Cyclical Trends Research Articles

A Study on Consumer Behaviour Towards Maruti Cars in Vellore District, Tamilnadu

The Indian passenger-car industry has been on the continuous growth trajectory in the recent past aided by different contributing factors of national development namely robust economic activity, increased environmental regulations, emerging organized retail industry, government’s increased focus on rural development, and development of infrastructure including roads supported by availability of funds for new projects as well as for easy vehicle financing for prospective buyers. The industry seems to be breaking out of its conventional cyclical trends and of the different sizes of cars. This study is also going to reveal the various motivating as well as influencing factors for the customers purchasing decision.

An Analysis of the Seasonal Cycle and the Business Cycle

Robert Barsky and Jeffrey Miron (1989) revealed the seasonal cycle of the U.S. economy from 1948 to 1985 was characterized by a “bubble-like” expansion in the second and fourth quarters, a “crash-like” contraction in the first quarter, and a mild contraction in the third quarter. We replicate, in part, their seasonal cycle analysis from 1946 to 2001. Our results are largely in line with theirs. Nonetheless, we find the seasonal cycle is not stable and can evolve across time. In particular, the Great Moderation affected both the business cycle and the seasonal cycle. Robert Barsky and Jeffrey Miron also found real aggregates, like the output, move together in the seasonal cycle across broadly defined sectors, similar to a phenomenon observed under the conventional business cycle. They posed a challenge question concerning why “the seasonal and the conventional business cycles are so similar.” To answer their question, we focus on a number of aggregate variables with a recursive application of the HP filter and find that aggregates, such as the GDP, consumption, the S&P 500 Index, and so forth, have a “bubble-like” expansion and a “crash-like” contraction in their cyclical trends in business cycle frequencies. Although preference shifts and production synergy are the two major forces that drive the seasonal cycle, we find the time-varying stochastic discount factor is the main cause of the business cycle and plays a more important role in macroeconomic fluctuations in business cycle frequencies than other factors.

Forecasting International Tourist Flows to a Small Island: The Case of Saipan

Sustainable tourism, unlike conventional tourism, explicitly strives to maintain economic, environmental, and social sustainability in tourist destinations. Saipan, the capital and the largest island of the chain of Pacific islands known as the Commonwealth of the Northern Mariana Islands (CNMI), is a tropical destination that heavily relies on the tourist industry as a source of income. When analyzing the trends of tourist arrivals from the top three countries that account for the majority of tourist arrivals in Saipan-China, Japan, and Korea-the necessity to implement sustainable tourism in Saipan becomes clear. This article forecasts tourism demand, which is one of the main factors in determining sustainable tourism on a small island. Analyzing both long-term and cyclical trends in tourist arrivals assists budget-constrained policymakers and related organizations in implementing sustainable tourism. Therefore, forecasting accuracy is essential in tourism policy and planning. The tourist arrival data obtained from Marianas Visitors Authority’s official Japanese website are made into a time series for Chinese, Japanese, and Korean visitor data from 2006 to 2016. Given the various time-series forecasting models, we employ simple but powerful forecasting techniques, such as ARIMA and ETS to control for the possible seasonality and trend in our tourism data. Using these forecast methods, the tourist arrival data is used to predict the future trends of tourist arrivals in Saipan for the next twelve months. The performance of our forecasting model in this study is evaluated based on the five measures of accuracy, as measured by comparison with actual tourism flows: mean error, mean absolute scaled error, mean percentage error, mean absolute error, and mean absolute percentage error. A comparison of the two popular forecast techniques in this study shows that the ETS model statistically outperforms the ARIMA model in terms of various measures of accuracy. A practical implication of this finding is that the ETS approach should be used when forecasting tourism demand for small islands. We predict the visitor arrivals from China, Japan, and Korea to Saipan by using the better model. The estimated 80% and 95% intervals for the number of tourist arrivals by the ETS model are presented in this study. The upper (lower) bound is the maximum (minimum) value of the estimated tourist arrivals. We find strong evidence that the tourist arrival trend for Korean visitors to Saipan will increase in the short run; although, the Chinese and Japanese tourist arrival trends seems to be stable, except for Japanese tourist arrivals in August. Nonetheless, with such an intense demand for Saipan’s natural resources, sustainable island tourism becomes necessary. Our estimated result is useful to practitioners in the sense that they are able to prepare for carrying capacities and the means of destination management on Saipan. Proper management for maintaining the island environmentally, economically, and socially requires the preservation of indigenous culture, local quality of life, natural resources, and economic stability while accommodating the demands of arriving tourists. It is crucial to preserve the assets of Saipan in order to maintain and improve its economy. Thus, our time series modeling for forecasting the number of visitors becomes necessary to develop overall economic, environmental, and social sustainability in the island. An interesting addition to implementing sustainable tourism in Saipan is the idea of maintaining a “cap” on the number of incoming tourists visiting the island. Perhaps, with more research on Saipan’s environment, specifically an estimate of how much of the island’s resources tourists exhaust and how much the island can withstand the aforementioned, this “cap” could be practical to prevent perpetual damage to Saipan’s biodiversity, resources, culture, and economy. Along with this idea, knowing the minimum number of tourists needed to sustain Saipan’s necessities is valuable.

