Chretien Jean-Paul

A pandemic caused by influenza A/H5N1 or another novel strain could kill millions of people and devastate economies worldwide. Recent computer simulations suggest that an emerging influenza pandemic might be contained in Southeast Asia through rapid detection, antiviral distribution, and other interventions [1]. To facilitate containment, the World Health Organization (WHO) has established large, global antiviral stockpiles and called on countries to develop rapid pandemic detection and response protocols [2]. However, developing countries in Southeast Asia would face significant challenges in containing an emerging pandemic. Limited surveillance coverage and diagnostic capabilities; poor communication and transportation infrastructure; and lack of resources to investigate outbreaks could cause critical delays in pandemic recognition. Wealthy countries have committed substantial funds to improve pandemic detection and response in developing countries, but tools to guide system planning, evaluation, and enhancement in such places are lacking.

Objective

We propose a framework for evaluating the ability of syndromic, laboratory-based, and other public health surveillance systems to contain an emerging influenza pandemic influenza in developing countries, and apply the framework to systems in Laos.

The Johns Hopkins Applied Physics Laboratory and the Armed Forces Health Surveillance Center have developed a hybrid processing engine that alerts monitors when a severe health condition exists based on corroboration among several sources of data. The system was designed to ingest a day's worth of recent data and provide results to monitors daily. In some theaters, the health of the US Forces must be determined at near-real time rates requiring a reassessment of current surveillance practices. Challenges exist in both acquiring data in real-time and in modifying automated alerting processes to re-evaluate as a new piece of evidence is received.

Objective

To develop a real-time surveillance capability that processes, fuses and assesses when data is received using a new fusion processing methodology and multiple sources health indicator data.

Researchers have developed varied methods for forecasting influenza activity using surveillance data with predictive models, but real-world applications in public health programs are rare. To inform consideration of whether and how public health practice should incorporate influenza forecasting, we conducted a systematic review of these methods.

Objective

To assess studies of epidemiological forecasting models for human influenza activity.

Difficulties in timely acquisition and interpretation of accurate data on communicable diseases can impede outbreak detection and control. These limitations are of global importance: they contribute to avoidable morbidity, economic losses, and social disruption; and, in a globalized world, epidemics can spread rapidly to other susceptible populations.

SARS and the potential for an influenza pandemic highlighted the importance of global disease surveillance. Similarly, the World Health Organization’s newly implemented 2005 International Health Regulations require member countries to provide notification of emerging infectious diseases of potential global importance. The challenges arise when Ministries of Health (MoH) in resource-poor countries add these mandates to already over-burdened and under-funded surveillance systems. Appropriately adapted, electronic disease surveillance systems could provide the tools and approaches MOHs need to meet today’s surveillance challenges.

Objective

In this presentation we will discuss the concept of electronic disease surveillance in resource-poor settings, and the issues to be considered during system planning and implementation.

A U.S. Department of Defense program is underway to assess health surveillance in resource-poor settings and to evaluate the Early Warning Outbreak Reporting System. This program has included several information-gathering trips, including a trip to Lao PDR in September, 2006.

Objective

This modeling effort will provide guidance for policy and planning decisions in developing countries in the event of an acute respiratory illness epidemic, particularly an outbreak with pandemic potential.

The revised International Health Regulations (IHR) have expanded traditional infectious disease notification to include surveillance diseases of international importance, including emerging infectious diseases.  However, there are no clearly established guidelines for how countries should conduct this surveillance, which types of syndromes should be reported, nor any means for enforcement.  The commonly established concept of syndromic surveillance in developed regions encompasses the use of pre-diagnostic information in a near real time fashion for further investigation for public health action.  Syndromic surveillance is widely used in North America and Europe, and is typically thought of as a highly complex, technology driven automated tool for early detection of outbreaks.  Nonetheless, applications of syndromic surveillance using technology appropriate for the setting are being used worldwide to augment traditional surveillance, and may enhance compliance with the revised IHR.

Objective:

To review applications of syndromic surveillance in developing countries