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Variability of climatic conditions can impact numerous important public health outcomes, including the prevalence of mosquito-borne diseases such as West Nile virus (WNV). 2002 and 2003 were epidemic years for WNV in the United States, with 685 cases of the novel virus reported from Chicago, IL alone. WNV’s prevalence is most noted in urban areas due to juvenile mosquito habitats provided by subterranean stormwater drainage systems. Transmission in urban environments is favored by suitable weather conditions that stimulate and sustain mosquito reproduction and growth of vector species. Field and laboratory studies indicate that temperature is a crucial factor for suitable conditions for juvenile development, while precipitation plays a dual role: it can provide moisture necessary for aquatic habitats to persist, yet high rain rates lead to flushing of larvae out of subterranean stormwater habitats. The duration of these conditions, and locational features (i.e. topography, elevation and depth below ground), matter too. This study addresses two significant questions: 1) What is the seasonality of suitable juvenile habitat conditions across different human land-use types that employ a mixture of subterranean and surface stormwater infrastructure, and 2) Whether the seasonality of these climatic conditions for mosquito reproduction have shifted over time, which in turn affects the timing and severity of mosquito-borne disease transmission. In our study, we consider over 20 years of multi-scale observations and fine resolution spatiotemporal reanalysis data during the 7-month warm season (April-November) for five small and midsize cities in north-central Illinois. We use weather station data from Illinois State Water Survey and NCEP/EMC 4 KM Gridded Stage IV Data to track the intersection of necessary temperature and precipitation thresholds known to support mosquito reproduction in subterranean and surface stormwater systems. Our analysis will thus identify when and where risk of WNV transmission is more prevalent across the urban-to-rural land-use gradient. Such results will be helpful in identifying the timing of mosquito-borne disease risk and inform policies to curb transmission.
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Extreme climate whiplash events drive divergent responses of mosquito-borne disease
Abstract There is mounting concern surrounding climate change effects on human health. Vector-borne diseases—transmitted by ectotherms like mosquitos—are sensitive to abiotic conditions, and there is a significant interest in modeling their response to future climate change. However, changing climate also contributes to increasing variability and frequency of extreme weather, including “climate whiplash” events, when weather conditions abruptly shift between extremes. These events may have more immediate effects on vector-borne diseases, though they have received less attention. Here, we use the series of extreme atmospheric rivers of 2022/2023 in California, following years of extreme drought, as a natural experiment to assess the effect of climate whiplash on vector-borne disease risk. Using high spatiotemporal resolution standing water remote sensing, mosquito and viral surveillance, and community science observations of key reservoir hosts, we estimate mosquito species- and virus-specific responses to flooding following atmospheric rivers using panel regression models. We find significant positive effects of flooding on abundance of the rural West Nile virus (WNV) vector in California's Central Valley, with the largest effects at landscape scales. We find no significant effects for the urban WNV vector, or globally invasive yellow fever mosquito. Finally, we find similarly divergent effects on WNV and Saint Louis Encephalitis virus (SLEV) infection: WNV rates decline significantly in the urban vector and SLEV increases significantly in the rural vector. These results reveal species-specific responses to climate whiplash that are predictable by mosquito ecology, relevant to globally important mosquito vectors and diseases, and inform public health response to future extreme events.
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- PAR ID:
- 10705193
- Editor(s):
- Campbell, Corey; Brenner, David
- Publisher / Repository:
- Oxford Academic
- Date Published:
- Journal Name:
- PNAS Nexus
- Volume:
- 4
- Issue:
- 8
- ISSN:
- 2752-6542
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Mosquito‐borne diseases contribute substantially to the global burden of disease, and are strongly influenced by environmental conditions. Ongoing and rapid environmental change necessitates improved understanding of the response of mosquito‐borne diseases to environmental factors like temperature, and novel approaches to mapping and monitoring risk. Recent development of trait‐based mechanistic models has improved understanding of the temperature dependence of transmission, but model predictions remain challenging to validate in the field. Using West Nile virus (WNV) as a case study, we illustrate the use of a novel remote sensing‐based approach to mapping temperature‐dependent mosquito and viral traits at high spatial resolution and across the diurnal cycle. We validate the approach using mosquito and WNV surveillance data controlling for other key factors in the ecology of WNV, finding strong agreement between temperature‐dependent traits and field‐based metrics of risk. Moreover, we find that WNV infection rate in mosquitos exhibits a unimodal relationship with temperature, peaking at ~24.6–25.2°C, in the middle of the 95% credible interval of optimal temperature for transmission of WNV predicted by trait‐based mechanistic models. This study represents one of the highest resolution validations of trait‐based model predictions, and illustrates the utility of a novel remote sensing approach to predicting mosquito‐borne disease risk.more » « less
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Abstract ObjectivesWest Nile virus (WNV) is the most common mosquito-borne disease in the United States. Predicting the location and timing of outbreaks would allow targeting of disease prevention and mosquito control activities. Our objective was to develop software (ArboMAP) for routine WNV forecasting using public health surveillance data and meteorological observations. Materials and MethodsArboMAP was implemented using an R markdown script for data processing, modeling, and report generation. A Google Earth Engine application was developed to summarize and download weather data. Generalized additive models were used to make county-level predictions of WNV cases. ResultsArboMAP minimized the number of manual steps required to make weekly forecasts, generated information that was useful for decision-makers, and has been tested and implemented in multiple public health institutions. Discussion and ConclusionRoutine prediction of mosquito-borne disease risk is feasible and can be implemented by public health departments using ArboMAP.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1093/pnasnexus/pgaf223
