About

Professor Aaron A. King leads the King Laboratory of Theoretical Ecology & Evolution at the University of Michigan. His research program centers on the use of mathematical models to address fundamental questions in ecology and evolutionary biology, particularly focusing on the ecology and evolution of infectious diseases. Recognizing the challenges posed by the long temporal and large spatial scales of ecological systems, which make controlled experimentation difficult and data collection expensive and sparse, Professor King emphasizes the efficient use of data through rigorous confrontation of process-based models with empirical observations. This approach accelerates ecological understanding by making precise, quantitative, and testable predictions about ecological processes. The King Lab employs sophisticated mathematical, computational, and statistical tools to formalize scientific hypotheses as mathematical models, enabling powerful inference about mechanisms operating in host-pathogen interactions and their evolutionary dynamics. The lab supports graduate students from diverse quantitative disciplines who share a strong interest in developing and testing theory for real ecological systems.

Research topics

• Artificial Intelligence
• Mathematics
• Geography
• Ecology
• Political Science
• Computer Science
• Sociology
• Machine Learning
• Statistics
• Criminology
• Biology
• Virology
• Data science
• Cartography
• Demography
• Econometrics
• Law
• Engineering

Selected publications

• Depression in Older Adults with Mobility Issues

  ScholarWorks - MoreheadState (Morehead State University) · 2026-04-15

  article1st authorCorresponding

  https://scholarworks.moreheadstate.edu/celebration_posters_2026/1043/thumbnail.jpg

  Publisher

• panelPomp: Analysis of Panel Data via Partially Observed Markov Processes in R

  The R Journal · 2025-08-07

  preprintOpen access

  Panel data arise when time series measurements are collected from multiple, dynamically independent but structurally related systems. Each system's time series can be modeled as a partially observed Markov process (POMP), and the ensemble of these models is called a PanelPOMP. If the time series are relatively short, statistical inference for each time series must draw information from across the entire panel. The component systems in the panel are called units; model parameters may be shared between units or may be unit-specific. Differences between units may be of direct inferential interest or may be a nuisance for studying the commonalities. The R package panelPomp supports analysis of panel data via a general class of PanelPOMP models. This includes a suite of tools for manipulation of models and data that take advantage of the panel structure. The panelPomp package currently highlights recent advances enabling likelihood based inference via simulation based algorithms. However, the general framework provided by panelPomp supports development of additional, new inference methodology for panel data.

  PublisherOA PDFDOI

• P-312. Elimination of barrier contact precautions for Vancomycin-Resistant <i>Enterococcus</i> (VRE)-colonized patients was not associated with the acquisition of VRE in a tertiary care center

  Open Forum Infectious Diseases · 2025-01-29

  articleOpen access

  Abstract Background In April 2020, the University of Michigan discontinued barrier contact precautions for Vancomycin-Resistant Enterococcus (VRE) colonized patients while maintaining VRE surveillance. We conducted a retrospective cohort study to assess the impact of this policy change on the risk of VRE acquisition Inclusion and Exclusion Criteria of the Cohort Methods We analyzed patients admitted between 1/2018 and 3/2021 who underwent weekly surveillance. The primary outcome was VRE-free survival; the main exposure was admission before or after the contact precaution policy. Demographics, Elixhauser comorbidity index, antibiotic and PPI exposure, and colonization pressure were incorporated as covariates in an extended Cox proportional hazard model of VRE-free survival. Time-varying covariates were accounted for and modeled using log-transformed time. Patients were censored at discharge or death. VRE-free survival before and after change in contact precautions policy Change in contact precaution policy was not associated with an increased hazard of VRE-acquisition in single variable analysis Results We identified 2,221 patients admitted before the policy change and 1,830 after, with 225 cases of VRE acquisition before and 196 cases after. No significant differences in age, demographics, antibiotic use, or PPI exposure were observed among VRE patients admitted before and after the policy change. However, VRE patients admitted before the policy change had more medical comorbidities (mean Elixhauser: 23.64 before, 20.77 after; p=0.008) and lower exposure to colonization pressure (mean VRE patient days: 13.4 before, 29.9 after; p=0.019). In a multivariable extended Cox proportional hazard model, admission before or after the change in contact precaution policy showed no significant association with VRE-free survival (HR 0.81-1.1), or infection with VRE (HR 1.01, 95% CI 0.92-1.21). The most significant risk factors for VRE acquisition were vancomycin treatment (HR 1.60, 95% CI 1.25-2.03, p&amp;lt; 0.001) and PPI treatment (HR 1.43, 95% CI 1.15-1.77, p=0.001). Multivariable cox-regression model of risk factors for VRE acquisition There was no association between the change in contact precautions policy and risk of VRE acquisition Conclusion The policy change to eliminate isolation and barrier precaution requirements for VRE patients was associated with no detectible increase in the hazard of VRE acquisition. The most significant risk factors for VRE acquisition were vancomycin and PPI treatment. Our results suggest that appropriate use of PPI and antimicrobial stewardship may have a greater impact on preventing the spread and acquisition of VRE than barrier precautions. Disclosures Laraine Washer, MD, Ferring Pharmaceuticals: Advisor/Consultant

