About

C. Jessica E. Metcalf is a professor in the Department of Ecology & Evolutionary Biology and the School of Public and International Affairs at Princeton University. She serves as the Director of Graduate Studies in the EEB department. Her research interests encompass evolutionary ecology and infectious disease dynamics, with a focus on how changing human demography influences the incidence and spread of infectious diseases. She investigates the dynamics of rubella and malaria, exploring their spatial and temporal patterns and implications for vaccine control and disease management. Additionally, her work examines reproductive strategies in plants, such as Arabidopsis thaliana, and the lifespan of trees to understand carbon turnover and forest pathogen recovery. Her lab develops methods to bridge the gap between within-host and between-host disease dynamics, aiming to understand how pathogen population growth, spread, and clearance within bodies shape disease ecology and evolution. Her research integrates basic ecological principles with applications relevant to public health, emphasizing the importance of cross-scale demography and disease dynamics from individual hosts to global populations.

Research topics

• Medicine
• Virology
• Environmental health
• Biology
• Geography
• Computer Science
• Immunology
• Political Science
• Business
• Data science
• Public relations
• Nursing
• Internet privacy
• Internal medicine

Selected publications

• The island biology of the host microbiome

  Trends in Microbiology · 2026-04-01

  article
  PublisherDOI

• Navigating a Human-Dominated Landscape: Habitat Selection and Movement Behavior of Fosa (Cryptoprocta ferox) in Eastern Madagascar

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Coevolution of host resistance and pathogen exploitation in a propagule-mediated infection model

  PLoS Computational Biology · 2026-03-10

  articleOpen accessSenior author

  Host populations often face infection risk from pathogens that can persist in the environment as free-living propagules. We develop a population-level model to understand how host resistance - defined as reduced susceptibility to infection - evolves in response to the exploitation strategy of a pathogen where transmission occurs exclusively via environmental propagules. Using an adaptive dynamics framework, we analyze how the coevolution of host resistance and pathogen exploitation strategy unfolds under the following fitness costs: reduced survival associated with investment in resistance reflected by additional background mortality for the host; and reduced average lifespan represented by increased infected host mortality for the pathogen. Calculating individual host and pathogen invasion fitness expressions using standard invasion analysis, we track how stable levels of investment in host resistance vary across different infection scenarios. We found that costly resistance is disfavoured when pathogen encounters are excessively high, with maximal resistance selected at intermediate levels of transmission. Coevolutionary feedbacks between host resistance and pathogen exploitation can lead to diverse outcomes, including stable evolutionarily singular strategies and, under weakly accelerating costs, evolutionary branching that generates coexistence in the resistance trait. We further quantify how coevolution shapes the equilibrium density of free propagules, revealing conditions under which coevolution suppresses or amplifies pathogen prevalence in comparison to non-evolving scenarios. Overall, our model framework built on survival-based costs offers testable predictions for environmentally transmitted host-pathogen systems.

  PublisherDOI

• Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus

  medRxiv · 2026-03-25

  articleOpen access

  Many respiratory pathogens co-circulate within human populations. Yet, how pathogen community structure shapes the dynamics of infectious diseases remains poorly understood. At the population level, investigating polymicrobial dynamics, with potential underlying competitive or cooperative interactions, is challenging, because of confounding factors such as differing seasonality. This is particularly true for endemic pathogens which typically exhibit stable periodic dynamics. Their disruption due to the implementation of non-pharmaceutical interventions during the COVID-19 pandemic thus represents a unique large-scale natural experiment that can be leveraged to provide valuable insights into the complex interplay between respiratory pathogens. Here, we focus on the population dynamics of human rhinovirus (common cold) and on the potential viral interference of influenza A virus (flu A), which is hypothesized to account for their asynchronous circulation. Using a Bayesian framework, we first show based on simulations that exogenous perturbations can be a powerful tool to disentangle the contribution of pathogen interaction from other epidemiological factors. We then apply our framework to long surveillance time series from the US and Canada spanning the COVID-19 pandemic. We estimate key parameters of rhinovirus but find no conclusive support for an influence of influenza A virus at the population level.

