Okhmatovskaia Anya

Many studies evaluate the timeliness and accuracy of outbreak detection algorithms used in syndromic surveillance. Of greater interest, however, is defining the outcome associated with improved detection. In case of a waterborne cryptosporidiosis outbreak, public health interventions are aimed exclusively at preventing new infections, and not at medical treatment of infected individuals. The effectiveness of these interventions in reducing morbidity and mortality will depend on their timeliness, the level of compliance, and the duration of exposure to pathogen. In this work, we use simulation modeling to examine several scenarios of issuing a boil-water advisory (BWA) as a response to outbreak detection through syndromic surveillance, and quantify the possible benefits of earlier interventions.

Objective

To quantitatively assess the benefit of issuing a boil-water advisory for preventing morbidity and mortality from a waterborne outbreak of cryptosporidiosis.

Outbreaks of waterborne gastrointestinal disease occur routinely in North America, resulting in considerable morbidity, mortality, and cost (Hrudey, Payment et al. 2003). Outbreak detection methods generally attempt to identify anomalies in time, but do not identify the type or source of an outbreak. We seek to develop a framework for both detection and classification of outbreaks using information in both space and time. Outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection.

Objective

To develop a methodological framework for detecting and classifying outbreaks of gastrointestinal disease on the island of Montreal, with the goal of improving early outbreak detection using simulated surveillance data.

There is growing recognition that an inability to access timely health indicators can hamper both the design and the effective implementation of infectious diseases control interventions. In malaria control, the global use of standard interventions has driven down the burden of disease in many regions. Further gains in high transmission areas and elimination in lower transmission settings, however, will require an enhanced understanding of malaria epidemiology, population characteristics, and efficacy of clinical and public health programs at the local level. Currently, there is a dearth of information available to fine-tune malaria control interventions at the local level. A key obstacle is the fragmentation of data into silos, as existing data cannot be brought together to estimate accurate and timely health metrics.

Objective

Driven by the need to bring malaria surveillance data from different sources together to support evidence-based decision making, we are conducting the “Scalable Data Integration for Disease Surveillance” (SDIDS) project. This project aims to foster the integration of existing surveillance data to support evidence-based decision-making in malaria control and demonstrate a model applicable to other diseases. Central to this initiative is collaboration between academia, governmental and NGO sectors.

Electronic data that could be used for global health surveillance are fragmented across diseases, organizations, and countries. This fragmentation frustrates efforts to analyze data and limits the amount of information available to guide disease control actions. In fields such as biology, semantic or knowledge-based methods are used extensively to integrate a wide range of electronically available data sources, thereby rapidly accelerating the pace of data analysis. Recognizing the potential of these semantic methods for global health surveillance, we have developed the Scalable Data Integration for Disease Surveillance (SDIDS) software platform. SDIDS is a knowledge-based system designed to enable the integration and analysis of data across multiple scales to support global health decision-making. A ‘proof of concept’ version of SDIDS is currently focused on data sources related to malaria surveillance in Uganda.

Objective

To develop a scalable software platform for integrating existing global health surveillance data and to implement the platform for malaria surveillance in Uganda.