ライフサイエンスコーパス: forecast

1 nly slightly selective and did not appear to forecast.

2 spread of influenza among localities can be forecast.

3 st method and less prone to producing a poor forecast.

4 warranted for current operational influenza forecast.

5 eus accumbens activity can support aggregate forecasts.

6 activity may support even more generalizable forecasts.

7 ments and match or exceed in accuracy expert forecasts.

8 ial to help refine time-dependent earthquake forecasts.

9 for accurate infectious diseases models and forecasts.

10 have not historically supplanted behavioral forecasts.

11 sts to create weighted-average superensemble forecasts.

12 xtensive post hoc investigation into seizure forecasting.

13 logical invasions that may aid in ecological forecasting.

14 rvational content but supports more accurate forecasting.

15 reproducible research that advanced seizure forecasting.

16 earity, and was the most accurate method for forecasting.

17 These results forecast a future tipping point in the operation of cold

18 Predictive models forecast a general increase in Scots pine growth at tree

19 cclusion development over a year, the method forecasts a danger over one month ahead of blockage.

20 seasons in the United States and assess both forecast accuracy and error.

21 for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensi

22 ion method can be generalized to improve the forecast accuracy of other infectious disease dynamical

23 tation of spatial structure affect influenza forecast accuracy within New York City.

24 e network model structure generally degrades forecast accuracy.

25 generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV ca

26 highlight the ability of neural features to forecast aggregate choice, which could inform applicatio

27 er than choice itself.SIGNIFICANCE STATEMENT Forecasting aggregate behavior with individual neural da

28 ork, which uses seasonal climate and El Nino forecasts, allows a prediction to be made at the start o

29 intrapair differences in childhood cortisol forecast amygdala-perigenual PFC rs-FC (R(2) = 0.84, FWE

30 The predictions correctly forecast an early peak in dengue incidence in March, 201

31 zed computational models that can accurately forecast an impact of a given meal on an individual's bl

32 is of a comprehensive set of data and demand forecasts, an interdisciplinary perspective on how best

33 e for simulations using the Weather Research Forecast and Community Multiscale Air Quality (CMAQ) mod

34 are still not well understood, hindering the forecast and mitigation of haze pollution.

35 rgency of incorporating mechanism into range forecasting and invasion management to understand how cl

36 pendence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.

37 have implications for flood susceptibility, forecasting and mitigation, including management of grou

38 Targets for improvement include drug forecasting and procurement, and addressing provider rel

39 neously improve the quality of economic risk forecasting and reinforce individual government and dono

40 lity poses greater challenges to operational forecasting and, consequently, greater coastal risk duri

41 On the demand side, inaccurate forecasts and sole sourcing lead to under-procurement.

42 st methods by geographic location, timing of forecast, and influenza season.

43 h uses including virtual biobanking, disease forecasting, and adaption to other disease outbreaks.

44 play in advancing theory, improving hind and forecasting, and enabling problem solving and management

45 ovel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of

46 influenza model, and develop a new improved forecast approach combining dynamical error correction a

47 nosed using error breeding, we develop a new forecast approach that combines dynamical error correcti

48 more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that fo

49 However, these better forecasts are achieved only if ecological responses to c

50 The data assimilation forecasts are empirically evaluated against actual postp

51 These forecasts are generated using ensemble simulations depic

52 We find that, overall, the superensemble forecasts are more accurate than any individual forecast

53 Forecasting assemblage-level responses to climate change

54 These findings suggest that observation and forecast at sub-municipal scales within New York City pr

55 , raising questions about the reliability of forecasts based on a single modeling approach.

56 ase dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to non

57 putational engines need to generate accurate forecasts based on limited datasets consistent with typi

58 represents a major unresolved challenge for forecasting biosphere responses to global change.

59 MEMS capacitive device is able to detect and forecast blockages, similar to early detection procedure

60 nd computational methods for epidemiological forecasting, but here we consider a serious alternative

61 uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mech

62 More accurate forecasting can help officials better respond to and pla

63 ical state transition thus affords a tool to forecast cell fate outcomes and can be used to optimize

64 growth model and climate-growth analyses to forecast changes in tree growth during the 21st century.

65 reatly improve our ability to understand and forecast climate-driven range dynamics.

