Weather and land cover influences on mosquito populations in Sioux Falls, South Dakota

Spatial distribution and temporal dynamics of invasive and native mosquitoes in a large Mediterranean city

Mosquitoes, including invasive species like the Asian tiger mosquito Aedes albopictus, alongside native species Culex pipiens s.l., pose a significant nuisance to humans and serve as vectors for mosquito-borne diseases in urban areas. Understanding the impact of water infrastructure characteristics, climatic conditions, and management strategies on mosquito occurrence and effectiveness of control measures to assess their implications on mosquito occurrence is crucial for effective vector control. In this study, we examined data collected during the local vector control program in Barcelona, Spain, focusing on 234,225 visits to 31,334 different sewers, as well as 1817 visits to 152 fountains between 2015 and 2019. We investigated both the colonization and recolonization processes of mosquito larvae within these water infrastructures. Our findings revealed higher larval presence in sandbox-sewers compared to siphonic or direct sewers, and the presence of vegetation and the use of naturalized water positively influenced larval occurrence in fountains. The application of larvicidal treatment significantly reduced larvae presence; however, recolonization rates were negatively affected by the time elapsed since treatment. Climatic conditions played a critical role in the colonization and recolonization of sewers and urban fountains, with mosquito occurrence exhibiting non-linear patterns and, generally, increasing at intermediate temperatures and accumulated rainfall levels. This study emphasizes the importance of considering sewers and fountains characteristics and climatic conditions when implementing vector control programs to optimize resources and effectively reduce mosquito populations.

Future land-use change predictions using Dyna-Clue to support mosquito-borne disease risk assessment

Mosquitoes are known vectors for viral diseases in Canada, and their distribution is driven by climate and land use. Despite that, future land-use changes have not yet been used as a driver in mosquito distribution models in North America. In this paper, we developed land-use change projections designed to address mosquito-borne disease (MBD) prediction in a 38 761 km2 area of Eastern Ontario. The landscape in the study area is marked by urbanization and intensive agriculture and hosts a diverse mosquito community. The Dyna-CLUE model was used to project land-use for three time horizons (2030, 2050, and 2070) based on historical trends (from 2014 to 2020) for water, forest, agriculture, and urban land uses. Five scenarios were generated to reflect urbanization, agricultural expansion, and natural areas. An ensemble of thirty simulations per scenario was run to account for land-use conversion uncertainty. The simulation closest to the average map generated was selected to represent the scenario. A concordance matrix generated using map pair analysis showed a good agreement between the simulated 2020 maps and 2020 observed map. By 2050, the most significant changes are predicted to occur mainly in the southeastern region's rural and forested areas. By 2070, high deforestation is expected in the central west. These results will be integrated into risk models predicting mosquito distribution to study the possibility of humans' increased exposure risk to MBDs.

Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States

BACKGROUND

West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence.

OBJECTIVES

Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans.

METHODS

We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R2=0.61.

RESULTS

Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels <23.3mm/month as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables.

DISCUSSION

We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change. https://doi.org/10.1289/EHP10986.

Predicting Culex pipiens/restuans Population Dynamics Using a Weather-Driven Dynamic Compartmental Population Model

Mosquitoes of the genus Culex are important vectors of a variety of arthropod-borne viral infections. In most of the northern parts of the USA, Cx. pipiens/restuans is the predominant representative of this genus. As vectors, they play a key role in the spreading of arboviruses and thus, knowledge of the population dynamic of mosquitoes is important to understand the disease ecology of these viruses. As poikilotherm animals, the vital rates of mosquitoes are highly dependent on ambient temperature, and also on precipitation. We present a compartmental model for the population dynamics of Cx. pipiens/restuans. The model is driven by temperature, precipitation, and daytime length (which can be calculated from the geographic latitude). For model evaluation, we used long-term mosquito capture data, which were averaged from multiple sites in Cook County, Illinois. The model fitted the observation data and was able to reproduce between-year differences in the abundance of the Cx. pipiens/restuans mosquitoes, as well as the different seasonal trends. Using this model, we evaluated the effectiveness of targeting different vital rates for mosquito control strategies. The final model is able to reproduce the weekly mean Cx. pipiens/restuans abundance for Cook County with a high accuracy, and over a long time period of 20 years.

Interpretable machine learning approach to analyze the effects of landscape and meteorological factors on mosquito occurrences in Seoul, South Korea

Mosquitoes are the underlying cause of various public health and economic problems. In this study, patterns of mosquito occurrence were analyzed based on landscape and meteorological factors in the metropolitan city of Seoul. We evaluated the influence of environmental factors on mosquito occurrence through the interpretation of prediction models with a machine learning algorithm. Through hierarchical cluster analysis, the study areas were classified into waterside and non-waterside areas, according to the landscape patterns. The mosquito occurrence was higher in the waterside area, and mosquito abundance was negatively affected by rainfall at the waterside. The mosquito occurrence was predicted in each cluster area based on the landscape and cumulative meteorological variables using a random forest algorithm. Both models exhibited good performance (both accuracy and AUROC > 0.8) in predicting the level of mosquito occurrence. The embedded relationship between the mosquito occurrence and the environmental factors in the models was explained using the Shapley additive explanation method. According to the variable importance and the partial dependence plots for each model, the waterside area was more influenced by the meteorological and land cover variables than the non-waterside area. Therefore, mosquito control strategies should consider the effects of landscape and meteorological conditions, including the temperature, rainfall, and the landscape heterogeneity. The present findings can contribute to the development of mosquito forecasting systems in metropolitan cities for the promotion of public health.

Comparing Satellite and Ground-Based Measurements of Environmental Suitability for Vector Mosquitoes in an Urban Landscape

Exposure to mosquito-borne diseases is influenced by landscape patterns and microclimates associated with land cover. These influences can be particularly strong in heterogeneous urban landscapes where human populations are concentrated. We investigated how land cover and climate influenced abundances of Ae. albopictus (Skuse) (Diptera: Culicidae) and Cx. quinquefasciatus (Say) (Diptera: Culicidae) in Norman, Oklahoma (United States). From June-October 2019 and May-October 2020 we sampled mosquitoes along an urban-rural gradient using CO2 baited BG Sentinel traps. Microclimate sensors at these sites measured temperature and humidity. We mapped environmental variables using satellite images from Landsat, Sentinel-2, and VIIRS, and the CHIRPS rainfall dataset. We also obtained meteorological data from the closest weather station. We compared statistical models of mosquito abundance based on microclimate, satellite, weather station, and land cover data. Mosquitoes were more abundant on trap days with higher temperature and relative humidity. Rainfall 2 wk prior to the trap day negatively affected mosquito abundances. Impervious surface cover was positively associated with Cx. quinquefasciatus and tree cover was negatively associated with Ae. albopictus. Among the data sources, models based on satellite variables and land cover data had the best fits for Ae. albopictus (R2 = 0.7) and Cx. quinquefasciatus (R2 = 0.51). Models based on weather station or microclimate data had weaker fits (R2 between 0.09 and 0.17) but were improved by adding land cover variables (R2 between 0.44 and 0.61). These results demonstrate the potential for using satellite remote sensing for mosquito habitat analyses in urban areas.

Integrated Forecasts Based on Public Health Surveillance and Meteorological Data Predict West Nile Virus in a High-Risk Region of North America

BACKGROUND

West Nile virus (WNV), a global arbovirus, is the most prevalent mosquito-transmitted infection in the United States. Forecasts of WNV risk during the upcoming transmission season could provide the basis for targeted mosquito control and disease prevention efforts. We developed the Arbovirus Mapping and Prediction (ArboMAP) WNV forecasting system and used it in South Dakota from 2016 to 2019. This study reports a post hoc forecast validation and model comparison.

OBJECTIVES

Our objective was to validate historical predictions of WNV cases with independent data that were not used for model calibration. We tested the hypothesis that predictive models based on mosquito surveillance data combined with meteorological variables were more accurate than models based on mosquito or meteorological data alone.

METHODS

The ArboMAP system incorporated models that predicted the weekly probability of observing one or more human WNV cases in each county. We compared alternative models with different predictors including a) a baseline model based only on historical WNV cases, b) mosquito models based on seasonal patterns of infection rates, c) environmental models based on lagged meteorological variables, including temperature and vapor pressure deficit, d) combined models with mosquito infection rates and lagged meteorological variables, and e) ensembles of two or more combined models. During the WNV season, models were calibrated using data from previous years and weekly predictions were made using data from the current year. Forecasts were compared with observed cases to calculate the area under the receiver operating characteristic curve (AUC) and other metrics of spatial and temporal prediction error.

