The New Innovator Award occupies a distinctive niche in the NIH funding landscape. Rather than requiring preliminary data or a conventional track record, the program seeks out scientists whose ideas are considered transformative precisely because they depart from established approaches. Daniela Friedman, the Arnold School’s associate dean for research and leadership development, described the recognition as extremely well deserved, noting that since joining the school Shi has built an outstanding research program and distinguished herself as an innovative investigator whose work has the potential to reshape her field. For a researcher who has been on the faculty for only a short time, the award signals both the ambition of the science and the confidence the institute has placed in it.

Shi’s path to this project began far from South Carolina. She studied preventive medicine at Shanghai Jiao Tong University in China, where her involvement in tobacco control and other public health research projects led her to a realization that would define her career: improving health requires addressing not only diseases themselves but also the social and behavioral factors that shape how people live. That conviction deepened during an intervention project for people living with HIV in China. Although antiretroviral therapy has transformed HIV from a fatal diagnosis into a manageable chronic condition, Shi observed firsthand how many patients continued to struggle with stigma, fear of disclosure, and discrimination. Even individuals who were effectively controlling the virus medically tended to isolate themselves from family and friends, a pattern that convinced her that biomedical advances alone are not enough and that social and structural barriers must be confronted alongside them.

That experience brought her to the Arnold School, where she enrolled in the doctoral program in health promotion, education, and behavior and later completed a postdoctoral fellowship with the department and the South Carolina SmartState Center for Healthcare Quality. During that period she worked closely with faculty members Xiaoming Li and Xueying Yang, whom she credits as exceptional mentors who encouraged her to ask meaningful research questions, think creatively, and pursue innovative approaches that combine big data and artificial intelligence to advance HIV prevention and care. She has said their guidance was instrumental in her development as an independent researcher, fostering an environment of collaboration, curiosity, and innovation while emphasizing that research should ultimately improve people’s lives. It is also the community she built there, she explains, that drew her to remain at the Arnold School as a faculty member.

The new project brings together the threads of that training into a single, data-intensive research program. Shi and her team will draw on the NIH’s All of Us Research Program, one of the largest and most diverse health data resources ever assembled, which offers researchers access to longitudinal health information from hundreds of thousands of participants across the United States. Using advanced computational techniques, including machine-learning tools, the team will analyze how patterns of sexually transmitted infections relate to HIV risk and to HIV treatment outcomes over time. The analytical challenge is considerable: STI diagnoses arrive in clinical systems as scattered events, and connecting them to downstream HIV outcomes requires models that can capture multilevel social, structural, and clinical determinants of health simultaneously.

The scientific rationale for the project rests on a well-documented but underexploited relationship. Sexually transmitted infections are known to biologically increase the risk of acquiring HIV, and they may also signal lapses in engagement with HIV care among people already living with the virus. Yet, as Shi points out, STI data remains markedly underutilized by healthcare systems and researchers when it comes to predicting who may be at higher risk of HIV infection or adverse HIV-related health outcomes. In most clinical settings, an STI diagnosis is treated as a discrete event to be treated and closed, rather than as a signal that could trigger risk stratification, intensified prevention counseling, pre-exposure prophylaxis evaluation, or re-engagement efforts. Her project aims to close that gap by turning routinely collected clinical data into actionable predictive insight.

The stakes of better prediction are substantial. Of the roughly 1.1 million Americans living with HIV, an estimated 13 percent are unaware of their status, 25 percent do not receive care, and 35 percent are not virally suppressed. Each of those gaps represents a point at which the care continuum fails both the individual and public health, since unsuppressed viral load sustains transmission risk while untreated infection progresses. The extraordinary advances of the past three decades in HIV treatment, including reduced transmission achieved through careful management and modern prevention measures, mean that these numbers do not have to be what they are. What is missing, Shi argues, is a systematic way for clinicians to identify which patients need additional support and when. Her findings are intended to help clinicians do exactly that, and to give other scientists and healthcare providers a foundation for developing more effective strategies for HIV prevention and care.

