Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures

Conventional laboratory investigation of rotavirus infection and its antigen in rectal swabs from infected persons uses Electron microscopy (EM) (i.e., non-acute cases), genome, and antigen-detecting assays. A recent update involves sorting, trapping, concentrating, and identifying infectious rotavirus particles in clinical samples leveraging activated magnetic microparticles with monoclonal antibodies. However, the routine detection of rotavirus in many specimens using the EM approach is laborious, costly, and requires highly skilled workers. A sustainable healthcare system should leverage the Internet of Things to operate Smart Health Infrastructures (SHI) for predictive control of contagious diseases such as the rotavirus. This paper proposes a biomedical model for predictive control of the virus spread based on Susceptible, Breastfeeding, Vaccinated, Infected, and Recovered (SBVIR) parameters. We introduce breastfeeding, vaccination, and saturated incidence rate variables to deconstruct the transmission dynamics. An efficiency test is conducted using RI control parameters B and V. Applying Lyapunov function analysis, we prove that the global stability of disease-free and endemic equilibria exists under breastfeeding and vaccination conditions when the primary reproduction number is less than unity. Numerical simulation results show that breastfeeding and vaccination are optimal with SBVIR compared to SVIR, SBIR, and SIR parameters for rotavirus infection control by 99%, 26%, 19%, and 18%, respectively. On top of these, we show that the SBVIR model strongly agrees with real-world data and can be used to forecast the infected population in a production health facility. Finally, we show multiple Internet of Things applications in SHI to control rotavirus transmission effectively.