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Inequality in acute respiratory infection outcomes in the United States: A review of the literature and its implications for public health policy and practice

Seasonal and pandemic respiratory viruses such as influenza and the novel coronavirus (SARS-COV-2) currently sweeping the globe have often been described as ‘equal opportunity infectors’, implying little socioeconomic disparity in susceptibility. …

Preferential observation of large infectious disease outbreaks leads to consistent overestimation of intervention efficacy

Data from infectious disease outbreaks in congregate settings are often used to elicit clues about which types of interventions may be useful in other facilities. This is commonly done using before-and-after comparisons in which the infectiousness of …

Towards a simulation framework for optimizing infectious disease surveillance: An information theoretic approach for surveillance system design

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 to conduct in silico surveillance system design, and allows the formulation of surveillance guidelines based on quantitative evidence, tailored to local realities and priorities. The approach facilitates greater collaboration between health planners and computational and data scientists to advance surveillance science and strengthen the architecture of surveillance networks.