Exciting new publication from lab alumnus Dr. Jason Olejarz
A binary prototype for time-series surveillance and intervention, published in Epidemics. The article presents a model for decision-making and design of public health surveillance systems. Great work from Jason, Yonatan, and HSPH research collaborators!
Here below you’ll find the abstract and link to the full article.
Abstract
Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, , whenever the system is abnormal and no action is taken, or an immediate cost, , with action, where . If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.
