Mathematical modeling of postcoinfection with influenza A virus and Streptococcus pneumoniae, with implications for pneumonia and COPD-risk assessment
Received 29 March 2017
Accepted for publication 5 June 2017
Published 5 July 2017 Volume 2017:12 Pages 1973—1988
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Dr Richard Russell
Yi-Hsien Cheng,1 Shu-Han You,2 Yi-Jun Lin,3 Szu-Chieh Chen,4,5 Wei-Yu Chen,6 Wei-Chun Chou,2 Nan-Hung Hsieh,7 Chung-Min Liao3
1Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA; 2National Institute of Environmental Health Sciences, National Health Research Institutes, Zhunan, 3Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 4Department of Public Health, 5Department of Family and Community Medicine, Chung Shan Medical University Hospital, Taichung, 6Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan; 7Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
Background: The interaction between influenza and pneumococcus is important for understanding how coinfection may exacerbate pneumonia. Secondary pneumococcal pneumonia associated with influenza infection is more likely to increase respiratory morbidity and mortality. This study aimed to assess exacerbated inflammatory effects posed by secondary pneumococcal pneumonia, given prior influenza infection.
Materials and methods: A well-derived mathematical within-host dynamic model of coinfection with influenza A virus and Streptococcus pneumoniae (SP) integrated with dose–response relationships composed of previously published mouse experimental data and clinical studies was implemented to study potentially exacerbated inflammatory responses in pneumonia based on a probabilistic approach.
Results: We found that TNFα is likely to be the most sensitive biomarker reflecting inflammatory response during coinfection among three explored cytokines. We showed that the worst inflammatory effects would occur at day 7 SP coinfection, with risk probability of 50% (likely) to develop severe inflammatory responses. Our model also showed that the day of secondary SP infection had much more impact on the severity of inflammatory responses in pneumonia compared to the effects caused by initial virus titers and bacteria loads.
Conclusion: People and health care workers should be wary of secondary SP infection on day 7 post-influenza infection for prompt and proper control-measure implementation. Our quantitative risk-assessment framework can provide new insights into improvements in respiratory health especially, predominantly due to chronic obstructive pulmonary disease (COPD).
Keywords: chronic obstructive pulmonary disease, pneumonia, influenza, coinfection, modeling, risk assessment
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