  PublisherDOI

• Effect of glucagon-like peptide-1 receptor agonists on vascular risk factors among adults with type 2 diabetes and established atherosclerotic cardiovascular disease

  American Journal of Preventive Cardiology · 2025-01-08 · 4 citations

  articleOpen access1st author

  Limited data exist on the cardiovascular effectiveness of once-weekly (OW) glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in real-world practice. We assessed the OW GLP-1 RA effects on vascular risk factors in adults with type 2 diabetes and atherosclerotic cardiovascular disease using data from a large-scale US electronic health record database (index date = first prescription of OW GLP-1 RA). Exploratory analyses were performed on patients newly initiating OW GLP-1 RAs (including and excluding semaglutide), and semaglutide. Changes in vascular risk factors were evaluated by comparing mean measures between the 12-month pre- and post-index periods. Analyses were conducted for all three cohorts and subpopulations including stratified by tercile of baseline vascular risk factor value. In the final cohorts (1. OW GLP-1 RA including semaglutide: n = 20,084; 2. OW GLP-1 RA excluding semaglutide: n = 16,894; 3. semaglutide: n = 3,435), significant mean reductions ( P < 0.001) were observed from baseline to post-index in hemoglobin A1c (%, 1] -1.1; 2] -1.1; 3] -1.2), low-density lipoprotein cholesterol (mg/dL, 1] -6.4; 2] -6.4; 3] -6.9), total cholesterol (mg/dL, 1] -11.0; 2] -11.1; 3] -10.7), triglycerides (mg/dL, 1] -31.8; 2] -31.4; 3] -33.1), systolic blood pressure (mmHg, 1] -1.5; 2] -1.2; 3] -3.1), body weight (kg, 1] -2.7; 2] -2.4; 3] -4.3) and body mass index (kg/m 2 ; 1] -0.9; 2] -0.8; 3] -1.4). Largest reductions were observed in the top tercile. Our data suggest GLP-1 RAs are associated with significant reductions in key vascular risk factors in real-world practice.

  PublisherDOI

• 732-P: Real-World Impact of Oral Semaglutide (SEMA) vs. DPP-4 Inhibitors on Weight, BMI, and HbA1c Outcomes in Type 2 Diabetes (T2D)—An Observational Study (PAUSE)

