Hospital-associated infections and antibiotic-resistant infections lead to the loss of millions of lives each year. One Health Trust is a member of the U.S. Centers for Disease Control and Prevention’s MInD-Healthcare network, which supports innovative transmission modeling research to understand what drives the spread of hospital-associated and antibiotic-resistant pathogens and estimate the benefits of preventive measures.

The Study

One Health Trust has conducted and published research in multiple areas:

  • Multi-level modeling to inform interventions to control multidrug-resistant organisms in healthcare networks

As part of the MInD network, OHT developed models to improve understanding of how biological and behavioral factors associated with the transmission of multidrug-resistant organisms affect interventions to reduce hospital-associated infections. Resulting publications discussed the frequency of carbapenem-resistant Enterobacteriaceae colonization and the role of networks within and between hospitals in the transmission of antibiotic-resistant pathogens.

  • In silico randomized control trial to assess infection control and prevention in hospitals

The models developed through this proposal explore how biological factors associated with the transmission of multidrug-resistant organisms affect interventions to reduce hospital-associated infections. This will aid predictions of patients’ risk for colonization and infection, as well as improve understanding of how interventions can be combined to reduce the likelihood that patients will suffer from these high-consequence pathogens.

  • Modeling of the transmission of COVID-19 and its drivers

The MInD network has run modeling projects, including a model of COVID-19 transmission that estimates the percentage of asymptomatic infections, assessments of the benefits of vaccination under different scenarios, and the role of humidity in COVID-19 transmission. OHT has also supported the state of Maryland’s and the Johns Hopkins Hospital Systems’ responses to the COVID-19 pandemic.

  • Community Transmission of Hospital-Associated Infections 

Hospital-associated pathogen transmissions are studied using mathematical and computational models to understand the prevalence of hospital-associated infections (HAIs). However, due to the limited understanding of community transmission of these pathogens, the contribution of community reservoirs is often ignored. As part of the MInd network, One Health Trust conducted a systematic review to evaluate modeling studies on community transmission of hospital-associated pathogens. 

  • Endogenous Behavior Scoping Review

Models incorporating behavior as endogenous variables (variables not attributable to external or environmental factors, with an internal cause or origin) are more effective in analyzing the changes in behavior in response to public health measures and epidemic dynamics, thus leading to a more nuanced understanding of disease transmission. One Health Trust conducted a systematic scoping review to understand the extent to which endogenous behavior was incorporated into COVID-19 transmission modeling between 2022 and 2023. 

  • Global Antibiotic Consumption

A multi-faceted approach is needed to reduce antibiotic resistance globally. Thus, the continued surveillance of global antibiotic consumption is required to ascertain trends and create shared responsibilities amongst nations to influence policies and programs to curb antibiotic resistance. One Health Trust is evaluating the trends in antibiotic consumption globally and the effect of the COVID-19 pandemic on antibiotic use.

  • Covid-19 and Antibiotic Prescribing

This was an ecological study using random-effects panel regression of monthly reported COVID-19 county case and antibiotic prescription data, controlling for seasonality, urbanicity, health care access, nonpharmaceutical interventions, and sociodemographic factors. 



The Centers for Disease Control and Prevention (CDC)


  • Lin, G., Tseng, K. K., Gatalo, O., Martinez, D. A., Hinson, J. S., Milstone, A. M., … & Klein, E. (2021). Cost-effectiveness of carbapenem-resistant Enterobacteriaceae (CRE) surveillance in Maryland. Infection Control & Hospital Epidemiology, 1-9. Available here.
  • Squire, M. M., Sessel, G. K., Lin, G., Squire Jr, E. N., & Igusa, T. (2021). Optimal Design of Paired Built Environment Interventions for Control of MDROs in Acute Care and Community Hospitals. HERD: Health Environments Research & Design Journal, 14(2), 109-129. Available here.
  • Hamilton, A. J., Strauss, A. T., Martinez, D. A., Hinson, J. S., Levin, S., Lin, G., & Klein, E. Y. (2021). Machine learning and artificial intelligence: applications in healthcare epidemiology. Antimicrobial Stewardship & Healthcare Epidemiology, 1(1). Available here.
  • Haghpanah, F., Lin, G., Levin, S. A., & Klein, E. (2021). Analysis of the potential impact of durability, timing, and transmission blocking of COVID-19 vaccine on morbidity and mortality. EClinicalMedicine, 35, 100863. Available here.
  • Haghpanah, F., Lin, G., Levin, S., & Klein, E. Y. (2021). Analysis of the Potential Efficacy and Timing of COVID-19 Vaccine on Morbidity and Mortality. Available at SSRN 3745195. Available here.
  • Goodman, K., Simner, P., Klein, E., Kazmi, A., Gadala, A., Toerper, M., Levin, S., Tamma, P.D., Rock. C., & Cosgrove, S.E. (2019). Predicting probability of perirectal colonization with carbapenem-resistant Enterobacteriaceae (CRE) and other carbapenem-resistant organisms (CROs) at hospital unit admission. Infection Control & Hospital Epidemiology. Available here.
  • Klein, E.Y., Tseng, K.K., Hinson, J., Goodman, K.E., Smith, A., Toerper, M., Amoah, J., Tamma, P.D., Levin, S., & Milstone, A.M. (2020). The Role of Healthcare Worker-Mediated Contact Networks in the Transmission of Vancomycin-Resistant Enterococci. Available here.
  • Lin, G., Tseng, K., Martinez, D., and Klein, E.. Multiscale Modeling of Patient Movement to Determine Effects of Surveillance on Healthcare-Associated Infections. (2020). Infection Control & Hospital Epidemiology. Available here.
  • Lin, G., Strauss, A.T., Pinz, M., Martinez, D.A., Tseng, K.K., Schueller, E., Bhaduri, A., Gatalo, O., Gaynor, A.T., Hernandez-Rivera, E., Yang, Y., Levin, S., & Klein, E.Y. (2020). Explaining the Bomb-Like Dynamics of COVID-19 with Modeling and the Implications for Policy. Available here.
  • Lin, G., Hamilton, A., Gatalo, O., Haghpanah, F., Igusa, T., & Klein, E. (2020). Investigating the effects of absolute humidity and human encounters on transmission of COVID-19 in the United States. Epidemiology. Available here.
  • Gatalo, O., Tseng, K., Hamilton, A., Lin, G., and Klein, E. (2021). Associations between phone mobility data and COVID-19 cases. The Lancet Infectious Diseases. Available here.
  • Lin, G., Hamilton, A., Gatalo, O., Haghpanah, F., and Klein, E. (2022) Investigating the effects of absolute humidity and movement on COVID-19 seasonality in the United States. Sci Rep 12, 16729. Available here.