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Burkitt lymphoma risk shows geographic and temporal associations with Plasmodium falciparum infections in Uganda, Tanzania, and Kenya
Endemic Burkitt lymphoma (eBL) is a pediatric cancer coendemic with malaria in sub-Saharan Africa, suggesting an etiological link …
DOI Online Access
COVID-19 vaccination and mask wearing behaviors in the United States, August 2020 - June 2021

Background: During the rollout of COVID-19 vaccination, many states relaxed mask wearing requirements for those who were vaccinated. …

DOI Online Access
Modeling rates of disease with missing categorical data
Covariates like age, sex, and race/ethnicity provide invaluable insight to public health authorities trying to interpret surveillance …
DOI Online Access
Rapid Response Modeling of SARS-CoV-2 Transmission
The COVID-19 pandemic has cemented the role of mechanistic infectious disease models as drivers of the scientific, public, and policy …
DOI Online Access
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 …
DOI Online Access
A guide to backward paper writing for the data sciences
In this perspective, we outline a set of best practices for the planning, writing, and revision of scientific papers and other forms of …
DOI Online Access
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 …

DOI Online Access
Has the relationship between wealth and HIV risk in Sub-Saharan Africa changed over time? A temporal, gendered and hierarchical analysis
This study examines the relationship between wealth and HIV infection in Sub-Saharan Africa to determine whether and how this …
DOI Online Access
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.
DOI Online Access
Pneumonia Following Symptomatic Influenza Infection Among Nicaraguan Children Before and After Introduction of the Pneumococcal Conjugate Vaccine
Influenza is associated with primary viral and secondary bacterial pneumonias; however, the dynamics of this relationship in …
DOI Online Access