  Diabetes · 2025-06-13

  articleSenior author

  Introduction and Objective: To assess the real-world effectiveness of oral sema on weight, BMI, and HbA1c outcomes among adults with T2D in the US, and compare it with DPP-4is. Methods: This observational study of adults with uncontrolled T2D (HbA1c ≥7%) initiating oral sema or DPP-4is (Oct 2019-Apr 2023; first prescription = index) used linked data from IQVIA PharMetrics® Plus adjudicated claims and Ambulatory Electronic Medical Records databases. Changes in weight, BMI and HbA1c outcomes from baseline to 6 months post-index were assessed for oral sema overall and in three subgroups: persistent (≤60-day gap in treatment), receiving dose ≥7 mg, and both persistent and receiving dose ≥7 mg. Furthermore, after inverse probability of treatment weighting (IPTW) for oral sema vs DPP-4is, a logistic regression model was used to evaluate adjusted outcomes. Results: In the overall oral sema sample (n=378) and subgroups, a significant mean reduction from baseline was seen in all outcomes (Figure). Post-IPTW, oral sema (n=259) was associated with significantly higher odds vs DPP-4is (n=486) in achieving composite post-index HbA1c &amp;lt;7% and ≥5% weight loss (odds ratio = 3.08; p&amp;lt;0.0001). Conclusion: Adults with T2D initiating oral sema had significant decreases in weight, BMI and HbA1c after 6 months, and higher odds of achieving the composite target vs DPP-4is. Disclosure J. Amamoo: Employee; Novo Nordisk. R.P. Doshi: None. J. Noone: Employee; Novo Nordisk. L. Xie: None. M. Guevarra: Employee; Novo Nordisk. V. Divino: Employee; IQVIA Inc. J. Chen: None. A.A. King: None. Funding Novo Nordisk Inc.

  PublisherDOI

• EXACT PHYLODYNAMIC LIKELIHOOD VIA STRUCTURED MARKOV GENEALOGY PROCESSES.

  PubMed · 2025-07-08

  preprintOpen access1st authorCorresponding

  We show that each member of a broad class of Markovian population models induces a unique stochastic process on the space of genealogies. We construct this genealogy process and derive exact expressions for the likelihood of an observed genealogy in terms of a filter equation, the structure of which is completely determined by the population model. We show that existing phylodynamic methods based on either the coalescent or the linear birth-death processes are special cases. We derive some properties of filter equations and describe a class of algorithms that can be used to numerically solve them. Our results open the door to statistically efficient likelihood-based phylodynamic inference for a much wider class of models than is currently possible.

  PublisherOA PDF

• Once-Weekly Semaglutide Versus Sodium-Glucose Co-transporter 2 Inhibitors: Real-World Impact on Weight, HbA1c, and Healthcare Resource Utilization in Type 2 Diabetes (PAUSE)

  Diabetes Therapy · 2025-03-27 · 1 citations

  articleOpen accessSenior author

  Clinical trials have demonstrated greater glycemic control and weight loss with once-weekly (OW) semaglutide versus other anti-diabetes medications, including sodium-glucose co-transporter 2 inhibitors (SGLT2is) in adults with type 2 diabetes (T2D), yet real-world evidence is limited. This observational study of adults with uncontrolled T2D (HbA1c ≥ 7.0%) initiating semaglutide OW or SGLT2is (January 2018–February 2022; first prescription = index) utilized linked data from IQVIA PharMetrics® Plus adjudicated claims and Ambulatory Electronic Medical Records databases. Among the all semaglutide OW cohort and subgroups (1: persistent [≤ 60-day gap in semaglutide OW supply]; 2: receiving maximum dose ≥ 1 mg; and 3: persistent and ≥ 1 mg dose), changes in weight, body mass index (BMI), and glycated hemoglobin (HbA1c) outcomes from baseline to 1 year post index were descriptively compared. For the main analysis, changes in weight, BMI, HbA1c, and all-cause healthcare resource utilization (HCRU) after 1 year were compared among adjusted semaglutide OW and comparator SGLT2i cohorts, following inverse probability of treatment weighting (IPTW). The all semaglutide OW cohort included 772 patients, and IPTW adjusted cohorts included 416 semaglutide OW patients and 1093 SGLT2i patients. Significant (P < 0.0001) mean changes from baseline were observed in the all semaglutide OW cohort and all subgroups, in weight (kg [all: − 4.4; 1: − 5.0; 2: − 4.9; 3: − 5.2]), BMI (kg/m2 [all: − 1.5; 1: − 1.8; 2: − 1.8; 3: − 1.9]), and HbA1c (% [all: − 1.5; 1: − 1.7; 2: − 1.5; 3: − 1.6]). Post-IPTW adjustment, the semaglutide OW cohort had significantly greater mean reductions versus the SGLT2i cohort in weight (− 4.4 versus − 3.4 kg, P = 0.0061), BMI (− 1.5 versus − 1.1 kg/m2, P = 0.0013), and HbA1c (− 1.6 versus − 1.2%, P < 0.0001), with similar all-cause HCRU. Adults with T2D initiating semaglutide OW in the real-world had significant decreases in weight, BMI, and HbA1c after 1 year, with greater improvements versus SGLT2i, and similar HCRU.

