Seasonal Index Research Articles

Trend of hand, foot and mouth disease before, during, and after China’s COVID control policies in Zhejiang, China

ObjectiveTo describe the trends in the incidence of hand, foot and mouth disease (HFMD) before, during, and after China’s Coronavirus Disease (COVID) control policies, and to interpret the influence on HFMD incidence at different control stages in Zhejiang Province.MethodsWe collected data on HFMD cases in Zhejiang between 2014 and 2023. We compared the constituent ratios of cases at different COVID control stages by sex, age, child groups, and pathogens and weekly seasonal indices to observe seasonal variations in the incidence of HFMD. An interrupted time-series segmented regression analysis was applied to estimate the influence on HFMD incidence at different control stages. Stratified and sensitivity analyses were conducted to validate the findings.ResultsA considerable proportion of cases occurred among children living separately. The proportions of children in kindergartens or nurseries and children aged 2–4 years were relatively low at the strict control stage compared to the other three stages. Enteroviruses other than enterovirus 71 and coxsackie virus A16 were the dominant HFMD pathogens, and the proportion showed an increasing trend. The usual spring–summer peak in HMFD incidence did not occur in 2020, and the periodicity of the biennial peak was disrupted for a year. The summer peak in 2023 was higher than that in the other years, and was delayed by 3 weeks. The trend changes in weekly HFMD cases during the strict control and regular control stages were − 15% (IRR: 0.85, 95% CI: 0.81–0.89) and 17% (IRR: 1.17, 95% CI: 1.12–1.23), respectively. However, the change was not statistically significant during the reopening stage (IRR: 1.41, 95% CI: 0.34–5.88). The expected number of cases increased by 1.12 times (95% CI: 243.17, 53.45%) during the reopening stage compared to what would have occurred if the zero-COVID policy had continued in 2023.ConclusionNon-pharmaceutical interventions (NPIs) for COVID-19 control can mitigate HFMD. However, after the dynamic zero-COVID policy ended, the HFMD incidence returned to historical levels. Strict NPIs such as traffic restrictions and kindergarten closures cannot be sustained long-term. NPIs such as improving personal hygiene for routine prevention are highly recommended.

Time Series Analysis of Dry Chilli Price Trends in Byadagi Market, Haveri District, Karnataka, India

Spices are conventional aromatic vegetables mainly utilized for flavoring of food. Among these, chilli (capsicum annuum), is one of most important spice used around the world. The present study examined dry chillies price behaviour in the Byadagi market of Haveri District, Karnataka. The study was based on secondary data from 2000-2022 collected in Agmarketnet.in. Time series analysis involves a complex mix of four main components: trend (Tt), seasonal (St), cyclical (Ct), and irregular (It) variations. The result revealed that there was a significant increasing trend in prices of dry chillies from 2000-2022. The Multiplicative model of TCSI was used in this study, the polynomial regression was also used for the trend analysis and found the Third-degree model best fit. The highest seasonal index was found in January (112.44) while the lowest seasonal index in the month of July (93.28) the analysis of seasonal indices in the price of dry chilli would help to understand the status of dry chilli marketing in the Byadagi market. The dry chilli prices exhibit no oscillatory movement and no definite periodicity during the study period.

Assessing Alterations of Rainfall Variability Under Climate Change in Zengwen Reservoir Watershed, Southern Taiwan

This study aims to detect changes in rainfall variability caused by climate change for various scenarios in the CMIP6 (Coupled Model Intercomparison Project Phase 6) multi-model ensemble. Projected changes in rainfall unevenness in terms of different timescale indices using three categories, namely WD50 (number of wettest days for half annual rainfall), SI (seasonality index), and DWR (ratio of dry-season to wet-season rainfall) are analyzed in Zengwen Reservoir watershed, southern Taiwan over near future (2021–2040) and midterm future (2041–2060) relative to the baseline period (1995–2014) under SSP2-4.5 and SSP5-8.5 scenarios. The projected rainfall for both baseline and future periods is derived from 25 GCMs (global climate models). The results indicate that noticeably deteriorated rainfall unevenness is projected in the Zengwen Reservoir watershed over future periods, which include decreased WD50, increased SI, and decreased DWR. Though there were noticeable differences in the rainfall projections by the different GCMs, the overall consensus reveals that uncertainties in future rainfall should not be ignored. In addition, WD50 has the greatest deviated relative change in mean, which implies that the short-timescale rainfall unevenness index is easily affected by climate change in the study area. Distributional changes in rainfall unevenness determined by simultaneously considering alterations in relative changes in mean and standard deviation indicated that there was no single dominant category. However, the top two categories, with summed frequencies exceeding 0.5, characterize different properties of rainfall unevenness indices. The top two categories of WD50 and SI commonly have decreased mean and increased mean, respectively, but nearly equal frequencies of the top two categories in DWR exhibit opposite variations. The proposed rainfall unevenness change detection approach provides a better understanding of the impacts of climate change on rainfall unevenness, which is useful for preparing adaptive mitigation measures for coping with disasters induced by climate change.

