disease modeling
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SARC-CoV-2 modeling: What have we learned from this pandemic about how (not) to model disease spread?
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.
“Super Spreaders in the Corona Epidemics”
Recently a powerful example of a replicating nano-machine entered our society. In principle, it’s just a normal disease, that one attempts to model with 3 or 4 simple coupled equations with 2 important parameters: a timescale, and a replication factor (the famous R0). Then one tries to guess how changes in society change R0 and perhaps adopt some more or less strong lock-down measures. However, this virus has more “personality” than that. It behaves differently in different persons, and persons behave differently. Presumably, only a few of us infect a lot, while most do not infect so much. This assumption is supported by the observation that couples living together only infect each other with about 15 percent probability, indicating that most infected people are not really infectious. I will discuss this and other aspects of Covid-19 in the perspective of models that describe heterogeneous individuals in a society. In particular, we suggest that limiting superspreading opportunities is a cost-effective strategy to mitigate Covid-19.
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