Comparison of piecewise linear change point detection with traditional analytical methods for ocean and climate data

The Earth’s atmosphere and oceans are largely determined by periodic patterns of solar radiation, from daily and seasonal, to orbital variations over thousands of years. Dynamical processes alter these cycles with feedbacks and delays, so that the observed climate response is a combination of cyclical features and sudden regime changes. A primary example is the shift from a glacial (ice age) state to interglacial, which is driven by a 100-thousand year orbital cycle, while the transition occurs over a period of hundreds of years. Traditional methods of statistical analysis such as Fourier and wavelet transforms are very good at describing cyclical behavior, but lack any characterization of singular events and regime changes. More recently, researchers have tested techniques in the statistical discipline of change point detection. This paper explores the unique advantages of a piecewise linear regression change point detection algorithm to identify events, regime shifts, and the direction of cyclical trends in geophysical data. It evaluates the reasons for choosing this particular change detection algorithm over other techniques by applying the technique to both observational and model data sets. A comparison of the proposed change detection algorithm to the more established statistical techniques shows the benefits and drawbacks of each method.

Impact of Weather and Herd Size Management on Beef Cow Profitability

AbstractProducers often contemplate expanding or contracting production to take advantage of cyclical cattle price trends. This study quantifies profitability and risk implications of (1) constant herd size, (2) dollar cost averaging, and (3) price signal-based, anticipatory countercyclical expansion/contraction strategies. Weather simulation on forages with different calving season and land use intensity showed fall calving herds with added hay sales from greater fertilizer use and the countercyclical herd size management strategy to be most profitable regardless of weather or time period analyzed. Income risk was comparable to least fertilizer use. Overall, holding herd size constant led to little regret.

Seasonal variation of mortality from external causes in Hungary between 1995 and 2014.

ObjectiveTo analyze trends in external mortality in Hungary between 1995 and 2014 by sex.MethodsData on the numbers of deaths due to external causes were obtained from the published nationwide population register. Negative binomial regression was applied to investigate the yearly trends in external-cause mortality rates. Cyclic trends were investigated using the Walter-Elwood method.ResultsSuicide and accidents accounted for approximately 84% of the all-external-cause of deaths in Hungary. Annual suicide, unintentional falls and traffic accidents mortality declined significantly (p-value for annual trend: p < 0.001) from 30.5 (95% CI: 29.5–31.5) to 15.8 (15.1–16.5), from 31.2 (30.2–32.2) to 12.2 (11.7–12.8) and from 17.2 (16.4–18) to 5.4 (5–5.8) per 100 000 persons per year, respectively, during the study period. A significant declining trend in annual mortality was also found for assault, cold/heating-related accidents and accidents caused by electric current. However, the declining trend for drowning-related accidents was significant only for males. Significant winter-peak seasonality was found in the mortality rates from accidental falls, cold/heat-related accidents, other accidents caused by submersion/obstruction and other causes. Seasonal trends with a peak from June to July were observed in death rates from suicide/self-harm, accidental drowning/submersion and accidents caused by electric current. A significant seasonal variation with a peak in September was revealed in the mortality due to traffic accidents.ConclusionsThis Hungarian study suggests that there was a significant seasonal effect on almost all kinds of deaths from external causes between 1995 and 2014. Environmental effects are involved in the aetiology of suicide and accidents.

