también reduce o alivian los dolores de cabeza,
NOMBRE COMERCIAL: TEMPRA, SEDALMERCK,
Background: The climatic conditions in Harris County, Texas, is conducive for the propagation of mosquito populations the have been implicated in the transmission of arboviral diseases. Method: Harris County has been demarcated into two hundred and sixty-eight areas. Surveys were conducted in each area gathering geographic, environmental and demographic data to determine which areas has the greatest potential for mosquito-borne disease transmission. Mosquito trapping events were conducted utilizing modified Center for Disease Control (CDC) miniature light traps in storm sewers, modified CDC Reiter Gravid Traps and BG Sentinel Traps on private and public properties. Traps were strategically positioned based on geographic proximity and ease of access within the County. After 18 hours, these
traps were retrieved, mosquito species collected were sorted, identified, pooled based on the order of importance for disease transmission, and sent to the laboratory for viral testing. Results: Each year, from 2011 to 2014, approximately 10,000 trapping events were conducted yielding on average more than 900,000 female mosquitoes. More than 55,000 pools were submitted for viral testing during that period resulting in 2538 positive isolates for West Nile Virus and four positive isolates for St Luis Encephalitis. Conclusion: Based on species emergence, adaptability, environmental conditions and both mosquito and human populations, the potential threat of emerging mosquito borne disease such as Dengue Fever, Chikungunia, and Malaria remains a major concern to the citizens of Harris County.
Board 228. Enhanced Testing of Norovirus-negative Gastroenteritis Outbreaks N. Gregoricus, L. Barclay, J. Lewis, J. Vinje; CDC, Atlanta, GA, USA
Background: The burden of viral gastroenteritis outbreaks in the United States is largely attributed to norovirus; however no etiologic agent is identified in up to 16% of norovirus negative outbreaks. The lack of etiological agents in these unexplained outbreaks warrants a systemic exploration of viruses involved to better understanding underlining disease mechanisms. Methods: To investigate the etiologic agents in norovirus negative outbreaks reported in 9 states were submitted to 3 CaliciNet Outbreak Support
Centers (California, Minnesota, and Oregon) and fecal specimens (at least 2 per outbreak) were tested for sapovirus, astrovirus, and rotavirus by realtime RT-PCR. Positive specimens were typed by
sequencing of conventional RT-PCR products. Results: From 2012-2014, specimens from 80 norovirus- negative outbreaks were analyzed of which 18 (22.5%) outbreaks tested positive for sapovirus, 2(2.5%) for astrovirus, 1 (1.2%) for rotavirus, and 1 (1.2%) outbreak had a mixed infection of sapovirus and rotavirus. Eleven (61%) of the sapovirus outbreaks could be genotyped; 3 as GI.1, 1 as a GI.2, and 5 as GIV. Of the 2 outbreaks positive for astrovirus 1 was typed as type 4 and one as type 1. The 2 rotavirus outbreaks could be typed as G12P[8]. Conclusions: Of the 80 norovirus negative outbreaks, 26.2% could be attributed to a known gastroenteritis virus, of which, sapovirus had the highest positivity rate (22.5%). Future testing will include deep sequence analysis which allows identification of established gastroenteritis viruses that were missed by current realtime RT-PCR assays or novel viruses. Board 229. Using Multiple Data Sources to Monitor Influenza Epidemics
M. Santillana1,2,3, A. T. Nguyen2, M. Dredze4, M. J. Paul4, J. S. Brownstein1,3; 1Boston Children’s Hosp.,
Boston, MA, USA, 2Harvard Sch. of Engineering and Applied Sci., Cambridge, MA, USA, 3Harvard Med.
Sch., Boston, MA, USA, 4Dept. of Computer Sci., Johns Hopkins Univ., Baltimore, MD, USA
Background: Seasonal and non-seasonal influenza outbreaks are unpredictable and cause 3,000 to 50,000 deaths a year in the United States of America. Non-traditional methods that leverage data sources such as: Google searches, crowd-sourced disease surveillance data, clinician’s databases, medical records management companies; to independently track influenza activity have recently been suggested as alternatives to get timely estimates of influenza activity ahead of the Centers for Disease Control influenza reports. Different methods and data sources have strengths and weaknesses when taken
independently. Here we present a methodology that combines the strengths of four data sources and produces a single influenza predictor capable of delivering improved influenza predictions. Methods: A machine learning ensemble approach is implemented to extract the most meaningful information from multiple non-traditional real-time influenza surveillance systems and produce a single influenza predictor. Results: The influenza predictor obtained with our methodology displays superior accuracy and
robustness (as measured by the root mean square error and the maximum absolute error), during the 2012-2013 and 2013-2014 flu seasons, than any of the real-time predictors separately. Moreover, our methodology delivers improved predictions up to four weeks ahead of the most recent influenza CDC reports, effectively providing forecasts of influenza activity up to two weeks ahead of current week. As expected, the accuracy of our predictions decreases as the number of weeks ahead of CDC reports increase. Conclusions: We show that efficiently combining multiple independent influenza predictors is a superior approach than just using the best predictor to monitor influenza activity over the US during two flu seasons.
