Primary Research Area: Kiel's primary area of study is the modeling of Infectious Disease Modeling through mathematical and statistical methods. Kiel's past modeling strategy include agent-based modeling and systems of ordinary equations (ODE) to predict mortality and prevalence rates for various environments. Presently, since Kiel is a active Bayesian statistician his current focusing is developing a new application of the Delayed Rejection Adaptive Mechanism (DRAM) that allow for the stepwise input of parameters within models for infectious disease prediction that are unknown rather just one set of unknown variables at a time. Additionally, Kiel is working on incorporating the DRAM method for AI training in other areas of epidemiological research that relate to infectious diseases, such as the connection between drug abuse and number of infections.
Objectives:
Optimize prevention strategies for infectious disease transmission that a variety of healthcare settings, such as urban anchors hospitals or nursing nursing home.
Improve overall outcome for patients that are diagnosed with a infection disease.
Obtain a deeper understanding behind transmission mechanics associated with infectious diseases.
Build more reliable models for the prediction of transmission rate, morality rate, and prevalence rates for common infectious diseases.
More detailed information about Kiel's previous projects in this area mentioned and current research projects is provided below:
Past Research Projects
1) Title: "An Agent-Based Model to Assess the Impact of Shared Staff and Occupancy Rates on Infectious Disease Burden in Nursing Homes"
Authorship: Primary Author.
Funding Source: Center for Diseases Control (CDC)
Summary: The COVID-19 pandemic revealed that infection could spread from one nursing home to another through asymptomatic staff members working in both nursing homes. Nevertheless, the transmission dynamics of infection both within and between nursing homes have not been entirely studied for how various levels of shared staff differ in their impact on disease incidence, prevalence, and mortality. Our project was attempting to provide more quantitative details about these impacts and see if there any significant trends in regards to transmission of a infectious diseases within the nursing home when you have different levels of shared staff. . To do this , we created a agent based model (ABM) to simulate COVID-19 outbreaks in two artificial nursing homes set to various levels of shared staff (0%, 5%, 10%, 15%, 20%, and 30%) and recorded the results in terms of prevalence, incidence, and mortality for each nursing home.
Key Highlights:
The model simulations indicate that reducing the percentage of shared staff below 5% plays a significant role in controlling the spread of infection from one nursing home to another through personal protective equipment usage, rapid testing, and vaccination .
As the percentage of shared staff increases to more than 30%, basic prevention measures (PPE use, vaccination, and isolation) become less effective, and the prevalence of infection reaches an endemic state in both nursing homes.
Estimated individual reproduction numbers suggest that certain staff and residents are more susceptible to becoming super spreaders of a infection once you start employing a 15% shared staff level.
Effectiveness of infection control and basic preventive measures (PPE use, vaccination, and isolation) decreases as the level of staff sharing between nursing homes increases leading to an endemic state in both nursing homes.
Impact on incidence, prevalence, and morality rates from shared staff are higher when then nursing home as a higher contact rate.
2) Title: " Bayesian Inference of Nosocomial Methicillin-Resistant Staphylococcus aureus Transmission Rates in an Urban Safety-Net Hospital"
Authorship: Primary Author.
Funding Source: Center for Diseases Control (CDC)
Summary: Methicillin-resistant Staphylococcus aureus (MRSA) is a strain of Staphylococcus aureus that poses significant challenges in treatment and infection control within healthcare settings. Recent research suggests that the incidence of healthcare-associated MRSA (HA-MRSA) is higher among patients treated in safety-net hospitals compared to those in non-safety-net hospitals. This study aimed to identify HA-MRSA transmission patterns across various nursing units of a safety-net hospital to improve to enhance patient outcomes and facilitate the implementation of targeted infection control measures. A retrospective analysis was conducted using surveillance data from 2019 to 2023. A compartmental disease model was applied to estimate MRSA transmission rates and basic reproduction number for each nursing unit of an urban, multicenter safety-net hospital before and during the COVID-19 pandemic. Posterior probability distributions for transmission, isolation, and hospital discharge rates were computed using the Delayed Rejection Adaptive Metropolis (DRAM) Bayesian algorithm.
