Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus.

BACKGROUND: Complex transmission models of healthcare-associated infections provide insight for hospital epidemiology and infection control efforts, but they are difficult to implement and come at high computational costs. Structuring more simplified models to incorporate the heterogeneity of the intensive care unit (ICU) patient-provider interactions, we explore how methicillin-resistant Staphylococcus aureus (MRSA) dynamics and acquisitions may be better represented and approximated. METHODS: Using a stochastic compartmental model of an 18-bed ICU, we compared the rates of MRSA acquisition across three ICU population interaction structures: a model with nurses and physicians as a single staff type (SST), a model with separate staff types for nurses and physicians (Nurse-MD model), and a Metapopulation model where each nurse was assigned a group of patients. The proportion of time spent with the assigned patient group (γ) within the Metapopulation model was also varied. RESULTS: The SST, Nurse-MD, and Metapopulation models had a mean of 40.6, 32.2 and 19.6 annual MRSA acquisitions respectively. All models were sensitive to the same parameters in the same direction, although the Metapopulation model was less sensitive. The number of acquisitions varied non-linearly by values of γ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the Metapopulation structure. DISCUSSION: Inclusion of complex population interactions within a modeled hospital ICU has considerable impact on model results, with the SST model having more than double the acquisition rate of the more structured metapopulation model. While the direction of parameter sensitivity remained the same, the magnitude of these differences varied, producing different colonization rates across relatively similar populations. The non-linearity of the model's response to differing values of a parameter gamma (γ) suggests simple model approximations are appropriate in only a narrow space of relatively dispersed nursing assignments. CONCLUSION: Simplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions. In many, if not most, cases more complex models that represent population mixing with higher granularity are justified.

Estimating SARS-CoV-2 transmission parameters between coinciding outbreaks in a university population and the surrounding community.

Prior studies suggest that population heterogeneity in SARS-CoV-2 (COVID-19) transmission plays an important role in epidemic dynamics. During the fall of 2020, many US universities and the surrounding communities experienced an increase in reported incidence of SARS-CoV-2 infections, with a high disease burden among students. We explore the transmission dynamics of an outbreak of SARS-CoV-2 among university students, how it impacted the non-student population via cross-transmission, and how it could influence pandemic planning and response. Using surveillance data of reported SARS-CoV-2 cases, we developed a two-population SEIR model to estimate transmission parameters and evaluate how these subpopulations interacted during the 2020 Fall semester. We estimated the transmission rate among the university students (ßU) and community residents (ßC), as well as the rate of cross-transmission between the two subpopulations (ßM) using particle Markov Chain Monte Carlo (pMCMC) simulation-based methods. We found that both populations were more likely to interact with others in their population and that cross-transmission was minimal. The cross-transmission estimate (ßM) was considerably smaller [0.04 × 10-5 (95% CI: 0.00 × 10-5, 0.15 × 10-5)] compared to the community estimate (ßC) at 2.09 × 10-5 (95% CI: 1.12 × 10-5, 2.90 × 10-5) and university estimate (ßU) at 27.92 × 10-5 (95% CI: 19.97 × 10-5, 39.15 × 10-5). The higher within population transmission rates among the university and the community (698 and 52 times higher, respectively) when compared to the cross-transmission rate, suggests that these two populations did not transmit between each other heavily, despite their geographic overlap. During the first wave of the pandemic, two distinct epidemics occurred among two subpopulations within a relatively small US county population where university students accounted for roughly 41% of the total population. Transmission parameter estimates varied substantially with minimal or no cross-transmission between the subpopulations. Assumptions that county-level and other small populations are well-mixed during a respiratory viral pandemic should be reconsidered. More granular models reflecting overlapping subpopulations may assist with better-targeted interventions for local public health and healthcare facilities.

Transient dynamics of infection transmission in a simulated intensive care unit.

Healthcare-associated infections (HAIs) remain a serious public health problem. In previous work, two models of an intensive care unit (ICU) showed that differing population structures had markedly different rates of Staphylococcus aureus (MRSA) transmission. One explanation for this difference is the models having differing long-term equilbrium dynamics, resulting from different basic reproductive numbers, R0. We find in this system however that this is not the case, and that both models had the same value for R0. Instead, short-term, transient dynamics, characterizing a series of small, self-limiting outbreaks caused by pathogen reintroduction were responsible for the differences. These results show the importance of these short-term factors for disease systems where reintroduction events are frequent, even if they are below the epidemic threshold. Further, we examine how subtle changes in how a hospital is organized-or how a model assumes a hospital is organized-in terms of the admission of new patients may impact transmission rates. This has implications for both novel pathogens introduced into ICUs, such as Ebola, MERS or COVID-19, as well as existing healthcare-associated infections such as carbapenem-resistant Enterobacteriaceae.

Excess Risk of COVID-19 to University Populations Resulting from In-Person Sporting Events.

BACKGROUND: One of the consequences of COVID-19 has been the cancelation of collegiate sporting events. We explore the impact of sports on COVID-19 transmission on a college campus. METHODS: Using a compartmental model representing the university, we model the impact of influxes of 10,000 visitors attending events and ancillary activities (dining out, visiting family, shopping, etc.) on 20,000 students. We vary the extent visitors interact with the campus, the number of infectious visitors, and the extent to which the campus has controlled COVID-19 absent events. We also conduct a global sensitivity analysis. RESULTS: Events caused an increase in the number of cases ranging from a 25% increase when the campus already had an uncontrolled COVID-19 outbreak and visitors had a low prevalence of COVID-19 and mixed lightly with the campus community to an 822% increase where the campus had controlled their COVID-19 outbreak and visitors had both a high prevalence of COVID-19 and mixed heavily with the campus community. The model was insensitive to parameter uncertainty, save for the duration a symptomatic individual was infectious. CONCLUSION: Sporting events represent a threat to the health of the campus community. This is the case even in circumstances where COVID-19 seems controlled both on-campus and among the general population.

Assessing the Potential Impact of a Long-Acting SkinDisinfectant in the Prevention of Methicillin-Resistant Staphylococcus aureus Transmission.

Healthcare-associated transmission of methicillin-resistant Staphylococcus aureus (MRSA)remains a persistent problem. The use of chlorhexidine gluconate (CHG) as a means of decolonizingpatients, either through targeted decolonization or daily bathing, is frequently used to supplementother interventions. We explore the potential of a long-acting disinfectant with a persistent effect,immediate decolonizing action in the prevention of MRSA acquisition, and clinical illness andmortality in an 18-bed intensive care unit, based on a previous model. A scenario with nointervention is compared to CHG bathing, which decolonizes patients but provides no additionalprotection, and a hypothetical treatment that both decolonizes them and provides protection fromsubsequent colonization. The duration and effectiveness of this protection is varied to fully explorethe potential utility of such a treatment. Increasing the effectiveness of the decolonizing agentreduces colonization, with a 10% increase resulting in a colonization rate ratio (RR) of 0.89 (95% CI:0.89,0.90). Increasing the duration of protection results in a much more modest reduction, with a 12-hour increase in protection resulting in an RR of 0.99 (95% CI: 0.99, 0.99). There is little evidence ofsynergy between the two.