A unified stochastic modelling framework for the spread of nosocomial infections

Understanding underlying physical mechanism reveals early warning indicators and key elements for adaptive infections disease networks

The study of infectious diseases holds significant scientific and societal importance, yet current research on the mechanisms of disease emergence and prediction methods still face challenging issues. This research uses the landscape and flux theoretical framework to reveal the non-equilibrium dynamics of adaptive infectious diseases and uncover its underlying physical mechanism. This allows the quantification of dynamics, characterizing the system with two basins of attraction determined by gradient and rotational flux forces. Quantification of entropy production rates provides insights into the system deviating from equilibrium and associated dissipative costs. The study identifies early warning indicators for the critical transition, emphasizing the advantage of observing time irreversibility from time series over theoretical entropy production and flux. The presence of rotational flux leads to an irreversible pathway between disease states. Through global sensitivity analysis, we identified the key factors influencing infectious diseases. In summary, this research offers valuable insights into infectious disease dynamics and presents a practical approach for predicting the onset of critical transition, addressing existing research gaps.

Transmission routes of antibiotic resistant bacteria: a systematic review

Background

Quantification of acquisition routes of antibiotic resistant bacteria (ARB) is pivotal for understanding transmission dynamics and designing cost-effective interventions. Different methods have been used to quantify the importance of transmission routes, such as relative risks, odds ratios (OR), genomic comparisons and basic reproduction numbers. We systematically reviewed reported estimates on acquisition routes’ contributions of ARB in humans, animals, water and the environment and assessed the methods used to quantify the importance of transmission routes.

Methods

PubMed and EMBASE were searched, resulting in 6054 articles published up until January 1st, 2019. Full text screening was performed on 525 articles and 277 are included.

Results

We extracted 718 estimates with S. aureus (n = 273), E. coli (n = 157) and Enterobacteriaceae (n = 99) being studied most frequently. Most estimates were derived from statistical methods (n = 560), mainly expressed as risks (n = 246) and ORs (n = 239), followed by genetic comparisons (n = 85), modelling (n = 62) and dosage of ARB ingested (n = 17). Transmission routes analysed most frequently were occupational exposure (n = 157), travelling (n = 110) and contacts with carriers (n = 83). Studies were mostly performed in the United States (n = 142), the Netherlands (n = 87) and Germany (n = 60). Comparison of methods was not possible as studies using different methods to estimate the same route were lacking. Due to study heterogeneity not all estimates by the same method could be pooled.

Conclusion

Despite an abundance of published data the relative importance of transmission routes of ARB has not been accurately quantified. Links between exposure and acquisition are often present, but the frequency of exposure is missing, which disables estimation of transmission routes’ importance. To create effective policies reducing ARB, estimates of transmission should be weighed by the frequency of exposure occurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07360-z.

Dimension-reduction of dynamics on real-world networks with symmetry

We derive explicit formulae to quantify the Markov chain state-space compression, or lumping, that can be achieved in a broad range of dynamical processes on real-world networks, including models of epidemics and voting behaviour, by exploiting redundancies due to symmetries. These formulae are applied in a large-scale study of such symmetry-induced lumping in real-world networks, from which we identify specific networks for which lumping enables exact analysis that could not have been done on the full state-space. For most networks, lumping gives a state-space compression ratio of up to 10 7 , but the largest compression ratio identified is nearly 10 12 . Many of the highest compression ratios occur in animal social networks. We also present examples of types of symmetry found in real-world networks that have not been previously reported.

Association between intrahospital transfer and hospital-acquired infection in the elderly: a retrospective case-control study in a UK hospital network

Background

Intrahospital transfers have become more common as hospital staff balance patient needs with bed availability. However, this may leave patients more vulnerable to potential pathogen transmission routes via increased exposure to contaminated surfaces and contacts with individuals.

Objective

This study aimed to quantify the association between the number of intrahospital transfers undergone during a hospital spell and the development of a hospital-acquired infection (HAI).

Methods

A retrospective case–control study was conducted using data extracted from electronic health records and microbiology cultures of non-elective, medical admissions to a large urban hospital network which consists of three hospital sites between 2015 and 2018 (n=24 240). As elderly patients comprise a large proportion of hospital users and are a high-risk population for HAIs, the analysis focused on those aged 65 years or over. Logistic regression was conducted to obtain the OR for developing an HAI as a function of intrahospital transfers until onset of HAI for cases, or hospital discharge for controls, while controlling for age, gender, time at risk, Elixhauser comorbidities, hospital site of admission, specialty of the dominant healthcare professional providing care, intensive care admission, total number of procedures and discharge destination.