66 light the value of a multi-model approach in forecasting climate change impacts and uncertainties and

67 utionary history of a cancer is important in forecasting clinical outlook.

68 sted drugs, and specific predictors aimed at forecasting clinical response to treatment with four spe

69 in their species compositions-is critical to forecast communities in the Anthropocene.

70 In the past 3 decades, the weather forecasting community has made significant advances in d

71 accurate is human judgment, and how do these forecasts compare to their more computational, data-driv

72 aluation suggests that the data assimilation forecasts compare well with specific glucose measurement

73 power, suggesting that advances in long-term forecasting could be exploited to markedly improve manag

74 olera and climate patterns coupled with ENSO forecasting could be used to notify countries in Africa

75 by examining ways to present predictions as forecast-derived range quantities and evaluate the compa

76 Seasonal forecasts did not predict the disruption, but analogous

77 lived in poverty across ages 11 to 18 years forecasted diminished left dentate gyrus (simple slope,

78 s with environmental drivers is necessary to forecast disease outbreaks, and to predict future change

79 ent, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation

80 Forecasting ecological responses to climate change, inva

81 e future, and prediction markets are used to forecast election results.

82 The same methods were utilized to forecast emissions from fuels out to 2040, indicating ma

83 ENSO amplitude forecast errors are most strongly associated with the er

84 We propose a log transformation of forecast errors for price projections and a modified non

85 ncreasing lead times, is a possible cause of forecast errors in the thermocline feedback and thus in

86 s that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncerta

87 ance surveys and WHO tuberculosis reports to forecast estimates of incident MDR and XDR tuberculosis

88 applications include political and economic forecasting, evaluating nuclear safety, public policy, t

89 We also participated in two live forecasting experiments.

90 perature and nitrate levels as well as those forecast for the future.

91 th further climate-driven change and melting forecast for the twenty-first century, our findings docu

92 concentrations relative to Muskrat Falls are forecasted for 11 sites across Canada, suggesting the ne

93 National data are estimated along with forecasts for 2 regions.

94 We generate retrospective forecasts for 95 cities over 10 seasons in the United St

95 However, seasonal dynamical model forecasts for European summers have very little skill, p

96 more severe droughts, with consistently dire forecasts for negative future impacts.

97 have received little attention, in spite of forecasted forest expansion.

98 roof-of-concept for implementing a circadian forecasting framework, and provide insight into new appr

99 Forecasts from models calibrated with data centred on 19

100 We compare statistical inference and forecasts from our hierarchical Bayesian model to phenom

101 However, forecasts from statistical models (e.g. species distribu

102 simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessme

103 Sea-level rise is a global problem, yet to forecast future changes, we must understand how and why

104 We used Gaussian process regression to forecast future coverage rates and provide a vaccine per

105 oth in the context of historical work and to forecast future directions and opportunities for the fie

106 tracts, small pilot studies and protocols to forecast future directions.

107 to build and validate a statistical model to forecast future platelet demand and thereby reduce wasta

108 sented here offer considerable potential for forecasting future conditions, highlight regions of conc

109 Additionally, migrants could benefit from forecasting future wind conditions, crossing on nights w

110 are accurate than with no forcing) and that forecasts generated using daily climatological humidity

111 For influenza forecasts generated using dynamical models, forecast ina

112 luenza transmission improves the accuracy of forecasts generated with those models.

113 based on age, climate and previous growth to forecast growth trends up to year 2100 using climatic pr

114 Contrastingly, the model forecasted growth declines at lowland-southern populatio

115 us disease incidence should be monitored and forecast has been little explored.

116 Previous forecasts have not considered the potential impact of tr

117 proven elusive; even when successful, neural forecasts have not historically supplanted behavioral fo

118 Therefore, accurate weather and climate forecasts hinge on good predictions of ice nucleation ra

119 namics, and benchmark our population model's forecast horizon against a simple null model.

120 rate that DA enhances the predictability and forecast horizon of complex community dynamics.

121 erstand species-habitat association and help forecast how fishes will be affected by the flattening o

122 and other purinergic-targeting therapies and forecast how these might develop in combination with oth

123 ilience of the source of aquatic C export to forecasted hydroclimatic changes.

