Mosquito-borne diseases cause significant public health burden, mostly in tropical and sub-tropical regions, and are widely emerging or re-emerging in areas where previously absent. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modeling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for the movement of individuals through mosquito habitats and responds to environmental factors such as rainfall and temperature. Our approach extends the capabilities of existing agent-based models for individual movement developed to predict the spread of directly transmitted pathogens in populations. To extend to mosquito-borne disease, agent-based models are coupled with differential equations representing `clouds' of mosquitoes in geographic patches that account for mosquito ecology, including heterogeneity in mosquito density, emergence rates, and extrinsic incubation period. We illustrate the method by adapting an agent-based model for human movement across a network to mosquito-borne disease. We investigated the importance of heterogeneity in mosquito population dynamics and host movement on pathogen transmission, motivating the utility of detailed models of individual behavior. We observed that the total number of infected people is greater in heterogeneous patch models with one high risk patch and high or medium human movement than it would be in a random mixing homogeneous model. Our hybrid agent-based/differential equation model is able to quantify the importance of the heterogeneity in predicting the spread and invasion of mosquito-borne pathogens. Mitigation strategies can be more effective when guided by realistic models created by extending the capabilities of existing agent-based models to include vector-borne diseases.

Statistical Learning methodology for analysis of large collections of cross-sectional observational data can be most effective when the approach used is both Nonparametric and Unsupervised. We illustrate use of our NU Learning approach on 2016 US environmental epidemiology data that we have made freely available. We encourage other researchers to download these data, apply whatever methodology they wish, and contribute to development of a broad-based ``consensus view'' of potential effects of Secondary Organic Aerosols (volatile organic compounds of predominantly biogenic or anthropogenic origin) within PM2.5 particulate matter on circulatory and/or respiratory mortality. Our analyses here focus on the question: ``Are regions with relatively high air-borne biogenic particulate matter also expected to have relatively high circulatory and/or respiratory mortality?''

One Health issues, such as the spread of highly pathogenic avian influenza~(HPAI), present significant challenges at the human-animal-environmental interface. Recent H5N1 outbreaks underscore the need for comprehensive modeling efforts that capture the complex interactions between various entities in these interconnected ecosystems. To support such efforts, we develop a methodology to construct a synthetic spatiotemporal gridded dataset of livestock production and processing, human population, and wild birds for the contiguous United States, called a \emph{digital similar}. This representation is a result of fusing diverse datasets using statistical and optimization techniques, followed by extensive verification and validation. The livestock component includes farm-level representations of four major livestock types -- cattle, poultry, swine, and sheep -- including further categorization into subtypes such as dairy cows, beef cows, chickens, turkeys, ducks, etc. Weekly abundance data for wild bird species identified in the transmission of avian influenza are included. Gridded distributions of the human population, along with demographic and occupational features, capture the placement of agricultural workers and the general population. We demonstrate how the digital similar can be applied to evaluate spillover risk to dairy cows and poultry from wild bird population, then validate these results using historical H5N1 incidences. The resulting subtype-specific spatiotemporal risk maps identify hotspots of high risk from H5N1 infected wild bird population to dairy cattle and poultry operations, thus guiding surveillance efforts.