Aaron A. King

Aaron A. King

University of Michigan

H-index: 40

North America-United States

About Aaron A. King

Aaron A. King, With an exceptional h-index of 40 and a recent h-index of 27 (since 2020), a distinguished researcher at University of Michigan, specializes in the field of theoretical ecology, mathematical ecology, epidemiology, infectious disease.

His recent articles reflect a diverse array of research interests and contributions to the field:

The number and pattern of viral genomic reassortments are not necessarily identifiable from segment trees

Red blood cell dynamics during malaria infection violate the assumptions of mathematical models of infection dynamics

Lesson 4. Iterated filtering: principles and practice

Lesson 8: Case study: Panel data on dynamic variation in sexual contact rates

Bagged filters for partially observed interacting systems

Lesson 5. Case study: Measles in large and small towns

Disentangling the causes of mumps reemergence in the United States

Lesson 1: Introduction to Simulation-based Inference for Epidemiological Dynamics

Aaron A. King Information

University

University of Michigan

Position

N.G. Hairston Professor of Ecology Evolutionary Biology & Complex Systems

Citations(all)

7808

Citations(since 2020)

3506

Cited By

5781

hIndex(all)

40

hIndex(since 2020)

27

i10Index(all)

66

i10Index(since 2020)

53

Email

University Profile Page

University of Michigan

Aaron A. King Skills & Research Interests

theoretical ecology

mathematical ecology

epidemiology

infectious disease

Top articles of Aaron A. King

The number and pattern of viral genomic reassortments are not necessarily identifiable from segment trees

Authors

Qianying Lin,Emma E Goldberg,Thomas Leitner,Carmen Molina-París,Aaron A King,Ethan O Romero-Severson

Journal

Molecular Biology and Evolution

Published Date

2024/4/22

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Because large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the `first-infection …

Red blood cell dynamics during malaria infection violate the assumptions of mathematical models of infection dynamics

Authors

Madeline Amanda Erzen Peters,Aaron A King,Nina Wale

Journal

bioRxiv

Published Date

2024

For decades, mathematical models have been used to understand the course and outcome of malaria infections (ie, infection dynamics) and the evolutionary dynamics of the parasites that cause them. A key conclusion of these models is that red blood cell (RBC) availability is a fundamental driver of infection dynamics and parasite trait evolution. The extent to which this conclusion holds will in part depend on model assumptions about the host-mediated processes that regulate RBC availability ie, removal of uninfected RBCs and supply of RBCs. Diverse mathematical functions have been used to describe host-mediated RBC supply and clearance, but it remains unclear whether they adequately capture the dynamics of RBC supply and clearance during infection. Here, we use a unique dataset, comprising time-series measurements of erythrocyte (ie, mature RBC) and reticulocyte (ie, newly supplied RBC) densities …

Lesson 4. Iterated filtering: principles and practice

Authors

Aaron A King,Edward L Ionides,Qianying Lin

Published Date

2023/8/7

• Expert opinion can be treated as data for non-Bayesian analysis. However, our primary task is to identify the information in the data under investigation, so it can be helpful to use methods that do not force us to make our conclusions dependent on quantification of prior beliefs.

Lesson 8: Case study: Panel data on dynamic variation in sexual contact rates

Authors

Aaron A King,Edward L Ionides

Published Date

2023/8/9

Objectives1. Discuss the use of partially observed Markov process (POMP) methods for panel data, also known as longitudinal data.

Bagged filters for partially observed interacting systems

Authors

Edward L Ionides,Kidus Asfaw,Joonha Park,Aaron A King

Journal

Journal of the American Statistical Association

Published Date

2023/4/3

Bagging (i.e., bootstrap aggregating) involves combining an ensemble of bootstrap estimators. We consider bagging for inference from noisy or incomplete measurements on a collection of interacting stochastic dynamic systems. Each system is called a unit, and each unit is associated with a spatial location. A motivating example arises in epidemiology, where each unit is a city: the majority of transmission occurs within a city, with smaller yet epidemiologically important interactions arising from disease transmission between cities. Monte Carlo filtering methods used for inference on nonlinear non-Gaussian systems can suffer from a curse of dimensionality (COD) as the number of units increases. We introduce bagged filter (BF) methodology which combines an ensemble of Monte Carlo filters, using spatiotemporally localized weights to select successful filters at each unit and time. We obtain conditions under which …

Lesson 5. Case study: Measles in large and small towns

Authors

Aaron A King,Edward L Ionides

Published Date

2023/8/8

Objectives• To display a published case study using plug-and-play methods with non-trivial model complexities.

