Multiscale modeling approach to assess the impact of antibiotic treatment for COVID-19 on MRSA transmission and alternative immunotherapy treatment options
Taye Faniran, M. O. Adewole, Catherine Chirouze, Antoine Perasso, Raluca Eftimie
{"title":"Multiscale modeling approach to assess the impact of antibiotic treatment for COVID-19 on MRSA transmission and alternative immunotherapy treatment options","authors":"Taye Faniran, M. O. Adewole, Catherine Chirouze, Antoine Perasso, Raluca Eftimie","doi":"10.5206/mase/16685","DOIUrl":null,"url":null,"abstract":"Methicillin-Resistant Staphylococcus Aureus (MRSA) infection can occur alongside or following COVID-19, which is a concern in healthcare settings. The effectiveness of antiviral treatments for COVID-19 depends on a functioning immune response, but antibiotics used for bacterial infections like MRSA can disrupt the immune response and reduce the effectiveness of antiviral treatments. The emergence of MRSA due to excessive antibiotic usage has led to the widespread use of vancomycin as an alternative treatment. Immunomodulatory antibiotics like azithromycin may also be considered. To study the dynamics of these coinfections, a multiscale model was developed. Parameter estimation and sensitivity analysis were performed, revealing influential parameters affecting the reproduction number. Numerical simulations showed that methicillin may increase the population of co-infected cells, while azithromycin can improve the host immune response but has limited impact on MRSA proliferation. Increased efficacy of vancomycin can lead to MRSA eradication. Combination of immunomodulatory antibiotics and vancomycin has minimal effect on co-infected cell population, but increased vancomycin efficacy can reduce coinfection severity. This study emphasizes the importance of continuous research, surveillance, and the development of effective strategies to combat the complexities of COVID-19 and MRSA coinfection.","PeriodicalId":93797,"journal":{"name":"Mathematics in applied sciences and engineering","volume":"17 4","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematics in applied sciences and engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5206/mase/16685","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Methicillin-Resistant Staphylococcus Aureus (MRSA) infection can occur alongside or following COVID-19, which is a concern in healthcare settings. The effectiveness of antiviral treatments for COVID-19 depends on a functioning immune response, but antibiotics used for bacterial infections like MRSA can disrupt the immune response and reduce the effectiveness of antiviral treatments. The emergence of MRSA due to excessive antibiotic usage has led to the widespread use of vancomycin as an alternative treatment. Immunomodulatory antibiotics like azithromycin may also be considered. To study the dynamics of these coinfections, a multiscale model was developed. Parameter estimation and sensitivity analysis were performed, revealing influential parameters affecting the reproduction number. Numerical simulations showed that methicillin may increase the population of co-infected cells, while azithromycin can improve the host immune response but has limited impact on MRSA proliferation. Increased efficacy of vancomycin can lead to MRSA eradication. Combination of immunomodulatory antibiotics and vancomycin has minimal effect on co-infected cell population, but increased vancomycin efficacy can reduce coinfection severity. This study emphasizes the importance of continuous research, surveillance, and the development of effective strategies to combat the complexities of COVID-19 and MRSA coinfection.