  PublisherOA PDFDOI

• Mechanistic models for panel data: Analysis of ecological experiments with four interacting species

  ArXiv.org · 2025-06-04

  preprintOpen access

  In an ecological context, panel data arise when time series measurements are made on a collection of ecological processes. Each process may correspond to a spatial location for field data, or to an experimental ecosystem in a designed experiment. Statistical models for ecological panel data should capture the high levels of nonlinearity, stochasticity, and measurement uncertainty inherent in ecological systems. Furthermore, the system dynamics may depend on unobservable variables. This study applies iterated particle filtering techniques to explore new possibilities for likelihood-based statistical analysis of these complex systems. We analyze data from a mesocosm experiment in which two species of the freshwater planktonic crustacean genus, Daphnia, coexist with an alga and a fungal parasite. Time series data were collected on replicated mesocosms under six treatment conditions. Iterated filtering enables maximization of the likelihood for scientifically motivated nonlinear partially observed Markov process models, providing access to standard likelihood-based methods for parameter estimation, confidence intervals, hypothesis testing, model selection and diagnostics. This toolbox allows scientists to propose and evaluate scientifically motivated stochastic dynamic models for panel data, constrained only by the requirement to write code to simulate from the model and to specify a measurement distribution describing how the system state is observed.

  PublisherOA PDFDOI

• 1884-LB: Real-World Impact of Once-Weekly Injectable Semaglutide (sema OW) vs. Sodium–Glucose Cotransporter 2 Inhibitors (SGLT2i) on HbA1c, Weight, and Health Care Resource Utilization (HCRU) Outcomes in Type 2 Diabetes (T2D)—An Observational Study (PAUSE)

  Diabetes · 2024-06-14

  articleSenior author

  Introduction &amp; Objective: This study aimed to assess the real-world impact of sema OW vs SGLT2i on HbA1c, weight and HCRU outcomes among patients (pts) with T2D in the US. Methods: This observational study of adults with uncontrolled T2D (HbA1c ≥7%) initiating sema OW or SGLT2i (Jan 2018—Feb 2022; first prescription = index) used data from IQVIA PharMetrics® Plus and Ambulatory Electronic Medical Records databases. Changes in HbA1c, weight, BMI and all-cause medical HCRU outcomes after 1 year were compared among inverse probability of treatment weighting (IPTW) adjusted cohorts. Results: Post-IPTW (sema OW: n=416; SGLT2i: n=1093) a significantly higher proportion of pts in the sema OW cohort had combined reductions in HbA1c/weight outcomes vs SGLT2i (Figure). The sema OW cohort had significantly greater mean decreases in HbA1c (-1.6 vs -1.2%, p&amp;lt;0.0001), weight (-4.4 vs -3.4 kg, p&amp;lt;0.0001) and BMI (-1.5 vs -1.1 kg/m2, p=0.013) vs SGLT2i. Over 1-yr follow-up, all-cause HCRU was similar between both cohorts (≥1 hospitalization: 6.0 vs 5.7%, p=0.8190; ≥1 ER visit: 15.2 vs 17.6%, p=0.2658; ≥1 physician office visit: 99.7 vs 99.1%, p=0.2134). Conclusion: Sema OW was associated with greater improvements in HbA1c and weight vs SGLT2i, but with similar HCRU after 1 year. Disclosure J. Amamoo: None. R.P. Doshi: None. J. Noone: Employee; Novo Nordisk. L. Xie: None. C.L. Gamble: Employee; Novo Nordisk. M. Guevarra: Stock/Shareholder; Novo Nordisk. V. Divino: Employee; IQVIA Inc. J. Chen: None. A.A. King: Speaker's Bureau; Abbott, MannKind Corporation, Novo Nordisk. Advisory Panel; Novo Nordisk. Speaker's Bureau; Lilly Diabetes. Advisory Panel; Lilly Diabetes. Speaker's Bureau; Dexcom, Inc. Advisory Panel; Dexcom, Inc. Speaker's Bureau; Astellas Pharma Inc. Funding Novo Nordisk