Is there an association between daylight hours and serum testosterone levels in men?

Background Studies assessing variability of serum testosterone levels associated with seasonal environmental factors have been contradictory. Design We assessed associations between the seasons and changes (δ) in seasonality indices and male serum total testosterone (δTT) variability. Patients and Measurements Data were collected in 144 men with paired serum TT samples (126 non-fasting/18 fasting) analysed at Walsall Manor Hospital, UK (52.3 degrees North). Seasonal factors (ambient temperature within 15 min of sampling, humidity, precipitation, duration of daylight on the day of sampling, monthly average ambient temperature, and precipitation) were obtained from local weather-station archives. Sign-rank test determined inter-sample differences between TT and seasonality indices. Linear regression analyses studied associations between δTT and δ seasonal indices in the total cohort and subgroups (stratified by medians of age, TT and men with paired non-fasting samples). Sign-rank determined whether serum TT differed between the seasons. Results Median inter-sample interval was 63 days. No significant inter-sample differences were evident regarding serum TT levels and seasonality indices. No associations were noted between δTT and δ seasonality indices in the total cohort and subgroups stratified by age and TT. Interestingly, δ ambient temperature (p = 0.012) and daylight duration (p = 0.032) were inversely associated with δTT in the 126 men in the non-fasting group (dependent variable). Only a small degree of the variability in the δTT was accounted by the above-mentioned independent variables. The seasons did not appear to influence serum TT values. Conclusions No relation was shown between seasonality and serum TT in the total cohort, thus possibly eliminating a confounding variable that could affect laboratory and clinical practice. It may be that seasonal variation in length of day is too modest at this latitude to demonstrate significant associations, hence our findings are latitude specific. We suggest that further data analysis to address this question in areas with greater seasonal variation would be appropriate.

Environmental Quality, Extreme Heat, and Healthcare Expenditures

Although the effects of the environment on human health are well-established, the literature on the relationship between the quality of the environment and expenditures on healthcare is relatively sparse and disjointed. In this study, the Environmental Quality Index developed by the Environmental Protection Agency and heatwave days were compared against per capita Medicare spending at the county level. A general additive model with a Markov Random Field smoothing term was used for the analysis to ensure that spatial dependence did not undermine model results. The Environmental Quality Index was found to hold a statistically significant (p < 0.05), multifaceted nonlinear association with spending, as was the average seasonal maximum heat index. The same was not true of heatwave days, however. In a secondary analysis on the individual domains of the index, the social and built environment components were significantly related to spending, but the air, water, and land domains were not. These results provide initial support for the simultaneous benefits of healthcare financing systems to mitigate some dimensions of poor environmental quality and consistently high air temperatures.

Assessing the impact of climate change on spring discharge using hydrological modelling in Musanze District, Rwanda

AbstractClimate change has far-reaching impacts on water availability globally, with changing precipitation patterns and global warming contributing to increasing scarcity and unreliability of spring water in many regions. Despite this understanding, the implication of climate change on the hydrological system remains limited in certain areas, including the district of Musanze, Rwanda. This study investigates the effects of climate change on the discharge of 14 springs in Musanze using the hydrological model V2Karst. CORDEX data from two global climate models of CMIP5 are used to simulate the future spring discharges over the period 2021–2100. The results reveal significantly higher annual discharges in the RCP2.6 scenario compared to the RCP8.5 for all springs from 2021 to 2100. Nevertheless, no significant long-term trend in spring discharge is observed between the early (2021–2050) and late (2071–2100) periods. However, the intra-annual temporal discharge patterns are changing with a significant increase in the seasonality index for the RCP8.5 scenario towards the end of the twenty-first century. Additionally, for both RCPs, there is a notable increase in the number of days with discharges below 40% of the mean of annual discharges during the baseline period. The overall findings of this study suggest that climate change significantly impacts the future evolution of spring discharges in Musanze, indicating potential risks to the future availability of water in the region.

