Residual Square Research Articles

Decomposition and reconstruction of ore-caused anomalies information by airborne gamma-ray spectrum based on fractal singular value decomposition method

Identification method research on ore-caused anomalies information is one of the core technologies of airborne gamma-ray spectrum data post-processing. This paper proposed a decomposition and reconstruction method of anomaly information called fractal singular value decomposition (FSVD), aiming at the current ubiquitous truncation error and boundary effect problems in filter design in ore-caused anomaly information identification methods in airborne gamma-ray spectrum. Based on the multi-fractal theory, this method decomposes the singular value of the specific nucleus activity data of the airborne gamma-ray spectrum under time series, and quantitatively calculates the fractal singular value truncation parameter according to residual square sum minimum principle. According to the calculation result, the original data is partially reconstructed to rebuild the ore-caused anomalies information. This paper used the 1:50000 airborne gamma-ray spectrum trial production flight data of a certain area in Inner Mongolia (the surveying area contains proved metal ore occurrences) to verify the method. The results show that: after the original signals are decomposed by FSVD, the high, medium and low singular values can represent the background, anomaly, and noise signal, respectively. At the same time, the false anomaly signals caused by flight error exist in high singular values. After reconstructing the decomposed signals according to median singular values, background noises can be effectively separated, the ore-caused anomaly in the known ore-caused anomalies can be identified and band-like false anomalies caused by time domain batches can be removed.

Comparison of two augmented classical least squares algorithms and PLS for determining nifuroxazide and its genotoxic impurities using UV spectroscopy

AbstractConcentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS) are reflecting the resolving power of spectrophotometric augmented mathematical techniques. The applied multivariate calibration methods were able to determine simultaneously the quinary mixture of nifuroxazide (NIF) and its four carcinogenic impurities, and a comparative study between the proposed models and the conventional partial least squares (PLS) algorithm was also done. Regression models were built covering the range of 10.00 to 50.00 μg mL−1 for NIF, 0.05 to 0.45 μg mL−1 (for each of impurities A and B), and 0.10 to 0.90 μg mL−1 (for impurities C and D). NIF and its four genotoxic impurities were successfully determined in the prepared mixtures and dosage forms. The efficiency of the applied algorithm for resolution and quantitation of the overlapped UV signals were compared, and the advantages over PLS mode were emphasized. The obtained results were statistically compared with each other along with those of the reported method.

Do shocks to electricity consumption revert to its equilibrium? Evidence from Indian states

This study investigates the stationary properties of electricity consumption at the aggregate and user-group levels (agricultural, industrial, commercial, domestic, and miscellaneous) for 18 Indian states over the period 1971-72 to 2015–16. Our analysis employs the two-step Lagrange Multiplier (LM) and the three-step Residual Augmented Least Square Lagrange Multiplier (RALS-LM) unit root tests. The results obtained suggest evidence of stationarity at aggregate and user groups in most states. The findings infer that shocks to electricity consumption in the Indian states have only a temporary effect. These results suggest that electricity demand can be modelled for forecasting and policymaking.

A reassessment of total factor productivity convergence: Evidence from cross-country analysis

This study revisits total factor productivity (TFP) convergence by employing the latest Lagrange multiplier and residual augmented least squares Lagrange multiplier unit root tests and Phillips and Sul panel club convergence technique. The study uses annual data for 44 developing and 29 developed countries covering the time-period 1970–2014. Our findings from these unit root tests support evidence of TFP convergence. Region-based results (Africa, Asia, and Latin America and the Caribbean) also confirm TFP convergence. Further, results derived from the Phillips and Sul test support evidence of TFP convergence, although the speed of convergence varies by region. The highest speed is noted in the Asia region, whereas, the lowest of speed productivity convergence is observed in the Africa region.

