Prolonged dry seasons lengthen coccidioidomycosis transmission seasons: implications for a changing California.

Coccidioidomycosis, a fungal disease caused by soil-borne spp., exhibits pronounced seasonal transmission, with incidence in California typically peaking in the fall. However, the influence of climate on the timing and duration of transmission seasons remains poorly understood.

Coccidioidomycosis seasonality in California: a longitudinal surveillance study of the climate determinants and spatiotemporal variability of seasonal dynamics, 2000-2021.

Background: Coccidioidomycosis, an emerging fungal disease in the western USA, exhibits seasonal patterns that are poorly understood, including periods of strong cyclicity, aseasonal intervals, and variation in seasonal timing that have been minimally characterized, and unexplained as to their causal factors. Coccidioidomycosis incidence has increased markedly in recent years, and our limited understanding of intra- and inter-annual seasonality has hindered the identification of important drivers of disease transmission, including climate conditions. In this study, we aim to characterize coccidioidomycosis seasonality in endemic regions of California and to estimate the relationship between drought conditions and coccidioidomycosis seasonal periodicity and timing.

Association between wildfires and coccidioidomycosis incidence in California, 2000-2018: a synthetic control analysis.

Unlabelled: The frequency and severity of wildfires in the Western United States have increased over recent decades, motivating hypotheses that wildfires contribute to the incidence of coccidioidomycosis, an emerging fungal disease in the Western United States with sharp increases in incidence observed since 2000. While coccidioidomycosis outbreaks have occurred among wildland firefighters clearing brush, it remains unknown whether fires are associated with an increased incidence among the general population.

Methods: We identified 19 wildfires occurring within California's highly endemic San Joaquin Valley between 2003 and 2015.

Effects of precipitation, heat, and drought on incidence and expansion of coccidioidomycosis in western USA: a longitudinal surveillance study.

Background: Drought is an understudied driver of infectious disease dynamics. Amidst the ongoing southwestern North American megadrought, California (USA) is having the driest multi-decadal period since 800 CE, exacerbated by anthropogenic warming. In this study, we aimed to examine the influence of drought on coccidioidomycosis, an emerging infectious disease in southwestern USA.

Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections.

With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.

Impacts of Fine Particulate Matter From Wildfire Smoke on Respiratory and Cardiovascular Health in California.

Increases in wildfire activity 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. This study evaluates the association between fine particulate matter (PM) from wildfire smoke and unscheduled cardiorespiratory hospital visits in California during the 2004-2009 wildfire seasons.

School closures reduced social mixing of children during COVID-19 with implications for transmission risk and school reopening policies.

School closures may reduce the size of social networks among children, potentially limiting infectious disease transmission. To estimate the impact of K-12 closures and reopening policies on children's social interactions and COVID-19 incidence in California's Bay Area, we collected data on children's social contacts and assessed implications for transmission using an individual-based model. Elementary and Hispanic children had more contacts during closures than high school and non-Hispanic children, respectively.

The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures.

Infectious disease surveillance systems provide vital data for guiding disease prevention and control policies, yet the formalization of methods to optimize surveillance networks has largely been overlooked. Decisions surrounding surveillance design parameters-such as the number and placement of surveillance sites, target populations, and case definitions-are often determined by expert opinion or deference to operational considerations, without formal analysis of the influence of design parameters on surveillance objectives. Here we propose a simulation framework to guide evidence-based surveillance network design to better achieve specific surveillance goals with limited resources.

The effect of school closures and reopening strategies on COVID-19 infection dynamics in the San Francisco Bay Area: a cross-sectional survey and modeling analysis.

Background Large-scale school closures have been implemented worldwide to curb the spread of COVID-19. However, the impact of school closures and re-opening on epidemic dynamics remains unclear. Methods We simulated COVID-19 transmission dynamics using an individual-based stochastic model, incorporating social-contact data of school-aged children during shelter-in-place orders derived from Bay Area (California) household surveys.

El Niño-Southern oscillation and under-5 diarrhea in Botswana.

Childhood diarrheal disease causes significant morbidity and mortality in low and middle-income countries, yet our ability to accurately predict diarrhea incidence remains limited. El Niño-Southern Oscillation (ENSO) has been shown to affect diarrhea dynamics in South America and Asia. However, understanding of its effects in sub-Saharan Africa, where the burden of under-5 diarrhea is high, remains inadequate.

Ensemble forecast and parameter inference of childhood diarrhea in Chobe District, Botswana.

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. Here, we develop and test an epidemiological forecast model for childhood diarrheal disease in Chobe District, Botswana. Our prediction system uses a compartmental susceptible-infected-recovered-susceptible (SIRS) model coupled with Bayesian data assimilation to infer relevant epidemiological parameter values and generate retrospective forecasts.

Climate Change and Physical Activity: Estimated Impacts of Ambient Temperatures on Bikeshare Usage in New York City.

Background: Physical activity is one of the best disease prevention strategies, and it is influenced by environmental factors such as temperature.

Objectives: We aimed to illuminate the relation between ambient temperature and bikeshare usage and to project how climate change-induced increasing ambient temperatures may influence active transportation in New York City.

Methods: The analysis leverages Citi Bike® bikeshare data to estimate participation in outdoor bicycling in New York City.

Hydrometeorology and flood pulse dynamics drive diarrheal disease outbreaks and increase vulnerability to climate change in surface-water-dependent populations: A retrospective analysis.

Background: The impacts of climate change on surface water, waterborne disease, and human health remain a growing area of concern, particularly in Africa, where diarrheal disease is one of the most important health threats to children under 5 years of age. Little is known about the role of surface water and annual flood dynamics (flood pulse) on waterborne disease and human health nor about the expected impact of climate change on surface-water-dependent populations.

Methods And Findings: Using the Chobe River in northern Botswana, a flood pulse river-floodplain system, we applied multimodel inference approaches assessing the influence of river height, water quality (bimonthly counts of Escherichia coli and total suspended solids [TSS], 2011-2017), and meteorological variability on weekly diarrheal case reports among children under 5 presenting to health facilities (n = 10 health facilities, January 2007-June 2017).

Influenza forecast optimization when using different surveillance data types and geographic scale.

Background: Advance warning of influenza incidence levels from skillful forecasts could help public health officials and healthcare providers implement more timely preparedness and intervention measures to combat outbreaks. Compared to influenza predictions generated at regional and national levels, those generated at finer scales could offer greater value in determining locally appropriate measures; however, to date, the various influenza surveillance data that are collected by state and county departments of health have not been well utilized in influenza prediction.

Objectives: To assess whether an influenza forecast model system can be optimized to generate accurate forecasts using novel surveillance data streams.

Meteorological variability and infectious disease in Central Africa: a review of meteorological data quality.

Central African countries may bear high climate change-related infectious disease burdens because of preexisting high rates of disease, poor healthcare infrastructure, land use changes, and high environmental change vulnerabilities. However, making connections between climate and infectious diseases in this region is hampered by the paucity of high-quality meteorological data. This review analyzes the sources and quality of meteorological data used to study the interactions between weather and infectious diseases in Central African countries.

Climate-driven migration: an exploratory case study of Maasai health perceptions and help-seeking behaviors.

Objectives: By 2050, over 250 million people will be displaced from their homes by climate change. This exploratory case study examines how climate-driven migration impacts the health perceptions and help-seeking behaviors of Maasai in Tanzania. Increasing frequency and intensity of drought is killing livestock, forcing Maasai to migrate from their rural homelands to urban centers in search of ways to support their families.