Results

Of the 24 240 spells, 2877 cases were included in the analysis. 72.2% of spells contained at least one intrahospital transfer. On multivariable analysis, each additional intrahospital transfer increased the odds of acquiring an HAI by 9% (OR=1.09; 95% CI 1.05 to 1.13).

Conclusion

Intrahospital transfers are associated with increased odds of developing an HAI. Strategies for minimising intrahospital transfers should be considered, and further research is needed to identify unnecessary transfers. Their reduction may diminish spread of contagious pathogens in the hospital environment.

A mathematical model and inference method for bacterial colonization in hospital units applied to active surveillance data for carbapenem-resistant enterobacteriaceae

Widespread use of antibiotics has resulted in an increase in antimicrobial-resistant microorganisms. Although not all bacterial contact results in infection, patients can become asymptomatically colonized, increasing the risk of infection and pathogen transmission. Consequently, many institutions have begun active surveillance, but in non-research settings, the resulting data are often incomplete and may include non-random testing, making conventional epidemiological analysis problematic. We describe a mathematical model and inference method for in-hospital bacterial colonization and transmission of carbapenem-resistant Enterobacteriaceae that is tailored for analysis of active surveillance data with incomplete observations. The model and inference method make use of the full detailed state of the hospital unit, which takes into account the colonization status of each individual in the unit and not only the number of colonized patients at any given time. The inference method computes the exact likelihood of all possible histories consistent with partial observations (despite the exponential increase in possible states that can make likelihood calculation intractable for large hospital units), includes techniques to improve computational efficiency, is tested by computer simulation, and is applied to active surveillance data from a 13-bed rehabilitation unit in New York City. The inference method for exact likelihood calculation is applicable to other Markov models incorporating incomplete observations. The parameters that we identify are the patient-patient transmission rate, pre-existing colonization probability, and prior-to-new-patient transmission probability. Besides identifying the parameters, we predict the effects on the total prevalence (0.07 of the total colonized patient-days) of changing the parameters and estimate the increase in total prevalence attributable to patient-patient transmission (0.02) above the baseline pre-existing colonization (0.05). Simulations with a colonized versus uncolonized long-stay patient had 44% higher total prevalence, suggesting that the long-stay patient may have been a reservoir of transmission. High-priority interventions may include isolation of incoming colonized patients and repeated screening of long-stay patients.

COVID-19 infection and diffusion among the healthcare workforce in a large university-hospital in northwest Italy

Backgroud: Since the beginning of the coronavirus disease 2019 (COVID-19) outbreak, healthcare workers (HCWs) have been the workers most likely to contract the disease. Intensive focus is therefore needed on hospital strategies that minimize exposure and diffusion, confer protection and facilitate early detection and isolation of infected personnel.

METHODS

To evaluate the early impact of a structured risk-management for exposed COVID-19 HCWs and describe how their characteristics contributed to infection and diffusion. Socio-demographic and clinical data, aspects of the event-exposure (date, place, length and distance of exposure, use of PPE) and details of the contact person were collected.

RESULTS

The 2411 HCWs reported 2924 COVID-19 contacts. Among 830 HCWs who were at 'high or medium risk', 80 tested positive (9.6%). Physicians (OR=2.03), and non-medical services -resulted in an increased risk (OR=4.23). Patient care did not increase the risk but sharing the work environment did (OR=2.63). There was a significant time reduction between exposure and warning, exposure and test, and warning and test since protocol implementation. HCWs with management postitions were the main source of infection due to the high number of interactions.

DISCUSSION

A proactive system that includes prompt detection of contagious staff and identification of sources of exposure helps to lower the intra-hospital spread of infection. A speedier return to work of staff who would otherwise have had to self-isolate as a precautionary measure improves staff morale and patient care by reducing the stress imposed by excessive workloads arising from staff shortages.

Using Artificial Intelligence in Infection Prevention

Purpose of Review

Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene.

Recent Findings

AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed.

Summary

Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.