Impacts of Climate Change on Rainfall ‘Seasonality Index’ and Its Potential Implications on Water Savings and Reliability through Household Rainwater Tanks

This study depicts potential climate change impacts on annual rainwater savings from household rainwater harvesting using two different climate projection models; ACCESS 1.0 and CSIRO-Mk3.6. This paper also investigates potential changes in the relationships of ‘water saving efficiency’ and reliability with rainfall ‘seasonality index’ under the mentioned climate change scenarios. The annual water savings were calculated for three weather conditions: dry, average, and wet. Historical daily rainfall amounts provided by the Australian Bureau of Meteorology were used for three locations within the city of Brisbane (Australia). For the same locations, projected future daily rainfall amounts were collected from an online data portal facilitated by the Australian government. Potential annual water savings, water saving efficiency, and reliability values for the selected locations were calculated through a widely used tool, eTank, developed on water balance methodology at a daily scale. It was found that for the coastal location, Manly, the future water savings are not likely to change significantly. However, for the inland location, Sunnybank, the future water savings are expected to decrease under all the weather conditions through both the considered climate projections. For the far inner location, Oxley, the water savings are likely to decrease in the dry year, whereas in wet year, they are likely to increase. Also, it was found that the overall average relationship of SI–water saving efficiency is steeper for ACCESS 1.0 projected data compared to that produced through CSIRO-Mk3.6 data, and that significant differences exist among individual relationships for each location. The overall reliabilities calculated through the model projected data show lower values compared to the reliabilities calculated using historical data.

Влияние глубины и интенсивности сезонных колебаний на деятельность гостиниц

Currently, hospitality enterprises are faced with the problem of loading the number of rooms. At the same time, many hotels are faced with seasonal fluctuations in the volume of sales of hotel services. This situation leads to a lack of profit in certain periods, uneven workload on the enterprise and personnel, etc. Different types of hotel enterprises are affected by seasonality to varying degrees. Seasonality is relevant for various hotels, both resort and city. At the same time, each hotel may have its own seasonality: the factors that caused seasonal fluctuations differ. In some cases, these are climatic conditions, in others, event events in the region, focus on serving a certain segment, etc. Thus, the impact of seasonality on hotel operations should not be underestimated. The main problem here lies in the need to forecast the volume of sales of services, taking into account seasonal fluctuations. When assessing the impact of seasonality on sales volumes of hotel services, it is necessary to take into account such parameters as the depth, intensity, and duration of seasonal fluctuations. The object of research in this article is the GRINN hotel complex, located in the city of Orel. The study of seasonal fluctuations for this hotel enterprise is relevant. The work uses a method for determining seasonal fluctuations, which consists in calculating seasonality indices. They were calculated as a percentage of average monthly levels for a number of years to the total average monthly volume of services sold for the entire billing period. For the calculation, reporting data provided by the GRINN Group of Companies for six years (2017-2022) were used. First of all, the attendance of the GRINN Group of Companies was analyzed, as well as load factors. The data obtained made it possible to calculate seasonality indices. On their basis, “seasonal waves” were built. At the same time, a comparison was made between the pre-pandemic period and the pandemic period. Next, we compared the load factor and turnover in the GRINN Group of Companies. During the research, the following results were obtained. The maximum attendance of the hotel complex was noted in 2022 (38,832 people), the maximum average annual occupancy rate was in 2019 (50.3%). The year 2020 was the least successful year for the company, which was due to the coronavirus pandemic. In 2021-2022, a gradual recovery of indicators begins. In general, the following conclusions were drawn. The GRINN Group of Companies is characterized by minor seasonal fluctuations. They are determined by the specifics of this hospitality enterprise. Since the company is focused on serving organized groups and business tourists, attendance is influenced by the business activity of the region, as well as event events. In addition, it was established that there is no close connection between the occupancy level of a hotel and its profitability. It was proven that fewer customers can generate more revenue and vice versa. It was determined that the GRINN Group of Companies has the necessary potential to combat seasonal fluctuations.