Disentangling the causes of mumps reemergence in the United States

Authors

Deven V Gokhale,Tobias S Brett,Biao He,Aaron A King,Pejman Rohani

Journal

Proceedings of the National Academy of Sciences

Published Date

2023/1/17

Over the past two decades, multiple countries with high vaccine coverage have experienced resurgent outbreaks of mumps. Worryingly, in these countries, a high proportion of cases have been among those who have completed the recommended vaccination schedule, raising alarm about the effectiveness of existing vaccines. Two putative mechanisms of vaccine failure have been proposed as driving observed trends: 1) gradual waning of vaccine-derived immunity (necessitating additional booster doses) and 2) the introduction of novel viral genotypes capable of evading vaccinal immunity. Focusing on the United States, we conduct statistical likelihood-based hypothesis testing using a mechanistic transmission model on age-structured epidemiological, demographic, and vaccine uptake time series data. We find that the data are most consistent with the waning hypothesis and estimate that 32.8% (32%, 33.5%) of …

Lesson 1: Introduction to Simulation-based Inference for Epidemiological Dynamics

Authors

Aaron A King,Edward L Ionides

Published Date

2023/8/7

• To understand the motivations for simulation-based inference in the study of epidemiological and ecological systems.

Lesson 3: Likelihood-based inference for POMP models

Authors

Aaron A King,Edward L Ionides,Qianying Lin

Published Date

2023/8/7

1. Gain an understanding of the nature of the problem of likelihood computation for POMP models.

Machine Learning for Mechanistic Models of Metapopulation Dynamics

Authors

Jifan Li,Edward L Ionides,Aaron A King,Mercedes Pascual,Ning Ning

Journal

arXiv preprint arXiv:2311.06702

Published Date

2023/11/12

Mathematical models in ecology and epidemiology must be consistent with observed data in order to generate reliable knowledge and sound policy. Metapopulation systems, which consist of a collection of sub-populations at various locations, pose technical challenges in statistical inference due to nonlinear, stochastic interactions. Difficulties encountered in these methodological issues can obstruct the core scientific questions concerning the link between the mathematical models and the data. Progress in statistically efficient simulation-based inference for partially observed stochastic dynamic systems has enabled the development of statistically rigorous approaches to the analysis of nonlinear but low-dimensional systems. Recently, an algorithm has been developed which enables comparable inference for higher-dimensional models arising in metapopulation systems. The COVID-19 pandemic provides a situation where mathematical models and their policy implications were widely visible, and we revisit an influential metapopulation model used to inform basic epidemiological understanding early in the pandemic. Our methods support self-critical data analysis, enabling us to identify and address model limitations, and leading to a new model with substantially improved statistical fit and parameter identifiability. Our results suggest that the lockdown initiated on January 23, 2020 in China was more effective than previously thought. We proceed to recommend statistical analysis standards for future metapopulation system modeling.

Lesson 2: Simulation of stochastic dynamic models

Authors

Aaron A King,Edward L Ionides,Qianying Lin

Published Date

2023/8/7

Objectives This tutorial develops some classes of dynamic models relevant to biological systems, especially for epidemiology.

