37 citations · 44 across the 7 of their papers we have counts for
9 papers
Stochastic Epidemic Model Criticism with Neural Evidence Estimation
Prayag Chatha, Fan Bu, Jon Zelner
Neural techniques for simulation-based inference (SBI) have become popular in scientific domains featuring complex simulation models. These methods train neural networks on simulat…
Neural Posterior Estimation for Stochastic Epidemic Modeling
Prayag Chatha, Fan Bu, Jeffrey Regier +2
Stochastic infectious disease models capture uncertainty in public health outcomes and have become increasingly popular in epidemiological practice. However, calibrating these mode…
Bayesian Methods for Modeling Cumulative Exposure to Extensive Environmental Health Hazards
Rob Trangucci, Jesse Contreras, Jon Zelner +2
Measuring the impact of an environmental point source exposure on the risk of disease, like cancer or childhood asthma, is well-developed. Modeling how an environmental health haza…
Vaccine efficacy for binary post-infection outcomes under misclassification without monotonicity
Rob Trangucci, Yang Chen, Jon Zelner
In order to meet regulatory approval, pharmaceutical companies often must demonstrate that new vaccines reduce the total risk of a post-infection outcome like transmission, symptom…
Modeling racial/ethnic differences in COVID-19 incidence with covariates subject to non-random missingness
Rob Trangucci, Yang Chen, Jon Zelner
Characterizing the cumulative burden of COVID-19 by race/ethnicity is of the utmost importance for public health researchers and policy makers in order to design effective mitigati…
There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk
Jon Zelner, Nina B. Masters, Ramya Naraharisetti +2
Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform non-pharma…