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Statistical Inference

Presented June 21, 2019.

In this talk, Dr. Daihai He presents his recent works on applications of likelihood-based inference with non-mechanistic and mechanistic models in infectious disease modeling. Examples include modeling of the transmission of influenza, measles, yellow-fever virus, Zika virus, and Lassa-fever virus. Combined non-mechanistic and mechanistic models, we gain new insight into the mechanisms under the transmission of infectious diseases. 


Adverse Childhood Experiences (ACEs) have been linked to a variety of detrimental health and social outcomes. In the last 20 years, the association between ACEs with several adult health risk behaviors, conditions, and diseases including suicides, and substance abuse, mental health disturbances and impaired memory, nervous, endocrine and immune systems impairments, and criminal activities have been studied. One of the challenges in studying and timely diagnosis of ACEs is that the links between specific childhood experiences and their health outcomes are not totally clear. Similarly, an integrated dataset builtfrom multiple sources is often required for effective ACEs surveillance. The SPACES project aims at providing a semantic infrastructure to facilitate data sharing and integration and answer causal queries to improve ACEs surveillance.

Objective: We introduce the Semantic Platform for Adverse Childhood Experiences (ACEs) Surveillance (SPACES). It facilitates the access to the relevant integrated information, enables discovering the causality pathways and assists researchers, clinicians, public health practitioners, social workers, and health organization in studying the ACEs, identifying the trends, as well as planning and implementing preventive and therapeutic strategies.

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