Seasonality of newly notified pulmonary tuberculosis in Japan, 2007\u20132015

BackgroundThe seasonality of pulmonary tuberculosis (TB) incidence may indicate season-specific risk factors that could be controlled if they were better understood. The aims of this study were to elucidate how the incidence of TB changes seasonally and to determine the factors influencing TB incidence, to reduce the TB burden in Japan.MethodsWe assessed the seasonality of newly notified TB cases in Japan using national surveillance data collected between 2007 and 2015. To investigate age and sex differences, seasonal variation was analyzed according to sex for all cases and then by stratified age groups (0–4, 5–14, 15–24, 25–44, 45–64, 65–74, and ≥ 75 years). We used Roger’s test to analyze the cyclic monthly trends in seasonal variation of TB incidence.ResultsA total of 199,856 newly notified TB cases (male, 62.2%) were reported over the past 9-year period. Among them, 60.6% involved patients aged ≥65 years. Overall, the peak months of TB incidence occurred from April to October, excluding September. In the analysis stratified by age group, a significant seasonal variation in TB cases was observed for age groups ≥15 years, whereas no seasonal variation was observed for age groups ≤14 years. For female patients aged ≥25 years, the peak TB epidemic period was seen from June to December, excluding November. Male patients in the same age groups exhibited declining TB incidence from September to March.ConclusionsTB incidence exhibits seasonality in Japan for people aged > 15 years and peaks in summer to fall. Monthly trends differ according to age and sex. For age groups ≥25 years, cases in women showed longer peaks from June to December whereas cases in men declined from September to December. These results suggest that the seasonality of TB incidence in Japan might be influenced by health checkups in young adults, reactivation of latent TB infection with aging, and lifestyle habits in older adults. These findings can contribute to establishing the potential determinants of TB seasonality in Japan.

Seasonal variation in childhood mortality

Aim: This ecological study was carried out to determine the cyclic trends in the rate of perinatal and infant mortality and among children aged under 15 years who died in traffic accidents or from drowning, respiratory disease or cancer.Methods: Monthly data on the numbers of live births, perinatal and infant deaths, and deaths from respiratory disease, cancer, traffic accidents, accidental drowning, and submersion in children aged under 15 years were obtained from the Hungarian nationwide population register. The data were aggregated over the study period and cyclic trends were investigated using the Walter–Elwood and negative binomial regression methods.Results: Significant double-peak (May and November) trends were found in mortality rates for perinatal and infant deaths, respectively. Additionally, significant (p-value < .001) seasonal variation in monthly mortality rates for deaths from respiratory diseases was observed with a peak in February, and a significant single peak was observed in mortality rates in July in both deaths from traffic accidents and drowning among children aged 0–14 years. However, there was no seasonal variation in monthly mortality rates for deaths from childhood cancer.Conclusions: Since cyclic trends in mortality suggest some effect of environmental factors in etiology, we might speculate that perinatal mortality may have been related to respiratory infections. A significant single peak was observed in mortality rates in July in both deaths from traffic accidents and drowning among children aged 0–14 years which could be related to environmental factors, such as temperature, air masses, and fronts. However, there was no seasonal variation in monthly mortality rates for deaths from childhood cancer. All of these novel findings could prove useful in preventive strategies, but further cohort studies are needed to investigate this hypothesis.What is knownSeasonality in infant mortality is known.What is newA significant cyclic trend was found in infants’ mortality, with a peak in deaths in winter during the post-perinatal period and double peaks in May and November during the perinatal period, which might be associated with respiratory syncytial virus infections.Winter-peak cyclic trends were observed in both preterm and low birthweight mortality.A significant cyclic trend was observed in mortality, with a peak in deaths from traffic accidents and drowning in July for children aged 0–14 years, but seasonal variation was not found in monthly deaths from childhood cancer.

