99 research outputs found
Estimating numerical errors due to operator splitting in global atmospheric chemistry models: Transport and chemistry
We present upper bounds for the numerical errors introduced when using
operator splitting methods to integrate transport and non-linear chemistry
processes in global chemical transport models (CTM). We show that (a) operator
splitting strategies that evaluate the stiff non-linear chemistry operator at
the end of the time step are more accurate, and (b) the results of numerical
simulations that use different operator splitting strategies differ by at most
10 percent, in a prototype one-dimensional non-linear chemistry-transport
model. We find similar upper bounds in operator splitting numerical errors in
global CTM simulations
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
We present a machine learning-based methodology capable of providing
real-time ("nowcast") and forecast estimates of influenza activity in the US by
leveraging data from multiple data sources including: Google searches, Twitter
microblogs, nearly real-time hospital visit records, and data from a
participatory surveillance system. Our main contribution consists of combining
multiple influenza-like illnesses (ILI) activity estimates, generated
independently with each data source, into a single prediction of ILI utilizing
machine learning ensemble approaches. Our methodology exploits the information
in each data source and produces accurate weekly ILI predictions for up to four
weeks ahead of the release of CDC's ILI reports. We evaluate the predictive
ability of our ensemble approach during the 2013-2014 (retrospective) and
2014-2015 (live) flu seasons for each of the four weekly time horizons. Our
ensemble approach demonstrates several advantages: (1) our ensemble method's
predictions outperform every prediction using each data source independently,
(2) our methodology can produce predictions one week ahead of GFT's real-time
estimates with comparable accuracy, and (3) our two and three week forecast
estimates have comparable accuracy to real-time predictions using an
autoregressive model. Moreover, our results show that considerable insight is
gained from incorporating disparate data streams, in the form of social media
and crowd sourced data, into influenza predictions in all time horizon
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Evaluation of Internet-Based Dengue Query Data: Google Dengue Trends
Dengue is a common and growing problem worldwide, with an estimated 70–140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ∼36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models
Gradient-based estimation of Manning's friction coefficient from noisy data
We study the numerical recovery of Manning's roughness coefficient for the
diffusive wave approximation of the shallow water equation. We describe a
conjugate gradient method for the numerical inversion. Numerical results for
one-dimensional model are presented to illustrate the feasibility of the
approach. Also we provide a proof of the differentiability of the weak form
with respect to the coefficient as well as the continuity and boundedness of
the linearized operator under reasonable assumptions using the maximal
parabolic regularity theory.Comment: 19 pages, 3 figure
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Evaluating the Performance of Infectious Disease Forecasts: A Comparison of Climate-Driven and Seasonal Dengue Forecasts for Mexico
Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model
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Antibiotic Resistance Increases with Local Temperature
Abstract Background: Antibiotic resistance is considered as one of our greatest emerging public health threats. Current understanding of the factors governing spread of antibiotic-resistant organisms and mechanisms among populations is limited. Methods: We explored the roles of local temperature, population density, and additional factors on the distribution of antibiotic resistance across the United States, using a database of regional antibiotic resistance that incorporates over 1.6 million bacterial pathogens from human clinical isolates over the years 2013–2015. Results: We identified that increasing local temperature as well as population density were associated with increasing antibiotic resistance in common pathogens. An increase in temperature of 10oC was associated with increases in antibiotic resistance of 4.2%, 2.2%, and 3.6% for the common pathogens Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. The effect of temperature on antibiotic resistance was robust across almost all classes of antibiotics and pathogens and strengthened over time. Conclusion: These findings suggest that current forecasts of the burden of antibiotic resistance could be significant underestimates in the face of a growing population and warming planet. Figure 1. Antibiotic resistance increases with increasing temperature. (A) A heatmap of mean normalized antibiotic resistance for E. coli for all antibiotics across the USA. (B) A heatmap of 30-year average minimum temperature (oC) across the USA. Disclosures All authors: No reported disclosures
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