STRUCTURAL BREAKS IN NATURAL GAS CONSUMPTION AND ECONOMIC GROWTH IN NIGERIA: EVIDENCE FROM NEW TIME SERIES TESTS THAT ALLOW FOR STRUCTURAL BREAKS

This paper analyzed the issue of structural breaks in natural gas consumption and economic growth in Nigeria. The newly residual augmented least squares (RALS-LM) unit root test with breaks also known as “RALS-LM test with trend breaks and non-normal errors” proposed by Meng-Lee-Payne (2017) and the new structural breaks testing proposed by Kejriwal–Perron (2010) are among the tools used for the investi-gation. Our empirical findings provide significant evidence that the series of natural gas consumption and economic growth are stationary with one or two trend breaks. Furthermore, the investigation identified significant incidences of structural breaks in the relationship between natural gas consumption and economic growth in 1990, 2004, 2009 and all the break dates were found to be significant. The evaluation of the sub-sample periods based on the break dates revealed that the first and second breaks are potential while the last is destructive. Moreover, the estimate of the long-run elasticity is significant where a 1% increase in natural gas consumption induces the growth of Nigerian economy by 0.15% and all the dummies that represent the breakpoints are also significant where the 2004 break had a bigger effect among other breaks. The implication of the results is that shocks in the series of natural gas consumption and economic growth in Nigeria have transitory effect, modeling the relationship between natural gas consumption and economic growth in Nigeria without taking structural breaks into consideration could produce biased and unreliable statistical results, and there is economically significant dependence of the Nigerian economy on natural gas consumption.

Modelling the relationship between financing by Islamic banking system and environmental quality: evidence from bootstrap autoregressive distributive lag with Fourier terms

The aim of the paper is to contribute to the extant empirical literature on the determinants of pollution by investigating the impact of financing by Islamic banking on a comprehensive index of emission in Malaysia. Using the stochastic environmental impact, population, affluence and technology (STIRPAT) framework, we controlled for the impact of industrial production index and demography. A residual augmented least squares (RALS) method is used to examine stationarity of the series. We have also used a recently proposed bootstrap autoregressive distributive lag (ARDL) test, which is further augmented with Fourier terms to cater for any nonlinearity in the estimation. The results show evidence for cointegration among the series. The long run estimates suggest that financing by Islamic banks has a negative impact on emission, while labour force increases it. The industrial production index does not have significant impact on emission. The policy implications and recommendations are discussed in the body of the paper.

Tarifs et tables de cubage d’arbres individuels du pin d’Alep, Pinus halepensis Mill., dans l’Aurès algérien

L’objectif de ce travail est d’élaborer des tarifs de cubage d’arbres individuels de pin d’Alep, Pinus halepensis Mill., de la pinède des Ouled-Yaâgoub en Algérie. Neuf fonctions linéaires et non linéaires ont été ajustées systématiquement sur des données collectées à partir de 340 arbres, représentatifs des différentes classes de diamètre et de hauteur, choisis parmi les arbres abattus dans le cadre des coupes d’exploitation finales dans les chantiers installés dans la zone étudiée. L’approche adoptée pour l’estimation des paramètres des différentes fonctions testées est basée sur la régression, linéaire et non linéaire, selon la technique des moindres carrés ordinaires pondérés. Des analyses numériques et graphiques basées essentiellement sur la distribution des résidus et la précision des tarifs obtenus ont été employées pour comparer les différentes fonctions étudiées. Deux fonctions ont été retenues pour le cubage d’arbres individuels dans la pinède du massif des Ouled-Yaâgoub en Algérie. La première à une entrée exprime le volume en fonction du diamètre de l’arbre à 1,30 m de hauteur, et la seconde à deux entrées exprime le volume en fonction du diamètre à 1,30 m de hauteur et de la hauteur totale. Les coefficients de détermination ajustés et les carrés moyens résiduels obtenus par ces deux fonctions ou tarifs sont respectivement 0,944 et 109,78 dm3 pour la première et 0,977 et 69,69 dm3 pour la seconde. Enfin, pour une gestion efficace du massif des Ouled-Yaâgoub, l’estimation du volume sur pied des arbres de pin d’Alep doit tenir compte de l’objectif souhaité. Le tarif à une entrée, plus rapide à matérialiser, peut être utilisé pour des interventions sylvicoles telles que les éclaircies alors que le tarif de cubage à deux entrées, qui est plus précis, peut être choisi pour des coupes finales dont le bois sera destiné à la vente où des précisions plus importantes sur les volumes produits sont nécessaires.