RESULTS

Mosquito and environmental models outperformed the baseline model that included county-level averages and seasonal trends of WNV cases. Combined models were more accurate than models based only on meteorological or mosquito infection variables. The most accurate model was a simple ensemble mean of the two best combined models. Forecast accuracy increased rapidly from early June through early July and was stable thereafter, with a maximum AUC of 0.85. The model predictions captured the seasonal pattern of WNV as well as year-to-year variation in case numbers and the geographic pattern of cases.

DISCUSSION

The predictions reached maximum accuracy early enough in the WNV season to allow public health responses before the peak of human cases in August. This early warning is necessary because other indicators of WNV risk, including early reports of human cases and mosquito abundance, are poor predictors of case numbers later in the season. https://doi.org/10.1289/EHP10287.

Effects of land use and weather on the presence and abundance of mosquito-borne disease vectors in a urban and agricultural landscape in Eastern Ontario, Canada

Weather and land use can significantly impact mosquito abundance and presence, and by consequence, mosquito-borne disease (MBD) dynamics. Knowledge of vector ecology and mosquito species response to these drivers will help us better predict risk from MBD. In this study, we evaluated and compared the independent and combined effects of weather and land use on mosquito species occurrence and abundance in Eastern Ontario, Canada. Data on occurrence and abundance (245,591 individuals) of 30 mosquito species were obtained from mosquito capture at 85 field sites in 2017 and 2018. Environmental variables were extracted from weather and land use datasets in a 1-km buffer around trapping sites. The relative importance of weather and land use on mosquito abundance (for common species) or occurrence (for all species) was evaluated using multivariate hierarchical statistical models. Models incorporating both weather and land use performed better than models that include weather only for approximately half of species (59% for occurrence model and 50% for abundance model). Mosquito occurrence was mainly associated with temperature whereas abundance was associated with precipitation and temperature combined. Land use was more often associated with abundance than occurrence. For most species, occurrence and abundance were positively associated with forest cover but for some there was a negative association. Occurrence and abundance of some species (47% for occurrence model and 88% for abundance model) were positively associated with wetlands, but negatively associated with urban (Culiseta melanura and Anopheles walkeri) and agriculture (An. quadrimaculatus, Cs. minnesotae and An. walkeri) environments. This study provides predictive relationships between weather, land use and mosquito occurrence and abundance for a wide range of species including those that are currently uncommon, yet known as arboviruses vectors. Elucidation of these relationships has the potential to contribute to better prediction of MBD risk, and thus more efficiently targeted prevention and control measures.

Population Fluctuations and Abundance Indices of Mosquitoes (Diptera: Culicid), as the Potential Bridge Vectors of Pathogens to Humans and Animals in Mazandaran Province, Northern Iran

Background

Seasonal activity patterns of mosquitoes are essential as baseline knowledge to understand the transmission dynamics of vector-borne diseases. This study was conducted to evaluate the monthly dynamics of the mosquito populations and their relation to meteorological factors in Mazandaran Province, north of Iran.

Methods

Mosquito adults and larvae were collected from 16 counties of Mazandaran Province using different sampling techniques, once a month from May to December 2014. "Index of Species Abundance" (ISA) along with "Standardized ISA" (SISA) was used for assessing the most abundant species of mosquitoes based on the explanations of Robert and Hsi. Pearson's correlation coefficient (R) was used to assess the relationships between the monthly population fluctuations and meteorological variables.

Results

Overall, 23750 mosquitoes belonging to four genera and nineteen species were collected and identified. The highest population density of mosquitoes was in July and the lowest in May. The ISA/SISA indices for Culex pipiens were both 1 for larvae and 1.25/0.973 for adults in total catch performed in human dwellings. For Cx. tritaeniorhynchus, the ISA/SISA were 1.68/0.938 in pit shelter method. A significant positive correlation was observed between population fluctuations of Cx. tritaeniorhynchus and mean temperature (R: 0.766, P< 0.027).

Conclusion

The results indicated that the mosquitoes are more active in July, and Cx. pipiens and Cx. tritaeniorhynchus were the most abundant species. Considering the potential of these species as vectors of numerous pathogens, control programs can be planed based on their monthly activity pattern in the area.

Random Forest Analysis of Impact of Abiotic Factors on Culex pipiens and Culex quinquefasciatus Occurrence

The Culex pipiens complex of mosquitoes are significant vectors of several pathogens resulting in infectious human diseases in North America, including but not limited to West Nile encephalitis, Rift Valley Fever, and Lymphatic filariasis. Among this complex are C. pipiens form pipiens and Culex quinquefasciatus. While morphologically similar, the mosquitoes exhibit unique life histories that suit them uniquely to divergent niches, wherein C. pipiens can thrive despite the cold winters of the northern United States and C. quinquefasciatus is able to survive periods of drought typical in the southern states. Here, Random Forests machine-learning algorithms were employed to model and explore which environmental parameters best explain mosquito occurrence in historical trapping data across the continental United States of America, and test correlation with abundance data. The models explained between 71 and 97% of the presence or absence of the two mosquitoes based on historical climatic data. The results of this study will improve vector management programs by explaining which environmental variables will provide the most accurate predictions of mosquito presence at a given site.

Updated distribution maps of predominant Culex mosquitoes across the Americas

BACKGROUND

Estimates of the geographical distribution of Culex mosquitoes in the Americas have been limited to state and provincial levels in the United States and Canada and based on data from the 1980s. Since these estimates were made, there have been many more documented observations of mosquitoes and new methods have been developed for species distribution modeling. Moreover, mosquito distributions are affected by environmental conditions, which have changed since the 1980s. This calls for updated estimates of these distributions to understand the risk of emerging and re-emerging mosquito-borne diseases.

METHODS

We used contemporary mosquito data, environmental drivers, and a machine learning ecological niche model to create updated estimates of the geographical range of seven predominant Culex species across North America and South America: Culex erraticus, Culex nigripalpus, Culex pipiens, Culex quinquefasciatus, Culex restuans, Culex salinarius, and Culex tarsalis.

RESULTS

We found that Culex mosquito species differ in their geographical range. Each Culex species is sensitive to both natural and human-influenced environmental factors, especially climate and land cover type. Some prefer urban environments instead of rural ones, and some are limited to tropical or humid areas. Many are found throughout the Central Plains of the USA.

CONCLUSIONS

Our updated contemporary Culex distribution maps may be used to assess mosquito-borne disease risk. It is critical to understand the current geographical distributions of these important disease vectors and the key environmental predictors structuring their distributions not only to assess current risk, but also to understand how they will respond to climate change. Since the environmental predictors structuring the geographical distribution of mosquito species varied, we hypothesize that each species may have a different response to climate change.

Vector Surveillance, Host Species Richness, and Demographic Factors as West Nile Disease Risk Indicators

West Nile virus (WNV) is the most common arthropod-borne virus (arbovirus) in the United States (US) and is the leading cause of viral encephalitis in the country. The virus has affected tens of thousands of US persons total since its 1999 North America introduction, with thousands of new infections reported annually. Approximately 1% of humans infected with WNV acquire neuroinvasive West Nile Disease (WND) with severe encephalitis and risk of death. Research describing WNV ecology is needed to improve public health surveillance, monitoring, and risk assessment. We applied Bayesian joint-spatiotemporal modeling to assess the association of vector surveillance data, host species richness, and a variety of other environmental and socioeconomic disease risk factors with neuroinvasive WND throughout the conterminous US. Our research revealed that an aging human population was the strongest disease indicator, but climatic and vector-host biotic interactions were also significant in determining risk of neuroinvasive WND. Our analysis also identified a geographic region of disproportionately high neuroinvasive WND disease risk that parallels the Continental Divide, and extends southward from the US-Canada border in the states of Montana, North Dakota, and Wisconsin to the US-Mexico border in western Texas. Our results aid in unraveling complex WNV ecology and can be applied to prioritize disease surveillance locations and risk assessment.