SST trends in certain areas of the Barents Sea in the winter season and mechanisms of their formation

The climate changes observed over the past few decades are most clearly manifested in the Arctic Ocean. Sea surface temperature (SST) is one of the most reliable indicators of climate change. In this paper we analyze the changes of winter SST for the western, northeastern and southeastern regions of the Barents Sea and examine the relationship of the emerging STS trends with the influence of various external factors. The working data set is represented by average monthly SST values taken from the ERA-5 reanalysis for the period 1949–2023 with a spatial resolution of 0.25×0.25° and average water temperature values on the Kola Meridian section in the 0–50 m layer. Additionally, the Arctic Oscillation (AO), Arctic Dipole (AD) and Atlantic Multidecadal Oscillation (AMO) indices were used as external factors that may affect SST variability. The time series analyzed was divided into three periods: 1949–1969, 1970–1990, 1991–2023, where the variability of the analyzed parameters was different. Thus, in the first period the trend in SST changes was negative, for the second period it was slightly negative or neutral, and for the third period it was positive. It is shown that SST in all the regions of the Barents Sea has undergone significant changes, which were most noticeable in the “warm” period of 1991–2023, when the rate of SST increasing was up to 10·10-2 °C/year in areas under the warm Atlantic water influence. The analysis of SST variability in the Barents Sea shows that the positive anomalies observed in the recent years are most likely associated with the changes in the atmospheric circulation. The Wavelet coherence analysis showed the closest agreement between the changes in the sea surface temperature and the AD index in the winter season, and with the AMO index.

Modern epidemiological features of viral acute intestinal infections in children and adolescents of Sakhalin region

Objective: to identify modern epidemiological features of viral acute intestinal infections in children and adolescents in the Sakhalin Region.Materials and methods. Data on acute intestinal infection incidence obtained from statistical reports of the Rospotrebnadzor during 2021-2023 was compared with long-term average incidence rates calculated over the period before the COVID-19 pandemic (2010-2019) to perform epidemiological analysis. Incidence was calculated for different age groups; according to months of the year, monthly long-term average incidence rates were established. Index of seasonality, coefficient of seasonality, and index of seasonal growth were determined.Results and discussion. Manifestations of acute intestinal infections epidemic process in the Sakhalin Region indicate persisting conditions for an increase in the registration of both sporadic cases of diseases and outbreaks of infectious diarrhea. Viruses were prevailing in the structure of cases of known etiology (mainly caused by rotaviruses and noroviruses). Their intra-annual dynamics showed changes in rotavirus and norovirus infection incidence rates: a shift in periods of increased incidence, a decrease in the coefficient of seasonality, and for rotavirus infection, a decrease in the index of seasonal growth. Age-adjusted rotavirus infection incidence showed predominance of the infection among preschool-aged children; norovirus infection was frequently detected among schoolchildren and adolescents. An increase in the focality index may indicate ongoing problems in the public catering sector, non-compliance with sanitary rules and hygiene standards, and untimely detection and isolation of patients with acute intestinal infections.Conclusion. Obtained results confirm the epidemiological significance of viral gastroenteritis and the necessity of anti-epidemic measures optimization.

The feasibility of using national‐scale datasets for classifying wetlands in Arizona with machine learning

AbstractThe advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national‐scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland‐specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non‐wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

Protozoan intestinal invasions in military personnel of the Northern region of Ukraine

Objective — to study the prevalence and spectrum of intestinal parasitic infestations (IPI) agents in groups of military personnel of the Northern region of Ukraine, which differed according to the criteria of having symptoms of gastrointestinal disorders and passing the serving in a combat zone. Materials and methods. From February to November 2023, the study covered 302 servicemen during inpatient medical care at the Military Medical Clinical Center of the Northern Region of Ukraine. Detection and identification of intestinal parasites in samples of feces (SF) was carried out by classical methods of microscopy using the enrichment procedure (formalin‑ethyl acetate sedimentation) and the preparation (from each enriched sediment) of four smears: two thick wet with 50% aqueous solution of glycerol and D’ Anthony iodine solution; two thin, fixed, permanently stained with Whitley’s modified trichrome (mWT) or Heidenhain’s iron hematoxylin (HIH) and the modified Ziehl‑Neelsen acid method (mZN). Smear microscopy results were evaluated according to the criteria of the US Clinical and Laboratory Standards Institute. Results. It was established that the level of prevalence of IPI among the examined servicemen reached 10.6% with variation in the value of this indicator in different groups of soldiers from 2.2% to 12.6%. A specific share of protozoan monoinvasions in soldiers reached 65.6%, and mixed invasions (with two parasites at the same time) — 34.4%. Soldiers with symptoms of gastrointestinal diseases had significantly higher rates of IPI than did asymptomatic. In addition, the infestation rates among soldiers were higher in the cold season (October‑February) with a seasonality index of 1.9. All cases of IPI in servicemen (n=32) were caused by protozoan parasites (n=39), in the etiological spectrum of which the vast majority (84.6%) were pathogenic species (Blastocystis sp., Dientamoeba fragilis, Giardia lamblia), and a smaller proportion (15.4%) were commensal species (Chilomastix mesnili, Iodamoeba butschliі). Among pathogenic protozoa (n=33), Blastocystis sp. (51.5%) and D. fragilis had the highest proportion (42.4%), G. lamblia (6.1%) had significantly less one. Conclusions. Optimizing the management tactics of military medicine to reduce the negative impact of IPI on the health and combat capacity of servicemen requires knowledge about the prevalence and spectrum of pathogens of invasions among specific groups of soldiers. A necessary condition for increasing the efficiency of microscopic detection/identification of protozoan parasites (primarily D. fragilis) in the SF is the production of permanently stained (mWT or HIH) smears. Microscopic examination of such smears at high resolution (100ґ objective, oil immersion) provides an increased incidence of detection/identification of Blastocystis sp. by 47.1% and D. fragilis by 100%.

