Advancing Analytical Tools to Quantify and Mitigate the Risk for Transitioning from Episodic to Endemic Transmission for Emerging Infections

About This Grant

This project will develop and apply computational tools for assessing the risk that diseases with episodic transmission become established in the general population. Our project is relevant to emerging zoonoses, re-emerging vaccine-preventable diseases, and healthcare-associated infections. Timely identification and control of such diseases could have significantly altered the course of mpox, SARS-CoV-2, and antimicrobial resistance. It is therefore important to have methods available to monitor and fully elucidate the transmission patterns of infectious diseases that can develop increased burden through pathogen evolution, reduced population immunity, or other sociodemographic changes. Any such method needs to consider patchy surveillance and differences in risk among those who are exposed to the disease. Furthermore, diseases that cause episodic outbreaks might require specific control strategies that are different from those that apply to epidemic or endemic diseases. Existing models that explore some of these aspects typically omit key factors, rely on untested assumptions, or are validated in a circular fashion using simulations based on the same assumptions they aim to test. This limits their reliability for real-world applications. To address this gap, we will combine statistical inference with simulation approaches to address key questions in quantifying and mitigating the risk from infectious diseases. We will extend methods for inference using branching process models to take into account imperfect observations and heterogeneity in both transmission and susceptibility. We will apply these methods to a range of applications to improve our ability to learn from data describing sporadic infection clusters. We will also use mobility and demographic data to construct synthetic populations representing situations where infections cause occasional outbreaks. This will permit stress-testing of inference methods and evaluation of control strategies. Our iterative approach will allow us to refine model assumptions, improve inference robustness, and identify the most informative data types for public health surveillance and control. The work will result in a greater understanding of how public health agencies can best use data from episodic disease transmission and computational tools for applying this understanding to coming threats. To demonstrate the breadth of applicability, we will apply our methodological advancements to (1) quantify the transmissibility of H5N1 influenza, (2) determine the probability of large measles outbreaks occurring annually, and (3) evaluate control strategies for reducing transmission of virulent, healthcare-associated MRSA strains. To promote scientific reproducibility, we will produce user-friendly software that integrates with existing packages and share synthetic population data. Our team is well-positioned to conduct this work since we have developed many existing tools and paradigms for analyzing episodic transmission, including branching process models and outbreak simulations in synthetic populations. By advancing the science of disease transmission and equipping public health agencies with actionable results, this work will reduce the risk of future pandemics.