The Influence of Ingestion Time on the Validity of Gastrointestinal Pill Temperature as an Index of Body Core Temperature During Work in the Heat

Ingestible, telemetric pills are increasingly used to estimate body core temperature from the gastrointestinal tract when gold‐standard measurements (e.g., rectal temperature) are unfeasible. While gastrointestinal temperature has been shown to provide acceptable validity, it has been suggested that the time between pill ingestion and the measurement period can influence the precision of those estimates, with sensors spending a greater time in the gastrointestinal tract providing better agreement with rectal temperature. However, a within‐subjects comparison of the effects of various sensor ingestion times on the validity of gastrointestinal temperature as an index of body core temperature during steady‐state and variable‐intensity exercise (more reflective of occupational tasks) is currently unavailable. We therefore sought to quantify the agreement between rectal temperature and gastrointestinal temperatures measured from telemetric pill sensors ingested at various periods prior to exercise in the heat. On two separate occasions, six young (mean±SD; 27±4 years) males consumed a telemetric pill sensor (Vital Sense capsule thermometer; Mini Mitter, Bend, OR) 12, 6, 3 and 1 hour(s) prior to arriving at the laboratory. Thereafter, participants performed 15‐min seated rest followed by 90‐min of either constant or variable intensity cycling eliciting an average rate of metabolic heat production of ~200 W/m2 and a 45‐min recovery, in hot, dry conditions (40°C, ~20% relative humidity). Metabolic heat production as well as rectal and gastrointestinal temperatures were measured continuously (1 min intervals). Agreement between pairs of measurements from each pill sensor and the criterion (rectal temperature) were assessed by the residual mean square error and the percentage of data points exceeding a meaningful threshold of acceptance (±0.25°C). Individual rectal temperature ranged between 36.62–38.43°C, while gastrointestinal temperature ranged between 36.51–38.32°C, 36.46–38.46°C, 36.29–38.34°C and 36.19–38.32°C for the sensors ingested 12, 6, 3 and 1 hour(s) prior to work in the heat (respectively) across both trials. During the constant work trial, the residual mean square error for each sensor averaged 0.12±0.10°C (12 hours), 0.14±0.11°C (6 hours), 0.12±0.09°C (3 hours) and 0.13±0.11°C (1 hour), with 13±16 %, 17±21 %, 11±14 % and 8±9 % of measurements exceeding the predefined acceptance threshold (±0.25°C). Residual square errors averaged 0.18±0.12°C (12 hours), 0.14±0.12°C (6 hours), 0.19±0.16°C (3 hours) and 0.14±0.10°C (1 hour) during the variable work trial, while 27±28 %, 14±15 %, 28±24 % and 14±7 % of measurements exceeding the predefined acceptance threshold. These preliminary outcomes indicate that ingestion times ranging between 12‐1 hour(s) prior to use do not demonstrably modify the precision of estimates of body core temperature from the gastrointestinal tract during either constant or variable work of moderate intensity under hot, dry conditions, with 72–92% of measurements falling within an acceptable error range.Support or Funding InformationFunded by the Government of Ontario and Natural Sciences and Engineering Research Council of Canada.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Will energy strategies to reduce oil imports by countries dependent on foreign oil be effective? Evidence from residual augmented least squares and cross-sectionally augmented panel unit root tests

The testing for stationarity of energy imports, in order to infer whether policies to reduce resource dependence are likely to have long term impact, is limited in the literature. A recently introduced residual augmented least squares procedure is used to examine the stationarity of crude oil imports per capita in 32 countries. The results provide evidence for nonstationarity in oil imports per capita of 10 countries (or 31% of the total sample size). In order to provide corroborating evidence, we employ recent panel unit root tests, which provide for the possibility of cross-sectional dependence among the members of a panel. The empirical findings provide support in favour of stationarity of imports per capita in the series. The policy implications of the results are discussed.

