Effective surveillance systems are essential to developing targeted, efficient public health responses to infectious disease. In many domestic and global settings, multiple systems are being employed with varying degrees of overlap and with each system designed to achieve different surveillance objectives. The objective of this research project is to develop and distribute a spatio-temporal data integration toolset (?OPTI-SURVEIL?) for analyzing data from multiple surveillance systems, reducing spatial uncertainty by linking data across systems, space and time. With the support and guidance of the US CDC and China CDC (see letters of support), we will develop practical, multi-system analytical tools that will: (1) provide improved estimates of disease burden across space and time; (2) assess individual-level and system-level sensitivity and bias in case ascertainment; and (3) identify redundancies and gaps in current surveillance strategies. Approach:
In Aim 1, we will develop spatial- temporal capture-recapture methods for performing data integration at the individual-level. The methods will account for individual-level heterogeneity in case ascertainment and key system-level parameters (sensitivity, bias, dependency). The methods will be extended to consider multiple diseases simultaneously, in order to address the increasing interest in the burden of co-infections.
In Aim 2, we will develop spatial-temporal Bayesian hierarchical models for performing data integration using aggregated surveillance data that commonly arise from public surveillance databases. The models will exploit spatial-temporal dependence in county-level case numbers, account for spatial-temporal missing data due to system availability, and probabilistically incorporate information on ascertainment sensitivity and bias at the individual-level and at the system-level.
In Aim 3, we will develop simulation-based methods to perform joint evaluation of multiple surveillance system designs. These methods will be applied to optimize a system?s design to maximize case detection while considering resource constraints, system sensitivity/bias, and the presence of other systems. We will apply OPTIM-SURVEIL to existing data in China, and will focus on four infectious diseases of global importance: tuberculosis (TB), malaria, schistosomiasis and hookworm. These diseases exhibit a diverse set of surveillance challenges, including diagnostic accuracy, increasingly rare case counts as elimination is approached, variability in disease severity, and challenges identifying key co-infections (e.g., TB and malaria). Expected Outcomes: The methods that we will develop and distribute will provide timely and practical tools for analyzing data from multiple disease surveillance systems. For the four target infections, we will answer specific questions about how information on specific surveillance architectures and properties?such as alternative spatial configurations of systems that have variable sensitivity and case ascertainment bias?can be used to improve the design of systems for achieving specific surveillance objectives. We will evaluate key tradeoffs in surveillance designs, and assess optimal surveillance approaches for solving particular public health challenges.
Effective infectious disease surveillance systems are essential to developing targeted, efficient public health responses. Advances in methods for analyzing surveillance data are needed to support infectious disease control campaigns. The proposed research project will develop novel spatial-temporal methods to combine information from multiple surveillance systems, with the goal of improving system design, reducing uncertainty, and ultimately preventing infectious disease spread.
|Jiang, Baoguo; Liang, Song; Peng, Zhong-Ren et al. (2017) Transport and public health in China: the road to a healthy future. Lancet 390:1781-1791|
|Sokolow, Susanne H; Jones, Isabel J; Jocque, Merlijn et al. (2017) Nearly 400 million people are at higher risk of schistosomiasis because dams block the migration of snail-eating river prawns. Philos Trans R Soc Lond B Biol Sci 372:|