ライフサイエンスコーパス: surveillance data

1 ase Control and Prevention respiratory virus surveillance data.

2 nt class and latent transition models to HIV surveillance data.

3 on are underexplored due to lack of national surveillance data.

4 for real-time estimation of AVR fitness from surveillance data.

5 months of pre- and post-vaccine introduction surveillance data.

6 odels with vital death records and influenza surveillance data.

7 imple method for estimating AVR fitness from surveillance data.

8 to estimate sparse Markov networks from AMR surveillance data.

9 luenza viruses was described using virologic surveillance data.

10 ue cohort-specific prevalence, using disease surveillance data.

11 S. influenza season with the help of digital surveillance data.

12 (STM) from nine years of Australian disease surveillance data.

13 rameters and true herd-level prevalence from surveillance data.

14 al biases and underreporting inherent in the surveillance data.

15 and plan treatment adherence programs using surveillance data.

16 31, 2007, by linking national laboratory and surveillance data.

17 using GeoSentinel surveillance data or other surveillance data.

18 demiology of cholera in Togo, using national surveillance data.

19 from the continuous capture of institutional surveillance data.

20 9-2010 were analyzed using enhanced national surveillance data.

21 ssion intensity of future epidemics by using surveillance data.

22 ysis using only Arizona-specific outcome and surveillance data.

23 CRC, inflammatory bowel disease, or without surveillance data.

24 s are still hindered by the lack of reliable surveillance data.

25 bacterial nosocomial pathogens using routine surveillance data.

26 much lower than expected from epidemiologic surveillance data.

27 y risks, yet there are a paucity of national surveillance data.

28 open-source, laboratory-confirmed influenza surveillance data.

29 ever incidence estimates from facility-based surveillance data.

30 ent global epidemiology, we analyzed measles surveillance data.

31 ws and epidemiological national and regional surveillance data.

32 D rates in 4 age groups that agree well with surveillance data.

33 mediate levels, a ubiquitous pattern seen in surveillance data.

34 cephaly not available in routinely collected surveillance data.

35 useholds, health systems, and reliability of surveillance data.

36 Lack of lipid, fibrosis, or HCC surveillance data.

37 nities is poorly understood owing to limited surveillance data.

38 demia-CDI coinfection using population-based surveillance data.

39 ng SR, epidemiological national and regional surveillance data.

40 ons warrants an evaluation of post marketing surveillance data.

41 d are difficult to measure using traditional surveillance data.

42 o account for underascertainment in sentinel surveillance data.

43 Surveillance data (2000-2010) and mortality data (2000-2

44 on of adults with CHD and the development of surveillance data across the life span to provide empiri

45 This retrospective review of global surveillance data analysed case-based data for cases of

46 mmunications & community engagement, disease surveillance & data analysis, technical quality & capaci

47 amics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2

48 ood levels, and use them in conjunction with surveillance data and a data assimilation method to fore

49 s conducted using routinely collected health surveillance data and chloroplatinate exposure data.

50 A virus subtype can be seen in US influenza surveillance data and differ between prepandemic and pan

51 Here, we use detailed geographic surveillance data and epidemic models to estimate the cr

52 partmental model to synthesise evidence from surveillance data and epidemiological and behavioural st

53 We analyzed surveillance data and estimated case fatality rates (CFR

54 for interpreting clinical and public health surveillance data and for the design and implementation

55 Surveillance data and modelling will help country stakeh

56 djunct treatment for severe malaria using US surveillance data and reviewed the literature to update

57 nfection is necessary to complement clinical surveillance data and statistical models.

58 ort study based on the linkage of laboratory surveillance data and the Danish Civil Registration Syst

59 understood owing to the absence of reliable surveillance data and the simplistic approaches underlyi

60 We quantified the links between mosquito surveillance data and the spatiotemporal patterns of 3,8

61 t fully capture uncertainty due to imperfect surveillance data and uncertainty about the transmission

62 ose derived from traditional epidemiological surveillance data and with those reported for prior outb

63 ational parasite movement, utilize real time surveillance data, and relax the steady state assumption

64 ogy can aid in the interpretation of disease surveillance data, and the results can potentially refin

65 llated WHO country burden estimates, routine surveillance data, and tuberculosis prevalence surveys f

66 Based on this surveillance data, approximately 0.1% of children who we

67 High-quality, comprehensive, and real-time surveillance data are essential to reduce the burden of

68 Existing epilepsy surveillance data are inadequate to address factors such

69 tification, health promotion, and capture of surveillance data are integral aspects of the eSHC.

