Application of Temporal Network on Potential Disease Transmission: Hospital Case Study

Early diagnosis of potential epidemic transmission of diseases such as influenza or coronavirus in hospitals where one-to-one contact occurs is central not only to save patients’ life, but also to prevent disease propagation to staff, nurses, medical doctors, and other workers. This paper aims to predict the risk threshold of influenza disease transmission in a temporal network; the hospital’s data in Lyon, France is taken as a case study. The network involves 46 health care workers and 29 patients. The Susceptible Infectious Recovered (SIR) model is used for the analysis. The SIR model is more fit for the influenza disease because a patient is not suspected to spread the disease after recovery. The results show that the disease propagation rate is lower in the temporal network compared with the corresponding aggregated network. It is found out that that the threshold of an epidemic occurs when the transmission percentage is 10%. Most importantly, it is found that the nurses and administrators are more likely to be infected than physicians or patients in this case study. The proposed model is applicable in hospitals, schools, or any work organization for epidemiologic control.