Chen’s route to computational epidemiology runs through a seemingly unrelated field: the study of connectivity in complex networks. Her doctoral research produced a unified framework for measuring and understanding how connectivity behaves across very different kinds of systems, from infrastructure networks such as power grids to the networks through which epidemics move. The unifying insight is that in any such system, the behavior of the whole depends on how its individual nodes are linked. In a power grid, engineers ask whether the remaining parts of the network can keep electricity flowing when a single node fails. In an epidemic, the nodes are people, and the connections between them, to co-workers, family members and strangers encountered in transit, become the pathways along which a pathogen travels through a geographic area.

That framing makes the difference between a contained outbreak and a pandemic a question of network dynamics. When transmission expands exponentially and crosses international boundaries, the same mathematical intuition that describes cascading failures in infrastructure applies to chains of infection. Chen describes her epidemic work as a natural extension of her dissertation idea, noting that people are connected to their co-workers and their families, and that the research aims to develop strategies for monitoring a population-level outbreak at different scales, with the ultimate goal of containing transmission and reducing the number of infections.

The technical core of the project addresses a well-known weakness in existing epidemic models. Forecasting and tracking systems can miss important pieces of the picture, capturing reported infections without fully incorporating how people move between communities or what wastewater monitoring suggests about infections that have never been reported. Mobility matters enormously, Chen explains, because someone traveling frequently from state to state establishes connections with many different people, and those connections can pose infection risk far from home. Wastewater surveillance adds another complementary signal, detecting viral traces shed by infected individuals regardless of whether they have sought testing, which became especially valuable during the COVID-19 pandemic when reported case counts were distorted by uneven testing and widespread home testing that never entered official statistics.

Bringing these heterogeneous data streams together in real time is a problem that only recently became tractable. Chen, a former Google software engineer, says the emergence of AI-powered analytical tools is what now allows the kind of data processing these projects require. Her industry experience shapes how she approaches the work: it is not enough to invent new algorithms, she argues, because the harder question is whether those algorithms can scale to large, complex datasets and ultimately prove useful in real-world settings. Her research interests in data mining, machine learning, computational epidemiology, and trustworthy and efficient AI converge on that goal, aiming to serve both scientific discovery and practical public health decision-making.

Trustworthiness is not an abstract virtue in this context. In high-stakes applications such as public health, Chen notes, AI models must account for uncertainty and potential errors and must produce results that researchers and decision-makers can understand and rely on. A forecast that cannot explain its own confidence, or that silently fails when conditions shift, is worse than no forecast at all when officials are deciding where to deploy limited resources. This requirement for interpretable, uncertainty-aware predictions is a recurring theme in the broader movement toward trustworthy AI, and Chen’s project embeds it directly into the design of epidemic forecasting tools rather than treating it as an afterthought.

Adaptability presents another technical challenge, because the pathogen itself does not stand still. The emergence of new variants during COVID-19 demonstrated how quickly the characteristics governing a disease and its transmission can change, potentially invalidating models trained on earlier conditions. Guanghui Min, a Ph.D. student working with Chen on the project, describes one exciting next step as combining different signals, such as case counts, mobility patterns and wastewater data, to obtain a more complete picture of how an outbreak is evolving. Min adds that the team believes making these models adapt when conditions change, so that forecasts can help identify where additional monitoring or intervention is needed, may be the most useful capability of all. In machine learning terms, this is a problem of distribution shift: the statistical relationships a model learned during one phase of an epidemic may no longer hold in the next.

The CAREER project is organized around several components: improving epidemic forecasting, developing computational tools to help evaluate possible interventions, designing better disease-surveillance strategies, and educating the public. Chen plans to concentrate heavily on forecasting during the first three years, creating a foundation for the intervention and surveillance tools that follow. The practical questions those tools are meant to answer are deceptively simple: What is happening now? What is likely to happen next? And if officials intervene, how might the trajectory change? Answering them requires models that can simulate counterfactuals, estimating how a vaccination campaign, school closure or targeted testing effort might bend the curve in one community while leaving another largely untouched.

