Real-time surveillance to support dynamic and predictive models of MDR/XDR TB

数学模型提供了评估公共卫生潜在功效的机制 干预措施。虽然传统的结核病模型依赖于从 根据历史观察，迫切需要开发更直接依赖于 实际监控和接触者追踪数据此外，患者的治疗结果 耐药结核病通常由于在详细的耐药性之前的延迟而受到损害 配置文件可用。实际上，这意味着耐药疾病患者 经常用不适当或无效的药物治疗数月。 我们以前开发了电子病历（EMR），以促进临床护理， 结核病患者;在这里，我们建议扩展这个平台作为一个研究工具。的 本项目的具体目标是：（1）建立多药耐药（MDR）监测系统; （2）根据秘鲁利马建立的EMR， 建立结核病传播的现实模型， 社区异质性，并使用这些模型来评估具体控制政策的影响; 以及（3）使用监测数据开发耐药谱的个体预测模型 这将成为选择适当结核病药物治疗方案的决策支持工具。

Mathematical models provide mechanisms to evaluate the potential efficacy of public health interventions. While traditional models of tuberculosis have relied on parameters obtained from historical observation, there is a pressing need to develop models that depend more directly on actual surveillance and contact-tracing data. Additionally, treatment outcomes of patients with drug-resistant tuberculosis are often compromised by the delay before detailed drug resistance profiles are available. In practical terms, this means that patients with drug-resistant disease are often treated with inappropriate or ineffective drugs for many months. We have previously developed an Electronic Medical Record (EMR) to facilitate clinical care of tuberculosis patients; here we propose to extend this platform to function as a research tool. The specific aims of this project are to: (1) develop a surveillance system for multi-drug resistant (MDR) and extensively drug resistant (XDR) tuberculosis based on the EMR established in Lima, Peru; (2) develop realistic models of tuberculosis transmission that capture important individual and community heterogeneities and use these models to assess the impact of specific control policies; and (3) use surveillance data to develop an individual prediction model for drug resistance profiles that will serve as a decision support tool for choosing appropriate tuberculosis drug regimens.