Objective: A simultaneous outbreak of coronavirus disease 2019 (COVID-19) and influenza occurred in the latter quarter of 2023. The study aimed to provide valuable data for patient care by comparing the clinical characteristics of those diagnosed with either COVID-19 or influenza in the emergency room. This information is intended to assist healthcare providers in managing cases where both diseases are encountered concurrently. Methods: The study involved 1,026 patients diagnosed with COVID-19 or influenza. The following were analyzed retrospectively: demographic information (age and sex), clinical symptoms (fever, fatigue, muscle pain, shortness of breath, sore throat, cough, sputum, runny nose, headache, and gastrointestinal symptoms), laboratory (white blood cell count, C-reactive protein) and imaging tests, and hospitalization rates. Results: COVID-19 patients were more likely to be older, experience fatigue and dyspnea, and require imaging tests and hospitalization than influenza patients. Influenza patients reported more fever, sore throat, cough, runny nose, and certain other symptoms. No significant differences were observed between the two groups regarding sex, myalgia, headache, and gastrointestinal symptoms. Conclusion Despite the significant differences in age, underlying conditions, clinical symptoms, imaging tests, and hospitalization rates between the two groups, distinguishing between COVID-19 and influenza based solely on these factors is challenging. Therefore, specific testing for each disease is essential during simultaneous outbreaks for an accurate diagnosis.

Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results The functional evaluation shows that the main function of this SAP architecture was operated successfully.DiscussionThe proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.