Investigation of recent decadal-scale cyclical fluctuations in salinity in the lower Colorado River

Beginning in the late 1970s, 10- to 15-year cyclical oscillations in salinity were observed at lower Colorado River monitoring sites, moving upstream from the international border with Mexico, above Imperial Dam, below Hoover Dam, and at Lees Ferry. The cause of these cyclical trends in salinity was unknown. These salinity cycles complicate the U.S. Bureau of Reclamation's (Reclamation) responsibility for managing salinity in the river for delivery of water to Mexico to meet treaty obligations. This study develops a conceptual model of the salinity cycles from time-series water quality, streamflow, and precipitation data in both the lower and upper Colorado River Basins in order to provide Reclamation the ability to understand, anticipate, and manage future salinity cycles in the lower river. Compared with the Lees Ferry record, both maximum and minimum salinity levels increase downstream by about 25% at Hoover Dam, by about 49% at Imperial Dam, and by about 69% at the northern international boundary with Mexico. In the upper basin, cyclical salinity trends are evident at the outflow of three major tributaries, where salinity is also noted to be inversely related to streamflow. Time series trends in precipitation within the catchments of the three upper basin tributaries indicate cyclical periods with above normal precipitation and periods with below normal precipitation. Periods of greater than normal precipitation in the contributing areas correspond with declines in salinity at the catchment monitoring sites and periods of less than normal precipitation correspond with rising salinity at the sites. Based on the conceptual model developed in this investigation, a multiple linear regression model was developed using a stepwise variable selection procedure to simulate salinity in Lake Powell inflow. Important variables in the explanation of salinity entering Lake Powell include flow from the three upper basin tributaries, seasonality, and mean precipitation in the upper basin, among others. The root mean square error of prediction for the MLR model was 31.48 mg/L (5.7%).

Econometric Analysis of the Developing Countries’ Trade Indicators

In this paper, the author attempted to study the patterns of the export and import shares of the developing countries and found out the relation of export and import share with its determinants like growth rate, inflation rate, FDI, current account balance, REER, concentration index, and diversification index respectively during 1980-2016 where FDI, REER, and diversification index significantly influenced the export and import shares respectively. Both the export and import shares have upward structural breaks and smooth cyclical trends. Their VAR models are unstable and non-stationary.

