About

Rachel Baker is the John and Elizabeth Irving Family Assistant Professor of Climate Health and an Assistant Professor of Epidemiology and Environment and Society at Brown University School of Public Health. Her research focuses on the implications of climate change for human health, with a particular emphasis on infectious diseases. She employs a combination of statistical inference and mechanistic disease modeling to understand and address health challenges posed by environmental changes. Her work has been published in prominent journals including Science, PNAS, Nature Communications, and Climatic Change. Additionally, her research has been featured in media outlets such as The New York Times, The Atlantic, WIRED Magazine, and Scientific American. Baker's contributions are part of broader efforts to fuse public health with environmental science, exploring how climate change threatens health and seeking solutions to mitigate these impacts.

Research topics

• Biology
• Medicine
• Virology
• Environmental health
• Geography
• Immunology
• Internal medicine
• Demography
• Zoology
• Genetics
• Evolutionary biology
• Ecology

Selected publications

• Spatial patterns and environmental influences of COVID-19 outbreaks, post-Omicron

  PLoS ONE · 2026-02-10 · 1 citations

  articleOpen accessSenior author

  The seasonality of many respiratory pathogen outbreaks, such as influenza and respiratory syncytial virus, is driven by climate factors, such as specific humidity or temperature. However, it remains unclear whether climate plays a role in determining the seasonality of COVID-19, given that the evolution of novel strains likely plays a key role in shaping outbreak dynamics. Here we use Emergency Department data to explore spatial differences in COVID-19 outbreak dynamics over three years, from April 2022 through March 2025. We observe that outbreak patterns varied across latitude, with southern states experiencing larger summer peaks and northern states facing more evenly distributed summer to winter outbreaks or larger winter peaks. We find that specific humidity and temperature at the state level are significantly associated with observed differences in ED visits with a COVID-19 diagnosis, even after controlling for state-level variation in vaccination status. Our results imply a role for climate in influencing COVID-19 outbreak dynamics. We anticipate these findings will provide a foundational understanding of factors shaping SARS-CoV-2 transmission as COVID-19 becomes endemic in the United States.

  PublisherDOI

• Interplay of Immunity, Climate, and Viral Evolution Explains Semiannual SARS-CoV-2 Dynamics with Implications for Control

  medRxiv · 2026-03-02 · 1 citations

  articleOpen access

  In the three years since Omicron emergence, SARS-CoV-2 dynamics have exhibited persistent twice-yearly waves in the United States, peaking in late summer and winter, with heterogeneity in timing and intensity across states. This semiannual pattern sharply contrasts with typical annual respiratory pathogen dynamics in the US, yet their underlying mechanisms and whether this pattern will persist remain poorly understood. Here, we tested several hypothesized mechanisms and found that a combination of waning immunity, climatic factors of relative humidity and temperature, variant activity, and vaccination captured divergent patterns in COVID-19 hospitalization incidence across 10 US states, from January 2022-November 2024. Applying a compartmental disease model, we identified that waning infection-derived immunity was the dominant driver of semiannual SARS-CoV-2 dynamics, with climate factors shaping the timing and magnitude of seasonal waves across US states. Scenario analyses indicated that if infection-derived immunity remains short in duration, semiannual dynamics influenced by climate are likely to persist, with attenuation in severe disease over time. In contrast, more durable infection-derived immunity, or a slower rate of immune-evading viral evolution, could lead to an epidemiologic transition to annual dynamics. In some states, summer waves approached the magnitude of winter waves, likely reflecting local climatic influences on transmission, suggesting that optimal vaccination strategies may vary by state. These findings have broad implications for understanding epidemic dynamics and informing vaccine policy, including seasonal timing and two-dose vaccine schedules for high-risk persons.

