Informatics for empirically supported influenza pandemic control strategies

描述（由申请人提供）： 每年，一种新的流感病毒株通过空中和地面交通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，就有可能造成灾难性后果。1918年，“西班牙流感”大流行在美国夺去了675，000多人的生命，全世界有数千万人死亡。 由于上一次是在1969年，人们越来越担心再次发生;而H5 N1高致病性禽流感病毒在东南亚家禽中的惊人传播只会加剧这种担忧。 令人惊讶的是，几乎没有关于流感如何在城市、地区、国家和地球仪传播的经验数据。 然而，通过加强对地方和大规模模式的了解，可以为有针对性的预防战略提供信息。 我们将利用NLM资助的自动化高级疾病监测系统（AEGIS），以实证方式衡量流感传播的关键决定因素。 我们的第一个目标是建立国家流感传播模型，为流行性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方法模拟每年流感流行的全国传播，以了解多变量因素的影响，包括人口流动和环境条件。 我们将绘制每年的传播模式，并确定代表主要网络路径的城市枢纽。 我们希望确定应该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、旅行禁令和航班禁令。 我们的第二个目标是建立大都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。 我们将开发基于经验的地方流感在主要大都市地区传播的空间模型，并使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，评估导致该国不同地区感染的地理和人口风险因素。 对于这两个目标，我们的模型和方法将在AEGIS监测系统中实施，以实时监测流感活动。 我们将制定将流感监测与预防和控制战略有效联系起来的方法。 这些方法广泛应用于自然发生和故意引入传染性病原体引起的灾害准备。

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.