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ABSTRACT Mosquito surveillance is critical to reduce the risk of West Nile virus (WNV) transmission to humans. In response to surveillance indicators such as elevated mosquito abundance or increased WNV levels, many mosquito control programs will perform truck-mounted ultra-low volume (ULV) adulticide application to reduce the number of mosquitoes and associated virus transmission. Despite the common use of truck-based ULV adulticiding as a public health measure to reduce WNV prevalence, limited evidence exists to support a role in reducing viral transmission to humans. We use a generalized additive and fused ridge regression model to quantify the location-specific impact of truck-mounted ULV adulticide spray efforts from 2010 to 2018 in the North Shore Mosquito Abatement District (NSMAD) in metropolitan Chicago, IL, on commonly assessed risk factors from NSMAD surveillance gravid traps: Culex abundance, infection rate, and vector index. Our model also takes into account environmental variables commonly associated with WNV, including temperature, precipitation, wind speed, location, and week of year. Since it is unlikely ULV adulticide spraying will have the same impact at each trap location, we use a spatially varying spray effect with a fused ridge penalty to determine how the effect varies by trap location. We found that ULV adulticide spraying has an immediate temporary reduction in abundance followed by an increase after 5 days. It is estimated that mosquito abundance increased more in sprayed areas than if left unsprayed in all but 3 trap locations. The impact on infection rate and vector index were inconclusive due to the large error associated with estimating trap-specific infection rates. 

Spatial extremes are common for climate data as the observations are usually referenced by geographic locations and dependent when they are nearby. An important goal of extremes modeling is to estimate the T-year return level. Among the methods suitable for modeling spatial extremes, perhaps the simplest and fastest approach is the spatial generalized extreme value (GEV) distribution and the spatial generalized Pareto distribution (GPD) that assume marginal independence and only account for dependence through the parameters. Despite the simplicity, simulations have shown that return level estimation using the spatial GEV and spatial GPD still provides satisfactory results compared to max-stable processes, which are asymptotically justified models capable of representing spatial dependence among extremes. However, the linear functions used to model the spatially varying coefficients are restrictive and may be violated.We propose a flexible and fast approach based on the spatial GEV and spatial GPD by introducing fused lasso and fused ridge penalty for parameter regularization. This enables improved return level estimation for large spatial extremes compared to the existing methods. Supplemental files for this article are available online.