124 e real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation out

125 As these forecasts improve, their public health utility should in

126 Forecasted improvements in immunity for April 2016 were

127 tion, minimum temperature, and Nino3.4 index forecasts in a Bayesian hierarchical mixed model to pred

128 to produce, evaluate, and rank probabilistic forecasts in this setting.

129 forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlin

130 We forecast incident HCC cases through 2030, using novel ag

131 ation of El Nino-Southern Oscillation (ENSO) forecasts, including the development of successful effor

132 ohydrological modeling of downscaled climate forecasts indicate overall increases in the area suitabl

133 iated with differences in climate; (iii) our forecasts indicate that ongoing climate change will like

134 lance data and a data assimilation method to forecast influenza activity.

135 ions are developed that make explicit use of forecast information.

136 cading Alternating Renewal Process (CARP) to forecast interconnected global risks.

137 vironment using experimental biogeography to forecast invasive and native species' potential ranges u

138 Climatic surveillance should be used to forecast invasive bacterial disease epidemics, and simpl

139 the predator to sensitively detect prey and forecast its mobile prey's future position on the basis

140 ysis, enhanced errors in ENSO amplitude with forecast lead times are found to be well represented by

141 To forecast likely future behavior in the earliest stages o

142 apy, and levels at completion of therapy may forecast long-term outcome.

143 This paper uses three forecasting machines: (i) data assimilation, a technique

144 This study uses retrospective forecasts made using an APEC Climate Center seasonal for

145 ed by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to t

146 ng sample, only NAcc activity generalized to forecast market funding outcomes weeks later on the Inte

147 om the neuroimaging sample, however, did not forecast market funding outcomes.

148 research, we find that neural responses can forecast market-level choice and outperform behavioral m

149 To test whether neural activity could forecast market-level crowdfunding outcomes weeks later,

150 ctors of individual choice can generalize to forecast market-level crowdfunding outcomes-even better

151 t pronounced warming (+1.4 to 4.8 degrees C) forecasted mean growth reductions of -10.7% and -16.4% i

152 applied by the authors to develop models to forecast methane emissions from the future HD transporta

153 ecasts are more accurate than any individual forecast method and less prone to producing a poor forec

154 d a modified nonparametric empirical density forecasting method.

155 rmance of these individual and superensemble forecast methods by geographic location, timing of forec

156 es among the predicted outcomes of competing forecast methods can limit their use in decision-making.

157 in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecast

158 ing performance of several empirical density forecasting methods, using the continuous ranked probabi

159 ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers

160 stimation by combining a cheap reduced-order forecast model and mixed observations of the large- and

161 s made using an APEC Climate Center seasonal forecast model to investigate the cause of errors in pre

162 evaluated, such as The Weather Research and Forecasting Model meteorology predictions, emission inve

163 We use the Weather Research and Forecasting Model with atmospheric chemistry (WRF-Chem)

164 unt the uncertainty related to the choice of forecasting model.

165 of the nonlinear error structure in current forecast models is needed so that this growth can be cor

166 nd demonstrates that the predictive power of forecasting models is improved by circadian information.

167 We developed an ensemble of 21 forecasting models, all of which probabilistically contr

168 eillance data provide opportunity to develop forecasting models.

169 A Markov model was used to forecast NAFLD disease progression.

170 Our aim was to forecast national age-specific mortality and life expect

171 This model provides a key element to forecast novel invaders and to extend pathway-level risk

172 led nutrient cycles, as well as modeling and forecasting nutrient controls over carbon-climate feedba

173 population projections from 2016 to 2030 to forecast obesity estimates and NASH-related LT waitlist

174 statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

175 ly to persist, as predicted by a statistical forecast of subsurface ocean temperatures and consistent

176 empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill th

177 f S. aureus populations could lead to better forecasting of antibiotic resistance and virulence of em

178 nstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable

179 Our findings aid improved understanding and forecasting of Sahel drought, paramount for successful a

180 rder-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria giv

181 Skilful seasonal forecasting of the surface climate in both Europe and No

182 ment, national inventories of tOPV, detailed forecasting of tOPV needs, bOPV licensing, scaling up of

183 f plant responses to extreme drought impedes forecasts of both forest and crop productivity under inc

184 hematical models of emergent pathogens allow forecasts of case numbers, investigation of transmission

185 ystem was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014

186 tanding of carbon cycle dynamics, as well as forecasts of ecosystem responses to climate change.