Spring temperature shapes West Nile virus transmission in Europe

West Nile Virus (WNV) is now endemic in many European countries, causing hundreds of human cases every year, with a high spatial and temporal heterogeneity. Previous studies have suggested that spring temperature might play a key role at shaping WNV transmission. Specifically, warmer temperatures in April-May might amplify WNV circulation, thus increasing the risk for human transmission later in the year. To test this hypothesis, we collated publicly available data on the number of human infections recorded in Europe between 2011 and 2019. We then applied generalized linear models to quantify the relationship between human cases and spring temperature, considering both average conditions (over years 2003-2010) and deviations from the average for subsequent years (2011-2019). We found a significant positive association both spatial (average conditions) and temporal (deviations). The former indicates that WNV circulation is higher in usually warmer regions while the latter implies a predictive value of spring conditions over the coming season. We also found a positive association with WNV detection during the previous year, which can be interpreted as an indication of the reliability of the surveillance system but also of WNV overwintering capacity. Weather anomalies at the beginning of the mosquito breeding season might act as an early warning signal for public health authorities, enabling them to strengthen in advance ongoing surveillance and prevention strategies.

Land cover affects microclimate and temperature suitability for arbovirus transmission in an urban landscape

The emergence of mosquito-transmitted viruses poses a global threat to human health. Combining mechanistic epidemiological models based on temperature-trait relationships with climatological data is a powerful technique for environmental risk assessment. However, a limitation of this approach is that the local microclimates experienced by mosquitoes can differ substantially from macroclimate measurements, particularly in heterogeneous urban environments. To address this scaling mismatch, we modeled spatial variation in microclimate temperatures and the thermal potential for dengue transmission by Aedes albopictus across an urban-to-rural gradient in Athens-Clarke County GA. Microclimate data were collected across gradients of tree cover and impervious surface cover. We developed statistical models to predict daily minimum and maximum microclimate temperatures using coarse-resolution gridded macroclimate data (4000 m) and high-resolution land cover data (30 m). The resulting high-resolution microclimate maps were integrated with temperature-dependent mosquito abundance and vectorial capacity models to generate monthly predictions for the summer and early fall of 2018. The highest vectorial capacities were predicted for patches of trees in urban areas with high cover of impervious surfaces. Vectorial capacity was most sensitive to tree cover during the summer and became more sensitive to impervious surfaces in the early fall. Predictions from the same models using temperature data from a local meteorological station consistently over-predicted vectorial capacity compared to the microclimate-based estimates. This work demonstrates that it is feasible to model variation in mosquito microenvironments across an urban-to-rural gradient using satellite Earth observations. Epidemiological models applied to the microclimate maps revealed localized patterns of temperature suitability for disease transmission that would not be detectable using macroclimate data. Incorporating microclimate data into disease transmission models has the potential to yield more spatially precise and ecologically interpretable metrics of mosquito-borne disease transmission risk in urban landscapes.

Predicting the effects of temperature on mosquito abundance and arbovirus transmission cycles is essential for mapping hot spots of disease risk and projecting responses to climate change. In urban landscapes, the built environment and natural features create distinctive environments. Buildings and roads generate warmer conditions through the urban heat island effect, while vegetation can have a cooling effect because of shading and evaporative heat loss. We used land cover data to map microclimate temperature in Athens-Clarke County, GA and applied a temperature-dependent vectorial capacity model to predict the effects of microclimate on dengue transmission by Aedes albopictus. The highest vectorial capacity was predicted in patches of trees located in the urbanized portion of the study area. These locations had relatively warm nighttime and cool daytime temperature, which kept temperatures close to the optimum for disease transmission. This work demonstrates the feasibility of predicting variation in mosquito microenvironments in urban landscapes using satellite Earth observations. Incorporating microclimate data into disease transmission models has the potential to yield more spatially precise and ecologically interpretable metrics of mosquito-borne disease transmission risk.

Using Climate to Explain and Predict West Nile Virus Risk in Nebraska

We used monthly precipitation and temperature data to give early warning of years with higher West Nile Virus (WNV) risk in Nebraska. We used generalized additive models with a negative binomial distribution and smoothing curves to identify combinations of extremes and timing that had the most influence, experimenting with all combinations of temperature and drought data, lagged by 12, 18, 24, 30, and 36 months. We fit models on data from 2002 through 2011, used Akaike's Information Criterion (AIC) to select the best-fitting model, and used 2012 as out-of-sample data for prediction, and repeated this process for each successive year, ending with fitting models on 2002-2017 data and using 2018 for out-of-sample prediction. We found that warm temperatures and a dry year preceded by a wet year were the strongest predictors of cases of WNV. Our models did significantly better than random chance and better than an annual persistence naïve model at predicting which counties would have cases. Exploring different scenarios, the model predicted that without drought, there would have been 26% fewer cases of WNV in Nebraska through 2018; without warm temperatures, 29% fewer; and with neither drought nor warmth, 45% fewer. This method for assessing the influence of different combinations of extremes at different time intervals is likely applicable to diseases other than West Nile, and to other annual outcome variables such as crop yield.

Epidemic West Nile Virus Infection Rates and Endemic Population Dynamics Among South Dakota Mosquitoes: A 15-yr Study from the United States Northern Great Plains

Mosquito surveillance has been conducted across South Dakota (SD) to record and track potential West Nile virus (WNV) vectors since 2004. During this time, communities from 29 counties collected nearly 5.5 million mosquitoes, providing data from over 60,000 unique trapping nights. The nuisance mosquito, Aedes vexans (Meigen) was the most abundant species in the state (39.9%), and most abundant in most regions. The WNV vector, Culex tarsalis Coquillett (Diptera: Culicidae), was the second most abundant species (20.5%), and 26 times more abundant than the other Culex species that also transmit WNV. However, geographic variation did exist between WNV vector species, as well as relative abundance of vector and nuisance mosquitoes. The abundance of Ae. vexans decreased from east to west in South Dakota, resulting in an increase in the relative abundance of Cx. tarsalis. Other species are reported in this study, with various relative abundances throughout the different regions of South Dakota. WNV infection rates of mosquitoes showed that Cx. tarsalis had the most positive sampling pools and the highest vector index of all the species tested. This study addressed the need for an updated summary of the predominant mosquito species present in the United States Northern Great Plain and provides infection rate data for WNV among these predominant species.

Infection and transmission of Cache Valley virus by Aedes albopictus and Aedes aegypti mosquitoes

Background

Cache Valley virus (CVV; Bunyavirales, Peribunyaviridae) is a mosquito-borne arbovirus endemic in North America. Although severe diseases are mainly observed in pregnant ruminants, CVV has also been recognized as a zoonotic pathogen that can cause fatal encephalitis in humans. Human exposures to CVV and its related subtypes occur frequently under different ecological conditions in the New World; however, neurotropic disease is rarely reported. High prevalence rates of neutralizing antibodies have been detected among residents in several Latin American cities. However, zoophilic mosquito species involved in the enzootic transmission are unlikely to be responsible for the transmission leading to human exposures to CVV. Mechanisms that lead to frequent human exposures to CVV remain largely unknown. In this study, competence of two anthropophilic mosquitoes, Aedes albopictus and Ae. aegypti, for CVV was determined using per os infection to determine if these species could play a role in the transmission of CVV in the domestic and peridomestic settings of urban and suburban areas.

Results

Aedes albopictus were highly susceptible to CVV whereas infection of Ae. aegypti occurred at a significantly lower frequency. Whilst the dissemination rates of CVV were comparable in the two species, the relatively long period to attain maximal infectious titer in Ae. aegypti demonstrated a significant difference in the replication kinetics of CVV in these species. Detection of viral RNA in saliva suggests that both Ae. albopictus and Ae. aegypti are competent vectors for CVV under laboratory conditions.

Conclusions

Differential susceptibility to CVV was observed in Ae. albopictus and Ae. aegypti, reflecting their relatively different capacities for vectoring CVV in nature. The high susceptibility of Ae. albopictus to CVV observed in this study suggests its potential role as an efficient vector for CVV. Complemented by the reports of multiple CVV isolates derived from Ae. albopictus, our finding provides the basis for how the dispersal of Ae. albopictus across the New World may have a significant impact on the transmission and ecology of CVV.