Technically, the project sits at the intersection of infectious disease epidemiology, behavioral science, and data science. By training machine-learning models on the rich, longitudinal All of Us dataset, the team hopes to detect combinations of STI histories, demographic characteristics, social determinants, and clinical indicators that reliably precede HIV acquisition or poor treatment outcomes. Such models, if validated, could eventually be embedded in electronic health record systems as risk-stratification tools, flagging patients who warrant proactive outreach. The guidelines Shi’s team plans to develop for healthcare providers will translate these statistical findings into concrete clinical workflows, addressing the persistent divide between what population data can reveal and what individual practitioners can act upon during a routine visit.

Beyond its immediate clinical aims, the project reflects a broader vision of prevention that Shi has carried since her early days in preventive medicine: one in which social context, stigma, and structural barriers are treated as measurable, modifiable components of risk rather than as background noise. Her own trajectory, from studying preventive medicine in China to leading federally funded research at a major American research university, is one she hopes will encourage other early-career researchers and international scholars to pursue ambitious ideas with the potential for meaningful public health impact. The award, by its design, rewards exactly that kind of trajectory, betting on investigators whose careers are just taking shape and whose questions are still unconventional.

Looking ahead, Shi describes the Arnold School and the Center for Healthcare Quality as a unique environment where expertise in public health, clinical research, and data science converge. She intends to use the New Innovator Award to build an independent research program that leverages large-scale data and artificial intelligence to improve HIV prevention and care, while eventually extending her methods to other infectious diseases and health disparities. She also plans to invest in the next generation of the field, mentoring students and collaborating across disciplines to translate research findings into real-world impact. For a scientist whose formative insight was that data alone is never enough, the project she has now begun represents an attempt to prove the converse as well: that when large-scale data is joined to an understanding of human behavior and social barriers, prediction can become prevention.

Subject of Research: Use of large-scale health data and artificial intelligence to identify sexually transmitted infection patterns that predict HIV infection risk and treatment outcomes in at-risk populations

Article Title: New faculty member Fanghui Shi awarded NIH grant to protect at-risk populations from contracting HIV

Article References: New faculty member Fanghui Shi awarded NIH grant to protect at-risk populations from contracting HIV. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: HIV, NIH Director's New Innovator Award, Fanghui Shi, sexually transmitted infections, machine learning, All of Us Research Program, HIV prevention, public health, health disparities, University of South Carolina, Arnold School of Public Health, data science

The cornerstone of this research lies in the integration of worldwide genomic data from disparate regional surveillance systems. Historically, many countries have operated in isolation, collecting viral genome sequences primarily for local outbreak response. However, fragmented data sets limit comprehensive understanding of viral evolution and transmission dynamics on a global scale. This study demonstrates how uniting datasets amplifies detection power, allowing scientists to identify novel viral variants rapidly and monitor their spread across borders, which is crucial for timely countermeasures.

Genomic surveillance employs high-throughput sequencing technologies, which generate detailed genetic blueprints of viruses circulating in human populations. These blueprints reveal subtle genetic mutations that might enhance transmissibility or alter pathogenicity, information that traditional diagnostic methods cannot provide in real-time. The authors argue that reliance on isolated, national sequencing endeavours hampers early recognition of dangerous variants, whereas a globally coordinated system vastly improves resolution and speed.

The authors meticulously detail how viral genomic data was shared through international networks to construct a comprehensive, continually updated repository. Advanced bioinformatics tools powered by machine learning algorithms analyze this data, scanning for mutations indicative of increased virulence or resistance to existing therapeutics. This analytic framework transforms raw sequence data into actionable insights, enabling public health officials to preemptively adjust strategies such as vaccine design or resource allocation.

