Research Initiatives

Climate and environental determinants of Valley fever risk

Our group studies the epidemiological and environmental determinants of coccidioidomycosis (Valley fever), a climate-sensitive fungal respiratory infection transmitted via inhalation of dust containing aerosolized fungal spores. In collaboration with the California Department of Public Health and Arizona Department of Health Services, we have several ongoing projects as part of this initiative:

Characterize heterogeneity in seasonal patterns of coccidioidomycosis across California and Arizona and investigate how climate conditions, such as soil moisture, temperature, and rainfall, impact the growth and dispersal of Coccidioides spores; we have found that drought conditions significantly impact coccidioidomycosis seasonal dynamics and incidence in California.

Understand the impacts of windblown dust exposure on coccidioidomycosis risk, with the goal of identifying specific dust conditions that pose the greatest risk for infection. This project represents an exciting opportunity to incorporate recent advances in dust science into epidemiological analyses at high spatial and temporal resolutions in order to generate insights into the role that climate and dust play in the current coccidioidomycosis epidemic.

Develop short-term forcasting systems and climate change projections for coccidioidomycosis. This work is in collaboration with communities, public health and healthcare workers to ensure that the forecast models are responsive to local needs and can enhance local efforts to prevent coccidioidomycosis infections.

The impacts of climate change on active transport

Physical activity is one of the best disease prevention strategies, and it is influenced by environmental factors such as temperature and precipitation. However, our understanding of how climate change may impact active transport, which has co-benefits of mitigating climate change and promoting physical activity, remains extremely limited.

In our first study, we aimed to understand the relationship between ambient temperature and bikeshare usage and to project how climate change-induced increasing ambient temperatures may influence active transportation in New York City. The analysis leveraged Citi Bike® bikeshare data to estimate participation in outdoor bicycling in New York City from 2013-2017. We estimated that daily hours and distance ridden on Citi Bike significantly increased as temperatures increased, but then declined at temperatures above 26–28°C. Using climate change temperature projections, we showed that annual bike usage may increase 3% by 2070. Future ridership increases during the winter, spring, and fall may more than offset future declines in summer ridership. Evidence suggesting nonlinear impacts of rising temperatures on health-promoting bicycle ridership demonstrates how challenging it is to anticipate the health consequences of climate change.

We are currently working to expand this work across many countries and cities to determine 1) whether the climate sensitivity of bikeshare program usage differs across different geographies, climates, and cultures, and 2) what are the important modifying factors of these relationships (e.g., green space, bike lanes, elevation, air pollution, demographics, cultural factors).

Hydrometeorological drivers of childhood diarrhea

From 2016-2019, we leveraged unique under-5 diarrhea incidence data to explore the effects of meteorological variability on childhood diarrhea incidence in Botswana, where diarrhea remains an important cause of childhood morbidity and mortality. We found that cases of under-5 diarrhea exhibited robust seasonal dynamics, and that diarrhea incidence was strongly associated with variability in local rainfall, river flooding, and surface water quality (published in PLoS Medicine). Next, we confirmed the existence of an El Niño-Southern Oscillation teleconnection with southern Africa by demonstrating that La Niña conditions are associated with cooler temperatures, increased rainfall, and higher flooding in Chobe District during the wet season. In turn, we showed that La Niña conditions lagged 0-5 months were associated with higher-than-average incidence of under-5 diarrhea (published in Nature Communications).

This work contributes to our understanding of the links between proximal and distal climatic variability and childhood diarrhea in arid regions of sub-Saharan Africa. Furthermore, we demonstrate the potential use of ENSO data, which are publicly available, to prepare for and mitigate diarrheal disease outbreaks in a low-resource setting up to 5 months in advance. Deaths caused by diarrhea are preventable using low-cost treatments. Hence, accurate predictions of diarrhea outbreak magnitudes could help healthcare providers and public health officials prepare for and mitigate the significant morbidity and mortality resulting from diarrhea outbreaks.

Currently, we are expanding this work to study the hydrometeorological drivers of diarrheal disease in Jordan as part of the Global Center on Climate Change and Water Energy Food Health Systems project. We will utilize administrative health data, water quality data, and climate data to draw connections between climate variability and diarrhea incidence in this arid environment.

Population health impacts of wildfire smoke and extreme heat in California

Increasing wildfire activity and extreme heat across the Western US pose a significant public health threat. While there is evidence that wildfire smoke is detrimental for respiratory health, the impacts on cardiovascular health remain unclear. Our recent study evaluated the association between fine particulate matter (PM2.5) from wildfire smoke and cardiorespiratory health in California during the 2004-2009 wildfire seasons. We found that smoke event days were associated with ~3% increase in hospital visits for all respiratory diseases and ~10% increase for asthma specifically. Stratifying by age, we found the largest effect for asthma among children ages 0-5y. We observed no significant association between smoke exposure and overall cardiovascular disease, but stratified analyses revealed increases in visits for all cardiovascular, ischemic heart disease, and heart failure among non-Hispanic white individuals and those older than 65y. Further, we found significant interaction between smoke event days and daily temperature for all cardiovascular disease visits, suggesting that days with high wildfire PM2.5 and high temperatures may pose greater disease risk. These results suggest increases in adverse health outcomes from wildfire smoke exposure and indicate the need for improved prevention and adaptations to protect vulnerable populations. Our ongoing work builds on this study to better understand the potentially synergistic impacts of wildfire smoke and extreme heat exposures on emergency department visits among children in San Diego, California.

Developing and testing infectious disease forecast systems

Diarrheal disease is the second largest cause of mortality in children younger than 5, yet our ability to anticipate and prepare for outbreaks remains limited. We developed and tested an epidemiological forecast model for childhood diarrheal disease in Chobe District, Botswana. Our prediction system used a compartmental susceptible-infected-recovered-susceptible (SIRS) model coupled with Bayesian data assimilation to infer relevant epidemiological parameter values and generate retrospective forecasts. Our model inferred two system parameters and accurately simulated weekly observed diarrhea cases from 2007-2017. Accurate retrospective forecasts for diarrhea outbreaks were generated up to six weeks before the predicted peak of the outbreak, and accuracy increased over the progression of the outbreak. Many forecasts generated by our model system were more accurate than predictions made using only historical data trends. Reliable predictions of under-5 diarrhea can greatly help officials anticipate, respond to, and mitigate childhood diarrhea outbreaks by helping inform vaccine distribution, hospital and clinic staffing, and the management of healthcare supplies and beds in anticipation of patient surges. We are currently working to apply this same model system to forecast diarrheal disease in Jordan.