Modeling the evolution of segment trees reveals deficiencies in current inferential methods for genomic reassortment

Authors

Qianying Lin,Emma E Goldberg,Thomas Leitner,Carmen Molina-París,Aaron A King,Ethan Romero-Severson

Journal

bioRxiv

Published Date

2023

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to new viral variants. Large-scale genome rearrangements, such as reassortment, have the potential to quickly generate new phenotypes, making the understanding of viral reassortment important to both evolutionary biology and public health research. In this paper, we argue that reassortment cannot be reliably inferred from incongruities between segment phylogenies using the established remove-and-rejoin or coalescent approaches. We instead show that reassortment must be considered in the context of a broader population process that includes the dynamics of the infected hosts. Using illustrative examples and simulation we identify four types of evolutionary events that are difficult or impossible to reconstruct with incongruence …

Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics

Authors

Victoria Romeo-Aznar,Laís Picinini Freitas,Oswaldo Gonçalves Cruz,Aaron A King,Mercedes Pascual

Journal

Nature communications

Published Date

2022/2/22

The spread of dengue and other arboviruses constitutes an expanding global health threat. The extensive heterogeneity in population distribution and potential complexity of movement in megacities of low and middle-income countries challenges predictive modeling, even as its importance to disease spread is clearer than ever. Using surveillance data at fine resolution following the emergence of the DENV4 dengue serotype in Rio de Janeiro, we document a pattern in the size of successive epidemics that is invariant to the scale of spatial aggregation. This pattern emerges from the combined effect of herd immunity and seasonal transmission, and is strongly driven by variation in population density at sub-kilometer scales. It is apparent only when the landscape is stratified by population density and not by spatial proximity as has been common practice. Models that exploit this emergent simplicity should afford …

Markov genealogy processes

Authors

Aaron A King,Qianying Lin,Edward L Ionides

Journal

Theoretical population biology

Published Date

2022/2/1

We construct a family of genealogy-valued Markov processes that are induced by a continuous-time Markov population process. We derive exact expressions for the likelihood of a given genealogy conditional on the history of the underlying population process. These lead to a nonlinear filtering equation which can be used to design efficient Monte Carlo inference algorithms. We demonstrate these calculations with several examples. Existing full-information approaches for phylodynamic inference are special cases of the theory.

SIMULATION OF MARKOV PROCESSES

Authors

AARON A KING

Published Date

2022/11/28

We have seen two definitions of the Wiener process. Both imply its key properties: its increments are independent and normally-distributed, with a variance proportional to the time-interval. Less obvious is the important fact that the sample paths of the Wiener process are continuous. The code in Box 1 shows how to simulate a sample path of a Wiener process. Box 2 shows how to accomplish the same thing using pomp.

Package ‘subplex’

Authors

Aaron A King,Maintainer Aaron A King

Published Date

2022/4/12

Subplex is a subspace-searching simplex method for the unconstrained optimization of general multivariate functions. Like the Nelder-Mead simplex method it generalizes, the subplex method is well suited for optimizing noisy objective functions. The number of function evaluations required for convergence typically increases only linearly with the problem size, so for most applications the subplex method is much more efficient than the simplex method.

The impact of infection-derived immunity on disease dynamics

Authors

Adam Le,Aaron A King,Felicia Maria G Magpantay,Afshin Mesbahi,Pejman Rohani

Journal

Journal of Mathematical Biology

Published Date

2021/12

When modeling infectious diseases, it is common to assume that infection-derived immunity is either (1) non-existent or (2) perfect and lifelong. However there are many diseases in which infection-derived immunity is known to be present but imperfect. There are various ways in which infection-derived immunity can fail, which can ultimately impact the probability that an individual be reinfected by the same pathogen, as well as the long-run population-level prevalence of the pathogen. Here we discuss seven different models of imperfect infection-derived immunity, including waning, leaky and all-or-nothing immunity. For each model we derive the probability that an infected individual becomes reinfected during their lifetime, given that the system is at endemic equilibrium. This can be thought of as the impact that each of these infection-derived immunity failures have on reinfection. This measure is useful …

A guide to state–space modeling of ecological time series

Authors

Marie Auger‐Méthé,Ken Newman,Diana Cole,Fanny Empacher,Rowenna Gryba,Aaron A King,Vianey Leos‐Barajas,Joanna Mills Flemming,Anders Nielsen,Giovanni Petris,Len Thomas

Published Date

2021/3/3

State–space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics, animal movement, and capture–recapture data, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological variation and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the …

