Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures
ABSTRACT 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 infectio...
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Wiley
2025-05-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70150 |
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| author | Titus Ifeanyi Chinebu Kennedy Chinedu Okafor Omowunmi Mary Longe Kelvin Anoh Henrietta Onyinye Uzoeto Victor Onukwube Apeh Ijeoma Peace Okafor Bamidele Adebisi Chukwunenye Anthony Okoronkwo |
| author_facet | Titus Ifeanyi Chinebu Kennedy Chinedu Okafor Omowunmi Mary Longe Kelvin Anoh Henrietta Onyinye Uzoeto Victor Onukwube Apeh Ijeoma Peace Okafor Bamidele Adebisi Chukwunenye Anthony Okoronkwo |
| author_sort | Titus Ifeanyi Chinebu |
| collection | DOAJ |
| description | ABSTRACT 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. |
| format | Article |
| id | doaj-art-e7b335f6a71b45cdbad37b5c89504190 |
| institution | OA Journals |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-e7b335f6a71b45cdbad37b5c895041902025-08-20T01:57:05ZengWileyEngineering Reports2577-81962025-05-0175n/an/a10.1002/eng2.70150Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health InfrastructuresTitus Ifeanyi Chinebu0Kennedy Chinedu Okafor1Omowunmi Mary Longe2Kelvin Anoh3Henrietta Onyinye Uzoeto4Victor Onukwube Apeh5Ijeoma Peace Okafor6Bamidele Adebisi7Chukwunenye Anthony Okoronkwo8Department of Applied Sciences Federal University of Allied Health Sciences Enugu NigeriaDepartment of Mechatronics Engineering Federal University of Technology Owerri NigeriaDepartment of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South AfricaCenter For Future Technologies University of Chichester Bognor Regis UKDepartment of Applied Sciences Federal University of Allied Health Sciences Enugu NigeriaDepartment of Applied Sciences Federal University of Allied Health Sciences Enugu NigeriaDepartment of Public Health Cardiff Metropolitan University, Llandaff Campus Cardiff UKDepartment of Engineering Manchester Metropolitan University Manchester UKDepartment of Mechatronics Engineering Federal University of Technology Owerri NigeriaABSTRACT 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.https://doi.org/10.1002/eng2.70150applied mathematicscomputational biomedical modelelectron microscopyinternet of thingsLyapunov functionsmart health infrastructure |
| spellingShingle | Titus Ifeanyi Chinebu Kennedy Chinedu Okafor Omowunmi Mary Longe Kelvin Anoh Henrietta Onyinye Uzoeto Victor Onukwube Apeh Ijeoma Peace Okafor Bamidele Adebisi Chukwunenye Anthony Okoronkwo Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures Engineering Reports applied mathematics computational biomedical model electron microscopy internet of things Lyapunov function smart health infrastructure |
| title | Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures |
| title_full | Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures |
| title_fullStr | Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures |
| title_full_unstemmed | Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures |
| title_short | Predicting and Controlling Multiple Transmissions of Rotavirus Using Computational Biomedical Model in Smart Health Infrastructures |
| title_sort | predicting and controlling multiple transmissions of rotavirus using computational biomedical model in smart health infrastructures |
| topic | applied mathematics computational biomedical model electron microscopy internet of things Lyapunov function smart health infrastructure |
| url | https://doi.org/10.1002/eng2.70150 |
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