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Correction for “An open challenge to advance probabilistic forecasting for dengue epidemics,” by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116 (Proc. Natl. Acad. Sci. U.S.A. 116, 24268–24274). The authors note that the following statement should be added to the Acknowledgments: “E.A.M. was supported by NIH Grant R35GM133439.

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.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a large risk to healthcare personnel (HCP). Quantifying the risk of coronavirus infection associated with workplace activities is an urgent need. Methods We assessed the association of worker characteristics, occupational roles and behaviors, and participation in procedures with the risk of endemic coronavirus infection among HCP who participated in the Respiratory Protection Effectiveness Clinical Trial (ResPECT), a cluster randomized trial to assess personal protective equipment to prevent respiratory infections and illness conducted from 2011 to 2016. Results Among 4689 HCP seasons, we detected coronavirus infection in 387 (8%). HCP who participated in an aerosol-generating procedure (AGP) at least once during the viral respiratory season were 105% (95% confidence interval, 21%–240%) more likely to be diagnosed with a laboratory-confirmed coronavirus infection. Younger individuals, those who saw pediatric patients, and those with household members <5 years of age were at increased risk of coronavirus infection. Conclusions Our analysis suggests that the risk of HCP becoming infected with an endemic coronavirus increases approximately 2-fold with exposures to AGPs. Our findings may be relevant to the coronavirus disease 2019 (COVID-19) pandemic; however, SARS-CoV-2, the virus that causes COVID-19, may differ from endemic coronaviruses in important ways. Clinical Trials Registration NCT01249625. [ABSTRACT FROM AUTHOR]

Importance: Clinical studies have been inconclusive about the effectiveness of N95 respirators and medical masks in preventing health care personnel (HCP) from acquiring workplace viral respiratory infections.Objective: To compare the effect of N95 respirators vs medical masks for prevention of influenza and other viral respiratory infections among HCP.Design, Setting, and Participants: A cluster randomized pragmatic effectiveness study conducted at 137 outpatient study sites at 7 US medical centers between September 2011 and May 2015, with final follow-up in June 2016. Each year for 4 years, during the 12-week period of peak viral respiratory illness, pairs of outpatient sites (clusters) within each center were matched and randomly assigned to the N95 respirator or medical mask groups.Interventions: Overall, 1993 participants in 189 clusters were randomly assigned to wear N95 respirators (2512 HCP-seasons of observation) and 2058 in 191 clusters were randomly assigned to wear medical masks (2668 HCP-seasons) when near patients with respiratory illness.Main Outcomes and Measures: The primary outcome was the incidence of laboratory-confirmed influenza. Secondary outcomes included incidence of acute respiratory illness, laboratory-detected respiratory infections, laboratory-confirmed respiratory illness, and influenzalike illness. Adherence to interventions was assessed.Results: Among 2862 randomized participants (mean [SD] age, 43 [11.5] years; 2369 [82.8%]) women), 2371 completed the study and accounted for 5180 HCP-seasons. There were 207 laboratory-confirmed influenza infection events (8.2% of HCP-seasons) in the N95 respirator group and 193 (7.2% of HCP-seasons) in the medical mask group (difference, 1.0%, [95% CI, -0.5% to 2.5%]; P = .18) (adjusted odds ratio [OR], 1.18 [95% CI, 0.95-1.45]). There were 1556 acute respiratory illness events in the respirator group vs 1711 in the mask group (difference, -21.9 per 1000 HCP-seasons [95% CI, -48.2 to 4.4]; P = .10); 679 laboratory-detected respiratory infections in the respirator group vs 745 in the mask group (difference, -8.9 per 1000 HCP-seasons, [95% CI, -33.3 to 15.4]; P = .47); 371 laboratory-confirmed respiratory illness events in the respirator group vs 417 in the mask group (difference, -8.6 per 1000 HCP-seasons [95% CI, -28.2 to 10.9]; P = .39); and 128 influenzalike illness events in the respirator group vs 166 in the mask group (difference, -11.3 per 1000 HCP-seasons [95% CI, -23.8 to 1.3]; P = .08). In the respirator group, 89.4% of participants reported "always" or "sometimes" wearing their assigned devices vs 90.2% in the mask group.Conclusions and Relevance: Among outpatient health care personnel, N95 respirators vs medical masks as worn by participants in this trial resulted in no significant difference in the incidence of laboratory-confirmed influenza.Trial Registration: ClinicalTrials.gov Identifier: NCT01249625. [ABSTRACT FROM AUTHOR]

The synthetic estrogen 17α-ethinylestradiol (EE2) is an endocrine-disrupting chemical released into aquatic environments from sewage treatment facilities. We tested the effects of two environmentally relevant concentrations of waterborne EE2, 10 and 100 ng L−1, on reproductive endpoints in the teleost fish Betta splendens. In the first experiment, testes were removed from males and sperm were exposed to EE2 directly through the activation water. Direct exposure to EE2 had no effect on any measure of sperm swimming performance. In the second experiment, we exposed sexually mature male B. splendens to EE2 using a semi-static exposure protocol for 4 weeks. There were no significant treatment effects in the 10 ng L−1 treatment group, but at the 100 ng L−1 dose we found that fish had smaller gonads and reduced sperm swimming velocity. When allowed to interact freely with female conspecifics, males exposed to 100 ng L−1 EE2 built smaller nests and showed a nonsignificant decrease in fertilization success. To investigate further the potential mechanism underlying the decrease in sperm quality, we repeated the chronic exposure experiment and analyzed the ATP content of sperm from fish in each treatment group. We found that males exposed to 100 ng L−1 of EE2 had fewer moles of ATP per sperm than did fish in the other two treatment groups, suggesting that a decrease in intracellular ATP caused a reduction in sperm swimming velocity. The current study adds to the growing body of literature that indicates the risks to aquatic organisms of exposure to environmentally relevant concentrations of EE2. © 2012 Wiley Periodicals, Inc. Environ Toxicol 29: 243-252, 2014. [ABSTRACT FROM AUTHOR]