Partially observed Markov processes with spatial structure via the R package spatPomp

Authors

Kidus Asfaw,Joonha Park,Aaron A King,Edward L Ionides

Journal

arXiv preprint arXiv:2101.01157

Published Date

2021/1/4

We introduce a computational framework for modeling and statistical inference on high-dimensional dynamic systems. Our primary motivation is the investigation of metapopulation dynamics arising from a collection of spatially distributed, interacting biological populations. To make progress on this goal, we embed it in a more general problem: inference for a collection of interacting partially observed nonlinear non-Gaussian stochastic processes. Each process in the collection is called a unit; in the case of spatiotemporal models, the units correspond to distinct spatial locations. The dynamic state for each unit may be discrete or continuous, scalar or vector valued. In metapopulation applications, the state can represent a structured population or the abundances of a collection of species at a single location. We consider models where the collection of states has a Markov property. A sequence of noisy measurements is made on each unit, resulting in a collection of time series. A model of this form is called a spatiotemporal partially observed Markov process (SpatPOMP). The R package spatPomp provides an environment for implementing SpatPOMP models, analyzing data using existing methods, and developing new inference approaches. Our presentation of spatPomp reviews various methodologies in a unifying notational framework. We demonstrate the package on a simple Gaussian system and on a nontrivial epidemiological model for measles transmission within and between cities. We show how to construct user-specified SpatPOMP models within spatPomp.

Choices and trade-offs in inference with infectious disease models

Authors

Sebastian Funk,Aaron A King

Journal

Epidemics

Published Date

2020/3/1

Inference using mathematical models of infectious disease dynamics can be an invaluable tool for the interpretation and analysis of epidemiological data. However, researchers wishing to use this tool are faced with a choice of models and model types, simulation methods, inference methods and software packages. Given the multitude of options, it can be challenging to decide on the best approach. Here, we delineate the choices and trade-offs involved in deciding on an approach for inference, and discuss aspects that might inform this decision. We provide examples of inference with a dataset of influenza cases using the R packages pomp and rbi.

See List of Professors in Aaron A. King University(University of Michigan)

Aaron A. King FAQs

What is Aaron A. King's h-index at University of Michigan?

The h-index of Aaron A. King has been 27 since 2020 and 40 in total.

What are Aaron A. King's top articles?

The articles with the titles of

The number and pattern of viral genomic reassortments are not necessarily identifiable from segment trees

Red blood cell dynamics during malaria infection violate the assumptions of mathematical models of infection dynamics

Lesson 4. Iterated filtering: principles and practice

Lesson 8: Case study: Panel data on dynamic variation in sexual contact rates

Bagged filters for partially observed interacting systems

Lesson 5. Case study: Measles in large and small towns

Disentangling the causes of mumps reemergence in the United States

Lesson 1: Introduction to Simulation-based Inference for Epidemiological Dynamics

...

are the top articles of Aaron A. King at University of Michigan.

What are Aaron A. King's research interests?

The research interests of Aaron A. King are: theoretical ecology, mathematical ecology, epidemiology, infectious disease

What is Aaron A. King's total number of citations?

Aaron A. King has 7,808 citations in total.

What are the co-authors of Aaron A. King?

The co-authors of Aaron A. King are Robert C Reiner, Jr, Ottar Nordal Bjornstad, Mercedes Pascual, Pejman Rohani, Bruce Kendall, Matthew Ferrari.

    Co-Authors

    H-index: 99
    Robert C Reiner, Jr

    Robert C Reiner, Jr

    University of Washington

    H-index: 79
    Ottar Nordal Bjornstad

    Ottar Nordal Bjornstad

    Penn State University

    H-index: 64
    Mercedes Pascual

    Mercedes Pascual

    University of Chicago

    H-index: 60
    Pejman Rohani

    Pejman Rohani

    University of Georgia

    H-index: 47
    Bruce Kendall

    Bruce Kendall

    University of California, Santa Barbara

    H-index: 47
    Matthew Ferrari

    Matthew Ferrari

    Penn State University

    academic-engine

    Useful Links