70 Genotype surveillance data are needed from many countries to impr

71 ative about HIV donors in ways that standard surveillance data are not.

72 anisms to ensure timely and complete cholera surveillance data are reported to the national level sho

73 We analysed national surveillance data around PsA-TT introduction to investig

74 using national influenza and RSV laboratory surveillance data as covariates.

75 s that incorporated weekly influenza and RSV surveillance data as covariates.

76 Trends were compared to provincial Chlamydia surveillance data by time-series analysis, using the cro

77 , tailoring these models to certain types of surveillance data can be challenging, and overly complex

78 s of influenza A(H3N2), so that sequence and surveillance data can be used synergistically.

79 We considered how participatory syndromic surveillance data can be used to estimate influenza atta

80 ngs demonstrate that participatory syndromic surveillance data can be used to gauge influenza attack

81 These surveillance data can be used to improve viral food-born

82 We show that the temporal extent of surveillance data can have a dramatic impact on inferenc

83 lyses identified unobserved risk patterns in surveillance data, characterized high-risk MSM, and quan

84 Surveillance data collected between 2006 and 2016 was us

85 plying 4 proposed UNAIDS metrics to national surveillance data collected between 2010 and 2015.

86 om routine, standardized, inpatient clinical surveillance data collected between 2015 and 2018 from 4

87 from routine standardised inpatient clinical surveillance data collected between 2015 and 2018 from 4

88 Surveillance data collected by specialist clinics may no

89 Limitation: Surveillance data collected by specialized clinics may n

90 eal-time--two weeks earlier than traditional surveillance data collected by the U.S. Centers for Dise

91 ed a latent transition analysis technique to surveillance data collected during clinic visits.

92 Retrospective analysis of longitudinal surveillance data collected from 2005-2006 through 2013-

93 lyze syndromic, virological, and serological surveillance data collected in England in 2009-2011 and

94 Surveillance data collected in Philadelphia during Septe

95 aths through generating causes of death from surveillance data combined with innovative diagnostic an

96 m which accommodates a wide range of disease surveillance data comprising any combination of recorded

97 Inaccurate or incomplete surveillance data delay a translational approach to the

98 Indeed, our current local surveillance data demonstrate that approximately half of

99 trends in its utilization, but national HIV surveillance data do not include PrEP uptake.

100 za virologic, hospitalization, and mortality surveillance data during 2000-2017 were analyzed for coh

101 the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks.

102 Surveillance data enabled the computation of severity sc

103 We analyzed surveillance data for all persons aged >/=13 years with

104 We collected regional syndromic surveillance data for epidemiological weeks 23 to 44, 20

105 ate recommended standards for the use of HAI surveillance data for external facility assessment to en

106 sed the challenges associated with using HAI surveillance data for external quality reporting, includ

107 where available and validated using national surveillance data for incidence of NAFLD-related HCC.

108 and Prevention's Emerging Infections Program surveillance data for invasive community-associated MRSA

109 active population-based and laboratory-based surveillance data for invasive GBS disease conducted thr

110 We summarize US population-based surveillance data for invasive listeriosis from 2004 thr

111 We analyzed Emerging Infections Program surveillance data for invasive S. aureus (SA) infections

112 We used nationwide surveillance data for IPD and a hierarchical Bayesian re

113 edictability based on high-quality influenza surveillance data for Israel; the model fit is corrobora

114 ing study, we adjusted routine malariometric surveillance data for known biases and used socioeconomi

115 observational cohort study, we used national surveillance data for meningococcal serogroup W and sero

116 We compiled and analysed surveillance data for nine countries in the meningitis b

117 e Cost and Utilization Project and influenza surveillance data for regions encompassing these states.

118 idemiologic studies should carefully dissect surveillance data for sex-specific effects.

119 Nationwide weekly surveillance data for suspect malaria cases reported bet

120 We aimed to assess European surveillance data for travel-related illness to profile

121 rs and >/=65 years) and scaled by laboratory surveillance data for viral types and subtypes, in the p

122 Prospectively collected MRSA surveillance data from 10 general wards at Guy's and St.

123 IPD surveillance data from 1986 to 2009 and carriage survey

124 t bloodstream infection (BSI) and meningitis surveillance data from 1998 to 2014.