The influence of weather and weather variability on mosquito abundance and infection with West Nile virus in Harris County, Texas, USA

Early warning systems for vector-borne diseases (VBDs) prediction are an ecological application where data from the interface of several environmental components can be used to predict future VBD transmission. In general, models for early warning systems only consider average environmental conditions ignoring variation in weather variables, despite the prediction from Schmalhausen's law about the importance of environmental variability for biological systems. We present results from a long-term mosquito surveillance program from Harris County, Texas, USA, where we use time series analysis techniques to study the abundance and West Nile virus (WNV) infection patterns in the local primary vector, Culex quinquefasciatus Say. We found that, as predicted by Schmalhausen's law, mosquito abundance was associated with the standard deviation and kurtosis of environmental variables. By contrast, WNV infection rates were associated with 8-month lagged temperature, suggesting environmental conditions during overwintering might be key for WNV amplification during summer outbreaks. Finally, model validation showed that seasonal autoregressive models successfully predicted mosquito WNV infection rates up to 2 months ahead, but did rather poorly at predicting mosquito abundance, a result that might reflect impacts of vector control for mosquito population reduction, geographic scale, and other artifacts generated by operational constraints of mosquito surveillance systems.

Long-term surveillance defines spatial and temporal patterns implicating Culex tarsalis as the primary vector of West Nile virus

West Nile virus (WNV) has become the most epidemiologically important mosquito-borne disease in the United States, causing ~50,000 cases since its introduction in 1999. Transmitted primarily by Culex species, WNV transmission requires the complex interplay between bird reservoirs and mosquito vectors, with human cases the result of epizootic spillover. To better understand the intrinsic factors that drive these interactions, we have compiled infection data from sentinel chickens, mosquito vectors, and human cases in Iowa over a 15 year period (2002-2016) to better understand the spatial and temporal components that drive WNV transmission. Supplementing these findings with mosquito abundance, distribution, and host preferences data, we provide strong support that Culex tarsalis is the most important vector of human WNV infections in the region. Together, our analysis provides new insights into WNV infection patterns in multiple hosts and highlights the importance of long-term surveillance to understand the dynamics of mosquito-borne-disease transmission.

Short-term Forecasting of Daily Abundance of West Nile Virus Vectors Culex pipiens-restuans (Diptera: Culicidae) and Aedes vexans Based on Weather Conditions in Southern Québec (Canada)

Since 2002, human cases of West Nile virus (WNV) have occurred every year in southern Canada, but WNV risk remains challenging to predict. Here, we explored the ability of weather-based forecasting models to predict the seasonal abundance of two WNV vector species (Culex pipiens-restuans and Aedes vexans) in Québec, Canada, and explored the importance of accounting for larvicide use and local habitat (forest park vs residential garden). A gamma-generalized linear model predicting mosquito abundance was developed based on an approach previously used in Ontario combining temperature and precipitation during the days preceding mosquito captures. This model was calibrated and validated for each species with independent entomological datasets from the Montréal region collected in 2013 and 2014. Culex pipiens-restuans abundance was associated with mean degree days (dd; >9°C) over the 22 d before mosquito capture and with mean precipitation over the 71 d before capture; Ae. vexans abundance with the mean dd (>12°C) over the 24 d before capture and mean precipitation over the 30 d before capture. These results are consistent with temperature effects on immature development rates and adult activity, and effects of precipitation on the abundance and suitability of breeding sites. Taking into account larvicide use and habitat significantly improved the predictions. This study provides evidence that weather conditions can yield robust short-term predictions of the regional daily mosquito abundance, particularly when accounting for local variation in habitat or mosquito control efforts, and may provide real-time indicators of WNV or other mosquito-borne disease risks during the summer.

Evaluation of Barrier Sprays for Controlling Mosquitoes in Eastern North Carolina: Can Land Cover and Spatial Analyses Improve Predictions of Efficacy?

Mosquitoes can be a nuisance and can transmit pathogens causing numerous diseases. Homeowners may hire private companies that use barrier sprays to alleviate mosquito-related issues, especially in areas where government funding for mosquito control programs is limited. Here, the spatial distribution of mosquitoes was evaluated in a suburban neighborhood during successive treatments with either Bifen Insecticide/Termiticide (active ingredient: bifenthrin) or Suspend Polyzone (active ingredient: deltamethrin) from May 17 to November 8, 2016. A total of 15,083 adult mosquitoes and 18,054 mosquito eggs were collected. Analysis of variance (P < 0.05) was used to analyze differences in abundance of key species between weeks, traps, and treatments. Weather trends were analyzed in relation to mosquito abundance using time-lagged weekly average temperatures and total rainfall. Kriging showed hot spots of mosquito abundance. The spatial pattern of abundance was different for oviposition and adults, and this was expected because of different types of traps used here. A land cover analysis was performed within the geographic information system (GIS) file to determine the extent to which land cover type could predict mosquito abundance. We show an uneven distribution of host-seeking mosquito abundance and that, in general, mosquitoes preferred areas that were lightly wooded or composed of small collections of trees or bushes, compared with larger densely wooded areas. Analyses of spatial distribution, land cover, and weather can be used to supplement an integrated mosquito management approach.

A Novel Sampling Method to Measure Socioeconomic Drivers of Aedes Albopictus Distribution in Mecklenburg County, North Carolina

Climate change, urbanization, and globalization have facilitated the spread of Aedes mosquitoes into regions that were previously unsuitable, causing an increased threat of arbovirus transmission on a global scale. While numerous studies have addressed the urban ecology of Ae. albopictus, few have accounted for socioeconomic factors that affect their range in urban regions. Here we introduce an original sampling design for Ae. albopictus, that uses a spatial optimization process to identify urban collection sites based on both geographic parameters as well as the gradient of socioeconomic variables present in Mecklenburg County, North Carolina, encompassing the city of Charlotte, a rapidly growing urban environment. We collected 3,645 specimens of Ae. albopictus (87% of total samples) across 12 weeks at the 90 optimized site locations and modelled the relationships between the abundance of gravid Ae. albopictus and a variety of neighborhood socioeconomic attributes as well as land cover characteristics. Our results demonstrate that the abundance of gravid Ae. albopictus is inversely related to the socioeconomic status of the neighborhood and directly related to both landscape heterogeneity as well as proportions of particular resident races/ethnicities. We present our results alongside a description of our novel sampling scheme and its usefulness as an approach to urban vector epidemiology. Additionally, we supply recommendations for future investigations into the socioeconomic determinants of vector-borne disease risk.

Improving the prediction of arbovirus outbreaks: A comparison of climate-driven models for West Nile virus in an endemic region of the United States

Models that forecast the timing and location of human arboviral disease have the potential to make mosquito control and disease prevention more effective. A common approach is to use statistical time-series models that predict disease cases as lagged functions of environmental variables. However, the simplifying assumptions required for standard modeling approaches may not capture important aspects of complex, non-linear transmission cycles. Here, we compared a set of alternative models of human West Nile virus (WNV) in 2004-2017 in South Dakota, USA. We used county-level logistic regressions to model historical human case data as functions of distributed lag summaries of air temperature and several moisture indices. We tested two variations of the standard model in which 1) the distributed lag functions were allowed to change over the transmission season, so that dependence on past meteorological conditions was time varying rather than static, and 2) an additional predictor was included that quantified the mosquito infection growth rate estimated from mosquito surveillance data. The best-fitting model included temperature and vapor pressure deficit as meteorological predictors, and also incorporated time-varying lags and the mosquito infection growth rate. The time-varying lags helped to predict the seasonal pattern of WNV cases, whereas the mosquito infection growth rate improved the prediction of year-to-year variability in WNV risk. These relatively simple and practical enhancements may be particularly helpful for developing data-driven time series models for use in arbovirus forecasting applications.

Permethrin Susceptibility for the Vector Culex tarsalis and a Nuisance Mosquito Aedes vexans in an Area Endemic for West Nile Virus

In 2016, we compared susceptibility to the insecticide, permethrin, between the West Nile virus vector, Culex tarsalis Coquillett, and a major nuisance mosquito, Aedes vexans (Meigen), using baseline diagnostic dose and time values determined using the CDC bottle bioassay protocol. Mosquitoes were collected in the wild in Brookings County, South Dakota, situated in the Northern Great Plains of the USA. The determined diagnostic dose and time were then used in 2017 to validate these measurements for the same 2 mosquito species, collected at a second location within Brookings County. The diagnostic dose was determined for multiple time periods and ranged from 27.0 µg/ml at 60 min to 38.4 µg/ml at 30 min. There was no significant difference detected in mortality rates between Cx. tarsalis and Ae. vexans for any diagnostic time and dose. For practical purposes, mosquitoes in 2017 were tested at 38 µg/ml for 30 min; expected mortality rates were 93.38% for Cx. tarsalis and 94.93% for Ae. vexans. Actual 2017 mortality rates were 92.68% for Cx. tarsalis and 96.12% for Ae. vexans, validating the usefulness of this baseline at an additional location and year.