Forward regression for Cox models with high-dimensional covariates

Forward regression, a classical variable screening method, has been widely used for model building when the number of covariates is relatively low. However, forward regression is seldom used in high-dimensional settings because of the cumbersome computation and unknown theoretical properties. Some recent works have shown that forward regression, coupled with an extended Bayesian information criterion (EBIC)-based stopping rule, can consistently identify all relevant predictors in high-dimensional linear regression settings. However, the results are based on the sum of residual squares from linear models and it is unclear whether forward regression can be applied to more general regression settings, such as Cox proportional hazards models. We introduce a forward variable selection procedure for Cox models. It selects important variables sequentially according to the increment of partial likelihood, with an EBIC stopping rule. To our knowledge, this is the first study that investigates the partial likelihood-based forward regression in high-dimensional survival settings and establishes selection consistency results. We show that, if the dimension of the true model is finite, forward regression can discover all relevant predictors within a finite number of steps and their order of entry is determined by the size of the increment in partial likelihood. As partial likelihood is not a regular density-based likelihood, we develop some new theoretical results on partial likelihood and use these results to establish the desired sure screening properties. The practical utility of the proposed method is examined via extensive simulations and analysis of a subset of the Boston Lung Cancer Survival Cohort study, a hospital-based study for identifying biomarkers related to lung cancer patients’ survival.

Parametric and non-parametric convergence analysis of electricity intensity in developed and developing countries.

This paper examines the pattern of convergence in electricity intensity in a sample of 79 countries. We apply the residual augmented least squares regression to the convergence of energy intensity. This method has been used in the convergence of per capita energy consumption but not convergence of energy intensity. Furthermore, in contrast to the previous studies which mainly used the conventional beta convergence approach to examine conditional convergence, we use a beta convergence method that is capable of identifying the actual number of countries that contribute to conditional convergence. The sigma and gamma convergences of electricity intensity are also examined. In addition to the full sample of countries, we also examine convergence in African countries, Asian and Oceanic countries, American countries and European countries, separately. Convergences in OECD and non-OECD countries are also examined, separately. In the full sample, the results show convergence exists in 54% of the countries in the total sample. There is convergence in 65% of the African countries, 61% of the American countries, 43% of the Asian and Oceanic countries and 33% of the European countries. In terms of the regional classification, it is also observed that convergence exists for 58% of the non-OECD countries and 31% of the OECD countries. There is evidence for sigma convergence in all the blocs with the exception of European and non-OECD countries. With the exception of African countries, there is evidence for gamma convergence in all the countries and the various blocs. The policy implications of the results are discussed.

Yapısal Kırılmalar Altında Yakınsaklık ve Eşbütünleşme Analizi: Türkiye Turizm Piyasaları Uygulaması

The purpose of this study is to examine the cointegration structure and to test the convergence hypothesis in Turkey’s tourism markets with monthly data over the period 1996-2016. To check the existence of convergence, we used international tourist arrivals to Turkey from each of Turkey’s 23 major markets. International tourist arrivals data of Turkey may comprise structural changes and volatility. These attributes may negatively affect the reliability of unit root and cointegration tests outcomes. Ignorance of volatility and breaks leads to decrease in the number of convergent tourism markets. Conventional Dickey Fuller unit root test, recently developed Lagrange multiplier (LM) and Residual Augmented Least Squares (RALS-LM) tests are performed to test the existence of convergence in the tourism market. Gregory Hansen and RALS based cointegration tests are used for checking long run relationship of the tourist arrivals data. The study reveals strong evidence that most of the tourism markets for Turkey are converging. This means that tourism policies and strategies are successful for convergent tourism markets. In addition, there is some long run relationship among the different tourism markets in the presence of structural break and volatility.

Convergence of Defence Burdens in Asia-Pacific Economies: A Residual Augmented Least Squares Approach

AbstractThis paper investigates convergence of military burdens in the Asia-Pacific countries. Two different datasets of military expenditure have been used in the analysis including the datasets from Stockholm International Peace Research Institute (SIPRI) and World Military Expenditures and Arms Transfers (WMEAT). Unlike the existing papers on convergence of military burdens, we adopt a two-step method that allows the identification of countries that contribute to both stochastic and beta convergence. In the course of investigating the stochastic convergence, we use a residual augmented least squares (RALS) unit root test. The results suggest a weak evidence for convergence of military burdens in Asia-Pacific region, which is a departure from the majority of the existing studies that have provided substantial evidence of convergence. We further examine sigma convergence and the results suggest mixed evidence for convergence of military burdens. The policy implications of the results are discussed in the conclusion section.

Convergence in CO 2 emissions, carbon footprint and ecological footprint: evidence from OECD countries.

