Background:The contemporary effectiveness of assisted partner notification services (APS) in the United States is uncertain.Setting:State and local jurisdictions in the United States that reported ≥300 new HIV diagnoses in 2018 and were participating in the Ending the Epidemic Initiative.Methods:The study surveyed health departments to collect data on the content and organization of APS and aggregate data on APS outcomes for 2019. Analyses defined contact and case-finding indices (i.e., sex partners named and newly diagnosed per index case receiving APS) and estimated staff case-finding productivity.Results:Sixteen (84%) of 19 jurisdictions responded to the survey, providing APS outcome data for 14 areas (74%). Most health departments routinely integrated APS with linkage of cases and partners to HIV care (88%) and pre-exposure prophylaxis (88%). A total of 19,164 persons were newly diagnosed with HIV in the 14 areas. Staff initiated APS investigations on 14,203 cases (74%) and provided APS to 9937 cases (52%). Cases named 6799 partners (contact index = 0.68), of whom 1841 (27%) had previously diagnosed HIV, 2202 (32%) tested HIV negative, 541 (8% of named and 20% of tested partners) were newly diagnosed with HIV, and 2215 (33%) were not known to have tested. Across jurisdictions, the case-finding index was 0.054 (median = 0.05, range 0.015–0.12). Health departments employed 292 full-time equivalent staff to provide APS. These staff identified a median of 2.0 new HIV infections per staff per year. APS accounted for 2.8% of new diagnoses in 2019.Conclusions:HIV case-finding resulting from APS in the United States is low.
Purpose
Every year volunteers play a crucial role in disaster responses around the world. Volunteer management is known to be more complex than managing a paid workforce, and this is only made worse by the uncertainty of rapidly changing conditions of crisis scenarios. The purpose of this paper is to address the critical problem of assigning tasks to volunteers and other renewable and non-renewable resources simultaneously, particularly under high-load conditions. These conditions are described by a significant mismatch between available volunteer resources and demands or by frequent changes in requirements.
Design/methodology/approach
Through a combination of literature reviews and interviews with managers from several major volunteer organizations, six key characteristics of crisis volunteer resource allocation problems are identified. These characteristics are then used to develop a general mixed integer programming framework for modeling these problems. Rather than relying on probabilistic resource or demand characterizations, this framework addresses the constantly changing conditions inherent to this class of problems through a dynamic resource reallocation-based approach that minimizes the undesirable impacts of changes while meeting the desired and changing objectives. The viability of this approach for solving problems of realistic size and scale is demonstrated through a large set of computational experiments.
Findings
Using a common commercial solver, optimal solutions to the allocation and reallocation problems were consistently obtained in short timespans for a wide variety of problems that have realistic sizes and characteristics.
Originality/value
The proposed approach has not been previously addressed in the literature and represents a computationally tractable method to allocate volunteer, renewable and non-renewable resources to tasks in highly volatile crisis scenarios without requiring probabilistic resource or demand characterizations.
Component Based Software Development (CBSD) has gained widespread acceptance as it often results in higher quality software with a significant reduction in development time and costs. A key idea behind CBSD is the extensive reuse and composition of preexisting modules into new software. In this paper we introduce the pliability metric, which is well suited to a component-based orientation and extends previous metrics. Pliability is a flexible measure that assesses software quality across different quality attributes in terms of the quality of its components. In addition, we have developed an optimal component selection model based on integer programming, for maximizing pliability. Through computational experimentation we demonstrate that this model is capable of finding optimal solutions to problems with a very large number of components and requirements in a short time.
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