125 extracted prospectively acquired Australian surveillance data from 2 studies nested within the Paedi

126 nessee Emerging Infections Program Influenza Surveillance data from 2006 to 2016 and the concurrent T

127 umber of PLWH was obtained from national HIV surveillance data from 2008 to 2016.

128 Using the national surveillance data from 2010 to 2013, we conducted this r

129 Using laboratory surveillance data from 2020, we estimate that RSV transm

130 We performed a retrospective study on HIV surveillance data from 5226 adult cases in Los Angeles C

131 We used nationally representative surveillance data from 63 emergency departments obtained

132 Surveillance data from a large measles outbreak in Mongo

133 ic analysis of HIV genetic sequence data and surveillance data from a US population of men who have s

134 Surveillance data from all Dutch sexually transmitted in

135 Surveillance data from all Dutch STI clinics between 200

136 Surveillance data from both COVIS and FoodNet indicate t

137 plements a Bayesian model using strain-typed surveillance data from both human cases and source sampl

138 xamined national population-based meningitis surveillance data from Burkina Faso using two sources, o

139 tious Diseases and Epidemiology Network, and surveillance data from Buruli ulcer control programmes i

140 We examined surveillance data from camera traps at bait sites and re

141 sease (GBD) Study 2016, national surveys and surveillance data from China, and qualitative data.

142 We looked at surveillance data from England and Wales to ascertain th

143 We used surveillance data from England over the years 2009 to 20

144 We fitted the transmission model to surveillance data from Hubei Province, China, and applie

145 Syndromic surveillance data from Illinois showed that the mean mon

146 We analyzed surveillance data from laboratory-confirmed cases of sal

147 We calibrated the model to match the HIV surveillance data from LAC across a 10-year period, star

148 ation in West Africa, we collected influenza surveillance data from ministries of health and influenz

149 Although surveillance data from most Western European countries s

150 We used acute flaccid paralysis (AFP) surveillance data from Nigeria collected between January

151 d through application to bovine tuberculosis surveillance data from Northern and the Republic of Irel

152 We analyzed WGS surveillance data from November 2016 to November 2017 fo

153 enza hospitalization from 2011 to 2018 using surveillance data from school district zip codes.

154 Using routine national surveillance data from Swaziland (a sub-Saharan country

155 o human rabies autopsy data and human rabies surveillance data from Tamil Nadu.

156 Surveillance data from the California tuberculosis regis

157 d active population and laboratory-based IPD surveillance data from the Centers for Disease Control a

158 We also included surveillance data from the Centers for Disease Control a

159 nal and regional) with traditional influenza surveillance data from the Centers for Disease Control a

160 Combining features of long-term surveillance data from The Netherlands with features of

161 The model was calibrated to surveillance data from the Norwegian national registry (

162 Making use of surveillance data from the past decade in conjunction wi

163 Jan 1, 2010, to Dec 31, 2017, and mortality surveillance data from the South African National Popula

164 Using surveillance data from the United Kingdom (UK) and the N

165 Using a stochastic model fit to seasonal flu surveillance data from the United States, we find that s

166 We examined RSV and influenza (flu) surveillance data from the US National Respiratory and E

167 ity for 125 countries using laboratory-based surveillance data from the WHO's FLUNET database and com

168 Influenza surveillance data from tropical, sub-Saharan African cou

169 Longitudinal surveillance data from two randomized controlled trials

170 extracted prospectively acquired Australian surveillance data from two studies nested within the Pae

171 By evaluating several decades of surveillance data from wild aquatic birds sampled along

172 Analysis of AMR surveillance data has focused on resistance to individua

173 These surveillance data help characterize the clinical manifes

174 Conclusion: These surveillance data help characterize the clinical manifes

175 We reviewed national surveillance data housed in the National Ministry of Hea

176 d potential for discovery using existing IAV surveillance data.IMPORTANCE Wild aquatic birds are the

177 Conclusions Preliminary surveillance data in Colombia suggest that maternal infe

178 Preliminary surveillance data in Colombia suggest that maternal infe

179 Enteric disease surveillance data in Minnesota were used to describe EAE

180 nd analysed nationwide case-based meningitis surveillance data in Niger.

181 = 0.70) and regional (R(2) = 0.74) norovirus surveillance data in the United States.

