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Browsing by Author "Johansson, Michael A."

Now showing 1 - 4 of 4
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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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    Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction
    Holcomb, Karen M.; Mathis, Sarabeth; Staples, J. Erin; Fischer, Marc; Barker, Christopher M.; Beard, Charles B.; Nett, Randall J.; Keyel, Alexander C.; Marcantonio, Matteo; Childs, Marissa L.; Gorris, Morgan E.; Rochlin, Ilia; Hamins-Puértolas, Marco; Ray, Evan L.; Uelmen, Johnny A.; DeFelice, Nicholas; Freedman, Andrew S.; Hollingsworth, Brandon D.; Das, Praachi; Osthus, Dave; Humphreys, John M.; Nova, Nicole; Mordecai, Erin A.; Cohnstaedt, Lee W.; Kirk, Devin; Kramer, Laura D.; Harris, Mallory J.; Kain, Morgan P.; Reed, Emily M. X.; Johansson, Michael A. (2023-01-12)
    Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).
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    An open challenge to advance probabilistic forecasting for dengue epidemics
    Johansson, Michael A.; Apfeldorf, Karyn M.; Dobson, Scott; Devita, Jason; Buczak, Anna L.; Baugher, Benjamin; Moniz, Linda J.; Bagley, Thomas; Babin, Steven M.; Guven, Erhan; Yamana, Teresa K.; Shaman, Jeffrey; Moschou, Terry; Lothian, Nick; Lane, Aaron; Osborne, Grant; Jiang, Gao; Brooks, Logan C.; Farrow, David C.; Hyun, Sangwon; Tibshirani, Ryan J.; Rosenfeld, Roni; Lessler, Justin; Reich, Nicholas G.; Cummings, Derek AT T.; Lauer, Stephen A.; Moore, Sean M.; Clapham, Hannah E.; Lowe, Rachel; Bailey, Trevor C.; Garcia-Diez, Markel; Carvalho, Marilia Sa; Rodo, Xavier; Sardar, Tridip; Paul, Richard; Ray, Evan L.; Sakrejda, Krzysztof; Brown, Alexandria C.; Meng, Xi; Osoba, Osonde; Vardavas, Raffaele; Manheim, David; Moore, Melinda; Rao, Dhananjai M.; Porco, Travis C.; Ackley, Sarah; Liu, Fengchen; Worden, Lee; Convertino, Matteo; Liu, Yang; Reddy, Abraham; Ortiz, Eloy; Rivero, Jorge; Brito, Humberto; Juarrero, Alicia; Johnson, Leah R.; Gramacy, Robert B.; Cohen, Jeremy M.; Mordecai, Erin A.; Murdock, Courtney C.; Rohr, Jason R.; Ryan, Sadie J.; Stewart-Ibarra, Anna M.; Weikel, Daniel P.; Jutla, Antarpreet; Khan, Rakibul; Poultney, Marissa; Colwell, Rita R.; Rivera-Garcia, Brenda; Barker, Christopher M.; Bell, Jesse E.; Biggerstaff, Matthew; Swerdlow, David; Mier-y-Teran-Romero, Luis; Forshey, Brett M.; Trtanj, Juli; Asher, Jason; Clay, Matt; Margolis, Harold S.; Hebbeler, Andrew M.; George, Dylan; Chretien, Jean-Paul (National Academy of Sciences, 2019-11-26)
    A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.
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    The Role of Vector Trait Variation in Vector-Borne Disease Dynamics
    Cator, Lauren J.; Johnson, Leah R.; Mordecai, Erin A.; El Moustaid, Fadoua; Smallwood, Thomas R. C.; LaDeau, Shannon L.; Johansson, Michael A.; Hudson, Peter J.; Boots, Michael; Thomas, Matthew B.; Power, Alison G.; Pawar, Samraat (2020-07-10)
    Many important endemic and emerging diseases are transmitted by vectors that are biting arthropods. The functional traits of vectors can affect pathogen transmission rates directly and also through their effect on vector population dynamics. Increasing empirical evidence shows that vector traits vary significantly across individuals, populations, and environmental conditions, and at time scales relevant to disease transmission dynamics. Here, we review empirical evidence for variation in vector traits and how this trait variation is currently incorporated into mathematical models of vector-borne disease transmission. We argue that mechanistically incorporating trait variation into these models, by explicitly capturing its effects on vector fitness and abundance, can improve the reliability of their predictions in a changing world. We provide a conceptual framework for incorporating trait variation into vector-borne disease transmission models, and highlight key empirical and theoretical challenges. This framework provides a means to conceptualize how traits can be incorporated in vector borne disease systems, and identifies key areas in which trait variation can be explored. Determining when and to what extent it is important to incorporate trait variation into vector borne disease models remains an important, outstanding question.