Informatics for empirically supported influenza pandemic control strategies

描述(由申请人提供)： 每年，一种新的流感毒株通过空中和地面交通相互连接的城市在全国范围内传播。因此，如果出现流感大流行毒株，可能会产生灾难性的后果。1918年，“西班牙流感”在美国夺走了675,000人的生命，在全世界夺走了数千万人的生命。因为上一次是在1969年，人们对疫情复发的担忧与日俱增；而H5N1高致病性禽流感病毒在东南亚家禽中令人担忧的传播只会加剧这种担忧。令人惊讶的是，关于流感如何在城市、地区、国家和全球范围内传播的经验数据很少。然而，可以通过加强对当地和大规模模式的了解来制定有针对性的预防战略。我们将利用NLM资助的自动化先进疾病监测系统(Aegis)对流感传播的关键决定因素进行经验性测量。我们的第一个目标是开发全国流感传播模型，为流行病和大流行性流感的公共卫生准备战略提供信息。我们将使用网络驱动的方法对每年流感疫情的全国传播进行建模，以了解包括人口流动和环境条件在内的多变量因素的影响。我们将绘制年度传播模式，并确定代表主要网络路径的城市枢纽。我们预计将确定应成为控制战略重点的哨点城市，这些战略可能包括有针对性的疫苗接种、旅行建议和飞行禁令。我们的第二个目标是开发大城市地区局部流感传播的空间模型，以确定监测和控制的目标。我们将建立以经验为基础的局部流感在主要大都市地区传播的空间模型，并利用监测数据确定反复出现的风险热点。我们将利用主要大都市地区感染空间模式的差异，评估导致全国不同地区感染的地理和人口风险因素。为了实现这两个目标，我们的模型和方法将在宙斯盾监测系统中实施，以实时监测流感活动。制定流感监测与防控战略有效衔接的办法。这些方法在自然发生和故意引入传染病病原体引起的事件的灾难准备方面有着广泛的应用。

DESCRIPTION (provided by applicant): Each year, a new influenza strain spreads across the nation, through cities interconnected by air and ground transportation. Consequently, there is potential for disastrous consequences, should an influenza pandemic strain emerge. In 1918, the "Spanish flu" pandemic claimed over 675,000 lives in the United States, and tens of millions worldwide. Because the last was in 1969, there is mounting concern over a recurrence; and the alarming spread of the H5N1 highly pathogenic avian influenza virus among poultry in Southeast Asia only heightens this concern. Surprisingly, there is little empirical data on how influenza spreads through cities, regions, nations and across the globe. Targeted prevention strategies could, however, be informed by enhanced understanding of local and large scale patterns. We will leverage an NLM-funded automated advanced disease surveillance system (AEGIS) to empirically measure the key determinants of influenza spread. Our first aim is to develop models of national influenza spread to inform public health preparedness strategies for epidemic and pandemic flu. We will model the national spread of the yearly influenza epidemic using network driven methods so as to understand the impact of multivariate factors, including population movement and environmental conditions. We will map the yearly pattern of spread and identify urban hubs that represent major network pathways. We expect to identify sentinel cities that should be the focus of control strategies which could include targeted vaccination, travel advisories, and flight bans. Our second aim is to develop spatial models of local influenza spread in metropolitan areas to identify targets for surveillance and control. We will develop empirically based spatial models of local influenza spread across major metropolitan areas and identify recurring hotspots of risk using surveillance data. We will exploit differences in spatial patterns of infection in major metropolitan areas to evaluate geographic and demographic risk factors that drive infection in different parts of the country. For both aims, our models and methods will be implemented in the AEGIS surveillance system for real-time monitoring of influenza activity. We will develop methods for effective linkage of influenza surveillance to prevention and control strategies. These methods have broad application for disaster preparedness for events caused by both naturally-occurring and deliberate introductions of infectious agents.