Browsing by Author "Cummings, Derek A. T."

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    Enhancing disease surveillance with novel data streams: challenges and opportunities
    Althouse, Benjamin M.; Scarpino, Samuel V.; Meyers, Lauren Ancel; Ayers, John W.; Bargsten, Marisa; Baumbach, Joan; Brownstein, John S.; Castro, Lauren; Clapham, Hannah; Cummings, Derek A. T.; Del Valle, Sara; Eubank, Stephen; Fairchild, Geoffrey; Finelli, Lyn; Generous, Nicholas; George, Dylan; Harper, David R.; Hebert-Dufresne, Laurent; Johansson, Michael A.; Konty, Kevin; Lipsitch, Marc; Millinovich, Gabriel; Miller, Joseph D.; Nsoesie, Elaine O.; Olson, Donald R.; Paul, Michael; Priedhorsky, Reid; Read, Jonathan M.; Rodriguez-Barraquer, Isabel; Smith, Derek J.; Stefansen, Christian; Swerdlow, David L.; Thompson, Deborah; Vespignani, Alessandro; Wesolowski, Amy; Polgreen, Philip M. (Springer, 2015)
    Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.
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    Modeling targeted layered containment of an influenza pandemic in the United States
    Halloran, Elizabeth M.; Ferguson, Neil M.; Eubank, Stephen; Longini, Ira M. Jr.; Cummings, Derek A. T.; Lewis, Bryan L.; Xu, Shufu; Fraser, Christophe; Vullikanti, Anil; Germann, Timothy C.; Wagener, Diane; Beckman, Richard J.; Kadau, Kai; Barrett, Christopher L.; Macken, Catherine A.; Burke, Donald S.; Cooley, Philip (NAS, 2008-03-25)
    Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with 8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.