Temporal trends of seasonal pollen indexes in a region of Northern Italy (2001–2022)

Increasing trends in allergic respiratory diseases may be linked to climate changes affecting pollen levels. This study aimed to investigate the temporal trends in seasonal indexes of 9 allergenic pollen types in the Veneto Region, Northern Italy. Available daily data from 20 monitoring stations covered 2001–2022 for Corylaceae, Cupressaceae, Poaceae, Oleaceae, and Urticaceae (pollen families), and 2006–2022 for Alnus, Betula, Ambrosia, and Artemisia (pollen genera). The 95-percentage method was used to identify the start/end dates of pollen seasons annually, and the seasonal cumulative pollen concentration (Seasonal Pollen Integral, SPIn) was calculated. The non-parametric Theil-Sen median slope method was used to examine trends in pollen seasons’ start day, duration, and SPIn in the whole region and for climatic areas (alpine, subcontinental eastern, subcontinental western). The results showed a clear upward trend in SPIn for several pollens, with median slopes ranging from 326 (Alnus) to 1089 p/m3 per 10yrs (Corylaceae); exceptions were Artemisia, whose SPIn decreased over time (−26 p/m3 per 10yrs), and Ambrosia, Betula, and Urticaceae, which did not show a clear trend. For families, pollen seasons showed a trend for anticipation (from −4.7, Urticaceae, to −13.5 days/10yrs, Oleaceae) and extended duration (from 8.3, Cupressaceae, to 13.3 days/10yrs, Oleaceae). An evident heterogeneity across the climatic areas was seen for the SPIn, with the subcontinental western area exhibiting the highest loads and most prominent increases over time. In conclusion, increased levels and prolonged seasons for several pollen taxa observed in the Veneto Region, particularly in subcontinental areas, suggest increasing risks for individuals with pollen allergies. Addressing this issue requires measures of mitigation and adaptation to climate-driven changes in pollen exposure.

Spatial and temporal analysis and forecasting of TB reported incidence in western China

ObjectiveTuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies.MethodsWe collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China.ResultsThe reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran’s I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05).ConclusionsAs a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different.

Characteristics and Indicative Significance of Groundwater Stable Isotopes in the Loess Plateau at the Regional Scale

Regional groundwater recharge is a critical scientific issue for sustainable groundwater resource development and management. However, spatial variations in groundwater recharge in the Loess Plateau （LP） remain poorly understood. To fill this knowledge gap, a systematic sampling campaign and stable isotope analysis were carried out for groundwater （shallow aquifer） in 13 major catchments during July 2019. The main objectives of this study were： ① to understandthe spatial distribution and influencing factors of stable isotopes in groundwater and ② to reveal the groundwater recharge sources and pathways and their spatial variations, combined with the precipitation stable isotope datasets. Stable isotopes in groundwater had poor spatial variations at the regional scale； however, they became isotopically depleted with the increase in annual average precipitation on the catchment scale （r = -0.87）. Compared with the stable isotope of precipitation, stable isotopes of groundwater were generally depleted and were similar to the precipitation of the rainy season （July-September）. These together indicated that there was pronounced seasonality of groundwater recharge, and the main recharge period was the rainy season. In particular, the recharge seasonality index （δP/G） was closely related to the catchment's average annual precipitation （r = -0.77） and leaf area index （r = -0.63）. In addition, groundwater lc-excess was generally negative, with the catchment-mean value ranging from -4.3‰ to -0.7‰. Hydrologically, this indicated that groundwater recharge pathways （ratio of matrix flow vs. preferential flow） were different among these catchments, which should be quantitatively determined by combining the saturated zone （groundwater） and the unsaturated zone （soil） in future work. Our findings can improve the understanding of groundwater recharge in LP and provide a scientific basis for sustainable management of groundwater resources at the regional scale.