Mortality surveillance in the Netherlands: severity of winter 2016/2017

ObjectiveWeekly numbers of deaths are monitored to increase the capacity to deal with both expected and unusual (disease) events such as pandemic influenza, other infections and non-infectious incidents. The monitoring information can potentially be used to detect, track and estimate the impact of an outbreak or incident on all-cause mortality.IntroductionThe mortality monitoring system (initiated in 2009 during the influenza A(H1N1) pandemic) is a collaboration between the Centre for Infectious Disease Control (CIb) of National Institute for Public Health and the Environment (RIVM) and Statistics Netherlands. The system monitors nation-wide reported number of deaths (population size 2017: 17 million) from all causes, as cause of death information is not available real-time. Data is received from Statistics Netherlands by weekly emails.MethodsOnce a week the number of reported deaths is checked for excess above expected levels at 2 different time-lags: within 1 and 2 weeks after date of death (covering a median 43% and 96% of all deaths respectively). A weekly email bulletin reporting the findings is sent to the Infectious Disease Early Warning Unit (at CIb) and a summary of results is posted on the RIVM website. Any known concurrent and possibly related events are also reported. When excess deaths coincide with hot temperatures, the bulletin is sent to the Heat Plan Team (also at RIVM). Data are also sent to EuroMOMO which monitors excess mortality at a European level. For the Dutch system baselines and prediction limits are calculated using a 5 year historical period (updated each July). A serfling-like algorithm based on regression analysis is used to produce baselines which includes cyclical seasonal trends (models based on historical data in which weeks with extreme underreporting have been removed. Also periods with high excess mortality in winter and summer were removed so as not to influence the baseline with previous outbreaks).ResultsIncreased mortality started two weeks after the influenza epidemic started and remained increased during the remainder of the influenza epidemic except for a drop in week 52 (coinciding with Christmas holidays) (Figure 1). Excess mortality was primarily observed in persons 75 years or older. Cumulative excess mortality was estimated at 7,503 deaths occurring during the 15 weeks of the 2016/2017 influenza epidemic (week 48 of 2016 through week 10 of 2017) and at 8,890 during the total winter season (44 weeks running from week 40 up to week 20 of the next year).ConclusionsIn terms of number of deaths during the winter season (weeks 40-20 the next year) and during the influenza epidemic (weeks 48-10), the 2016/2017 season in the Netherlands was of high severity compared with the previous five seasons. Mortality was only higher in the 2014/2015 season when the longest influenza epidemic was recorded of 21 weeks.

A Scoping Review of Enterovirus D-68

Objective: To create a scoping review on enterovirus D-68 (EV-D68) that will serve as a useful tool to guide future research with the aim of filling critical information gaps and supporting the development of public health preparedness activities.Introduction: EV-D68 is a non-polio enterovirus, primarily resulting in respiratory illness, with clinical symptoms ranging from mild to severe. Infection has also been associated with severe neurological conditions like acute flaccid myelitis (AFM). EV-D68 was first discovered in 1962, with infrequent case reports until 2014 at which point a widespread multi-national outbreak mostly affecting the pediatric population occurred across North America, Europe, Southeast Asia and Africa. This outbreak was associated with an increase in AFM, with cases being reported in Canada, the United States, Norway, and France. With this new and emerging threat, public health and other organizations were called upon to implement response measures such as establishment of case definitions, surveillance mechanisms, and recommendations for clinical and public health management. The response to the 2014 outbreak in Canada highlighted several important EV-D68 evidence gaps including a lack of risk factor and clinical information available for non-severe cases, and uncertainty around seasonal, cyclical and secular trends. Given the increased reporting of EV-D68 cases associated with severe outcomes, it's critical that public health establishes what is known about EV-D68 in order to support decision-making, education and other preparedness activities and to highlight priority areas for future research to fill critical knowledge gaps. Scoping reviews provide a reproducible and updateable synthesis research methodology to identify and characterise all the literature on a broad topic as a means to highlight where evidence exists and where there are knowledge gaps. In order to systematically characterise the EV-D68 knowledge base, a scoping review was conducted to map the current body of evidence.Methods: A literature search of published and grey literature on EV-D68 was conducted on May 1, 2017. A standardized search algorithm was implemented in four bibliographic databases: Medline, Embase, Global Health and Scopus. Relevant grey literature was sought from a prioriidentified sources: the World Health Organization, United States Centers for Disease Control and Prevention, the Public Health Agency of Canada, the European Centre for Disease Prevention and Control, and thesis registries. Two-level relevance screening (title/abstract followed by full-text) was performed in duplicate by two independent reviewers using pretested screening forms. Conflicts between the reviewers were reconciled following group discussion with the study team. English and French articles were included if they reported on EV-D68 as an outcome. There were no limitations by date, publication type, geography or study design. Conference abstracts were excluded if they did not provide sufficient outcome information to characterize. The articles were then characterized by two independent reviewers using a pretested study characterization form. The descriptive characteristics of each article were extracted and categorized into one of the following broad topic categories: 1) Epidemiology and Public Health, 2) Clinical and Infection Prevention and Control (IPC), 3) Guidance Products, 4) Public Health Surveillance, 5) Laboratory, and 6) Impact. The Epidemiology and Public Health category contained citations describing prevalence, epidemiological distribution, outbreak data and public health mitigation strategies. Clinical and IPC citations included details regarding symptoms of EV-D68 infection, patient outcomes, clinical investigation processes, treatment options and infection prevention and control strategies. The Guidance category included citations that assess risk, provide knowledge translation or provide practice guidelines. Public Health Surveillance citations provided details on surveillance systems. Citations in the laboratory category included studies that assessed the genetic characteristics of circulating EV-D68 (phylogeny, taxonomy) and viral characteristics (proteins, viral properties). Lastly, the Impact category contained citations describing the social, economic and resource burden of EV-D68 infection. Each broad topic category was subsequently characterised further into subtopics.Results: The search yielded a total of 384 citations, of which 300 met the inclusion criteria. Twenty-six of forty-three potentially relevant grey literature sources were also included. Preliminary literature characterization suggests that the majority of the published literature fell under the topic categories of Epidemiology, Clinical, and Laboratory. There were limited published articles on public health guidance, IPC, surveillance systems and the impact of EV-D68. The grey literature primarily consisted of webpages directed towards the public (what EV-D68 is, how to prevent it, what to do if ill, etc.). This scoping review work is presently underway and a summary of the full results will be presented at the 2018 Annual Conference.Conclusions: The body of literature on EV-D68 has increased since the 2014 outbreak, but overall remains small and contains knowledge gaps in some areas. To our knowledge, this scoping review is the first to classify the entirety of literature relating to EV-D68. It will serve as a useful tool to guide future research with the aim of filling critical information gaps, and supporting development of public health preparedness activities.