The aim of this paper is to augment the existing literature on convergence of CO2 emissions, by adding carbon footprint per capita and ecological footprint per capita to the convergence debate. We use the residual augmented least squares regression to examine the stochastic convergence of the environmental indices in 27 OECD countries. Furthermore, in contrast to the previous studies which mainly used the conventional beta-convergence approach to examine conditional convergence, we use a beta-convergence method that is capable of identifying the actual number of countries that contribute to conditional convergence. The sigma-convergence of the environmental indices is also examined. The results suggest that conditional convergence exists in 12 countries for CO2 emissions per capita, 15 countries for carbon footprint per capita and also 13 countries for ecological footprint per capita. There is evidence for sigma-convergence for all the three indicators. The policy implications of the results are discussed in the body of the paper.

Failure Rate Prediction Model of Substation Equipment Based on Weibull Distribution and Time Series Analysis

The failure rate is an important indicator to assess the reliability of substation equipment, and the failure rate prediction is an effective way to master the operation status of substation equipment. According to the characteristics of failure rate data, such as failure period segmentation and stochastic variation, this paper proposes a combined prediction method by the data decomposition of failure rate time series and the segmentation theory of failure periods and then establishes a new failure rate prediction model of the substation equipment based on the Weibull distribution and time series analysis. Compared with the traditional Weibull distribution function model which cannot describe the stochastic variation of failure rate data and cannot identify the failure period automatically, the proposed model in this paper uses the minimum sum algorithm of residual squares obtained by the Weibull distribution to accurately identify the demarcation point between different failure periods and then establishes the autoregressive moving average (ARMA) model to achieve combined prediction. Finally, the effectiveness of the proposed model is verified by the engineering example and comparison with the traditional failure rate prediction methods.

The Parameter Identification of Least Absolute Deviation Based on an Improved Gravitational Search Algorithm

The least squares (LS) identification algorithm is vulnerable to outliers and has large residual square when the measured data is mixed with impulse noise which obeys symmetrical alpha stable (SaS) distribution, so the least absolute deviation (LAD) is selected as the objective function to get better identification performance when impulse noise exists. And taking the non-difference of least absolute deviation into consideration, we adopt an improved gravitational search algorithm as optimal algorithm to search for optimal solution globally. Then the parameter identification method based on LAD objective function using an improved gravitational search algorithm (LAD-IGSA) is put forward creatively. Simulation results show that the LAD-IGSA method can restrain the influence of impulse noise effectively and achieve higher identification accuracy. Moreover, LAD-IGSA method presents better robustness and bettidentification accuracy than LP method with small data sets.

Inhibitory Kinetics of Free Ammonia (FA) on Ammonia-oxidizing Bacteria (AOB)

In this study, a sequencing batch reactor (SBR) was operated to investigate the inhibitory kinetics of free ammonia (FA) on ammonia-oxidizing bacteria (AOB). At the beginning of the experiment, FA concentrations in influent were altered to achieve stable short-cut nitrification and enrich AOB. Nitritation sludge was then employed to study variations in the specific nitrite production rate (SNiPR) during the ammonia oxidation process of batch tests. Furthermore, a kinetic model of FA inhibition on AOB activity was fitted for statistical analysis. Results showed that SNiPR increased rapidly with increase in FA concentration (0.7 mg·L-1 ≤ FA ≤ 50.2 mg·L-1) but decreased with an increase in FA concentration (FA ≥ 50.2 mg·L-1). SNiPR was maintained at 0 g·(g·d)-1 when FA concentration was higher than 687.1 mg·L-1, implying that AOB activity was completely inhibited. Statistical analysis showed that, compared to Haldane, Edwards-1#, Edwards-2#, and Luong inhibition kinetics models, the Aiba model was the most suitable for describing the inhibitory effect of FA on AOB activity. The statistical constants, i.e., residual square sum (RSS) correlation coefficient (R2), F value of the fitting equation, and confidence degree (P) were 0.005, 0.932, 181.7, and 1.06×10-9, respectively. The dynamic constant values, i.e., maximum specific nitrite production rate (rmax), half saturation constant (KS), and inhibition constant (KI) were 0.37 g·(g·d)-1, 11.78 mg·L-1, and 153.74 mg·L-1, respectively.