182 e of chronic shedding was only apparent when surveillance data included at least two outbreaks and th

183 Surveillance data included numbers of residents diagnose

184 FINDINGS: Overall trends of the ANC surveillance data indicated a substantial HIV prevalence

185 proposes a framework to integrate influenza surveillance data into transmission models.

186 Continued close attention to surveillance data is needed to monitor the impact of rec

187 ct and analyze mortality and hospitalization surveillance data is needed to rapidly establish the sev

188 -risk schools, as identified by school-level surveillance data, may experience substantial caries-pre

189 We linked national vital records with surveillance data of clinically or laboratory-confirmed

190 trospective analysis of population-based IPD surveillance data of the general population residing in

191 An annual update of antimicrobial resistance surveillance data of uropathogens may permit targeted tr

192 ral Brong Ahafo region in Ghana, we combined surveillance data on 11,274 deliveries with quality of c

193 Our study is a secondary analysis of surveillance data on 119 244 pregnancies from two large

194 and characteristics of infections, national surveillance data on diagnoses in England and Wales from

195 Surveillance data on IMD for patients aged 15 to 64 year

196 A retrospective study using Dutch surveillance data on IMD from June 1999 to June 2011.

197 Surveillance data on influenza virus activity permitted

198 lign with those derived from epidemiological surveillance data on MERS and Ebola, underscoring the im

199 We analyzed National HIV Behavioral Surveillance data on MSM from 20 cities.

200 In the absence of quality surveillance data on privately treated patients, commerc

201 We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 19

202 other settings using analogous, multiseason surveillance data on self-reported ILI together with sep

203 ailability of weekly Web-based participatory surveillance data on self-reported influenza-like illnes

204 ing serotypes emphasizes the urgent need for surveillance data on serotype distribution and serotype-

205 We analysed surveillance data on suspected and confirmed cases of me

206 Our findings demonstrate that surveillance data on the predominant pathogens associate

207 Using recent surveillance data on virologically confirmed infections

208 Surveillance data on water quality and diarrhea were col

209 yndrome analytical studies using GeoSentinel surveillance data or other surveillance data.

210 e available literature and the postmarketing surveillance data, proposed a clinically based grading o

211 We analyzed surveillance data prospectively submitted from 29 U.S. s

212 Malaria surveillance data provide opportunity to develop forecas

213 V Synthesis Model, to multiple data sources (surveillance data provided by Public Health England and

214 CV was developed using data synthesized from surveillance data, published literature, expert opinion,

215 ld were identified from prospective clinical surveillance data recorded routinely at four referral ho

216 ffects logistic regression models to routine surveillance data recording the presence of poliomyeliti

217 We collected national surveillance data regarding cases of pregnant women with

218 Recent national surveillance data report stable rates of invasive GAS di

219 HCV surveillance data reported to the Philadelphia Departmen

220 Cholera surveillance data reported to the Uganda Ministry of Hea

221 We reviewed the cholera surveillance data reported to the World Health Organizat

222 , children were matched with NC kindergarten-surveillance data representing school-level mean untreat

223 nking the mortality database to the national surveillance data set and the Scottish Morbidity Record.

224 ortance, we analyzed a longitudinal mosquito surveillance data set from Connecticut (CT), United Stat

225 sed inference schemes to analyze the largest surveillance data set of Shigella sonnei in the United S

226 This detailed surveillance data set provided an invaluable insight int

227 uartile from administrative data, use of the surveillance data set resulted in performance grades of

228 s to transplant varied by step, and national surveillance data should be collected on early transplan

229 Utilizing long-term influenza surveillance data since 1998, we are able to estimate th

230 atistical framework for integrating multiple surveillance data sources to evaluate the adequacy of tr

231 performed in routine clinical practice; and surveillance data suffer from confounding problems commo

232 ccine development has been unsuccessful, but surveillance data suggest that outer membrane vesicle me

233 US surveillance data suggest that the case fatality ratio i

234 An initial analysis of surveillance data suggested that such a polymorphism in

235 red (April-May and September-November), with surveillance data suggesting locally acquired infections

236 ata on antiretroviral therapy or viral load, surveillance data suggests that a small proportion of me

237 the potential to generate rich epidemiologic surveillance data that will be widely accessible to mala

238 On the basis of postmarketing surveillance data, the Food and Drug Administration issu

239 cohort study linked South Carolina HIV case surveillance data to 3 statewide healthcare databases.