Scale is the central obstacle between detailed local knowledge and useful national guidance. Inside a hospital, researchers might map interactions between infected individuals in fine detail; across a city, state or country, that becomes far more difficult. Chen’s framework is designed to work gracefully with different levels of available information. Publicly accessible disease reports, wastewater measurements and mobility data can provide a broad view of spread, and when more detailed records such as localized case counts, testing data and other health surveillance information become available for research, the framework can incorporate them to yield a more granular understanding of transmission and sharper predictions. The same logic points toward smarter surveillance: if a region lacks sufficient information, a model might show officials where an additional wastewater monitoring point would most improve forecasts, turning the monitoring network itself into an optimization problem.

Beyond the models and decision-support tools for officials, Chen envisions educational tools for the public, with a long-term goal of an accessible website that helps community members understand disease conditions and risk in the places around them, answering questions such as what the viral level is in a nearby area and what the risk level might be for someone visiting. Sandhya Dwarkadas, Walter N. Munster Professor and chair of the Department of Computer Science, observes that these are interesting times, with access to unprecedented amounts of data, analytical tools and real-world insight from COVID-19 about how to apply them, and that she appreciates how Chen is maximizing that advantage to help make a difference for communities worldwide by informing public health decision makers. The framework itself is not tied to any single pathogen: by changing how a model represents transmission and contact patterns, Chen says, the same approach could potentially be adapted to different infectious diseases. For Chen, the ultimate measure of success is whether better computational models can turn scattered information into useful guidance, helping officials respond to epidemics, prevent pandemics and give individuals a clearer picture of the risks around them.

Subject of Research: Artificial intelligence for infectious disease epidemic forecasting and surveillance

Article Title: Chen’s NSF CAREER Award supports UVA Engineering artificial intelligence research to better forecast infectious disease epidemics

Article References: Chen’s NSF CAREER Award supports UVA Engineering artificial intelligence research to better forecast infectious disease epidemics. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: epidemic forecasting, artificial intelligence, NSF CAREER Award, wastewater surveillance, public health, machine learning, network science, disease surveillance, human mobility data, computational epidemiology, trustworthy AI, University of Virginia

The interdisciplinary research team brings together a wealth of expertise, combining insights from engineering, infectious diseases, and health care policy. Led by Dr. Saswati Sarkar, a Professor in Electrical and Systems Engineering, alongside Assistant Professor Dr. Shirin Saeedi Bidokhti, Doctor of Infectious Diseases Dr. Harvey Rubin, and doctoral student Raghu Arghal, the team designed their framework to navigate the complexity inherent in vaccination strategies while remaining accessible to public health agencies that may lack extensive computational resources typically associated with high-end supercomputing facilities.

One of the most significant challenges in determining effective vaccination strategies is the scale of communities affected by COVID-19. The framework developed by the research team is capable of processing vast amounts of community data—potentially involving populations that range from hundreds of thousands to millions—within seconds, relying entirely on the processing power of a standard personal laptop. This accessibility means that even under-resourced communities can utilize the framework to develop rapid response plans tailored to their specific needs.

Understanding the dynamics of vaccination distribution requires a meticulous approach to categorizing populations. The researchers defined three essential groups: the high-risk group, which includes the elderly and immunocompromised individuals; the high-contact group, comprised of essential workers who are vital in maintaining public health and safety; and the baseline group, which encompasses the remainder of the population. By employing network theory, the team was able to design a numerical model that integrates the complexities of these grouped populations and yields effective strategies for vaccination rollout.

