Construction of A New Dose–Response Model for Staphylococcus aureus Considering Growth and Decay Kinetics on Skin

Abstract

In order to determine the relationship between an exposure dose of Staphylococcus aureus (S. aureus) on the skin and the risk of infection, an understanding of the bacterial growth and decay kinetics is very important. Models are essential tools for understanding and predicting bacterial kinetics and are necessary to predict the dose of organisms post-exposure that results in a skin infection. One of the challenges in modeling bacterial kinetics is the estimation of model parameters, which can be addressed using an inverse problem approach. The objective of this study is to construct a microbial kinetic model of S. aureus on human skin and use the model to predict concentrations of S. aureus that result in human infection. In order to model the growth and decay of S. aureus on skin, a Gompertz inactivation model was coupled with a Gompertz growth model. A series of analyses, including ordinary least squares regression, scaled sensitivity coefficient analysis, residual analysis, and parameter correlation analysis were conducted to estimate the parameters and to describe the model uncertainty. Based on these analyses, the proposed model parameters were estimated with high accuracy. The model was then used to develop a new dose-response model for S. aureus using the exponential dose-response model. The new S. aureus model has an optimized k parameter equivalent to 8.05 × 10 −8 with 95 th percentile confidence intervals between 6.46 × 10 −8 and 1.00 × 10 −7 . their nose persistently, while another 20% to 30% carry intermittently [14]. The typical transmission route of S. aureus is from the nose to the hand of a person [15], then to a surface (e.g., a door knob), and/or via the hand to the nose of a second person [16,17]. Activities involving close physical contact and the risk of minor injuries are positively correlated with S. aureus spread and acquisition [18]. Even a brief contact of fingers with a S. aureus contaminated surface may cause the transfer of a large amount of organisms resulting in a potential infection hazard [19]. The transfer rate is higher from moist contaminated surfaces than dry surfaces [20,21].S. aureus can survive on dry surfaces between 2 and 4 days, and then can be easily transferred to hands and foods [22]. Other experiments showed more than a day of survival in hospital fabrics (cotton, terry, blend, and polyester) to over 90 days of survival in polyethylene [23]. These long survival times indicate a potential high risk of transmission of S. aureus through the surface-to-hand pathway. Once S. aureus is in the human body, it is believed to form biofilms, which makes the pathogen less vulnerable to host immune responses and allows them to cause colonization and local infections [14].S. aureus is an opportunistic pathogen and does not usually pose a fatal risk to humans even if it colonizes human mucosa or skin [14]. However, in some cases, S. aureus can cause severe or fatal infections. S. aureus infections progress in five stages: colonization, local infection, systematic dissemination, me...

Construction of multifunctional porcine acellular dermal matrix hydrogel blended with vancomycin for hemorrhage control, antibacterial action, and tissue repair in infected trauma wounds

“…they cause cell membrane damage [ 19 ], DNA damage [ 20 ], oxidative stress [ 21 ], and the formation of reactive oxygen species (ROS) [ 22 ]. Staphylococcus aureus ( S. aureus ) is the most common type of bacterium that causes skin infection [ 23 , 24 ]. As the first generation of glycopeptide antibiotic, vancomycin has been demonstrated to possess remarkable bactericidal properties, especially against S. aureus .…”

Quantitative microbial risk assessment and sensitivity analysis for workers exposed to pathogenic bacterial bioaerosols under various aeration modes in two wastewater treatment plants

“… Aerosol ingestion rate (ag) Unitless Supplementary Materials Table 2 – Removal fraction by employing PPE (F PPE ) Unitless Uniform distribution (Min = 0.95; Max = 0.99) Haas et al, 2017 Annual exposure frequency (n) Number of times 183 According to the field survey in this research. Staphylococcus aureus bioaerosol Exponential dose–response model (dose–infection model) k Unitless Uniform (Min = 6.46E−8; Max = 1.00E−7) Esfahanian et al, 2019 Prevalence P ill/inf Unitless 1 Busgang et al, 2018 Health burden (HB) DALYs/case 2.60E-3 Havelaar et al, 2012 Escherichia coli bioaerosol Beta-Poisson dose–response model (dose–infection model) α Unitless 1.55E-01 Shi et al, 2018 N 50 Unitless 2.11E+06 Shi et al, 2018 Exponential dose–response model (dose–illness model) k Unitless 1.22E-08 Shi et al, 2018 Health burden (HB) DALYs/case 4.55E-2 Shi et al, 2018

Fig. 1 Flow chart of quantitative microbial risk assessment.

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“…aureus bioaerosol, the exponential dose–response model as a dose–infection model was used to determine the relationship between the dose and the infection risks (Eq. (2) ) ( Esfahanian et al, 2019 ): where P ( d ) inf is the estimated daily probability of infection, and k is the parameter of the model ( Table 1 ).…”

“…Health risk is usually quantified by the annual probability of infection (P ( a )inf ) and disease burden (DB) ( Haas et al, 2014 ). The P ( a )inf and DB of bioaerosol exposure could be estimated by quantitative microbial risk assessment (QMRA) ( Haas et al, 2014 ; Shi et al, 2018 ; Esfahanian et al, 2019 ). QMRA commonly follows four classical working steps: (a) hazard identification, (b) exposure assessment, (c) dose–response assessment, and (d) risk characterization ( Haas et al, 2014 ).…”

Microbiological Health Risk Assessment of Water Conservation Strategies: A Case Study in Amsterdam

“…An exponential model was used for Staphylococcus aureus, Pseudomonas aeruginosa, Legionella pneumophila , and Mycobacterium Avium Complex, whereas a beta-poisson model was used for E. coli O157:H7. Whether an exponential or a beta-poisson model is used for certain pathogens is because certain models fit better to the dose–response data of the pathogen compared to other models [ 33 , 52 , 53 , 54 , 55 ].…”