Outbreak Detection

We implemented the CDC EARS algorithms in our DADAR (Data Analysis, Detection, and Response) situational awareness platform. We encountered some skepticism among some of our partners about the efficacy of these algorithms for more than the simplest tracking of seasonal flu.

We analyzed several flu outbreaks observed in our data, including the H1N1 outbreaks in 2009, and noted that, using the C1 algorithm, even with our adjustable alerting thresholds, there was an uncomfortable number of false alarms in the noisy steady-state data, when the number of reported cases of flu-like symptoms was less than five per day.

We developed an algorithm, RecentMax, that could offer better performance in analyzing our flu data.

Objective

To develop an algorithm for detecting outbreaks of typical transmissible diseases in time series data that offers better sensitivity and specificity than the CDC EARS C1/C2/C3 algorithms while offering much better noise handling performance.

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.