Key Highlights:
Analysis of 187,040 patient records revealed that inpatient nursing units exhibited the highest MRSA transmission rates in three out of the five years studied (2019 to 2023).
Transmission pattern found by our simulations consist of relative calm for a few years then a spike and then back to relative calm transmission.
Notable transmission rates were observed in certain inpatient and progressive care units (0.55 per individual per month; 0.018 per individual per day) and the surgical ICU (0.44 per individual per month; 0.015 per individual per day).
In contrast, the Nursery NICU and Medical ICU had the lowest transmission rates. Although MRSA transmission rates significantly declined across all units in 2021.
When comparing this to regular hospital average transmission rate of 0.30 the mean transmission rates from our study indicate moderate infection control, though occasional spikes highlight areas for improvement.
3) Title: " Assessing the Impact of Biosecurity Compliance on Farmworker and Livestock Health within a One Health Modeling Framework"
Authorship: Secondary Author.
Funding Source: Center for Diseases Control (CDC)
Summary: The adherence to protocols designed to prevent outbreaks and spread of infectious diseases is known as biosecurity compliance. While several models only focus on animal health, the present work develops a one health modeling framework to investigate how different degrees of compliance can influence the health of both animals and farmworkers. The model consists of a set of ordinary differential equations representing the spread of drug-susceptible and drug resistant pathogens in the environment and animal population, coupled with a set of stochastic differential equations representing the spread of pathogens in the farm worker population. The next-generation matrix approach is employed to estimate the basic reproduction numbers (R0) under three different hypotheses of transition, transition–reservoir, and reservoir both for drug-susceptible and drug-resistant pathogens, where they all agree on the same threshold value (i.e. R0 >1 implies disease outbreak and R0 <1 results in the disappearance of infection). Using the data from the existing literature, the model is validated and certain parameter values are estimated. Also, the conditions for the existence and stability of disease free and three different endemic equilibria are established based on the R0 expressions. Focusing on transmission of infection within adult dairy cows on a dairy and the risk of spread to farmworkers, we investigate the effects five different biosecurity measures and their combinations: (1) animal movement control and quarantine, (2) disease monitoring and reporting(3) hygiene and disinfection, (4) feeding and watering practices, (5) antimicrobial stewardship, and (6) manure and contamination management.
Key Highlights:
Numerical simulations indicate that compliance with measures associated with host-to-host transmission has the highest impact on the prevention of outbreaks and improving the health of both animals and farmworkers.
Particularly, low compliance with this measure results in high prevalence and incidences of infection, whereas good or excellent levels of compliance prevents outbreaks or results in minimal incidences of Salmonella infection in cows.
Further model analysis indicates that full compliance with the other three biosecurity measures does not guarantee the prevention of outbreaks in a dairy farm.
These results are consistent with the local and global sensitivity analyses of the model .
The modeling framework developed in this study can be applied to other zoonotic infections and it can be used as a guiding tool for optimal resource allocation leading to minimization of disease spread both in animal and human populations.
4) Title: " Comparative Analysis of Machine Learning Models for Predicting Hospital- and Community-Associated Urinary Tract Infections Using Demographic, Hospital, and Socioeconomic Predictors"
Authorship: Secondary Author.
Funding Source: Center for Diseases Control (CDC).
Summary: Urinary tract infections (UTI) are among the most common infections encountered in both community and healthcare settings. Differentiating between community-associated UTI (CA-UTI) and healthcare-associated UTI (HA-UTI) is crucial for understanding their epidemiology, identifying risk factors, and developing appropriate treatment strategies. Machine learning (ML) techniques have shown significant potential in improving the accuracy of predicting these infections, enabling more effective interventions and better patient outcomes. While previous studies have demonstrated the utility of ML models in various healthcare settings, there is still a need for a comparative analysis of different ML approaches, particularly in distinguishing between CA-UTI and HA-UTI and assessing the risk of UTI among hospitalized patients.Using 2019-2023 patient demographics, hospital, and socioeconomic data, this study aims to build, validate, and compare machine learning models—Decision Tree (DT), Neural Network (NN), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to differentiate between the incidences of HA-UTI and CA-UTI. Additionally, it seeks to identify key predictors of UTI using demographic, hospital, and socioeconomic variables.