Convergence hypothesis in tourism markets and activities in Taiwan

Tourism policies do not only focus on how to improve arrivals from different tourism markets but also for different tourism activities. However, studies on convergence hypothesis of tourist arrivals, which can provide guidelines on how tourism policies should be conducted, have concentrated on convergence of tourism markets. The main contribution of this study is that in addition to convergence hypothesis in tourism markets, we have considered convergence hypothesis in tourism activities. We focus on Taiwan, and using a recently developed residual augmented least squares unit root test that allows for structural breaks and non-normality, we observe that convergence exists in the 15 major tourism markets and in 4 of the 5 major tourism activities in Taiwan. As a robustness check, we have also used a club convergence approach, and the results provide dominant evidence for club convergence in the tourism sector of Taiwan. The policy implications of the findings are provided within the article.

The Developmental Profile Inventory: Constructing a Clinically Useful Self-Report for Levels of Psychodynamic Personality Functioning.

The Developmental Profile Inventory (DPI) was constructed to assess psychodynamic personality functioning by self-report. On the basis of the frame of reference of the Development Profile interview method, a self-report was developed covering 3 domains, self, interpersonal functioning, and problem-solving strategies, which represent 6 maladaptive and 3 adaptive developmental levels of psychodynamic functioning. The DPI was administered to patients with personality disorders who were receiving psychotherapy (N=179) and to normal controls (N=228). The internal reliabilities of the subscales were in general in the fair to good range in the patient sample, (α=0.67 to 0.88, ωh=0.52 to 0.87) and adequate to good in the healthy controls (α=0.71 to 0.91, ωh=0.71 to 0.90). Mean item-rest correlations were adequate (0.30 to 0.50). Test-retest reliability was good (intraclass correlation=0.73 to 0.91). The hypothesized factorial structure of the DPI with 9 subscales organized in 3 clusters was partly confirmed by confirmatory factor analysis χ/df=2.37, root mean square error of approximation=0.060, root mean residual square=0.078, and comparative fit index=0.630, with each factor showing over 80% standardized loadings >0.30, and at least 75% loadings >0.40. The DPI discriminated patients and healthy controls in a meaningful way. Correlations among the DPI and other self-report measures of global personality pathology and psychological complaints showed satisfactory convergent and discriminant validity. The DPI is a promising self-report measure for assessing both adaptive and maladaptive patterns of psychodynamic personality functioning. The appropriate initial psychometric properties justify proceeding with more formal tests of construct validity and predictive performance in broader mental health settings.

Updating syndromic surveillance baselines following public health interventions

ObjectiveTo adjust modelled baselines used for syndromic surveillance to account for public health interventions. Specifically to account for a change in the seasonality of diarrhoea and vomiting indicators following the introduction of a rotavirus vaccine in England.IntroductionPublic Health England's syndromic surveillance service monitor presentations for gastrointestinal illness to detect increases in health care seeking behaviour driven by infectious gastrointestinal disease. We use regression models to create baselines for expected activity and then identify any periods of signficant increases. The introduction of a rotavirus vaccine in England during July 2013 (Bawa, Z. et al. 2015) led to a reduction in incidence of the disease, requiring a readjustment of baselines.MethodsWe identified syndromes where rates had dropped significantly following the vaccine’s introduction. For these indicators, we introduced new variables into the regression models used to create baselines. Specifically we tested for a ‘step-change’ drop in rates and a change in the seasonality of baselines. Finally we checked the new models accuracy against actual syndromic data before and after the vaccine introduction.ResultsWe were able to improve model fit post-intervention, with the best-fitting models based on a change in seasonality. All post-intervention regression models had reduced average residual square error. Reductions in residual errors ranged from <1% to 60% when a ‘step-change’ variable was included and 4% to 75% when accounting for seasonality. Furthermore, every syndrome showed a better model fit when a change in seasonality was included.ConclusionsPrior to the vaccine’s introduction, rotavirus caused a spring-time peak in vomiting and diarrhoea recorded by syndromic surveillance systems. Failure to account for the reduction in this peak post-vaccine would have made surveillance systems less effective. In particular, any increased activity during spring may have been undetected. Moreover, models that did not account for changes in seasonality would increase the chances of false alarms during other seasons. By adjusting our baselines for the changes in seasonality due to the vaccine we were able to maintain effective surveillance systems.ReferencesBawa, Z., et al. Assessing the Likely Impact of a Rotavirus Vaccination Program in England: The Contribution of Syndromic Surveillance. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2015;61(1):77-85.