Estimating the maximum risk of measles outbreaks due to heterogeneous fall in immunization rates
Wu, N.; Moon, S. A.; Falk, A.; Marathe, A.; Vullikanti, A.
Show abstract
Immunization rates for childhoold vaccines, such as MMR, have seen a reduction over the recent years; this fall has only been accentuated after the COVID-19 pandemic. However, there is limited data on where the rates have reduced, and prior work has shown that heterogeneity in the drop in immunization rates has a significant impact on the risk of an outbreak. An important question from a public health perspective is: what is the maximum size of an outbreak in a region, when limited information is available on the fall in immunization rates within the region? This turns out to be a very hard computational problem. We develop a Bayesian optimization based approach for estimating the maximum outbreak size, and use it on a measles model for the state of Virginia. Our results show that the maximum outbreak size is several orders of magnitude higher than estimated in a baseline which assumes homogeneous fall. Even for a 5% reduction in the statewide immunzation rate, the expected outbreak size can be very high. The maximum outbreak size depends crucially on the importation location, i.e., where the disease starts, and importation in an urban region leads to a significantly higher outbreak. The outbreak size remains high even if the drop in immunization is bounded in health service areas in the state.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Estimating the impact of interventions against COVID-19: from lockdown to vaccination 97%
- Reopening California : Seeking Robust, Non-Dominated COVID-19 Exit Strategies 97%
- Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts 96%
Similar papers in this journal
Similar papers in this journal
- Bayesian model discrimination for partially-observed epidemic models 96%
- Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution 95%
- COVID-19 optimal vaccination policies: a modeling study on efficacy, natural and vaccine-induced immunity responses 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.