Epidemic

The threat of epidemics due to non-human strains of influenza A viruses is ever present1. Surveillance is a critical aspect of pandemic preparedness for early case detection2. Identification of the index cases of a pandemic virus can trigger public health mitigation efforts3. To develop an appropriate surveillance process, it is important to understand the two possibilities of pandemic evolution. A new pandemic may begin with mild cases, during which surveillance should be concentrated on work/school absenteeism and in physician offices. The other possibility begins with severe cases, characterized by sCAP, respiratory failure, and ICU admission. As the syndrome of pneumonia is not reportable to health agencies for public health surveillance, a year-round, hospital-based surveillance mechanism may be an important tool for early case detection in the event of an epidemic of sCAP. To fill these gaps, we developed a statewide, hospital-based surveillance network for sCAP surveillance in Kentucky.

Objective

To present the development and implementation of the SIPS project, a statewide, hospital-based surveillance system for severe community-acquired pneumonia (sCAP) in Kentucky.

Informal surveillance systems like HealthMap are effective at the early detection of outbreaks. However, reliance on informal sources such as news media makes the efficiency of these systems vulnerable to newsroom constraints, namely high-profile disease events drawing reporting resources at the expense of other potential outbreaks and diminished staff over weekends and holidays. To our knowledge, this effect on informal or syndromic surveillance systems has yet to be studied.

Objective

Reporting about large public health events may reduce effective disease surveillance by syndromic or informal surveillance systems. The goal is to determine to what extent this problem exists and characterize situations in which it is likely to occur.

With an estimated 500 million people infected each year, dengue ranks as one of the most significant mosquito-borne viral human diseases, and one of the most rapidly emerging vectorborne diseases. A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist.

Objective

We aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics.

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition [1]. SSS seek early detection by focusing on pre-diagnostic symptoms that by themselves may not alarm clinicians. We have previously determined the performance of various Case Detector (CD) algorithms at finding cases of influenza-like illness (ILI) recorded in the electronic medical record of the Veterans Administration (VA) health system. In this work, we measure the impact of using CDs of increasing sensitivity but decreasing specificity on the time it takes a VA-based SSS to identify a modeled community-wide influenza outbreak. Objective This work uses a mathematical model of a plausible influenza epidemic to test the influence of different case-detection algorithms on the performance of a real-world syndromic surveillance system (SSS).

Yearly epidemics of respiratory diseases occur in children. Early recognition of these and of unexpected epidemics due to new agents or as acts of biological/chemical terrorism is desirable. In this study, we evaluate the ordering of chest radiographs as a proxy for early identification of epidemics of lower respiratory tract disease. This has the potential to act as a sensitive real-time surveillance tool during such outbreaks.

Objective:

Create a tool for monitoring respiratory epidemics based on chest radiograph ordering patterns.

We present the EpiEarly, EpiGrid, and EpiCast tools for mechanistically-based biological decision support. The range of tools covers coarse-, medium-, and fine-grained models. The coarse-grained, aggregated time-series only data tool (EpiEarly) provides a statistic quantifying epidemic growth potential and associated uncertainties. The medium grained, geographically-resolved model (EpiGrid) is based on differential equation type simulations of disease and epidemic progression in the presence of various human interventions geared toward understanding the role of infection control, early vs. late diagnosis, vaccination, etc. in outbreak control. A fine-grained hybrid-agent epidemic model (EpiCast) with diurnal agent travel and contagion allows the analysis of the importance of contact-networks, travel, and detailed intervention strategies for the control of outbreaks and epidemics.

Objective:

We will demonstrate tools that allow mechanistic contraints on disease progression and epidemic spread to play off against interventions, mitigation, and control measures. The fundamental mechanisms of disease progression and epidemic spread provide important constraints on interpreting changing epidemic cases counts with time and geography in the context of on-going interventions, mitigations, and controls. Models such as these that account for the effects of human actions can also allow evaluation of the importance of categories of epidemic and disease controls.

Global Mass gatherings (MGs) such as Olympic Games, FIFA World Cup, and Hajj (Muslim pilgrimage to Makkah), attract millions of people from different countries. The gathering of a large population in a proximity facilitates transmission of infectious diseases. Attendees arrive from different geographical areas with diverse disease history and immune responses. The associated travel patterns with global events can contribute to a further disease spread affecting a large number of people within a short period and lead to a potential pandemic. Global MGs pose serious health threats and challenges to the hosting countries and home countries of the participants. Advanced planning and disease surveillance systems are required to control health risks in these events. The success of computational models in different areas of public health and epidemiology motivates using these models in MGs to study transmission of infectious diseases and assess the risk of epidemics. Computational models enable simulation and analysis of different disease transmission scenarios in global MGs. Epidemic models can be used to evaluate the impact of various measures of prevention and control of infectious diseases.

Objective:

To develop a computational model to assess the risk of epidemics in global mass gatherings and evaluate the impact of various measures of prevention and control of infectious diseases.

Each significant outbreak and epidemic raises questions that must be answered in order to better inform future preparedness and response efforts, such as:

• What are the systems and resources needed to characterize an outbreak?
• What systems and resources are needed to bring an outbreak to a close?

While we can anticipate these types of questions, the absence of dedicated mechanisms to record operational experiences and challenges can result in valuable, ephemeral data that are crucial for improving outbreak response not being consistently collected or analyzed. Participation in outbreaks by external experts can be instrumental in ensuring that this important operational information is documented, analyzed and shared with the broader public health community. There is a particular need for observers external to the response who can capture and analyze applied data about the operational response to outbreaks—eg, the systems and strategies involved in responding to the such events ”in order to improve our understanding of best practices for detecting and responding to these events. These can then be shared so that the entire public health community can access and incorporate lessons learned into their own preparedness and response plans. External observers can also help describe the important work performed by local responders during outbreaks and advocate for necessary preparedness and response program resources. The Outbreak Observatory is currently in a pilot phase and is looking for international and US partners who may be interested in collaborating with members of our team during their next outbreak response.

Objective:

The Outbreak Observatory (OO) aims to:

• Strengthen outbreak/epidemic preparedness and response activities through real-time, one-the-ground observations and analyses ●Identify best practices based on operational experience that are broadly applicable across outbreak response agencies
• Serve as an independent voice to advocate for policies that support preparedness and response activities based on expert assessment of the resources required to build and maintain necessary outbreak response capabilities Support local practitioners’ efforts to publish their experiences
• Sharing the firsthand experience of responders is critical for building outbreak preparedness and response capacity, and OO will serve as a dedicated mechanism to collect, analyze and disseminate this information