) is a clinically significant pathogen and a highly genetically diverse species due to its large accessory genome. The functional consequence of this diversity remains unknown mainly because, to date, functional genomic studies in have been primarily performed on reference strains. Given the growing public health threat of infections, understanding the functional genomic differences among clinical isolates can provide more insight into how its genetic diversity influences gene essentiality, clinically relevant phenotypes, and importantly, potential drug targets. To determine the functional genomic diversity among strains, we conducted transposon-sequencing (TnSeq) on 21 genetically diverse clinical isolates, including 15 . subsp. isolates and 6 . subsp. isolates, cataloging all the essential and non-essential genes in each strain. Pan-genome analysis revealed a core set of 3,845 genes and a large accessory genome of 11,507. We identified 259 core essential genes across the 21 clinical isolates and 425 differentially required genes, representing ~10% of the core genome. We also identified genes whose requirements were subspecies, lineage, and isolate-specific. Finally, by correlating TnSeq profiles, we identified 19 previously uncharacterized genetic networks in . Altogether, we find that clinical isolates are not only genetically diverse but functionally diverse as well.

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.