  PublisherOA PDFDOI

• Climate change and infectious diseases

  Nature Medicine · 2026-05-01

  article1st authorCorresponding
  PublisherDOI

• Climate influences on hospitalization patterns in Mexico: Evidence from 30 million records

  Environmental Research Communications · 2026-01-01

  articleOpen accessSenior author

  Abstract Climate change is expected to have wide-ranging effects on human health, yet the extent to which environmental factors drive health outcomes is poorly understood, particularly in tropical locations. Here, we leverage a large dataset of approximately 30 million individual-level hospitalizations from Mexico, linked with locally resolved climate data, to understand the seasonality of morbidity and the role of climate in driving these patterns. We first apply a Fourier transform to identify disease categories that exhibit significant seasonal signals. Next, we apply fixed effect regression models to identify climate drivers of these seasonal patterns for both broad disease categories specified by the International Classification of Diseases (ICD) and a comprehensive range of specific disease subcategories defined by the World Health Organization (WHO). We found that half of the ICD disease category hospitalizations had a significant seasonal signal. Among these, 89% exhibited a significant positive association with temperature, 33% exhibited a significant positive association with precipitation, and 11% exhibited a significant negative association with precipitation. Overall, we found that temperature is a significant driver of 26% of disease subcategories defined by the WHO. The disease areas most influenced by climate are infectious, cardiovascular, respiratory, injury, and maternal conditions. These findings highlight how precipitation and temperature drive seasonal hospitalization patterns for communicable diseases, non-communicable diseases, and injuries in tropical and temperate climates.

  PublisherDOI

• Impact of weather extremes on the spatiotemporal dynamics of visceral leishmaniasis in Brazil

  PLoS neglected tropical diseases · 2025-07-28 · 2 citations

  articleOpen accessCorresponding

  BACKGROUND: Vector-borne diseases are highly sensitive to environmental and climatic conditions, which can directly affect vector behavior, parasite development, and transmission dynamics. Identifying the key meteorological drivers of these diseases and understanding the timing of their impacts is crucial for enhancing public health preparedness. This study focuses on visceral leishmaniasis (VL) in Brazil; a parasitic vector-borne disease spread by the bite of infected sandflies whose distribution is heavily influenced by environmental conditions. METHODOLOGY: We analyzed monthly confirmed VL cases from 2007-2022 using distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical model framework to assess the nonlinear, time-lagged associations between locally defined weather anomalies and VL risk across space. We evaluated the exposure-lag-response relationships between anomalies in monthly average temperature, precipitation, and relative humidity; and VL incidence across Brazilian microregions, considering lags ranging from 0 to 4 months. PRINCIPAL FINDINGS: Among the 53,968 VL cases reported during the study period, the majority occurred in the Northeast and Central North regions. Our model revealed statistically significant nonlinear relationships between meteorological anomalies and VL risk. Associations were most pronounced in rural and deforested microregions, where climatic extremes intensified transmission risk. CONCLUSIONS AND SIGNIFICANCE: This analysis identified an increased VL risk at higher-than-usual temperatures and a lower risk with higher-than-usual humidity and precipitation across various lags. We offer novel foundational insights for the future development of early warning systems, especially relevant to regions like Brazil facing a substantial VL burden.

  PublisherDOI

• Urban contact patterns shape respiratory syncytial virus epidemics with implications for vaccination

  Science Advances · 2025-11-26 · 2 citations

  articleOpen accessSenior authorCorresponding

  Urban environments may alter the landscape of disease transmission with implications for control. Yet, it is unclear whether urban-rural differences exist in the dynamics of childhood respiratory diseases, given specific mixing patterns in younger age groups. Here, we leverage county-level data on respiratory syncytial virus (RSV) from the United States to reveal an urban-rural gradient in both the intensity and age structure of the RSV epidemic, where urban locations experience more prolonged epidemics with higher burden in infants (under 1 year of age). We develop a mechanistic epidemiological model to show that these differences can be explained by daycare utilization rates in children under 5. Using our model to consider control measures, we find that expanding seasonal immunization access in urban and rural areas may limit the risk of off season RSV epidemics.

