The SARS-CoV-2 pandemic is awash in data, including daily, spatially-resolved COVID case data, virus sequence data, patients `omics data, and mobility data. Journals are now also awash in studies that make use of quantitative modeling approaches to gain insight into the geographic spread of SARS-CoV-2 and its temporal dynamics, as well as studies that predict the impact of control strategies on SARS-CoV-2 circulation. Some, but by no means all, of these studies are informed by the massive amounts of available data. Some, but by no means all, of these studies have been useful — in that their predictions revealed something beyond simple back of the envelope calculations. To summarize some of these findings, in this symposium, we will address questions such as: What do we want from models of disease spread? What can and should be predicted? Which data are the most useful for predictions? When do we need mechanistic models? What have we learned about how to model disease spread from unmet and/or conflicting predictions? The workshop speakers will explore these questions from different perspectives on what data need to be considered and how models can be evaluated. As at other TMLS workshops, each speaker will deliver a 10-minute talk with ample time set aside for moderated questions/discussion. We expect the talks to be provocative and bold, while respecting different perspectives.

A globally invasive form of the mosquito Aedes aegypti has evolved to specialize in biting humans, making it an efficient vector of dengue, yellow fever, Zika, and chikungunya. Host-seeking females identify humans primarily by smell, strongly preferring human odour over the odor of non-human animals. Exactly how they discriminate, however, is unclear. Human and animal odors are complex blends that share most of the same chemical components, presenting an interesting challenge in sensory coding. I will talk about recent work from the lab showing that (1) human and animal blends can be distinguished by the relative concentration of a diverse array of compounds and that (2) these complex chemical differences translate into a neural code for human odor that involves as few as two to three olfactory glomeruli in the mosquito brain. Our work demonstrates how organisms may evolve to discriminate complex odor stimuli of special biological relevance with a surprisingly simple combinatorial code and reveals novel targets for the design of next-generation mosquito control strategies.