Systems Modeling Approach for Reducing the Risk of Healthcare-Associated Infections

Healthcare-associated infections (HAIs) are a major cause of concern because of the high levels of associated morbidity, mortality, and cost. In addition, children and intensive care unit (ICU) patients are more vulnerable to these infections due to low levels of immunity. Various medical interventions and statistical process control techniques have been suggested to counter the spread of these infections and aid early detection of an infection outbreak. Methods such as hand hygiene help in the prevention of HAIs and are well-documented in the literature. This chapter demonstrates the utilization of a systems methodology to model and validate factors that contribute to the risk of HAIs in a pediatric ICU. It proposes an approach that has three unique aspects: it studies the problem of HAIs as a whole by focusing on several HAIs instead of a single type, it projects the effects of interventions onto the general patient population using the system-level model, and it studies both medical and behavioral interventions and compares their effectiveness. This methodology uses a systems modeling framework that includes simulation, risk analysis, and statistical techniques for studying interventions to reduce the transmission likelihood of HAIs.

A unified stochastic modelling framework for the spread of nosocomial infections

Over the last years, a number of stochastic models have been proposed for analysing the spread of nosocomial infections in hospital settings. These models often account for a number of factors governing the spread dynamics: spontaneous patient colonization, patient–staff contamination/colonization, environmental contamination, patient cohorting or healthcare workers (HCWs) hand-washing compliance levels. For each model, tailor-designed methods are implemented in order to analyse the dynamics of the nosocomial outbreak, usually by means of studying quantities of interest such as the reproduction number of each agent in the hospital ward, which is usually computed by means of stochastic simulations or deterministic approximations. In this work, we propose a highly versatile stochastic modelling framework that can account for all these factors simultaneously, and which allows one to exactly analyse the reproduction number of each agent at the hospital ward during a nosocomial outbreak. By means of five representative case studies, we show how this unified modelling framework comprehends, as particular cases, many of the existing models in the literature. We implement various numerical studies via which we (i) highlight the importance of maintaining high hand-hygiene compliance levels by HCWs, (ii) support infection control strategies including to improve environmental cleaning during an outbreak and (iii) show the potential of some HCWs to act as super-spreaders during nosocomial outbreaks.

A Multicompartment SIS Stochastic Model with Zonal Ventilation for the Spread of Nosocomial Infections: Detection, Outbreak Management, and Infection Control

In this work, we study the environmental and operational factors that influence airborne transmission of nosocomial infections. We link a deterministic zonal ventilation model for the airborne distribution of infectious material in a hospital ward, with a Markovian multicompartment SIS model for the infection of individuals within this ward, in order to conduct a parametric study on ventilation rates and their effect on the epidemic dynamics. Our stochastic model includes arrival and discharge of patients, as well as the detection of the outbreak by screening events or due to symptoms being shown by infective patients. For each ventilation setting, we measure the infectious potential of a nosocomial outbreak in the hospital ward by means of a summary statistic: the number of infections occurred within the hospital ward until end or declaration of the outbreak. We analytically compute the distribution of this summary statistic, and carry out local and global sensitivity analysis in order to identify the particular characteristics of each ventilation regime with the largest impact on the epidemic spread. Our results show that ward ventilation can have a significant impact on the infection spread, especially under slow detection scenarios or in overoccupied wards, and that decreasing the infection risk for the whole hospital ward might increase the risk in specific areas of the health-care facility. Moreover, the location of the initial infective individual and the protocol in place for outbreak declaration both form an interplay with ventilation of the ward.

A Multicompartment SIS Stochastic Model with Zonal Ventilation for the Spread of Nosocomial Infections: Detection, Outbreak Management, and Infection Control

In this work, we study the environmental and operational factors that influence airborne transmission of nosocomial infections. We link a deterministic zonal ventilation model for the airborne distribution of infectious material in a hospital ward, with a Markovian multicompartment SIS model for the infection of individuals within this ward, in order to conduct a parametric study on ventilation rates and their effect on the epidemic dynamics. Our stochastic model includes arrival and discharge of patients, as well as the detection of the outbreak by screening events or due to symptoms being shown by infective patients. For each ventilation setting, we measure the infectious potential of a nosocomial outbreak in the hospital ward by means of a summary statistic: the number of infections occurred within the hospital ward until end or declaration of the outbreak. We analytically compute the distribution of this summary statistic, and carry out local and global sensitivity analysis in order to identify the particular characteristics of each ventilation regime with the largest impact on the epidemic spread. Our results show that ward ventilation can have a significant impact on the infection spread, especially under slow detection scenarios or in overoccupied wards, and that decreasing the infection risk for the whole hospital ward might increase the risk in specific areas of the health-care facility. Moreover, the location of the initial infective individual and the protocol in place for outbreak declaration both form an interplay with ventilation of the ward.