187 entation in models to make skillful seasonal forecasts of ENP TCs.

188 nt predictive ability to near- and long-term forecasts of flood risk.

189 ce methods can be used to generate real-time forecasts of future infectious disease incidence.

190 In retrospective forecasts of historical influenza outbreaks for 95 US ci

191 then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, Ne

192 To generate personalized forecasts of how patients with open-angle glaucoma (OAG)

193 The forecasts of increasing global temperature and sea level

194 lters, and argue for evolutionarily informed forecasts of invasive spread by exotic species or climat

195 In particular, some advocate forecasts of life expectancy based on period trends; oth

196 re provide an upper bound on the accuracy of forecasts of major outbreaks that are constructed using

197 Accurate forecasts of mosquito infection rates are generated befo

198 enerate personalized and dynamically updated forecasts of OAG progression under different target IOP

199 s in simpler ecological niche models improve forecasts of observed range shifts.

200 nge dynamics will be essential for realistic forecasts of patterns of biodiversity under climate chan

201 We generated retrospective forecasts of peak timing, peak incidence, and total inci

202 Six-month forecasts of poliomyelitis incidence by district for 201

203 ormation Criterion [AIC]) to produce 6-month forecasts of poliomyelitis incidence.

204 t [Formula: see text] used here, when making forecasts of population abundance.

205 has been levelled at climate-change-induced forecasts of species range shifts that do not account ex

206 ry widely across environmental gradients but forecasts of species' responses to environmental change

207 ng realistic coastal baroclinic processes in forecasts of storm intensity and impacts will be increas

208 ults have immediate application to empirical forecasts of summer rainfall for the United Kingdom, Ire

209 Forecasts of summer weather patterns months in advance w

210 sion-making tool that generates personalized forecasts of the trajectory of OAG progression at differ

211 els, however, have limited skill in seasonal forecasts of the winter NAO.

212 Presently, operational real-time forecasts of total influenza incidence are produced at t

213 impact of model vertical resolution on track forecasts of tropical cyclones.

214 Trusted decadal forecasts of UV dosage over the United States in summer

215 Forecasts of widespread range shifts with climate change

216 fied according to the characteristics of the forecast or geographic location.

217 direct implications for improving heavy rain forecasting over the IMR, by developing realistic land c

218 We performed a discrete event simulation to forecast patient characteristics and rate of waitlist dr

219 Thus, the CES signature forecasts patient response to adjuvant chemotherapy or r

220 rall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak

221 We evaluate the out-of-sample forecasting performance of several empirical density for

222 Models forecasting plant community responses to global change i

223 terize current solitarious distributions and forecast potential recession range shifts under two extr

224 ns of formation of known deposits as well as forecasting potential exploration targets.

225 Global climate forecasts predict changes in the frequency and intensity

226 h, but it is a necessary consideration given forecasts predicting that these events will increase in

227 can be combined into a single more accurate forecast product for operational delivery in real time.

228 In vitro experiments are widely used to forecast reef-building coral health into the future, but

229 Forecasting refugee movements is important, as accurate

230 However, such forecasts require extrapolation into new locations and e

231 re key to improving short-term and long-term forecasts, respectively.

232 Here we develop such a system to study and forecast respiratory syncytial virus (RSV).

233 We then evaluated the forecast retrospectively with available epidemiological

234 esearch has produced a number of methods for forecasting seasonal influenza outbreaks.

235 We found that forecasts separated by type/subtype generally produced m

236 These forecasts show the need for development of early prevent

237 We ran forecast simulations using projected climate data throug

238 ded so that this growth can be corrected and forecast skill improved.

239 a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, in

240 strategies in different environments and may forecast species survival and range shifts under climate

241 n dynamic environments, which is critical in forecasting species distributions, as well as the divers

242 ) tend to have higher predictive accuracy in forecasting species range shifts than structurally simpl

243 Recent forecasts suggest that African countries must triple the

244 models typically used for such biogeographic forecasts-suggest the urgency of incorporating mechanism

245 ar to be promising for developing a seasonal forecast system supporting fire management strategies.