Board 230. Participatory Surveillance: The Influenza Experience
R. Chunara1,2, A. W. Crawley3, O. Wojcik2, K. Baltrusaitis2,4, J. Olsen3, J. S. Brownstein1,2, M. Smolinski3; 1Harvard Med. Sch., Boston, MA, USA, 2Boston Children’s Hosp., Boston, MA, USA, 3Skoll Global
Threats Fndn., San Francisco, CA, USA, 4Boston Univ., Boston, MA, USA
Background: Information about the activity of influenza-like-illness (ILI) in the United States (U.S.) is limited and traditional surveillance systems take time to aggregate data. Methods: Flu Near You (FNY) is a participatory surveillance system that was developed to capture influenza-related symptoms reported by participants on a weekly basis providing estimates of influenza-like-illness incidence. Individuals
contribute an email address, gender, age and zip code. On the website (flunearyou.org) or through mobile phone app they are requested to fill in a short survey asking if they had any of 10 symptoms: fever, cough, sore throat, shortness of breath, chills/night sweats, fatigue, nausea or vomiting, diarrhea, body aches and headache. Users can also enroll their household members and report weekly for them. Spatio- temporal trends of the symptom reports were compared to measures of ILI-incidence from the Centers for Disease Control and Prevention (CDC) ILINet. In subsequent work, we examined how other subsets of the symptom reports compared to repots from HealthMap, FOOD database, NoroCORE, CalciNet and NoroStat, by developing exclusion and inclusion criteria in regards to two contemporaneous disease outbreaks in the U.S.; norovirus and enterovirus EV-D68. Results: 54,747 FNY individuals had at least two reports between October 1, 2012 and May 19 2014; ILI from those reports correlated with ILINet (ρ = 0.906). ILI reports peaked the week of December 23rd for both FNY and CDC at 3.4% and 4.6%,
respectively (2013-2014 season). In examining non-flu syndromes, we found increases in our defined “norovirus case” reports contemporaneous with norovirus prevalence and in common enterovirus symptoms (breathlessness, cough, body aches) at times of reported enterovirus outbreaks in U.S. populations in 2014. Conclusions: Participatory surveillance can be useful for infectious diseases; with further specificity desirable for distinguishing different infectious disease outbreaks.
Board 231. Coccidioides immitis: Developing a One Health Approach for Surveillance of a Newly Emerging Fungal Pathogen in Washington State
O. McCotter1, R. Worhle2, P. VanderKelen2, M. Goldoft3, T. Chiller1, A. Litvintseva1, H. Oltean3, W.
Cliford2; 1CDC, Atlanta, GA, USA, 2Washington State Dept. of Hlth., Tacoma, WA, USA, 3Washington
State Dept. of Hlth., Shoreline, WA, USA
Background: Coccidioides immitis is an emerging fungal pathogen for the Pacific Northwest region. Three human cases have been reported and identified as locally acquired in the South-central region of Washington. Environmental sampling from the site of exposure in two cases resulted in detection of C. immitis DNA and recovery of viable C. immitis isolates from soil. Whole genome sequencing of the recovered isolates confirmed genetic identity between isolates from soils and one of the case-patients indicating local acquisition. A review of veterinary data indicates animal cases back to mid-1990. Methods: We brought together experts in environmental health, veterinarians, and public health to develop a surveillance strategy for this emerging infection. We combined multiple surveillance sources, including soil sampling, rodent trapping and testing, veterinary serosurveys, veterinary and human case investigations, air sampling, and spatial analysis, to enhance surveillance for this emerging pathogen. Much of the geography in the south-central region of Washington State is similar to the location where C. immitis was identified. Results: We established a surveillance strategy for coccidioidomycosis. Enhanced surveillance has identified human cases without reported travel to previously defined endemic regions for this infection. Environmental sample testing is strategically targeted near likely soil exposure locations. Positive environmental samples are assisting the understanding of the ecological niches and boundaries of this pathogen. Conclusions: Utilizing human, animal, and environmental approaches we have established surveillance for coccidioidomycosis in a one health model. The surveillance is important to understand this emerging pathogen in this region, and to provide education for local health care providers and veterinarians.
