scholarly journals Synthesis of public water supply use in the United States: Spatio‐temporal patterns and socio‐economic controls

2017 ◽  
Vol 5 (7) ◽  
pp. 771-788 ◽  
Author(s):  
A. Sankarasubramanian ◽  
J. L. Sabo ◽  
K. L. Larson ◽  
S. B. Seo ◽  
T. Sinha ◽  
...  
Keyword(s):  
United States ◽  
Water Supply ◽  
Temporal Patterns ◽  
The United States ◽  
Public Water Supply ◽  
Spatio Temporal

scholarly journals An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study (Preprint)

2018 ◽  
Author(s):  
Prathyush Sambaturu ◽  
Parantapa Bhattacharya ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
Madhav Marathe ◽  
...  
Keyword(s):  
United States ◽  
Compression Ratio ◽  
Seasonal Influenza ◽  
Minimum Description Length ◽  
Temporal Patterns ◽  
The United States ◽  
Disease Spread ◽  
Influenza Like Illness ◽  
Incidence Data ◽  
Spatio Temporal

BACKGROUND Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. OBJECTIVE Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. METHODS We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). RESULTS We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. CONCLUSIONS Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

scholarly journals An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study

10.2196/12842 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e12842
Author(s):  
Prathyush Sambaturu ◽  
Parantapa Bhattacharya ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
Madhav Marathe ◽  
...  
Keyword(s):  
United States ◽  
Compression Ratio ◽  
Seasonal Influenza ◽  
Minimum Description Length ◽  
Temporal Patterns ◽  
The United States ◽  
Disease Spread ◽  
Influenza Like Illness ◽  
Incidence Data ◽  
Spatio Temporal

Background Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

scholarly journals Response of crop yield to different time-scales of drought in the United States: Spatio-temporal patterns and climatic and environmental drivers

2019 ◽  
Vol 264 ◽  
pp. 40-55 ◽  
Author(s):  
Marina Peña-Gallardo ◽  
Sergio M. Vicente-Serrano ◽  
Steven Quiring ◽  
Marc Svoboda ◽  
Jamie Hannaford ◽  
...  
Keyword(s):  
United States ◽  
Time Scales ◽  
Crop Yield ◽  
Temporal Patterns ◽  
The United States ◽  
Environmental Drivers ◽  
Spatio Temporal ◽  
Different Time Scales

Understanding current and future impacts of public water supply on global nitrogen cycling

2021 ◽  
Author(s):  
Elizabeth M. Flint ◽  
Matthew J. Ascott ◽  
Daren C. Gooddy ◽  
Benjamin W.J. Surridge ◽  
Mason O. Stahl
Keyword(s):  
United States ◽  
Water Supply ◽  
Water Security ◽  
Distribution Networks ◽  
Developed Countries ◽  
The United States ◽  
Public Water Supply ◽  
Data Sets ◽  
Relative Significance ◽  
N Fluxes

<p>Within in the United States some 54 km<sup>3</sup> of water is withdrawn annually for public supply. Around 16% of this water is subsequently lost through leakage as it moves through distribution networks. These processes not only have implications both economically and for water security, but the substantial redistribution of water has also been shown to cause significant perturbations in elemental cycling. Due to its importance for ecological health and global food production, this research attempts to quantify the nitrogen (N) fluxes associated with a range of Public Water Supply processes, such as abstraction and leakage. Using county level data sets, these N fluxes will be determined across the contiguous United States, and the significance of results evaluated through comparisons with other quantified N fluxes. Assessments will also be made on how the absolute and relative significance of these fluxes may change in the future, such as due to evolving water demands as a result of the combined drivers of changing climate and increasing population. Outputs from the US will form part of a wider global assessment, including comparisons with less developed countries.</p>

Sign in / Sign up

Export Citation Format

Share Document