CoLabo at 2: looking for a postdoc to work on genetic-epi models using data from a real-life outbreak simulator

Developing a realistic “real-life” outbreak simulator

Operation Outbreak started as an experiential learning activity and classroom module on public health and epidemiology for middle and high schools we created back in 2016 with collaborators from the Broad Institute and the Inspire Project. The OO learning activity consists of an outbreak simulation where students try to stop the outbreak by playing roles as general population, health responders, epidemiologists, and government. This simulation is enabled by a smartphone app that uses Bluetooth to spread the virtual pathogen among the participants in the simulation. We developed the first version the OO app in early 2018, tested it with great success with middle school students at the time, and piloted at many different settings since then (read this recent article on Harvard Health Policy Review for more details about the history of the project.)

Video of OO presentation at AI4PAN

Validating genetic-epi models with synthetic ground-truth data

Understanding transmission dynamics is essential for control of emerging infectious diseases, yet it is difficult to observe the complete transmission process due to individual heterogeneity and inadequate sampling methods. During the pandemic, attempts to infer transmission dynamics using epidemiological and genetic data have relied on incomplete records of information. At the same time, computational advances could make it possible to apply increasingly rigorous statistical frameworks to reconstruct the transmission tree of an outbreak. Contact tracing data with high accuracy and precision is valuable to characterize the performance of these new inference methods, as well as for estimating routes of transmission, analyzing social determinants of disease transmission, and designing new mitigation measures. Attempts to collect high-resolution contact data have been limited by small sample size, low-resolution distance estimation technology, or both.

Diagram describing the stages in our proposed modeling platform: live simulations, generation of ground-truth data, construction of predictive models, validation of models during the live simulations.

From the microbiology of host-pathogen interaction to population-level epidemiological dynamics

We also believe that our outbreak simulator also give us a framework incorporate molecular-level features that characterize the interaction between pathogen and host cells as inputs into the models that predict individual phenotypes of the disease, such as viral load kinetics. The predictions of these models could be used to drive the dynamics of the live outbreak, for example, by determining infection and recovery rates.

Each “player” in the OO simulation (1a) will be assigned an individual-level model of viral kinetics (1b), which will determine their infectivity during the outbreak. The transmission network (2), generated in real-time during the simulation will result in a time tree (3) that will be used to calculate coalescence times in the intra-host models of sequence evolution (4–5).

Join us at CoLabo!

I hope you enjoyed reading our ideas and plans regarding OO and using this novel tool to generate better epidemiological datasets and models. When not developing apps or running simulations, we like to take occasional day trips to enjoy nature or city attractions:

Members of CoLabo at the New England aquarium, earlier in the Spring

With ❤️ from the CoLabo team!

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CoLabo

CoLabo

Colubri Lab at the University of Massachusetts Medical School