COVID-19 Learning Modules

COVID-19 Learning Modules are learning experiences that use dynamic animations and modeling to educate users about the spread of infectious disease.

Michigan COVID-19 Mapping

COVIDMAPPING is a web tool that puts Michigan COVID-19 data in its spatial context.

Our Publications

A detailed overview of individual publications can be found on our publications page. Check the list below for direct links to publications. 2022 There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk A guide to backward paper writing for the data sciences 2021 Measuring the impact of spatial perturbations on the relationship between data privacy and validity of descriptive statistics Evaluating Michigan’s administrative rule change on nonmedical vaccine exemptions Pneumonia following symptomatic influenza infection among Nicaraguan children before and after introduction of the pneumococcal conjugate vaccine Accounting for uncertainty during a pandemic Measuring Multiple Dimensions and Indices of Nonvaccination Clustering in Michigan, 2008–2018 Has the relationship between wealth and HIV risk in Sub-Saharan Africa changed over time?

There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk

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-pharmaceutical interventions. Such models have played a pivotal role in the …

Evaluating Michigan’s Administrative Rule Change on Nonmedical Vaccine Exemptions

OBJECTIVES: Vaccine hesitancy is a growing threat to health in the United States. Facing the fourth highest vaccine exemption rate in the United States in 2014, Michigan changed its state Administrative Rules, effective January 1, 2015, requiring …

The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures

Disease surveillance systems are essential for understanding the epidemiology of infectious diseases and improving population health. A well-designed surveillance system can achieve a high level of fidelity in estimates of interest (e.g., disease trends, risk factors) within its operational constraints. Currently, design parameters that define surveillance systems (e.g., number and placement of the surveillance sites, target populations, case definitions) are selected largely by expert opinion and practical considerations. Such an informal approach is less tenable when multiple aspects of surveillance design—or multiple surveillance objectives—need to be considered simultaneously, and are subject to resource or logistical constraints. Here we propose a framework to optimize surveillance system design given a set of defined surveillance objectives and a dynamical model of the disease system under study. The framework provides a platform for in silico surveillance system design, and allows the formulation of surveillance guidelines based on quantitative evidence, tailored to local realities and priorities. The framework is designed to facilitate greater collaboration between health planners and computational and data scientists to advance surveillance science and strengthen the architecture of surveillance networks.

Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns

Screening household members of newly detected tuberculosis cases is an efficient method for finding previously undiagnosed cases in high-burden settings. Despite the intuitive appeal of this approach, randomized trials examining the population-level effects of these interventions in settings with sustained community transmission have shown mixed results. One explanation for these inconclusive findings is that household transmission is responsible for a varying proportion of overall tuberculosis burden between locations, with the impact of household transmission being a function of both the overall incidence and the relative intensity of disease-transmitting contacts in the community and the household. In this manuscript, we use an individual-based network model to explore how local incidence levels and patterns of community contact impact the effectiveness of household-based approaches for interrupting tuberculosis transmission. Our analyses suggest that protective benefits of household-based interventions are maintained across a wide range of epidemiological settings. Our findings provide evidence for the robustness of household-based interventions and suggest that variable results from trials may be primarily due to implementation challenges rather than inherent limitations of these interventions.

Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data

The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic …

Children as sentinels of tuberculosis transmission: disease mapping of programmatic data

Background: Identifying hotspots of tuberculosis transmission can inform spatially targeted active case-finding interventions. While national tuberculosis programs maintain notification registers which represent a potential source of data to …

Social patterning of acute respiratory illnesses in the Household Influenza Vaccine Evaluation (HIVE) Study 2014–2015

Social patterning of infectious diseases is increasingly recognised. Previous studies of social determinants of acute respiratory illness (ARI) have found that highly educated and lower income families experience more illnesses. Subjective social …