  PublisherDOI

• Interplay between climate, childhood mixing, and population-level susceptibility explains a sudden shift in RSV seasonality in Japan

  medRxiv · 2025-03-03 · 1 citations

  preprintOpen access

  Titrating the relative importance of endogenous and exogenous drivers for dynamical transitions in host-pathogen systems remains an important research frontier towards predicting future outbreaks and making public health decisions. 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. This shift was not observed in any other countries. 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 mean specific humidity and transmission, with minimum transmission occurring at intermediate humidity. 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 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 susceptibility through increased contact rates with susceptible 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

• Urban environment and RSV: a retrospective observational study of neighbourhood factors associated with the risk of severe disease in the infant population of a metropolitan area, Lyon, France

  BMC Public Health · 2025-10-14

  articleOpen access

  Severe acute respiratory infections (SARI) caused by a human respiratory syncytial virus (RSV) are a leading cause of hospitalisation among infants. We aimed to estimate the incidence of RSV SARI across neighbourhoods in the Lyon metropolitan area and to assess how urban environmental factors at the metropolitan scale are associated with spatial variation in incidence. Laboratory-confirmed cases of RSV SARI (< 2 years of age) were extracted from the university hospitals of Lyon laboratory database 2015–2023. We calculated and mapped the incidence to assess spatial variation. Remote sensing data were used to derive spectral indices characterising the metropolitan area and to model temperature and air humidity at this scale. Temporal and spatial regression models were fitted using variables selected based on prior knowledge and data availability. Cumulative incidence varied significantly across neighbourhoods (0 to 1166 cases per 100,000 people at risk; p-value < 0.001). The best spatial multivariate model (r2 = 0.39, Akaike information criterion (AIC) = 6103), explained a substantial portion of this variation, and included neighbourhood median income that was negatively associated with incidence; and neighbourhood winter temperature as well as particulate matter < 10 µm pollution, both positively associated with incidence. Additionally, urban index (UI) and normalised difference moisture index (NDMI) demonstrated strong (p-value < 0.001) univariate associations (UI: r2 = 0.23, AIC = 6216; NDMI: r2 = 0.21, AIC = 6229), accounting for a significant portion of the incidence variation. Substantial neighbourhood-level differences in RSV SARI incidence exist in a large European metropolis. These differences are associated with the urban environment such as particulate pollution. The use of spectral indices shows promise in identifying vulnerable populations within cities to guide public health measures and to integrate public health and urban planning.

  PublisherOA PDFDOI

• 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

• A Climate-Aware Deep Learning Framework for Generalizable Epidemic Forecasting

  ArXiv.org · 2025-10-22 · 1 citations

  preprintOpen accessSenior author

  Precise outbreak forecasting of infectious diseases is essential for effective public health responses and epidemic control. The increased availability of machine learning (ML) methods for time-series forecasting presents an enticing avenue to enhance outbreak forecasting. Though the COVID-19 outbreak demonstrated the value of applying ML models to predict epidemic profiles, using ML models to forecast endemic diseases remains underexplored. In this work, we present ForecastNet-XCL (an ensemble model based on XGBoost+CNN+BiLSTM), a deep learning hybrid framework designed to addresses this gap by creating accurate multi-week RSV forecasts up to 100 weeks in advance based on climate and temporal data, without access to real-time surveillance on RSV. The framework combines high-resolution feature learning with long-range temporal dependency capturing mechanisms, bolstered by an autoregressive module trained on climate-controlled lagged relations. Stochastic inference returns probabilistic intervals to inform decision-making. Evaluated across 34 U.S. states, ForecastNet-XCL reliably outperformed statistical baselines, individual neural nets, and conventional ensemble methods in both within- and cross-state scenarios, sustaining accuracy over extended forecast horizons. Training on climatologically diverse datasets enhanced generalization furthermore, particularly in locations having irregular or biennial RSV patterns. ForecastNet-XCL's efficiency, performance, and uncertainty-aware design make it a deployable early-warning tool amid escalating climate pressures and constrained surveillance resources.

  PublisherOA PDFDOI

Frequent coauthors

• Bryan T. Grenfell

  Princeton Public Schools

  90 shared

• C. Jessica E. Metcalf

  Princeton University

  65 shared

• Chadi M. Saad-Roy

  38 shared

• Caroline E. Wagner

  McGill University

  24 shared

• Jeremy Farrar

  Wellcome Trust

  19 shared

• Sinead E. Morris

  Professional Services Group (United States)

  16 shared

• Andrea L. Graham

  Princeton University

  16 shared

• Simon A. Levin

  Princeton University

  16 shared