Board 232. Genetic Heterogeneity of Listeria monocytogenes in Northern Italy
E. Amato1, A. Parisi2, P. Huedo1, M. Gori1, V. Filipello3, C. Mammina4, M. Pontello1; 1Univ. of Milan,
Milano, Italy, 2Experimental Zooprophylactic Inst. of Apulia and Basilicata, Foggia, Italy, 3Univ. of Turin,
Turin, Italy, 4Univ. of Palermo, Palermo, Italy
Background: Invasive listeriosis is a rare foodborne disease. Septicaemia and meningoencephalitis with high letality are the most common clinical presentations in vulnerable population. The etiologic agent, Listeria monocytogenes, displays a high genetic heterogeneity. Thus, implementation of strain discrimination techniques is of major importance for prompt outbreak detection and improvement of surveillance. The aim of this study was to detect emerging strains and monitor their prevalence in the Lombardy region, Northern Italy. Methods: In Lombardy, the mandatory notification system has been integrated since 2005 with a laboratory-based surveillance network based on voluntary referral of clinical isolates to a Regional Reference Laboratory. All human L. monocytogenes isolates collected during the
period 2005-2013 (n=245) were serotyped and subtyped by Multi-locus Sequence Typing (MLST). Sequence Types (STs) were assigned in accordance to the Listeria MLST database (Pasteur Institute, France). Results: The predominant serotypes were 1/2a (59.6%), 4b (26.1%) and 1/2b (10.6%). Forty- nine STs were identified, of which 36 had been previously reported in the same area. The 6 most
common STs were ST38 (16.7%), ST1 (11.8%), ST8 (9.4%), ST155 (6.5%), ST2 (5.7%) and ST3 (4.9%), accounting for 55% of the strains. The high prevalence of ST38 and ST155 led us to hypothesize the likely occurrence of two outbreaks which had gone undetected by local health authorities during the period 2009-2011. In particular, identification of ST155 isolates began in 2008 (n=1), peaked in 2011 (n=10) and continued in 2013 (n=5) with 3 pregnancy-related cases closely linked in space and time. For these cases, an epidemiological investigation had been implemented which failed to identify their source. Only in one case (ST8), the implicated food (brique cheese) was recognized. Finally, ST1, ST2 and ST3 showed a persistent trend being widely disseminated across time and space. Conclusions: This study identified the prominent STs circulating in our regional area and contributed to our understanding of the genetic heterogeneity of L. monocytogenes isolates. Monitoring STs will provide essential information for preventing listeriosis and supporting epidemiological investigations in this field, which are often
challenging.
Board 233. Real Time Surveillance of Influenza Using Wireless Reporting of RIDT Results J. L. Temte1, S. Barlow1, A. Schemmel1, E. G. Temte1, M. Landsverk1, T. Haupt2, E. Reisdorf3, M.
Wedig3, P. A. Shult3, D. Booker4, J. Tamerius4; 1Univ. of Wisconsin Sch. of Med. and Publ. Hlth.,
Madison, WI, USA, 2Wisconsin Div. of Publ. Hlth., Madison, WI, USA, 3Wisconsin State Lab. of Hygiene,
Madison, WI, USA, 4Quidel Corportation, San Diego, CA, USA
Background: Delays in reporting are inherent in influenza surveillance. Consequently, public health response to outbreaks is offset by 2-to-3 weeks. The feasibility of a statewide array of rapid influenza detection test (RIDT) analyzers using wireless connectivity to report in real time was assessed over a two- year period. Methods: We evaluated the feasibility of a real-time primary care influenza surveillance network, including site recruitment, engagement, training, and implementation of analyzers. Secondary objectives included assessment of performance over two seasonal outbreaks of influenza as compared to existing influenza surveillance programs. We used the Quidel Sofia Influenza A+B FIA--a RIDT with wireless connectivity. Analyzers were deployed in all public health regions of Wisconsin, starting in October 2013. Twenty primary care practices located in urban, suburban and rural locations participated. We compared the total number of influenza A and B detections per week to the prevalence of influenza- like illness visits at primary care sites in Wisconsin (ILI-Net) and detections from existing PCR and RIDT networks. Results: IRB exemption and university clearances were obtained by late August 2013. The full complement of practices was recruited by 11/20/2013. Installations of Sofia analyzers were completed by 12/18/2013. Data accumulated as soon as sites were activated and reporting from 16 sites (80%) was fully operationalized by 12/31/2013. Data were aggregated and analyzed on a daily and weekly basis public health region, and for the Wisconsin composite. The system identified the onsets of the 2013-2014
and 2014-2015 seasonal influenza outbreak extremely early. High correlations existed between the weekly real-time influenza detections and ILI-Net ILI visits (r=0.769; P<0.001), PCR detections, (r=0.896; P<0.001), and RIDT detections (r=0.914; P<0.001). Conclusions: Effortless, real time reporting of RIDT results was achievable over a very short time frame in practices using RIDT coupled with immediate wireless transmission of results. Such reporting eliminated the need for clinicians or laboratorians to take time to aggregate and transmit information. This approach is a reasonable model for public health surveillance for any pathogen identifiable by clinic-based technology.
Board 234. Crowdsource Reporting of Infectious Diseases