Browsing by Author "Zhang, Xianyang"

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ATD: Collaborative Research: Predicting The Threat of Vector-Borne Illnesses Using Spatiotemporal Weather Patterns
Statistics; TAMU; https://hdl.handle.net/20.500.14641/620; National Science Foundation
Vector-borne diseases affect virtually everyone on earth. Mosquitoes are the most widely distributed disease vectors and are a serious threat to human life and health. West Nile virus (WNV) is one of the mosquito-borne diseases for which there is still no effective treatment; to date, the Centers for Disease Control and Prevention has reported over 40,000 cases across the United States. Temperature and precipitation are the two most important weather variables that affect mosquito populations and thus affect the WNV transmission cycle. The mosquito infection rate (MIR) is considered an important mediator to study WNV risk. Based on surveillance data for WNV in Illinois, this project aims to develop new methodologies and algorithms to study WNV and MIR using weather and environmental variables. Specifically, the investigators plan first to make predictions of MIR and then characterize the spatial pattern of temperature and precipitation to identify the risk level of WNV human illness and MIR. They will also establish a WNV Index to provide a reliable and interpretable warning for vector-borne disease risk. Finally, since mosquito-borne diseases are particularly affected by rising temperatures, changing precipitation patterns, and a higher frequency of extreme weather events, the project aims to both quantitatively and qualitatively project the current risk to the future under climate change. The research will foster fundamental statistical methodology development as well as collaborations between statistics and public health. Graduate and undergraduate students will be engaged in aspects of the scientific research. The project will provide new results on the impact of climate change on national security, of general interest and importance to the wider public and policymakers. The methods of this project include a spatially-varying-coefficient model with functional weather covariates to make predictions of MIR, as well as a multiple-testing approach to characterize the spatial pattern of temperature and precipitation for ultimately classifying the weather pattern into different risk levels with respect to WNV. The statistical models and algorithms learned from the historical data will be applied to downscaled future weather data to study the impact of climate change on WNV human illness and MIR. The analyses will be based on massive data including WNV human cases, MIR, current and future spatio-temporal stochastic weather processes, land cover, and the length of daylight. The statistical methods used in the project are not only effective for this WNV study but can be a general methodology for a wide range of vector-borne diseases. The spatially-varying-coefficient model with functional covariates takes the continuous and dynamic influence of the retrospective weather on MIR into account while allowing the relationship between MIR and weather and other environmental variables to vary over a spatial domain. The characterization of the spatial weather pattern and the establishment of WNV Index provide a new perspective to study and prevent WNV risk. Compared to previous methods that evaluate the difference between two spatio-temporal random fields as a whole, the multiple-testing approach in this project can detect exactly where the differences occur. This feature is crucial for regional risk detection. Quantifying the impact of climate change on vector-borne diseases is essential to policymakers; the results of the project are expected to provide a reliable resource for such purposes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Leveraging Covariate and Structural Information for Efficient Large-Scale and High-Dimensional Inference
Statistics; TAMU; https://hdl.handle.net/20.500.14641/620; National Science Foundation
The proliferation of big data is accompanied by a vast number of questions, in the form of hypothesis tests, which call for effective methods to conduct large-scale and high-dimensional inferences. These influential methods must involve statistical analysis on many study units simultaneously. Conventional simultaneous inference procedures often assume that hypotheses for different units are exchangeable. However, in many scientific applications, external covariate and structural information regarding the patterns of signals are available. Exploiting such side information efficiently and accurately will lead to improved statistical power, as well as enhanced interpretability of research results. The main thrust of this research is to advance statistical methodologies and theories for large-scale and high-dimensional inference with a particular focus on integrating potentially useful external covariate and structural information into inferential procedures. This research aims to develop innovative methodologies and theories to address several significant problems in large-scale and high-dimensional inference. In Project 1, the PI will introduce a new multiple testing procedure that can automatically select relevant covariates to improve the efficiency in inference when a large number of external covariates are available. In Project 2, the PI will develop a new multiple testing framework, which can integrate various forms of structural information. Because prior information is seldom perfectly accurate, a particular focus will be on developing procedures that are robust to misspecified/imperfect prior information. In Project 3, the PI shall propose new procedures for simultaneous inference in high-dimensional regressions with side information. The statistical tools will be used to identify skilled fund managers, assess the performance of climate field reconstructions, and analyze genomic data in an integrative way. Methods and computer code developed will be made publicly available. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.