Outbreak Detection

Currently Centers for Disease Control and Prevention (CDC) employ threshold rules to declare epidemic outbreaks, such as influenza, separately in each population. However each year influenza starts in one population and spreads population-to-population throughout the country. Therefore there is a need for an algorithm to declare the epidemic that uses information from multiple populations.

Objective

Detect epidemics over multiple Populations using computational methods

Infectious disease outbreaks during crises can be controlled by detecting epidemics at their earliest possible stages through cost effective and time efficient data analytical approaches. The slow or non reporting is a real gap in existing reporting systems that delays in receiving the disease alerts and outbreaks, and hence delays in response causing high burden of morbidity and mortality, especially during crises situation. As on contrary, the functioning electronic databases for fast and reliable disease early warning and response networks (EWARN) have been found very effective in early detection, confirmation and response to disease outbreaks but launching the implementation of such systems is always time consuming due to resource constraints and other limitations during crises. Hence introduction of time efficient data analytical approaches can serve as a fast and reliable alternative for electronic databases during the launching phases, and may facilitate assessment of epidemics and outbreak situation by ensuring immediate, reliable and fully functional disease reporting and analysis until online database becomes fully functional and adopted by authorities.

Objective

To assess the epidemic and outbreak situations during emergencies through development and application of a data summarization techniques while launching electronic disease early warning systems (eDEWS) in resource poor countries

The majority of farmed animals are sent to slaughterhouses, making them a focal point for potential collection of health data. However, these data are not always available to health officials, and remain under-used for cattle health monitoring. Meat inspection data are mainly non-diagnostic (condemned portion and reasons for condemnation) and cover a large population. These characteristics make them a good candidate for syndromic surveillance. Whole carcass condemnation rate is linked to acute infections which reduces the dilution bias due to the variable period of time between cattle infection and the detection of lesions at the slaughterhouse.

Objective

The objective of the work was to assess the performance of several algorithms for outbreak detection based on weekly proportions of whole carcass condemnation

The Connecticut Department of Public Health (DPH), in collaboration with Yale Emerging Infections Program (EIP), receives funding to particpate in the Foodborne Diseases Active Surveillance Network (FoodNet) and Foodborne Disease Centers for Outbreak Response Enhancement (FoodCORE). FoodNet is an active population-based surveillance network that monitors trends for ten enteric diseases and conducts special studies to better understand the causes of foodborne illness. FoodCORE develops best practices related to the detection, investigation, and control or disease outbreaks, particularly those due to to Salmonella, Shiga toxin-producing E. coli, and Listeria (SSL). Foodborne disease surveillance and response is a collaborative effort requiring real-time data sharing between key stakeholders including: DPH Epidemiology, DPH Laboratory, DPH Food Protection Program, Yale EIP, and local health department (LHD) staff.

Objective

To develop an integrated system for routine enteric disease surveillance, cluster detection and monitioring, information sharing among key stakeholders, and documentation.

Influenza is a contagious disease that causes epidemics in many parts of the world. The World Health Organization estimates that influenza causes three to five million severe illnesses each year and 250,000-500,000 deaths. Predicting and characterizing outbreaks of influenza is an important public health problem and significant progress has been made in predicting single outbreaks. However, multiple temporally overlapping outbreaks are also common. These may be caused by different subtypes or outbreaks in multiple demographic groups. We describe our Multiple Outbreak Detection System (MODS) and its performance on two actual outbreaks. This work extends previous work by our group by using model-averaging and a new method to estimate non-influenza influenza-like illness (NI-ILI). We also apply MODS to a real dataset with a double outbreak.

Previous research identifies social media as an informal source of near-real time health data that may add value to disease surveillance systems by providing broader access to health data across hard-toreach populations. This indirect health monitoring may improve public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. The Philippines consists of over 7,000 islands and is prone to meteorological (storms), hydrological (floods), and geophysical disasters (earthquakes and volcanoes). In these situations, evacuation centers are used for safety and medical attention and often house up to 50K people each for 2 or more months, sometimes with unclean water sources and improper sanitation. Consequently, these conditions are a perfect venue for communicable disease transmission and have been proposed to cause disease outbreaks weeks after the original disaster occurred. Coined the social media capital of the world1, the Philippines provides a perfect opportunity to evaluate the potential of social media use in disease surveillance.

Objective

To determine the potential of Twitter data as an early warning of a likely communicable disease outbreak following a natural disaster, and if successful, develop an open-source algorithm for use by interested parties.