Epidemiology Project

摘要 为确保亚太区域疟疾传播继续下降，至关重要的是，国家 疟疾控制方案能够更有效地针对高传播率和高人口的特定地区 风险最高。为了做到这一点，他们需要能够准确地识别和描绘残留物的口袋 传播，并确定高危群体的存在和性质。这将需要新的方法和 更有效的流行病学研究设计，基于对关键宿主和 有助于在不同情况下维持残留传播的寄生虫因素。特别是， 揭开相对的寄生虫的生物学基础(如寄生虫种群遗传学、宿主遗传学、免疫学) 无症状感染的高流行率现在比以往任何时候都更加重要。该计划的总体目标是 因此，亚太ICEMR流行病学项目是为了更好地了解宿主和寄生虫因素 尽管加强了控制，但有助于维持疟疾的残留传播，并将这一知识应用于 开发新的方法来准确识别和描绘残存传输区。为了 要实现这一点，疟疾感染和疾病风险的时空模式将通过 结合严格流行病学的大型横断面和纵向队列研究 研究设计采用最先进的疟原虫分子检测和基因分型。感染和一种 评估宿主免疫反应及其与暴露和预防临床症状的联系 疟疾。然后，这些信息将用于评估改进的监视战略，通过创新的 计算机模拟与现场应用相结合。这种深入的疟疾流行病学的结合， 分子和抗体图谱的生态学及其在数学建模中的应用 硅胶实验，随后在该领域的应用是高度创新的，以前得到了非常 在疟疾监测方面很少受到关注。

Summary To ensure that malaria transmission continues to decline in the Asia-Pacific region, it is critical that national malaria control programs be able to more efficiently target specific areas of high transmission and populations at highest risk. In order to do this they need to be able to accurately identify and delineate pockets of residual transmission and identify the presence and nature of high-risk groups. This will require new approaches and more efficient epidemiological study designs that are based on an in-depth understanding of key host and parasite factors that contribute to sustaining residual transmission in different scenarios. In particular, unraveling the biological basis (e.g. parasite population genetics, host genetics, immunity) for the relatively high prevalence of asymptomatic infections is now more important than ever before. The overall aim of the Asia-Pacific ICEMR Epidemiology Project is thus to better understand host and parasite factors that contribute to sustaining residual malaria transmission despite intensified control and to apply this knowledge to develop novel ways of accurately identifying and delineating pockets of residual transmission. In order to achieve this spatio-temporal patterns of risk of malaria infection and disease will be investigated through a combination of large cross-sectional and longitudinal cohort studies that combine rigorous epidemiological study designs with state-of-the-art molecular detection and genotyping of Plasmodium spp. infections and an assessment of host immune responses and their link to exposure and protection from clinical symptoms of malaria. This information will then be used to evaluate improved surveillance strategies through an innovative combination of computer simulations and field application. This combination of in-depth malaria epidemiology, ecology and application of molecular and antibody profiling feeding into mathematical modeling to allow in silico experimentation, followed by application in the field is highly innovative and has previously received very little attention in the context of malaria surveillance.