  PublisherDOI

• Navigating a Human-Dominated Landscape: Habitat Selection and Movement Behavior of Fosa (Cryptoprocta ferox) in Eastern Madagascar

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Toward AI foundation models for epidemics: Promise, challenges, and paths forward

  Proceedings of the National Academy of Sciences · 2026-03-13

  articleOpen access

  Foundation models—large AI systems pretrained on broad, heterogeneous data—are transforming scientific discovery. These models (e.g., GPT, GenCast, AlphaFold) excel at learning generalizable representations and adapting to new tasks with limited data. Yet, epidemic modeling has not experienced a comparable transformation. Traditional models remain pathogen-specific and often struggle to generate rapid insights during emerging outbreaks, as starkly illustrated by the SARS-CoV-2 pandemic. This Perspective asks whether the foundation model paradigm can extend to epidemic science: Can we build a single, pretrained model that captures the shared principles of infectious disease dynamics across pathogens, populations, and settings? Such a model could be fine-tuned to new contexts with minimal data, enabling faster forecasting, inference, and response, especially valuable in resource-limited settings. We argue that the growing convergence of epidemiological insight and modern AI makes this goal both urgent and increasingly plausible. We outline the main challenges in building foundation models for epidemics—nonstationarity, fragmented surveillance data, presence of diverse dynamical regimes, and the need for interpretability. We then propose a roadmap toward epidemic foundation models, emphasizing both algorithmic innovations to address these challenges and progress beyond algorithms, including investments in open datasets and cross-disciplinary training and collaboration. Developing epidemic foundation models offers a potentially transformative opportunity to strengthen global health security, particularly by improving preparedness in underresourced settings. If successful, they will serve as powerful, generalizable tools that complement existing efforts. The process of building these models will itself be valuable, exposing critical data gaps and guiding investments in global surveillance.

  PublisherOA PDFDOI

• Evaluating how demography and temperature increase might alter the burden of congenital Toxoplasmosis in Africa

  PLoS neglected tropical diseases · 2026-03-06 · 1 citations

  articleOpen accessSenior author

  The impact of climate change on environmental pathogens is a question whose importance will amplify in coming years. The protozoan parasite and global zoonosis Toxoplasma gondii is one such: empirical evidence indicates that oocyst survival is reduced at high temperatures. Paradoxically, a decline in incidence of T. gondii infections could amplify the burden of this disease, as the most damaging outcome occurs subsequent to first infection during pregnancy, and reductions in the incidence of the infection will increase the average age of first infection. We blend models of infection dynamics rooted in occurrence across the African continent with models of human demography to bound expectations for the future burden of this pathogen, accounting for the effects of changing temperatures. We discuss targeting efforts and approaches for mitigation.

  PublisherDOI

• Age-structured dynamics and susceptibility in the face of infection and vaccination

  medRxiv · 2026-02-11

  articleOpen access

  Abstract Background Strikingly low allocation of SARS-CoV-2 vaccine to the African Continent limits its capacity to control transmission. Characterizing the trajectory of vaccination efforts and their impact on the expected burden of SARS-CoV-2 will help planning vaccine delivery strategies, and public health interventions more broadly. As the burden is strongly age-dependent, this requires an understanding of the age-structured dynamics of susceptible individuals, accounting for the combined effects of vaccination and infection induced immunity. Methods and Findings We illustrate with projections for diverse African LMIC demographics. To this end, we develop an age-structured mathematical model with vaccination to assess the likely time-horizon to reach target vaccine coverage of high-risk groups, and how susceptibility patterns across age will shift as a result of both infection, and the broadening of vaccination targets from a focus on risk groups to efforts to reach the general population. We base our assessment on the demography, contact patterns and public health capacity of 16 African countries with diverse age pyramids. We identify a considerable divergence in the projected horizon of expanded targeting from prioritized age groups to general vaccination, with longer time among those with higher mean age and lower vaccination capacity. We parameterize the model using realistic demographies and contact patterns to project the changing age profile of susceptibles. We demonstrate that contacts and vaccination jointly drive the early age profile; while immune duration contributes to the transition of age-susceptibility profile in the intermediate future. Conclusions Our model framework provides a flexible and critical preparedness-tools to inform decision making against future epidemic waves and beyond Covid-19.