Key Highlights:
Results: The DT model demonstrated the highest sensitivity, particularly in handling the highly imbalanced data of HAI, with a sensitivity of 87%. LR achieved the best overall accuracy, at 95.9% for HA-UTI and 93.2% for HA-UTI vs. CA-UTI. RF performed best in cross-validation, reaching 99.1% for HA-UTI and 96.2% for HA-UTI vs. CA-UTI. NN showed the highest specificity, at 93.4%, for HA-UTI vs. CA-UTI. The AUC values further supported these findings, ranging from 71.9% for NN to 96% for RF, reflecting the robustness of these models across different annual datasets.
Among patient demographics, hospital, and socioeconomic variables, all models consistently identified the nurse units (e.g., inpatient units and mental health units) as the most significant predictors of UTI. In addition to nurse units, LR and DT identified location (e.g., various clinics and medical centers) as a key predictor. For HA-UTI versus CA-UTI, variations were observed across the years, with patient age, median household income, and gender intermittently emerging as key predictors.
In addition to nurse units, LR and DT identified location (e.g., various clinics and medical centers) as a key predictor. For HA-UTI versus CA-UTI, variations were observed across the years, with patient age, median household income, and gender intermittently emerging as key predictor.
he predictive accuracy of the machine learning models is relatively the same, with some differences in sensitivity and specificity for identifying both HA-UTI vs. CA-UTI and HA-UTI. Nurse units consistently emerge as the most significant predictors across all years.
The importance of all predictors, such as socioeconomic factors and location, varies from year to year, highlighting the need for incorporating those variables in the surveillance systems to optimize the accuracy of predictions.
5) Title: "Advancing Mathematical Models of Healthcare-Associated Infections to Evaluate the Impact of Antimicrobial Stewardship and Staffing Strategies on Health Equity "
Authorship: Secondary Author.
Funding Source: Center for Diseases Control (CDC).
Summary: Creating new methods for Healthcare - Associated Infections (HAIs) model construction for the purpose gaining new insight into how antimicrobial stewardship and staffing strategies affect health equity.
Key Highlights: In progress.
Current Projects
Title: "Understanding the Connection Between Illicit Drug Use and Infectious Disease Spread Through AI Based Tools."
Authorship: Primary Author.
Funding Source: National Research Foundation
Summary: Emergence and growth of infectious diseases within populations found across the country is highly correlated with drug abuse. This correlation is due to the fact that behaviors associated with drug abuse, such as the use of shared needles, can cause compromised immune systems and widespread transmission of infectious disease. Our research proposal aims at utilizing, modifying, and developing AI tools designed for infectious diseases that are correlated with drug abuse. These tools can be used to create effective control and prevention strategies for reducing drug abuse and disease spread. By utilizing predictive AI techniques, simulated behavior of drug abusers, and optimization of significant recovery factors, we believe that our study will improve public health in the areas of both disease transmission and drug recovery. Individuals who participate in drug abuse are contributing to greater levels of infectious diseases transmission, such as hepatitis C,Tuberculosis, or methicillin-resistant Staphylococcus aureus [9]. The end result of both increased drug abuse and infectious diseases within communities can create major challenges in the management of public health in different subpopulations. Standard approaches to understanding this relationship have had limited success in adding new tools for analyzing the connection. Integrating AI and statistical learning methods has the potential to significantly change how to identify prevention factors, recovery factors, user behavior, predict rates for morality and prevalence from drug abuse, and enable better prevention strategies for both drug abuse and transmission of infectious spread.
Key Highlights: In progress