The influence of ecological factors on mosquito abundance and occurrence in Galápagos

We sampled mosquitoes across 18 sites established at different elevations and stretching from the north to the south of Isla Santa Cruz, Galápagos. Two commonly occurring species, Ae. taeniorhynchus and Cx. quinquefasciatus, were collected along with environmental variables characteristic of the trapping sites to assess their influence on mosquito abundance and occurrence in the dry season of 2015. We captured Ae. taeniorhynchus at 14 out of 18 sites and Cx. quinquefasciatus at low and high elevation sites on Santa Cruz. We utilized two generalized linear models; the first assessed the influence of environmental variables on abundances of Ae. taeniorhynchus and the second assessed the influence of these variables on the presence of Cx. quinquefasciatus. Populations of both mosquito species declined with elevation. Rainfall data were limited, as we sampled during the dry season of 2015. Distance to mangroves and maximum humidity were significant in influencing the abundance of Ae. taeniorhynchus, while maximum humidity was found to significantly influence the presence of Cx. quinquefasciatus. Both species occurred in sites where temperature, precipitation, and humidity should allow for mosquito development as well as parasitic development of the protozoan parasites that cause avian malaria. Further research involving year-round sampling of mosquitoes and accompanying meteorological data as well as experimental studies on vector competence are required to understand disease dynamics of parasites such as avian malaria in Galápagos.

Environmental and social-demographic predictors of the southern house mosquito Culex quinquefasciatus in New Orleans, Louisiana

BACKGROUND

Understanding the major predictors of disease vectors such as mosquitoes can guide the development of effective and timely strategies for mitigating vector-borne disease outbreaks. This study examined the influence of selected environmental, weather and sociodemographic factors on the spatial and temporal distribution of the southern house mosquito Culex quinquefasciatus Say in New Orleans, Louisiana, USA.

METHODS

Adult mosquitoes were collected over a 4-year period (2006, 2008, 2009 and 2010) using CDC gravid traps. Socio-demographic predictors were obtained from the United States Census Bureau, 2005-2009 American Community Survey and the City of New Orleans Department of Code Enforcement. Linear mixed effects models and ERDAS image processing software were used for statistical analysis and image processing.

RESULTS

Only two of the 22 predictors examined were significant predictors of Cx. quinquefasciatus abundance. Mean temperature during the week of mosquito collection was positively associated with Cx. quinquefasciatus abundance while developed high intensity areas were negatively associated with Cx. quinquefasciatus abundance.

CONCLUSION

The findings of this study illustrate the power and utility of integrating biophysical and sociodemographic data using GIS analysis to identify the biophysical and sociodemographic processes that increase the risk of vector mosquito abundance. This knowledge can inform development of accurate predictive models that ensure timely implementation of mosquito control interventions.

Multi-Scale Clustering of Lyme Disease Risk at the Expanding Leading Edge of the Range of Ixodes scapularis in Canada

Since its detection in Canada in the early 1990s, Ixodes scapularis, the primary tick vector of Lyme disease in eastern North America, has continued to expand northward. Estimates of the tick's broad-scale distribution are useful for tracking the extent of the Lyme disease risk zone; however, tick distribution may vary widely within this zone. Here, we investigated I. scapularis nymph distribution at three spatial scales across the Lyme disease emergence zone in southern Quebec, Canada. We collected ticks and compared the nymph densities among different woodlands and different plots and transects within the same woodland. Hot spot analysis highlighted significant nymph clustering at each spatial scale. In regression models, nymph abundance was associated with litter depth, humidity, and elevation, which contribute to a suitable habitat for ticks, but also with the distance from the trail and the type of trail, which could be linked to host distribution and human disturbance. Accounting for this heterogeneous nymph distribution at a fine spatial scale could help improve Lyme disease management strategies but also help people to understand the risk variation around them and to adopt appropriate behaviors, such as staying on the trail in infested parks to limit their exposure to the vector and associated pathogens.

Use of temperature to improve West Nile virus forecasts

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

West Nile virus (WNV) is the leading cause of domestically acquired arthropod-borne viral disease in the United States. Here we show that accurate retrospective forecasts of mosquito infection rates and human WNV cases can be generated for a variety of locations in the U.S. Incorporation of temperature forcing into a baseline dynamic model improves our ability to accurately forecast WNV outbreaks and provides further evidence that temperature modulates rates of WNV transmission. These findings provide a foundation for implementation of a statistically rigorous system for real-time short-term and seasonal forecast of WNV. Such a decision support tool would help public health officials and mosquito control programs target control of infectious mosquito populations, alert the public to future periods of elevated WNV spillover transmission risk, and identify when to intensify blood donor screening.

Health Co-Benefits of Green Building Design Strategies and Community Resilience to Urban Flooding: A Systematic Review of the Evidence

Climate change is increasingly exacerbating existing population health hazards, as well as resulting in new negative health effects. Flooding is one particularly deadly example of its amplifying and expanding effect on public health. This systematic review considered evidence linking green building strategies in the Leadership in Energy and Environmental Design^® (LEED) Rating System with the potential to reduce negative health outcomes following exposure to urban flooding events. Queries evaluated links between LEED credit requirements and risk of exposure to urban flooding, environmental determinants of health, co-benefits to public health outcomes, and co-benefits to built environment outcomes. Public health co-benefits to leveraging green building design to enhance flooding resilience included: improving the interface between humans and wildlife and reducing the risk of waterborne disease, flood-related morbidity and mortality, and psychological harm. We conclude that collaborations among the public health, climate change, civil society, and green building sectors to enhance community resilience to urban flooding could benefit population health.

Integrating Environmental Monitoring and Mosquito Surveillance to Predict Vector-borne Disease: Prospective Forecasts of a West Nile Virus Outbreak

INTRODUCTION

Predicting the timing and locations of future mosquito-borne disease outbreaks has the potential to improve the targeting of mosquito control and disease prevention efforts. Here, we present and evaluate prospective forecasts made prior to and during the 2016 West Nile virus (WNV) season in South Dakota, a hotspot for human WNV transmission in the United States.

METHODS

We used a county-level logistic regression model to predict the weekly probability of human WNV case occurrence as a function of temperature, precipitation, and an index of mosquito infection status. The model was specified and fitted using historical data from 2004-2015 and was applied in 2016 to make short-term forecasts of human WNV cases in the upcoming week as well as whole-year forecasts of WNV cases throughout the entire transmission season. These predictions were evaluated at the end of the 2016 WNV season by comparing them with spatial and temporal patterns of the human cases that occurred.

RESULTS

There was an outbreak of WNV in 2016, with a total of 167 human cases compared to only 40 in 2015. Model results were generally accurate, with an AUC of 0.856 for short-term predictions. Early-season temperature data were sufficient to predict an earlier-than-normal start to the WNV season and an above-average number of cases, but underestimated the overall case burden. Model predictions improved throughout the season as more mosquito infection data were obtained, and by the end of July the model provided a close estimate of the overall magnitude of the outbreak.

CONCLUSIONS

An integrated model that included meteorological variables as well as a mosquito infection index as predictor variables accurately predicted the resurgence of WNV in South Dakota in 2016. Key areas for future research include refining the model to improve predictive skill and developing strategies to link forecasts with specific mosquito control and disease prevention activities.

Effects of Climate and Climate Change on Vectors and Vector-Borne Diseases: Ticks Are Different

There has been considerable debate as to whether global risk from vector-borne diseases will be impacted by climate change. This has focussed on important mosquito-borne diseases that are transmitted by the vectors from infected to uninfected humans. However, this debate has mostly ignored the biological diversity of vectors and vector-borne diseases. Here, we review how climate and climate change may impact those most divergent of arthropod disease vector groups: multivoltine insects and hard-bodied (ixodid) ticks. We contrast features of the life cycles and behaviour of these arthropods, and how weather, climate, and climate change may have very different impacts on the spatiotemporal occurrence and abundance of vectors, and the pathogens they transmit.