Seasonality of Common Pediatric Infectious Diseases.

Traditional sources cite seasonal patterns for common infectious diseases, often based on microbiologic data, but little is known about cyclical trends in clinically diagnosed infectious conditions in the emergency department (ED). We leveraged the publicly available Nationwide Emergency Department Sample database to measure the seasonality of the most common pediatric infectious diseases diagnosed in US EDs. We searched the Nationwide Emergency Department Sample database to identify infectious diagnoses comprising at least 1% of all diagnosis codes ascribed to patients 21 years and younger in US EDs from 2009 to 2013. We used Fourier regression to examine seasonal trends in disease and calculated the peak-to-nadir ratio for each infectious condition. Over 20% of pediatric visits during the study period were for infectious conditions. Upper respiratory infection, otitis media, gastroenteritis, urinary tract infection/pyelonephritis, cellulitis/abscess, and pneumonia showed a seasonal pattern that matched trends found in prior regional or microbiologic-based studies. The strongest seasonal trend as measured by goodness of model fit was found in pneumonia (peak-to-nadir incidence ratio of 2.7), followed by otitis media (2.0), cellulitis/abscess (2.0), gastroenteritis (1.6), upper respiratory infection (3.2), and urinary tract infection/pyelonephritis (1.4). Pharyngitis did not show a strong seasonal trend. Many of the most common pediatric infectious diseases diagnosed in US EDs exhibited seasonal patterns. Large administrative databases can be used to track seasonal disease patterns, with the advantage that they reflect clinician diagnosis beyond microbiologic confirmation. This methodology could aid in resource planning, infection control, and public health educational initiatives.

Time-varying effects of cyclical fluctuations in China's energy industry on the macro economy and carbon emissions

This study first used principal component analysis to calculate an operating index for China's energy industry and applied the index to investigate cyclical fluctuation trends. Subsequently, a time-varying parameter structural vector auto-regressive model was adopted to analyse the time-varying characteristics of the cyclical fluctuations in the macro economy and carbon emissions. The results showed that cyclical changes in the energy industry lagged fluctuations in the macroeconomic environment. The average length of the cycle was three years. Industrial shocks had a clear pro-cyclical impact on production levels and prices. During periods of prosperous growth, these positive shocks can sustain increased output and price levels. During recessionary periods, positive shocks have a short-term effect and are not favourable for output growth or price rebounds in the long run. The macroeconomic effect of cyclical fluctuations in the energy industry clearly has a stronger price effect than output effect and a greater impact on producer price inflation than consumer price inflation. In addition, the effect of energy shocks on carbon emissions has gradually weakened over time. We argue that promoting supply-side structural reform policies and eliminating excessive energy capacity within the industry while continuing to promote green energy strategies are key solutions to reversing supply-demand imbalances and downward price trends.