Notably, the study showcases multiple case studies where global genomic cooperation detected potentially pandemic-prone respiratory viruses months before widespread outbreaks occurred. These early warnings provided critical lead time for healthcare providers to prepare hospital capacity, accelerate vaccine research, and implement targeted containment policies, significantly mitigating the viruses’ impact.

This initiative also addresses key hurdles previously constraining global pathogen surveillance. These challenges include disparities in sequencing infrastructure, data sharing policies, and equitable access to technology in low-resource settings. By promoting capacity building and fostering trust among nations, the project not only democratizes genomic surveillance but also enhances the fidelity of global health intelligence systems.

Moreover, the researchers emphasize the importance of data standardization and interoperability. Harmonizing sequencing protocols and metadata formats ensures that datasets from diverse sources can be seamlessly integrated. This cohesiveness is vital for accurate phylogenetic analyses that track viral lineage divergence, revealing the epidemiological pathways responsible for viral dissemination across continents.

The study advocates for sustained investment in next-generation sequencing capacity and bioinformatics expertise worldwide. Importantly, it introduces a decentralized surveillance model amplified by cloud computing platforms, which overcome geographical and logistical barriers to data sharing. Such infrastructure allows real-time global monitoring, making it feasible to spot new viral threats as they emerge rather than react after the fact.

In addition to technical advancements, the authors highlight sociopolitical dimensions that underlie effective genomic surveillance. International solidarity is paramount for transparent data exchange, overcoming competitive national interests, and securing funding commitments. The trust forged by mutual collaboration enables quicker consensus on public health interventions that transcend political borders.

On the clinical front, this surveillance paradigm shift supports personalized medicine approaches against respiratory viruses. By identifying genetic variants with resistance to antivirals, clinicians can tailor treatment regimens to improve patient outcomes. Furthermore, vaccine developers can use up-to-date genomic maps to adjust antigenic targets before immunity wanes due to viral evolution, maintaining vaccine efficacy.

Crucially, the paper addresses ethical considerations involved in genomic data collection and sharing, including privacy safeguards and equitable benefit distribution. The authors propose frameworks incorporating local community engagement and adherence to international guidelines, ensuring that genomic surveillance serves the interests of all populations without exacerbating disparities.

The researchers foresee that this enhanced genomic infrastructure will extend beyond respiratory viruses to encompass a broader spectrum of pathogens, fortifying global preparedness against future pandemics. The scalable model described offers a blueprint for continuous pathogen monitoring, integrating innovations in artificial intelligence and portable sequencing devices to reach remote areas rapidly.

This pioneering global strategy therefore represents an inflection point in public health surveillance, marking a transition from reactive to predictive capabilities. By embracing unity and leveraging cutting-edge genomic technology, the world can anticipate and attenuate the impact of viral epidemics before they escalate into devastating crises.

The implications of this work resonate beyond academic circles, informing policies of organizations such as the World Health Organization and national health agencies tasked with pandemic preparedness. With infectious diseases poised to remain a persistent threat, fostering collaborative genomic surveillance networks is an essential investment in safeguarding humanity’s future.

In conclusion, the study by de Jong et al. not only underscores the feasibility of global solidarity in genomic surveillance but also establishes its indispensable value in detecting and combating acute respiratory virus threats early. This integrative vision combines technology, policy, and international cooperation to create a resilient defense system capable of outpacing viral evolution. It is a compelling call to unify efforts against invisible enemies that know no borders.

Subject of Research: Genomic surveillance of acute respiratory viruses and global data sharing to improve early detection of viral threats.

Article Title: Global solidarity in genomic surveillance improves early detection of acute respiratory virus threats.

Article References:

de Jong, S.P.J., Nichols, B.E., de Ruijter, A. et al. Global solidarity in genomic surveillance improves early detection of acute respiratory virus threats. Nat Commun (2026). https://doi.org/10.1038/s41467-025-67442-9

Image Credits: AI Generated