The Impact of Cycling Temperature on the Transmission of West Nile Virus

West Nile virus (WNV) is an important cause of disease in humans and animals. Risk of WNV infection varies seasonally, with the greatest risk during the warmest parts of the year due in part to the accelerated extrinsic incubation rate of the virus in mosquitoes. Rates of extrinsic incubation have been shown in constant-temperature studies to increase as an approximately linear function of temperature, but for other vector-borne pathogens, such as malaria or dengue virus, nonlinear relationships have been demonstrated under cycling temperatures near the thermal limits of pathogen replication. Using typical daily air temperature profiles from three key periods of WNV amplification in a hyperendemic area of WNV activity in California's Central Valley, as well as a fourth temperature profile based on exposures that would result from daily mosquito host-seeking and resting behavior, we explored the impacts of cycling temperatures on WNV transmission by Culex tarsalis Coquillett, one of the principal vectors in the western United States. The daily cycling temperature ranges studied were representative of those that occur across much of California, but they did not significantly alter the extrinsic incubation period of WNV compared with estimates from mean temperatures alone. This suggests that within the relatively broad range we studied, WNV incubation rates are a simple function of mean temperature. Realistic daily temperature patterns that reflected mosquitoes' avoidance of daytime high temperatures during summer reduced transmission over time compared with air temperatures, indicating that adjustment for mosquito exposure temperatures would be prudent for calculating risk.

Assessing Chikungunya risk in a metropolitan area of Argentina through satellite images and mathematical models

BACKGROUND

Chikungunya fever is a viral disease that recently invaded the American continent. In America, it is transmitted mainly by the mosquito Aedes aegypti, but Aedes albopictus is the main vector in other regions of the world. This work estimates the risk of disease emergence and the corresponding population at risk for the case of a naive population in the metropolitan area of Buenos Aires, the capital city of Argentina.

METHODS

A classic metapopulation epidemiological model, that considers human and mosquito populations, was extended in order to include different environmental signals. First, the vital rates of the mosquitoes were affected by local temperature. Second, habitat availability estimated from satellite images was used to determine the carrying capacity for local mosquito populations. Disease invasion was proposed to occur at different moments of the year. For each scenario, Monte Carlo simulations were used to estimate the risk of disease invasion and the population at risk.

RESULTS

The risk of a Chikungunya outbreak displays strong temporal (seasonal) patterns as well as spatial variability at the level of neighborhoods in the study area. According to the model, Summer and Fall display high risk for a Chikungunya invasion. The population at risk displays less variation over the year underlying the importance of preventive actions.

CONCLUSIONS

The ability of mapping habitat quality for vector-borne diseases allows developing risk analysis at scales that are easily manageable for public health officers. For this location, the correlation of disease risk with the season of the year and the habitat availability could provide information to develop efficient control strategies. This also underlines the importance of involving the whole community when developing control measures for Chikungunya fever and other recently invading vector-borne diseases such as Zika fever.

Software to Facilitate Remote Sensing Data Access for Disease Early Warning Systems

Satellite remote sensing produces an abundance of environmental data that can be used in the study of human health. To support the development of early warning systems for mosquito-borne diseases, we developed an open-source, client based software application to enable the Epidemiological Applications of Spatial Technologies (EASTWeb). Two major design decisions were full automation of the discovery, retrieval and processing of remote sensing data from multiple sources, and making the system easily modifiable in response to changes in data availability and user needs. Key innovations that helped to achieve these goals were the implementation of a software framework for data downloading and the design of a scheduler that tracks the complex dependencies among multiple data processing tasks and makes the system resilient to external errors. EASTWeb has been successfully applied to support forecasting of West Nile virus outbreaks in the United States and malaria epidemics in the Ethiopian highlands.

ECOLOGICAL DETERMINANTS OF AVIAN INFLUENZA VIRUS, WEST NILE VIRUS, AND AVIAN PARAMYXOVIRUS INFECTION AND ANTIBODY STATUS IN BLUE-WINGED TEAL (ANAS DISCORS) IN THE CANADIAN PRAIRIES

The Canadian prairies are one of the most important breeding and staging areas for migratory waterfowl in North America. Hundreds of thousands of waterfowl of numerous species from multiple flyways converge in and disperse from this region annually; therefore this region may be a key area for potential intra- and interspecific spread of infectious pathogens among migratory waterfowl in the Americas. Using Blue-winged Teal (Anas discors, BWTE), which have the most extensive migratory range among waterfowl species, we investigated ecologic risk factors for infection and antibody status to avian influenza virus (AIV), West Nile virus (WNV), and avian paramyxovirus-1 (APMV-1) in the three prairie provinces (Alberta, Saskatchewan, and Manitoba) prior to fall migration. We used generalized linear models to examine infection or evidence of exposure in relation to host (age, sex, body condition, exposure to other infections), spatiotemporal (year, province), population-level (local population densities of BWTE, total waterfowl densities), and environmental (local pond densities) factors. The probability of AIV infection in BWTE was associated with host factors (e.g., age and antibody status), population-level factors (e.g., local BWTE population density), and year. An interaction between age and AIV antibody status showed that hatch year birds with antibodies to AIV were more likely to be infected, suggesting an antibody response to an active infection. Infection with AIV was positively associated with local BWTE density, supporting the hypothesis of density-dependent transmission. The presence of antibodies to WNV and APMV-1 was positively associated with age and varied among years. Furthermore, the probability of being WNV antibody positive was positively associated with pond density rather than host population density, likely because ponds provide suitable breeding habitat for mosquitoes, the primary vectors for transmission. Our findings highlight the importance of spatiotemporal, environmental, and host factors at the individual and population levels, all of which may influence dynamics of these and other viruses in wild waterfowl populations.

Modeling Occurrence of Urban Mosquitos Based on Land Use Types and Meteorological Factors in Korea

Mosquitoes are a public health concern because they are vectors of pathogen, which cause human-related diseases. It is well known that the occurrence of mosquitoes is highly influenced by meteorological conditions (e.g., temperature and precipitation) and land use, but there are insufficient studies quantifying their impacts. Therefore, three analytical methods were applied to determine the relationships between urban mosquito occurrence, land use type, and meteorological factors: cluster analysis based on land use types; principal component analysis (PCA) based on mosquito occurrence; and three prediction models, support vector machine (SVM), classification and regression tree (CART), and random forest (RF). We used mosquito data collected at 12 sites from 2011 to 2012. Mosquito abundance was highest from August to September in both years. The monitoring sites were differentiated into three clusters based on differences in land use type such as culture and sport areas, inland water, artificial grasslands, and traffic areas. These clusters were well reflected in PCA ordinations, indicating that mosquito occurrence was highly influenced by land use types. Lastly, the RF represented the highest predictive power for mosquito occurrence and temperature-related factors were the most influential. Our study will contribute to effective control and management of mosquito occurrences.

Meteorological conditions associated with increased incidence of West Nile virus disease in the United States, 2004-2012

West Nile virus (WNV) is a leading cause of mosquito-borne disease in the United States. Annual seasonal outbreaks vary in size and location. Predicting where and when higher than normal WNV transmission will occur can help direct limited public health resources. We developed models for the contiguous United States to identify meteorological anomalies associated with above average incidence of WNV neuroinvasive disease from 2004 to 2012. We used county-level WNV data reported to ArboNET and meteorological data from the North American Land Data Assimilation System. As a result of geographic differences in WNV transmission, we divided the United States into East and West, and 10 climate regions. Above average annual temperature was associated with increased likelihood of higher than normal WNV disease incidence, nationally and in most regions. Lower than average annual total precipitation was associated with higher disease incidence in the eastern United States, but the opposite was true in most western regions. Although multiple factors influence WNV transmission, these findings show that anomalies in temperature and precipitation are associated with above average WNV disease incidence. Readily accessible meteorological data may be used to develop predictive models to forecast geographic areas with elevated WNV disease risk before the coming season.

Seasonal variation in size estimates of Aedes albopictus population based on standard mark-release-recapture experiments in an urban area on Reunion Island

The implementation of the sterile insect technique for area-wide vector control requires that natural population density be accurately estimated to determine both the appropriate time to treat and the adequate number of sterile males for release. Herein, we used mark-release-recapture (MRR) to derive seasonal abundance estimates of Aedes albopictus population sizes within a delimited geographical area in Reunion Island. Population size of Ae. albopictus was estimated through four mark-release-recapture experiments carried out separately in different seasons. Marked males and females were released each time, and recaptured using BG sentinel traps for six consecutive days. Data were used to estimate the population size using a conceptual model that incorporates the variation in daily mortality rates. The likely influence of environmental factors on the magnitude of catches and on population fluctuation was analyzed. A total of 2827 mosquitoes (1914 males and 913 females) were marked and released on four occasions during dry and wet seasons. After release, 138 males (7.21%) and 86 females (9.41%) of the marked specimens were recaptured in subsequent samplings. The effectiveness of the daily captures of wild and released mosquitoes was significantly influenced by meteorological conditions such as temperature, rainfall, wind speed and light intensity. The estimates of Ae. albopictus population size obtained with our model estimator ranged from 298 to 1238 males and 604 to 2208 females per ha, with seasonal variability - higher population size in the humid season. The presented results will be essential in designing more effective sterile male release strategies for long-term suppression of wild Ae. albopictus populations.