240 We used US population-based surveillance data to characterize clinical risk factors

241 We analyzed California surveillance data to characterize the outcomes of patien

242 tion of chikungunya in 2015, by using active surveillance data to correct reported dengue case data f

243 We used national surveillance data to describe pertussis epidemiology, in

244 spital service with national epidemiological surveillance data to describe the use of surgical proced

245 care utilization survey and population-based surveillance data to estimate disease incidence.

246 We used serological and surveillance data to estimate the probability of infecti

247 analyzed characteristics of cases from 2016 surveillance data to evaluate the utility of laboratory

248 ddition to providing high-quality laboratory surveillance data to help guide disease control, elimina

249 developed methods to aggregate county-level surveillance data to inform provincial-level analysis, a

250 on and academic scientists, these models use surveillance data to make quantitative predictions regar

251 In this analysis, we use surveillance data to provide an estimate of influenza-as

252 resolution can be combined with longitudinal surveillance data to test hypotheses about routes and dr

253 cation months of ventilator-associated event surveillance data to the National Healthcare Safety Netw

254 a multidecade, continental-scale approach of surveillance data to understand trends of seasonal IAV s

255 In this study, we use all available U.S. surveillance data to: 1) fit a set of mathematical model

256 ission, informed by detailed behavioural and surveillance data, to assess the effect of seven differe

257 Available hospital-wide genome surveillance data traced the origins of the outbreak to

258 We analyzed epidemiological surveillance data, travel surveys, parasite genetic data

259 Surveillance data used by epidemic alert systems are typ

260 by partner type and fitted the model to HIV surveillance data using Latin hypercube sampling.

261 from vital registration, verbal autopsy, and surveillance data using the Cause of Death Ensemble Mode

262 A descriptive analysis of surveillance data was performed.

263 artin on pages 368-9.)Using population-based surveillance data, we analyzed antiviral treatment among

264 Using active surveillance data, we evaluated geographic and temporal

265 From surveillance data, we found that two of these substituti

266 strict to state influenza-like illness (ILI) surveillance data, we measured its effect on community l

267 Coupled with Ebola virus surveillance data, we modelled the expected number of in

268 Using population-based surveillance data, we quantified the secondary invasive

269 Here, using ILI records and virologic surveillance data, we show that ILI signal can be disagg

270 e data used by ALERT are routinely collected surveillance data: weekly case counts of laboratory-conf

271 bacteria for which robust weekly laboratory surveillance data were available.

272 Individual-level malaria surveillance data were collected from 1 outpatient depar

273 Surveillance data were collected through patient and pro

274 Surveillance data were collected through physician and p

275 Ten years of Austrian surveillance data were compared, including 10 960 labora

276 United States national surveillance data were gathered from the Centers for Dis

277 al-level administrative data sets and active surveillance data were joined to estimate influenza-asso

278 Annual medical evaluations and injury surveillance data were linked to compare levels of aerob

279 Health surveillance data were obtained from 1437 households wit

280 California surveillance data were reviewed to identify all children

281 California Department of Public Health surveillance data were reviewed to identify cases; demog

282 Case surveillance data were used primarily to derive stages 1

283 HIV surveillance data were used to assess demographic, clini

284 hia Department of Public Health and enhanced surveillance data were used to determine where individua

285 boratory-confirmed influenza hospitalization surveillance data were used to examine the association b

286 Yearly parasite surveys and routine surveillance data were used to monitor the primary outco

287 METHODS AND Routinely-collected hospital surveillance data were used to undertake a pragmatic com

288 rably according to whether administrative or surveillance data were used, suggesting that administrat

289 ate was 0.15% (95% CI, 0.13% to 0.17%); when surveillance data were used, the rate was 2.0% (CI, 1.8%

290 ta was compared with the grade assigned when surveillance data were used.

291 We reviewed national surveillance data where available.

292 ictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse

293 Following recent release of time-stamped surveillance data, which better reflects real-time opera

294 trol and Prevention's influenza-like illness surveillance data with aggregated prescription data.

295 clude hybrid systems that couple traditional surveillance data with data from search queries, social

296 echanistic framework to integrate individual surveillance data with geolocation data and aggregate mo

297 Combining active surveillance data with routine dengue reports improved n

298 dministrative data) or examined adults (from surveillance data) with at least 1 stage II or greater H

299 Postmarketing surveillance data would be useful in assessing whether t

300 de conjugate vaccine GBS6 was designed using surveillance data yielded by whole-genome sequencing of