The findings of the study illustrate that the traditional approach of prioritizing vaccinations for the high-risk group may not always be the most effective strategy. In over 42% of the scenarios simulated by their framework, the team identified that prioritizing the high-contact group—those most likely to spread the virus—could lead to a more significant reduction in overall mortality rates. This flexible adaptability of their model reveals the nuanced nature of public health responses, emphasizing that one approach cannot be uniformly applied across different communities with unique characteristics and needs.

The research also underscores the importance of interdisciplinary collaboration in confronting public health challenges. By combining the rigor of engineering principles with the practical knowledge of medical professionals, the team has created a framework that resonates with the realities of vaccination distribution and community protection. Dr. Saeedi Bidokhti highlighted the essential nature of this collaboration, noting that linking theoretical modeling with field applications could lead to more informed decisions in real-world scenarios.

As the nature of infectious diseases continues to evolve, with new variants and outbreaks emerging, the research team’s framework has far-reaching implications beyond COVID-19. Their work lays the groundwork for addressing future public health concerns, including concurrent outbreaks and vaccination strategies for various respiratory diseases. Dr. Rubin pointed out that collaborative efforts across disciplines will be indispensable in formulating comprehensive strategies to tackle not only existing health threats but also new ones that will inevitably arise.

Looking ahead, the researchers are keen to expand their framework’s capabilities. Future projects aim to incorporate additional variables, such as the spread of public opinions regarding vaccination and health behaviors, into their model. By leveraging the same network theory methodologies developed for viral transmission, the research team seeks to create a more holistic approach to disease prevention, integrating social dynamics with medical strategy to foster voluntary cooperation among populations.

The COVID-19 pandemic provided an unprecedented opportunity for Arghal and his fellow researchers to challenge themselves and apply engineering principles to pressing societal issues. The urgency of devising effective distribution strategies for limited vaccine supplies propelled their work, marking the beginning of their research careers amid one of the most significant public health crises of our time. Their efforts showcase how engineering can play a critical role in areas typically dominated by medical and public health expertise.

Not only does this research provide essential insights into the optimal distribution of vaccines, but it also serves as a valuable lesson for the next generation of engineers. By integrating real-world problem-solving into their educational frameworks, the researchers are inspiring students to think creatively and develop solutions that extend beyond traditional engineering domains. In doing so, they are nurturing a new wave of professionals equipped to tackle multifaceted societal challenges.

The collaborative spirit fostered in this research endeavor is emblematic of a broader shift in how engineering disciplines approach public health. As the complexities of health crises deepen, the necessity for interdisciplinary cooperation becomes increasingly vital. Researchers are now called to build bridges between theory and practical application, ensuring that engineering innovations translate into tangible benefits for communities that need them most.

By enabling public health officials to make informed decisions based on their findings, the Penn research team sets a precedent for future academic inquiry. Their work stands as a testament to the power of innovation, collaboration, and the potential for engineers to impact the health and well-being of populations globally. As they move forward, their framework promises to serve as a cornerstone in the face of ongoing and future health challenges.

As a culmination of their research efforts, the insights gained will likely play a significant role in shaping how communities respond to health emergencies, ultimately paving the way for enhanced public health strategies. The synergy of engineering, medical research, and community engagement presents a promising pathway toward improving the effectiveness of health initiatives and safeguarding populations against disease.

The framework developed by the research team at Penn not only purports to address the immediate challenges posed by the COVID-19 pandemic but signifies a broader commitment to applying scientific knowledge to enhance public health globally. The implications of their work extend beyond the present moment, illustrating the vital role of engineering and technology in shaping a healthier future for all.

Subject of Research: COVID-19 vaccine distribution strategies
Article Title: Protect or prevent? A practicable framework for the dilemmas of COVID-19 vaccine prioritization
News Publication Date: 22-Jan-2025
Web References: [Link to study in PLOS One]
References: National Science Foundation grants NSF-2047482, NSF-1910594, and NSF-2008284
Image Credits: [Photo credits if applicable]

Keywords

COVID-19 vaccines, vaccination strategy, public health, computational modeling, engineering, pandemic response, community health