Mosquito surveillance revealed lagged effects of mosquito abundance on mosquito-borne disease transmission: a retrospective study in Zhejiang, China

Mosquito-borne diseases (MBDs) are still threats to public health in Zhejiang. In this study, the associations between the time-lagged mosquito capture data and MBDs incidence over five years were used to examine the potential effects of mosquito abundance on patterns of MBDs epidemiology in Zhejiang during 2008–2012. Light traps were used to collect adult mosquitoes at 11 cities. Correlation tests with and without time lag were performed to investigate the correlations between MBDs incidence rates and mosquito abundance by month. Selected MBDs consisted of Japanese encephalitis (JE), dengue fever (DF) and malaria. A Poisson regression analysis was performed by using a generalized estimating equations (GEE) approach, and the most parsimonious model was selected based on the quasi-likelihood based information criterion (QICu). We identified five mosquito species and the constituent ratio of Culex pipiens pallens, Culex tritaeniorhynchus, Aedes albopictus, Anopheles sinensis and Armigeres subalbatus was 66.73%, 21.47%, 6.72%, 2.83% and 2.25%, respectively. The correlation analysis without and with time lag showed that Culex mosquito abundance at a lag of 0 or 1 month was positively correlated with JE incidence during 2008–2012, Ae. albopictus abundance at a lag of 1 month was positively correlated with DF incidence in 2009, and An. sinensis abundance at a lag of 0–2 months was positively correlated with malaria incidence during 2008–2010. The Poisson regression analysis showed each 0.1 rise of monthly mosquito abundance corresponded to a positive increase of MBD cases for the period of 2008–2012. The rise of mosquito abundance with a lag of 0–2 months increased the risk of human MBDs infection in Zhejiang. Our study provides evidence that mosquito monitoring could be a useful early warning tool for the occurrence and transmission of MBDs.

Regional variation of climatic influences on West Nile virus outbreaks in the United States

The national resurgence of human West Nile virus (WNV) disease in 2012 raised questions about the factors responsible for WNV outbreaks. Interannual climatic variations may influence WNV amplification and transmission to humans through multiple pathways, including mosquito breeding habitats, gonotrophic cycles, extrinsic incubation, avian communities, and human behavior. We examined the influences of temperature and precipitation anomalies on interannual variation in human WNV cases in three regions of the United States. There were consistent positive influences of winter temperatures, weaker and more variable positive effects of spring and summer temperatures, and highly variable precipitation effects that ranged from positive to negative. The overwintering period may be a particularly important climatic constraint on the dynamics of WNV in cold-temperate regions of North America. Geographic differences in the seasonal timing and relative importance of climatic drivers of WNV risk likely reflect underlying variability in key ecological and social characteristics.

Modeling Culex tarsalis abundance on the northern Colorado front range using a landscape-level approach

Remote sensing and Geographic Information System (GIS) data can be used to identify larval mosquito habitats and predict species distribution and abundance across a landscape. An understanding of the landscape features that impact abundance and dispersal can then be applied operationally in mosquito control efforts to reduce the transmission of mosquito-borne pathogens. In an effort to better understand the effects of landscape heterogeneity on the abundance of the West Nile virus (WNV) vector Culex tarsalis, we determined associations between GIS-based environmental data at multiple spatial extents and monthly abundance of adult Cx. tarsalis in Larimer County and Weld County, CO. Mosquito data were collected from Centers for Disease Control and Prevention miniature light traps operated as part of local WNV surveillance efforts. Multiple regression models were developed for prediction of monthly Cx. tarsalis abundance for June, July, and August using 4 years of data collected over 2007-10. The models explained monthly adult mosquito abundance with accuracies ranging from 51-61% in Fort Collins and 57-88% in Loveland-Johnstown. Models derived using landscape-level predictors indicated that adult Cx. tarsalis abundance is negatively correlated with elevation. In this case, low-elevation areas likely more abundantly include habitats for Cx. tarsalis. Model output indicated that the perimeter of larval sites is a significant predictor of Cx. tarsalis abundance at a spatial extent of 500 m in Loveland-Johnstown in all months examined. The contribution of irrigated crops at a spatial extent of 500 m improved model fit in August in both Fort Collins and Loveland-Johnstown. These results emphasize the significance of irrigation and the manual control of water across the landscape to provide viable larval habitats for Cx. tarsalis in the study area. Results from multiple regression models can be applied operationally to identify areas of larval Cx. tarsalis production (irrigated crops lands and standing water) and assign priority in larval treatments to areas with a high density of larval sites at relevant spatial extents around urban locations.

Patterns of spatio-temporal distribution, abundance, and diversity in a mosquito community from the eastern Smoky Hills of Kansas

Nearly 30% of emerging infectious disease events are caused by vector-borne pathogens with wildlife origins. Their transmission involves a complex interplay among pathogens, arthropod vectors, the environment and host species, and they pose a risk for public health, livestock and wildlife species. Examining habitat associations of vector species known to transmit infectious diseases, and quantifying spatio-temporal dynamics of mosquito vector communities is one aspect of the holistic One Health approach that is necessary to develop effective control measures. A survey was conducted from May to August, 2010 of the abundance and diversity of mosquito species occurring in the mixed-grass prairie habitat of the Smoky Hills of Kansas. This region is an important breeding ground for North America's grassland nesting birds and, as such, it could represent an important habitat for the enzootic amplification cycle of avian malaria and infectious encephalitides, as well as spill-over events to humans and livestock. A total of 11 species, belonging to the three genera Aedes, Anopheles, and Culex, was collected during this study. Aedes nigromaculis, Ae. sollicitans, Ae. taeniorhynchus, Culex salinarius, and Cx. tarsalis accounted for 98% of the collected species. Multiple linear regression models suggested that mosquito abundances in the grasslands of the central Great Plains were explained by meteorological and environmental variables. Temporal dynamics in mosquito abundances were well supported by models that included maximum and minimum temperature indices (adjusted R(2) = 0.73). Spatial dynamics of mosquito abundances were best explained by a model containing the following environmental variables (adjusted R(2) =0.37): ground curvature, topographic wetness index, distance to woodland, and distance to road. The mosquito species we detected are known vectors for infectious encephalitides, including West Nile virus. Understanding the microhabitat characteristics of these mosquito species in a grassland ecosystem will aid in the control and management of these disease vectors.

Spatio-temporal epidemiology of human West Nile virus disease in South Dakota

Despite a cold temperate climate and low human population density, the Northern Great Plains has become a persistent hot spot for human West Nile virus (WNV) disease in North America. Understanding the spatial and temporal patterns of WNV can provide insights into the epidemiological and ecological factors that influence disease emergence and persistence. We analyzed the 1,962 cases of human WNV disease that occurred in South Dakota from 2002-2012 to identify the geographic distribution, seasonal cycles, and interannual variability of disease risk. The geographic and seasonal patterns of WNV have changed since the invasion and initial epidemic in 2002-2003, with cases shifting toward the eastern portion of South Dakota and occurring earlier in the transmission season in more recent years. WNV cases were temporally autocorrelated at lags of up to six weeks and early season cumulative case numbers were correlated with seasonal totals, indicating the possibility of using these data for short-term early detection of outbreaks. Epidemiological data are likely to be most effective for early warning of WNV virus outbreaks if they are integrated with entomological surveillance and environmental monitoring to leverage the strengths and minimize the weaknesses of each information source.

