Detection of anomalous patterns in water consumption: an overview of approaches

The water distribution system constantly aims at improving and efficiently distributing water to the city. Thus, understanding the nature of irregularities that may interrupt or exacerbate the service is at the core of their business model. The detection of technical and non-technical losses allows water companies to improve the sustainability and affordability of the service. Anomaly detection in water consumption is at present a challenging task. Manual inspection of data is tedious and requires a large workforce. Fortunately, the sector may benefit from automatized and intelligent workflows to reduce the amount of time required to identify abnormal water consumption. The aim of this research work is to develop a methodology to detect anomalies and irregular patterns of water consumption. We propose the use of algorithms of different nature that approach the problem of anomaly detection from different perspectives that go from searching deviations from typical behavior to identification of anomalous pattern changes in prolonged periods of time. The experiments reveal that different approaches to the problem of anomaly detection provide complementary clues to contextualize household water consumption. In addition, all the information extracted from each approach can be used in conjunction to provide insights for decision-makingThis research work is cofounded by the European Regional Development Fund (FEDER) under the FEDER Catalonia Operative Programme 2014–2020 as part of the R+D Project from RIS3CAT Utilities 4.0 Community with reference code COMRDI16-1-0057.Peer ReviewedPostprint (author's final draft

Marked long-term decline in ambient CO mixing ratio in SE England, 1997–2014:Evidence of policy success in improving air quality

Atmospheric CO at Egham in SE England has shown a marked and progressive decline since 1997, following adoption of strict controls on emissions. The Egham site is uniquely positioned to allow both assessment and comparison of ‘clean Atlantic background’ air and CO-enriched air downwind from the London conurbation. The decline is strongest (approximately 50ppb per year) in the 1997–2003 period but continues post 2003. A ‘local CO increment’ can be identified as the residual after subtraction of contemporary background Atlantic CO mixing ratios from measured values at Egham. This increment, which is primarily from regional sources (during anticyclonic or northerly winds) or from the European continent (with easterly air mass origins), has significant seasonality, but overall has declined steadily since 1997. On many days of the year CO measured at Egham is now not far above Atlantic background levels measured at Mace Head (Ireland). The results are consistent with MOPITT satellite observations and ‘bottom-up’ inventory results. Comparison with urban and regional background CO mixing ratios in Hong Kong demonstrates the importance of regional, as opposed to local reduction of CO emission. The Egham record implies that controls on emissions subsequent to legislation have been extremely successful in the UK

Reconstruction of epidemic curves for pandemic influenza A (H1N1) 2009 at city and sub-city levels

To better describe the epidemiology of influenza at local level, the time course of pandemic influenza A (H1N1) 2009 in the city of Hong Kong was reconstructed from notification data after decomposition procedure and time series analysis. GIS (geographic information system) methodology was incorporated for assessing spatial variation. Between May and September 2009, a total of 24415 cases were successfully geocoded, out of 25473 (95.8%) reports in the original dataset. The reconstructed epidemic curve was characterized by a small initial peak, a nadir followed by rapid rise to the ultimate plateau. The full course of the epidemic had lasted for about 6 months. Despite the small geographic area of only 1000 Km2, distinctive spatial variation was observed in the configuration of the curves across 6 geographic regions. With the relatively uniform physical and climatic environment within Hong Kong, the temporo-spatial variability of influenza spread could only be explained by the heterogeneous population structure and mobility patterns. Our study illustrated how an epidemic curve could be reconstructed using regularly collected surveillance data, which would be useful in informing intervention at local levels

Novel survey method finds dramatic decline of wild cotton-top tamarin population

For conservation purposes, accurate methods are required to track cotton-top tamarins in their natural habitat. As existing census methods are not appropriate for surveying these monkeys, a lure-transect method combined with playback vocalization was used here to allow accurate counting of the animals

Gastroesophageal reflux disease in 2006: The imperfect diagnosis

There continues to be significant controversy related to diagnostic testing for gastroesophageal reflux disease (GERD). Clearly, barium contrast fluoroscopy is superior to any other test in defining the anatomy of the upper gastrointestinal (UGI) tract. Although fluoroscopy can demonstrate gastroesophageal reflux (GER), this observation does not equate to GERD. Fluoroscopy time should not be prolonged to attempt to demonstrate GER during barium contrast radiography. There are no data to justify prolonging fluoroscopy time to perform provocative maneuvers to demonstrate reflux during barium contrast UGI series. Symptoms of GERD may be associated with physiologic esophageal acid exposure measured by intraesophageal pH monitoring, and a significant percentage of patients with abnormal esophageal acid exposure have no or minimal clinical symptoms of reflux. Abnormal acid exposure defined by pH monitoring over a 24-h period does not equate to GERD. In clinical practice presumptive diagnosis of GERD is reasonably assumed by substantial reduction or elimination of suspected reflux symptoms during therapeutic trial of acid reduction therapy

How to determine life expectancy change of air pollution mortality: a time series study

<p>Abstract</p> <p>Background</p> <p>Information on life expectancy (LE) change is of great concern for policy makers, as evidenced by discussions of the "harvesting" (or "mortality displacement") issue, i.e. how large an LE loss corresponds to the mortality results of time series (TS) studies. Whereas loss of LE attributable to chronic air pollution exposure can be determined from cohort studies, using life table methods, conventional TS studies have identified only deaths due to acute exposure, during the immediate past (typically the preceding one to five days), and they provide no information about the LE loss per death.</p> <p>Methods</p> <p>We show how to obtain information on population-average LE loss by extending the observation window (largest "lag") of TS to include a sufficient number of "impact coefficients" for past exposures ("lags"). We test several methods for determining these coefficients. Once all of the coefficients have been determined, the LE change is calculated as time integral of the relative risk change after a permanent step change in exposure.</p> <p>Results</p> <p>The method is illustrated with results for daily data of non-accidental mortality from Hong Kong for 1985 - 2005, regressed against PM₁₀and SO₂with observation windows up to 5 years. The majority of the coefficients is statistically significant. The magnitude of the SO₂coefficients is comparable to those for PM₁₀. But a window of 5 years is not sufficient and the results for LE change are only a lower bound; it is consistent with what is implied by other studies of long term impacts.</p> <p>Conclusions</p> <p>A TS analysis can determine the LE loss, but if the observation window is shorter than the relevant exposures one obtains only a lower bound.</p

Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali

BACKGROUND: Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. METHODOLOGY/PRINCIPAL FINDINGS: In this longitudinal retrospective (01/1996-06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. CONCLUSIONS/SIGNIFICANCE: The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel