creation of a distinctive risk management system aiming at COVID-19 infection control in outpatients, designed with the capacity to offer precise and hierarchical control built on risk estimates. The study uses the auto-regressive integrated moving average (ARIMA) model to estimate infection risk. The ARIMA model relies on weekly surveillance data related to influenza-like illness (ILI) from outpatients collected at Xuanwu Hospital, Capital Medical University, and Baidu search data from 2021 and 2022. The model’s effectiveness in predicting risk was evaluated by determining the mean absolute percentage error (MAPE). Recommendations made for sterilization and personal safety measures helped in combating the virus during predicted high and low risk periods.

The algorithms developed through this model produced accurate projections for both ILI and search engine data. With the use of this model, medical professionals can implement hierarchical preventative measures, establish criteria for adapting these strategies, and save on costs. The technique proved successful for healthcare workers directly interacting with COVID-19 patients. It was not only efficient but allowed a 41% deduction in the cost associated with maintaining high-level infection prevention measures. Notably, this advancement also contributed to better control of respiratory infections. In the face of the COVID-19 pandemic, hospitals continue to juggle between rising infection rates and ensuing socio-economic consequences.

The virus’s high mutation and transmission potential increase the insurmountable task of diagnosis and immunization. The use of technological advances in prediction models, artificial intelligence, and computer technologies has improved clinical diagnoses, classification of pathogens, and estimations of infectious disease trends. The ARIMA model, one such tool, utilizes time-series analysis and has proved instrumental in accurately predicting the periodic spikes in infectious conditions, including COVID-19. The research study underlines the extended application of the ARIMA model, coupling it with hierarchical respiratory control measures for an effective early warning system. With the dynamic and tailored risk assessment provided by this novel model, the healthcare sector can be better armed to manage the ongoing pandemic.


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