Modeling monthly variation of Culex tarsalis (Diptera: Culicidae) abundance and West Nile Virus infection rate in the Canadian Prairies

The Canadian prairie provinces of Alberta, Saskatchewan, and Manitoba have generally reported the highest human incidence of West Nile virus (WNV) in Canada. In this study, environmental and biotic factors were used to predict numbers of Culex tarsalis Coquillett, which is the primary mosquito vector of WNV in this region, and prevalence of WNV infection in Cx. tarsalis in the Canadian prairies. The results showed that higher mean temperature and elevated time lagged mean temperature were associated with increased numbers of Cx. tarsalis and higher WNV infection rates. However, increasing precipitation was associated with higher abundance of Cx. tarsalis and lower WNV infection rate. In addition, this study found that increased temperature fluctuation and wetland land cover were associated with decreased infection rate in the Cx. tarsalis population. The resulting monthly models can be used to inform public health interventions by improving the predictions of population abundance of Cx. tarsalis and the transmission intensity of WNV in the Canadian prairies. Furthermore, these models can also be used to examine the potential effects of climate change on the vector population abundance and the distribution of WNV.

Climate change and West Nile virus in a highly endemic region of North America

The Canadian prairie provinces of Manitoba, Saskatchewan, and Alberta have reported the highest human incidence of clinical cases of West Nile virus (WNV) infection in Canada. The primary vector for WVN in this region is the mosquito Culex tarsalis. This study used constructed models and biological thresholds to predict the spatial and temporal distribution of Cx. tarsalis and WNV infection rate in the prairie provinces under a range of potential future climate and habitat conditions. We selected one median and two extreme outcome scenarios to represent future climate conditions in the 2020 (2010–2039), 2050 (2040–2069) and 2080 (2070–2099) time slices. In currently endemic regions, the projected WNV infection rate under the median outcome scenario in 2050 raised 17.91 times (ranged from 1.29-27.45 times for all scenarios and time slices) comparing to current climate conditions. Seasonal availability of Cx. tarsalis infected with WNV extended from June to August to include May and September. Moreover, our models predicted northward range expansion for Cx. tarsalis (1.06–2.56 times the current geographic area) and WNV (1.08–2.34 times the current geographic area). These findings predict future public and animal health risk of WNV in the Canadian prairie provinces.

West Nile virus infection rates and avian serology in east-central Illinois

Understanding the geographic role of different species of mosquito vectors and vertebrate hosts in West Nile virus (WNV) transmission cycles can facilitate the development and implementation of targeted surveillance and control measures. This study examined the relationship between WNV-antibody rates in birds and mosquito infection rates and bloodfeeding patterns in east-central Illinois. The earliest detection of WNV-RNA by reverse transcription-polymerase chain reaction TaqMan was from Culex restuans; however, amplification typically coincided with an increase in abundance of Cx. pipiens. Trap type influenced annual estimates of infection rates in Culex species, as well as estimation of blood meal source. Bird species with the highest WNV-antibody rates (i.e., Mourning Doves [Zenaida macroura], Northern Cardinals [Cardinalis cardinalis], American Robins [Turdus migratorius], and House Sparrows [Passer domesticus]) were also the common species found in Culex blood meals. Although antibody rates were not directly proportional to estimated avian abundance, the apparent availability of mammal species did influence proportion of mammal to bird blood meals. Antibody prevalence in the American Robin was lower than expected based on the strong attraction of Culex to American Robins for blood meals. Age-related differences in serology were evident, antibody rates increased in older groups of robins and sparrows, whereas 1st-year hatch and older adults of Mourning Doves and Northern Cardinals had equally high rates of antibody-positive serum samples. The vector and host interactions observed in east-central Illinois (Champaign County), an urban area surrounded by agriculture, are compared to studies in the densely population areas of southern Cook County.

Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

Background

Culex pipiens/restuans mosquitoes are important vectors for a variety of arthropod borne viral infections. In this study, the associations between 20 years of mosquito capture data and the time lagged environmental quantities daytime length, temperature, precipitation, relative humidity and wind speed were used to generate a predictive model for the population dynamics of this vector species.

Methods

Mosquito population in the study area was represented by averaged time series of mosquitos counts captured at 6 sites in Cook County (Illinois, USA). Cross-correlation maps (CCMs) were compiled to investigate the association between mosquito abundances and environmental quantities. The results obtained from the CCMs were incorporated into a Poisson regression to generate a predictive model. To optimize the predictive model the time lags obtained from the CCMs were adjusted using a genetic algorithm.

Results

CCMs for weekly data showed a highly positive correlation of mosquito abundances with daytime length 4 to 5 weeks prior to capture (quantified by a Spearman rank order correlation of r_S = 0.898) and with temperature during 2 weeks prior to capture (r_S = 0.870). Maximal negative correlations were found for wind speed averaged over 3 week prior to capture (r_S = −0.621). Cx. pipiens/restuans population dynamics was predicted by integrating the CCM results in Poisson regression models. They were used to simulate the average seasonal cycle of the mosquito abundance. Verification with observations resulted in a correlation of r_S = 0.899 for daily and r_S = 0.917 for weekly data. Applying the optimized models to the entire 20-years time series also resulted in a suitable fit with r_S = 0.876 for daily and r_S = 0.899 for weekly data.

Conclusions

The study demonstrates the application of interval lagged weather data to predict mosquito abundances with a feasible accuracy, especially when related to weekly Cx. pipiens/restuans populations.

West Nile virus in American White Pelican chicks: transmission, immunity, and survival

West Nile virus (WNV) causes significant mortality of American White Pelican chicks at northern plains colonies. We tested oropharyngeal/cloacal swabs from moribund chicks for shed WNV. Such shedding could enable chick-to-chick transmission and help explain why WNV spreads rapidly in colonies. WNV was detected on swabs from 11% of chicks in 2006 and 52% of chicks in 2007; however, viral titers were low. Before onset of WNV mortality, we tested blood from < 3-week-old chicks for antibodies to WNV; 5% of chicks were seropositive, suggesting passive transfer of maternal antibodies. Among near-fledged chicks, 41% tested positive for anti-WNV antibodies, indicating that they survived infection. Among years and colonies, cumulative incidence of WNV in chicks varied from 28% to 81%, whereas the proportion of chicks surviving WNV (i.e., seropositive) was 64-75%. Our data revealed that WNV kills chicks that likely would fledge in the absence of WNV, that infection of chicks is pervasive, and that significant numbers of chicks survive infection.

Remote sensing of climatic anomalies and West Nile virus incidence in the northern Great Plains of the United States

The northern Great Plains (NGP) of the United States has been a hotspot of West Nile virus (WNV) incidence since 2002. Mosquito ecology and the transmission of vector-borne disease are influenced by multiple environmental factors, and climatic variability is an important driver of inter-annual variation in WNV transmission risk. This study applied multiple environmental predictors including land surface temperature (LST), the normalized difference vegetation index (NDVI) and actual evapotranspiration (ETa) derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) products to establish prediction models for WNV risk in the NGP. These environmental metrics are sensitive to seasonal and inter-annual fluctuations in temperature and precipitation, and are hypothesized to influence mosquito population dynamics and WNV transmission. Non-linear generalized additive models (GAMs) were used to evaluate the influences of deviations of cumulative LST, NDVI, and ETa on inter-annual variations of WNV incidence from 2004–2010. The models were sensitive to the timing of spring green up (measured with NDVI), temperature variability in early spring and summer (measured with LST), and moisture availability from late spring through early summer (measured with ETa), highlighting seasonal changes in the influences of climatic fluctuations on WNV transmission. Predictions based on these variables indicated a low WNV risk across the NGP in 2011, which is concordant with the low case reports in this year. Environmental monitoring using remote-sensed data can contribute to surveillance of WNV risk and prediction of future WNV outbreaks in space and time.

Satellite Microwave Remote Sensing for Environmental Modeling of Mosquito Population Dynamics

Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically. This study compared environmental measurements from the NASA Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and in situ weather station data to examine their ability to predict the abundance of two important mosquito species (Aedes vexans and Culex tarsalis) in Sioux Falls, South Dakota, USA from 2005 to 2010. The AMSR-E land parameters included daily surface water inundation fraction, surface air temperature, soil moisture, and microwave vegetation opacity. The AMSR-E derived models had better fits and higher forecasting accuracy than models based on weather station data despite the relatively coarse (25-km) spatial resolution of the satellite data. In the AMSR-E models, air temperature and surface water fraction were the best predictors of Aedes vexans, whereas air temperature and vegetation opacity were the best predictors of Cx. tarsalis abundance. The models were used to extrapolate spatial, seasonal, and interannual patterns of climatic suitability for mosquitoes across eastern South Dakota. Our findings demonstrate that environmental metrics derived from satellite passive microwave radiometry are suitable for predicting mosquito population dynamics and can potentially improve the effectiveness of mosquito-borne disease early warning systems.