Modeling transmission of pathogens in healthcare settings

Purpose of review Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings. Recent findings The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts. As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

Automated summary

Mathematical, statistical, and computational models offer insights into healthcare-associated infections transmission and control. Recent studies have focused on modeling the transmission of pathogens in healthcare settings, especially with the impact of the COVID-19 pandemic on transmission dynamics of SARS-CoV-2 and the need for effective interventions. Efforts are being made to address inequities in COVID-19 outcomes and incorporate genomic data into modeling, while gaps still exist in producing generalizable models across different time periods, geographic locations, and populations.