Structural and Cyclical Trends in the Supplemental Nutrition Assistance Program

Participation in the Supplemental Nutrition Assistance Program (SNAP) has increased sharply over the past 20 years. Average monthly participation grew from 17.3 million people in 2001 to a peak of 47.6 million people in 2013. Although participation declined somewhat as the economy recovered from the Great Recession, SNAP participation remains well above its pre-recession level. {{p}} Kelly D. Edmiston investigates the forces driving long-term patterns in SNAP participation as well as its cyclical variation. He finds that three structural factors—legislative and programmatic changes, poverty, and a rising share of the working population not in the labor force—have made the largest contributions to SNAP participation over time. His results suggest growth in SNAP participation is unlikely to unwind in the near future.

Shipbuilding and economic cycles: a non-linear econometric approach

Purpose Economic studies have always underlined the cyclical trends of many industries and their different relations to the macro-economic cycles. Shipping is one of those industries and it has been often characterised by peaks that have influenced both the trade patterns and industry investment structure (e.g. fleet, shipyard activity, freight rates). One of the main issues related with the cycles is the effect on overcapacity and prices for newbuilding and how the understanding of these patterns can help in preventing short-hand strategies. The purpose of this paper is to evaluate different effects of business elements on shipbuilding activity, in relation to different economic-cycle phases. Design/methodology/approach This paper proposes a non-linear econometric model to identify the relations between shipbuilding and economic cycles over the past 30 years. The research focuses on identifying the cycle characteristics and understanding the asymmetrical effect of economic- and business-related variables on its development. Findings The study underlines the presence of an asymmetric effect of several business variables on the shipbuilding productions, depending on the cyclical phases (i.e. market expansion or economic slowdown). Moreover, lagged effects seem to be stronger than contemporaneous variables. Originality/value The paper is a first attempt of using non-linear modelling to shipbuilding cycles, giving indications that could be included in relevant investment policies.

Visceral leishmaniasis cyclical trends in Bihar, India – implications for the elimination programme.

Background: Visceral leishmaniasis (VL) is a vector-borne disease of public health importance in India, with the highest burden of disease in the states of Bihar, Jharkhand, West Bengal and Uttar Pradesh. The disease is currently targeted for elimination (annual incidence to less than one per 10,000 population) using indoor residual spraying, active case detection and treatment. Historically the disease trend in India has been regarded as cyclical with case resurgence characteristically occurring every 15 years. Understanding this pattern is essential if the VL elimination gains are to be sustained. To better understand the cyclical trends, annual climatic indicators including rainfall, temperature and humidity over time were compared with annual VL case incidence data. Methods: Annual climate data (rainfall, average and maximum temperature and specific humidity) from 1956-2004 were used to identify potential factors influencing VL incidence. Months relevant to the VL life-cycle were identified and defined (Monsoon, Sand-fly Peak, Pre-Sand-fly Peak and Annual) for analysis. The Kruskall-Wallis test was used to determine significant difference between categorical rainfall and VL incidence, whilst univariate negative binomial regression models were used to determine predictors of disease incidence. Results: The negative binomial regression model showed statistically significant associations (p <0.05) for VL incidence and maximum temperature, and average temperature, when considering annual and pre-sand fly peak time periods. No other associations between humidity, rainfall or temperature and VL incidence were detected (all values p >0.05). Conclusion: The VL programme in Bihar has made significant progress in adopting best practices for improved treatment and vector control, with the aim to achieve VL elimination. However, open access granular programme data for indoor residual spray activities and case detection is required to fully understand the role of climate in disease transmission and potential resurgence.