  PublisherOA PDFDOI

• Seroprevalence of endemic and emergent coronaviruses among SARS-COV-2 patients and healthcare workers in Abidjan, Côte d’Ivoire

  BMC Infectious Diseases · 2026-03-27

  articleOpen access

  Understanding the serological landscape of endemic and emergent coronaviruses is critical to interpreting early-pandemic immune responses and evaluating hypotheses of cross-reactivity. It was proposed that prior exposure to endemic coronaviruses could affect susceptibility or shape symptom severity through cross-reactive antibody responses. However, little was known about baseline coronavirus seroprevalence in many global regions, including Abidjan, Côte d’Ivoire. Characterizing this landscape provides key insights into early pandemic immunity and the potential influence of prior coronavirus exposures on SARS-CoV-2 immune response. Here, we probe this using data from syndromic surveillance in Abidjan, Côte d’Ivoire, collected between September 2020 and July 2021. We quantified IgG antibody levels to both spike and nucleocapsid proteins for emergent coronaviruses (SARS-CoV-1, SARS-CoV-2, and MERS-CoV) and endemic coronaviruses (HKU1, OC43, NL63 and 229E) using high-throughput multiplex bead assay. Samples were collected from SARS-CoV-2 negative healthcare workers (N = 202) and SARS-CoV-2 positive patients (N = 207). SARS-CoV-2 positive patients returned for repeat sampling at day 28 (N = 131). SARS-CoV-2 negative healthcare workers had higher SARS-CoV-1 seropositivity [0.27 (CI: 0.21–0.33) vs. 0.18 (CI: 0.13–0.24)] and SARS-CoV-2 seropositivity [0.52 (CI: 0.46–0.59) vs. 0.37 (CI: 0.31–0.44)] than SARS-CoV-2 positive patients. There were no significant differences among endemic coronaviruses between the healthcare workers and patients. Among the endemic coronaviruses, seropositivity was highest for 229E at 0.96 (95% CI: 0.94–0.98) and lowest for HKU1 at 0.56 (95% CI: 0.51–0.61) We found no significant sex difference in seropositivity to any coronavirus. These findings provide a snapshot of endemic and emergent coronavirus seroprevalences during the beginning of the COVID-19 pandemic in Abidjan. We observed high seroprevalence to endemic alphacoronaviruses (229E and NL63), slightly lower levels for betacoronaviruses (HKU1 and OC43), and cross-reactive antibody signals to SARS-CoV-1. Among SARS-CoV-2 positive patients sampled again after 28 days, we did not observe evidence of boosting antibody levels to endemic coronaviruses, suggesting no cross-reactive responses. However, studies incorporating conserved S2 regions are needed to more fully assess cross-reactivity.

  PublisherDOI

• Interplay between climate and childhood mixing can explain a sudden shift in RSV seasonality in Japan

  Nature Communications · 2025-12-13

  articleOpen access

  Titrating the importance of endogenous and exogenous drivers for host-pathogen systems remains an important research frontier towards predicting future outbreaks. In Japan, respiratory syncytial virus (RSV), a major childhood respiratory pathogen, displayed a sudden, dramatic shift in outbreak seasonality (from winter to fall) in 2016. We use mathematical models to identify processes that could lead to this outcome. In line with previous analyses, we identify a robust quadratic relationship between transmission against mean specific humidity and mean temperature, with maximum transmission occurring at low and high humidity as well as low and high temperature. This drives semiannual patterns of seasonal transmission rates that peak in summer and winter. Under this transmission regime, a subtle increase in population-level susceptibility or transmission can cause a sudden shift in seasonality, where the degree of shift is primarily determined by the interval between the two peaks of seasonal transmission rate. We hypothesize that an increase in children attending childcare facilities may have contributed to the increase in the overall RSV transmission through increased contact rates between susceptible and infected hosts. Our analysis underscores the power of studying infectious disease dynamics to titrate the roles of underlying drivers of dynamical transitions in ecology.

  PublisherOA PDFDOI

Frequent coauthors

• Bryan T. Grenfell

  Princeton Public Schools

  467 shared

• Ottar N. Bjørnstad

  Pennsylvania State University

  98 shared

• Jeremy Farrar

  Wellcome Trust

  90 shared

• Andrew J. Tatem

  University of Southampton

  86 shared

• Justin Lessler

  University of North Carolina at Chapel Hill

  85 shared

• Michael J. Mina

  83 shared

• Rachel E. Baker

  Brown University

  65 shared

• Daniel C. Douek

  National Institute of Allergy and Infectious Diseases

  63 shared