  PublisherDOI

• 857-P: Real-World Impact of Once-Weekly Injectable Semaglutide on Weight, BMI, and HbA1c Outcomes in Type 2 Diabetes—An Observational Study (PAUSE)

  Diabetes · 2024-06-14

  articleSenior author

  Introduction &amp; Objective: This study aimed to determine the real-world effectiveness of injectable semaglutide once weekly (sema OW) on weight and HbA1c outcomes among people with type 2 diabetes (T2D) in the US. Methods: This observational cohort study involved adults with uncontrolled T2D (HbA1c ≥7%) initiating sema OW from January 2018—February 2022 (first prescription = index) using IQVIA PharMetrics® Plus and Ambulatory Electronic Medical Records databases. Change in weight, BMI and HbA1c outcomes from baseline to 1-year post-index were descriptively compared overall, as well as for subgroups of those persistent (≤60-day gap in treatment, subgroup 1), those receiving maintenance dose ≥1 mg (subgroup 2), and those who were persistent and receiving dose ≥1 mg (subgroup 3). Results: Among the final sample (n=772) and all subgroups, a significant mean change from baseline was seen in weight, BMI and HbA1c (all p&amp;lt;0.0001; Fig.). Mean changes in weight and BMI were numerically greatest in subgroup 3 (Fig.). The proportion of patients with obesity (BMI ≥30 kg/m2) significantly decreased from 84.0% at baseline to 77.1% post-index (p&amp;lt;0.0001). Conclusion: Adults with T2D initiating sema OW had statistically significant decreases in weight, BMI and HbA1c after 1 year, with better weight outcomes in patients persistent to treatment and receiving maintenance dose ≥1 mg. Disclosure J. Amamoo: None. R.P. Doshi: None. J. Noone: Employee; Novo Nordisk. L. Xie: None. C.L. Gamble: Employee; Novo Nordisk. M. Guevarra: Stock/Shareholder; Novo Nordisk. V. Divino: Employee; IQVIA Inc. J. Chen: None. A.A. King: Speaker's Bureau; Abbott, MannKind Corporation, Novo Nordisk. Advisory Panel; Novo Nordisk. Speaker's Bureau; Lilly Diabetes. Advisory Panel; Lilly Diabetes. Speaker's Bureau; Dexcom, Inc. Advisory Panel; Dexcom, Inc. Speaker's Bureau; Astellas Pharma Inc. Funding Novo Nordisk

  PublisherDOI

Recent grants

• Collaborative Research: A Direct Modeling Approach for Phylogenetic Comparative Analysis

  NSF · $144k · 2005–2010

• Integrating immunology, epidemiology, and evolution to understand and control per

  NIH · $1.7M · 2012–2019

Frequent coauthors

• Pejman Rohani

  Children's Medical Center

  128 shared

• Edward L. Ionides

  University of Michigan–Ann Arbor

  64 shared

• Mercedes Pascual

  New York University

  29 shared

• Matthieu Domenech de Cellès

  24 shared

• Ottar N. Bjørnstad

  Pennsylvania State University

  23 shared

• Carles Bretó

  22 shared

• William M. Schaffer

  18 shared

• F. M. G. Magpantay

  17 shared

Education

• Ph.D., Program in Applied Mathematics

  University of Arizona

  1998

• M.A., Mathematics

  University of Hawai'i

  1992

• B.A., summa cum laude, Mathematics

  Rice University

  1989