Detecting seasonal and cyclical trends in agricultural runoff water quality-hypothesis tests and block bootstrap power analysis.

Seasonal and cyclic trends in nutrient concentrations at four agricultural drainage ditches were assessed using a dataset generated from a multivariate, multiscale, multiyear water quality monitoring effort in the agriculturally dominant Lower Rio Grande Valley (LRGV) River Watershed in South Texas. An innovative bootstrap sampling-based power analysis procedure was developed to evaluate the ability of Mann-Whitney and Noether tests to discern trends and to guide future monitoring efforts. The Mann-Whitney U test was able to detect significant changes between summer and winter nutrient concentrations at sites with lower depths and unimpeded flows. Pollutant dilution, non-agricultural loadings, and in-channel flow structures (weirs) masked the effects of seasonality. The detection of cyclical trends using the Noether test was highest in the presence of vegetation mainly for total phosphorus and oxidized nitrogen (nitrite + nitrate) compared to dissolved phosphorus and reduced nitrogen (total Kjeldahl nitrogen-TKN). Prospective power analysis indicated that while increased monitoring can lead to higher statistical power, the effect size (i.e., the total number of trend sequences within a time-series) had a greater influence on the Noether test. Both Mann-Whitney and Noether tests provide complementary information on seasonal and cyclic behavior of pollutant concentrations and are affected by different processes. The results from these statistical tests when evaluated in the context of flow, vegetation, and in-channel hydraulic alterations can help guide future data collection and monitoring efforts. The study highlights the need for long-term monitoring of agricultural drainage ditches to properly discern seasonal and cyclical trends.

Visceral leishmaniasis cyclical trends in Bihar, India - implications for the elimination programme.

Background: Visceral leishmaniasis (VL) is a vector-borne disease of public health importance in India, with the highest burden of disease in the states of Bihar, Jharkhand, West Bengal and Uttar Pradesh. The disease is currently targeted for elimination (annual incidence to less than one per 10,000 population) using indoor residual spraying, active case detection and treatment. Historically the disease trend in India has been regarded as cyclical with case resurgence characteristically occurring every 15 years. Understanding this pattern is essential if the VL elimination gains are to be sustained. To better understand the cyclical trends, annual climatic indicators including rainfall, temperature and humidity over time were compared with annual VL case incidence data. Methods: Annual climate data (rainfall, average and maximum temperature and specific humidity) from 1956-2004 were used to identify potential factors influencing VL incidence. Months relevant to the VL life-cycle were identified and defined (Monsoon, Sand-fly Peak, Pre-Sand-fly Peak and Annual) for analysis. The Kruskall-Wallis test was used to determine significant difference between categorical rainfall and VL incidence, whilst univariate negative binomial regression models were used to determine predictors of disease incidence. Results: The negative binomial regression model showed statistically significant associations (p <0.05) for VL incidence and maximum temperature, and average temperature, when considering annual and pre-sand fly peak time periods. No other associations between humidity, rainfall or temperature and VL incidence were detected (all values p >0.05). Conclusion: The VL programme in Bihar has made significant progress in adopting best practices for improved treatment and vector control, with the aim to achieve VL elimination. However, open access granular programme data for indoor residual spray activities and case detection is required to fully understand the role of climate in disease transmission and potential resurgence.

Growth in Public Interest and Scientific Research on Kinesiology Taping

Kinesiology taping has grown in popularity, though there are a number of unsubstantiated claims made by some kinesiology taping advocates. An investigation was conducted into trends in general and scientific interest in kinesiology taping. Data in the public domain (Google Trends and Google Scholar data) indicated significant growth of interest both in terms of public search queries and scientific publications relevant to kinesiology taping. Cyclical trends in interest relevant to kinesiology taping were identified. Segmented regression indicated some of the growth in interest in kinesiology taping may be attributed to exposure of spectators of the Olympic Games to kinesiology taping in athletes. Despite substantial growth in research on kinesiology taping there remain unsubstantiated claims.