246 We created the web-based "Epicast" forecasting system which collects and aggregates epidemi

247 nd also suggest that current dynamical model forecast systems have large potential for improvement.

248 y, is a consequence of weaknesses in current forecast systems.

249 at enable the development of epidemiological forecasting systems.

250 Here, we explore whether forecasts targeted to predict influenza by type and subt

251 nited States were more or less accurate than forecasts targeted to predict total influenza incidence.

252 in significant improvements in the skill for forecasting TC activity at daily and seasonal time-scale

253 nd consistently outperform alternative naive forecasting techniques.

254 ectancy based on period trends; others favor forecasts that hinge on cohort differences.

255 ogical humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (

256 oid pathology but have also been deployed to forecast the clinical course.

257 enrichment are required, the software cannot forecast the enrichment effort required.

258 Bayesian approach is used to understand and forecast the growth and geographic spread in the prevale

259 y, we estimate current biological status and forecast the impacts of contrasting management regimes o

260 Decision-makers can use this model to forecast the improvement that any proposed biodiversity

261 In order to forecast the potential health benefits deriving from the

262 of space weather because it is impossible to forecast the solar eruptions that can cause these terres

263 e then build stochastic demography models to forecast the viability of the populations under differen

264 The model forecasted the percentage of MDR tuberculosis among inci

265 Despite their importance for eruption forecasting the causes of seismic rupture processes duri

266 thermal environment is largely ignored when forecasting the dynamics of non-native species.

267 ction modes: a universal predictor, aimed at forecasting the efficacy of untested drugs, and specific

268 standing the fate of gravel is important for forecasting the response of rivers to large influxes of

269 ers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zik

270 rovide a quantitative physical mechanism for forecasting the strength and duration of bursty seasons

271 We designed a regression model that forecasts the accuracy of residue-residue contact predic

272 early warning triggers tied to precipitation forecasts, these intense fire episodes will reoccur duri

273 episodes, respectively, with median advance forecast times (AFT) of 12 and 0 min.

274 he-meter (BTM) electric storage capacity are forecast to eclipse grid-side electrochemical storage by

275 Blacks are forecast to have the highest rate among women (age-stand

276 incidence rates among men, and Hispanics are forecast to have the highest rates among men (age-standa

277 Thus, by 2030, Asians are forecast to have the lowest incidence rates among men, a

278 NTERPRETATION: MDR and XDR tuberculosis were forecast to increase in all four countries despite impro

279 the second cause of deaths worldwide and is forecasted to affect more that 22 million people in 2020

280 pulation growth and economic development are forecasted to impose unprecedented levels of extinction

281 mmunity against serotype-2 poliomyelitis was forecasted to improve in April 2016 compared to the firs

282 Prevalent NAFLD cases are forecasted to increase 21%, from 83.1 million (2015) to

283 Global riverine N2 O emission rates are forecasted to increase by 35%, 25%, 18% and 3% in 2050 c

284 o 2015, further disaggregation by cause, and forecasts to 2020.

285 development pressure, and then compare these forecasts to any chosen policy target.

286 ecast methods, and combined these individual forecasts to create weighted-average superensemble forec

287 In this study, we used climate forecasts to predict the evolution of the 2016 dengue se

288 ease, dementia, disability, and mortality to forecast trends in life expectancy and the burden of dis

289 ted on Jan 1, 2016, producing monthly dengue forecasts until November, 2016.

290 lacking owing to difficulties in evaluating forecasts using real-world data.

291 make statistical inference and probabilistic forecasts, using mechanistic ecological models.

292 oming decades, temperature and precipitation forecasts vary by latitude and geographic region suggest

293 The models forecast very distinct trajectories for the lizard speci

294 onal design of safer vaccine strains and for forecasting virulence of viruses.

295 iciency could further constrain responses to forecasted warming and drying.

296 l-time by human participants, and with these forecasts we ask two questions: how accurate is human ju

297 To test whether birds forecast, we developed a movement model, calculated to w

298 In addition, for forecasting, we estimated a dynamic parametric model of

299 stem that exhibits a high degree of skill in forecasting wildfire probabilities and drought for 10-23

300 dynamics, which have been optimized prior to forecast with observations of influenza incidence and da