| 主讲人 |
Le Bao |
简介 |
<p><span style="font-family: 'Times New Roman', serif;">Recently, HIV interventions and policies have required more information at sub-national and sub-population levels to support local planning, decision making and resource allocation. Unfortunately, many areas and high-risk groups lack sufficient data for deriving stable and reliable results. One solution is to borrow information from other areas and groups within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time consuming. In this talk, we propose a simple and innovative way to incorporate the hierarchical information into the dynamic systems. The proposed method efficiently uses information from multiple areas and risk groups within each country without increasing the computational burden. As a result, the new model improves predictive ability in general with especially significant improvement in areas and risk groups with sparse data.</span></p> |
