Prophylactic interventions such as vaccine allocation are some of the most effective public health policy planning tools. The supply of vaccines, however, is limited and an important challenge is to optimally allocate the vaccines to minimize epidemic impact. This resource allocation question (which we refer to as VaccIntDesign) has multiple dimensions: when, where, to whom, etc. Most of the existing literature in this topic deals with the latter (to whom), proposing policies that prioritize individuals by age and disease risk. However, since seasonal influenza spread has a typical spatial trend, and due to the temporal constraints enforced by the availability schedule, the when and where problems become equally, if not more, relevant. In this paper, we study the VaccIntDesign problem in the context of seasonal influenza spread in the United States. We develop a national scale metapopulation model for influenza that integrates both short and long distance human mobility, along with realistic data on vaccine uptake. We also design GreedyAlloc, a greedy algorithm for allocating the vaccine supply at the state level under temporal constraints and show that such a strategy improves over the current baseline of pro-rata allocation, and the improvement is more pronounced for higher vaccine efficacy and moderate flu season intensity. Further, the resulting strategy resembles a ring vaccination applied spatiallyacross the US.

In a number of cases, the Quantile Gaussian Process (QGP) has proven effective in emulating stochastic, univariate computer model output (Plumlee and Tuo, 2014). In this paper, we develop an approach that uses this emulation approach within a Bayesian model calibration framework to calibrate an agent-based model of an epidemic. In addition, this approach is extended to handle the multivariate nature of the model output, which gives a time series of the count of infected individuals. The basic modeling approach is adapted from Higdon et al. (2008), using a basis representation to capture the multivariate model output. The approach is motivated with an example taken from the 2015 Ebola Challenge workshop which simulated an ebola epidemic to evaluate methodology. of realizations can be averaged, preprocessed, or accounted for in the estimation scheme for the emulator. In other cases the distribution of computer model outcomes (at a given input setting) can be described by its mean and variance. One strategy is to model the output distribution as normal, conditional on the mean and variance and use a Gaussian process (GP) to model the mean and variance as a function of the inputs (Henderson et al., 2009;Marrel et al., 2012). Adapting similar strategies to non-Gaussian distributions has also been carried out -see Reich et al. (2012) for an example of modeling the exposure distribution as a mixture of normals, using a formulation that depends on space, time, and individual-level covariates.An alternative is the quantile kriging approach described in Plumlee and Tuo (2014). Here quantiles of the output distribution are estimated from the collection of realizations produced at each input setting. A GP is then fit to the augmented input space consisting of the original inputs combined with the quantile estimates. Once fit to the model output, this results in a GP whose realizations give continuous, univariate distributions at each input setting.In this paper we extend the quantile-kriging emulator to handle functional, stochastic computer model output. We also embed this new emulation approach within a Bayesian computer model calibration framework (Kennedy and O'Hagan, 2001;Higdon et al., 2008) to take on a challenging problem in characterizing epidemic behavior, using an ABM that models interaction and contact at the individual level. This statistical modeling is implemented in the software of GPMSA (Gattiker et al., 2016), taking advantage of thoughtful preprocessing of the epidemic observations and the simulation output. The Ebola Challenge ProblemThe 2015 ebola challenge problem (ebola-challenge.org) was organized by the Research and Policy for Infectious Disease Dynamics (RAPIDD) program at the National Institutes for Health (NIH). Its goal is to motivate the development, assessment, and comparison of different approaches for modeling, predicting, and managing infectious disease epidemics.The challenge involved multiple scenarios for the nation of Liberia, each simulating an epidemic patterned after the Ebola outbrea...

Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model’s performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model’s ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.

In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models -providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation of, and results from, various methods.

The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counterfactual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6-9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.