Returned Rate and Changed Patterns of Systemic Antibiotic Use in Ambulatory Care in Hungary after the Pandemic-A Longitudinal Ecological Study.

The COVID-19 pandemic affected the epidemiology of infectious diseases and changed the operation of health care systems and health care seeking behavior. Our study aimed to analyze the utilization of systemic antibiotics in ambulatory care in Hungary after the COVID-19 pandemic and compare it to the period before COVID. We defined three periods (24 months each): Before COVID, COVID, and After COVID. Monthly trends in systemic antibiotic (J01) use were calculated using the WHO ATC-DDD index and expressed as DDD/1000 inhabitants/day (DID) and number of exposed patients per active agent. The data were further categorized by the WHO AWaRe classification. In the After COVID period, we detected almost the same (11.61 vs. 11.11 DID) mean monthly use of systemic antibiotics in ambulatory care compared to the Before COVID period. We observed a decrease in the seasonality index in the After COVID period (46.86% vs. 39.86%). In the After COVID period, the use of cephalosporins and quinolones decreased significantly, while in the case of macrolides, a significant increase was observed compared to the Before COVID period, with excessive azithromycin use (66,869 vs. 97,367 exposed patients). This study demonstrated significant changes in the pattern of ambulatory care antibiotic use in Hungary.

PyMTRD: A Python package for calculating the metrics of temporal rainfall distribution

Temporal rainfall distribution facilitates the understanding of rainfall patterns at various time scales, extreme events, and corresponding water resources implications. Researchers have developed various metrics of temporal rainfall distribution but there exist no easy-to-use software packages for calculating these metrics. To address this gap, we developed the PyMTRD package, which can be conveniently used to calculate the metrics of temporal rainfall distribution and conduct rainfall pattern analysis. The metrics calculated in the package included rainfall intensity, rainfall frequency, consecutive dry days, Gini index, unranked Gini index, wet-day Gini index, precipitation concentration index, dimensionless seasonality index, and seasonality index. This paper documented our Python software development, which included the architecture design, the Application Programming Interfaces design and algorithms for calculating each metric, and also the point and global scale applications.

Tracking Forest Disturbance in Northeast China’s Cold-Temperate Forests Using a Temporal Sequence of Landsat Data

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.

ASSESSMENT OF THE FINANCIAL RESULT OF PUBLIC-PRIVATE PARTNERSHIP PROJECT IMPLEMENTATION USING THE EXAMPLE OF A CONCESSION AGREEMENT

The article is aimed at developing a methodological approach to improving the efficiency of assessing the financial results of implementing Ukrainian and European business projects. In this context, the object of study is a specific individual partnership and cooperation project. The research has established that one of the key informational aspects of such a partnership is the assessment and forecasting of the financial result. The goal of the research is to present the methodological approach to assessing financial performance for an individual project in the most detailed form possible. The article presents an approach based on modern simulation modelling methods, systems analysis methodology, and the method of composite seasonal indices. All modelling processes were conducted using MATLAB software. MATLAB is a high-level programming language and interactive environment for numerical computation, visualization, and programming. This program is widely used for modelling and data processing in fields such as engineering, scientific research, and mathematics. The research result is presented as an approach to the mathematical modelling of assessing the financial performance of partnership projects of Ukraine with representatives of EU business. It is necessary to emphasize that the presented research has limitations related to changes in Ukraine's hyperdynamic external environment provoked by war. Forecasts may vary depending on the development of military actions. Therefore, the prospects for further research should be directed towards improving the informational provision in the system of partnership and cooperation with EU countries to increase their own investment security.

Original Research Article Analyzing Price Behaviour and Constraints in Marketing of Small Onion in Tamil Nadu, India

This study examines market trends and seasonal patterns in small onion Markets in Tamil Nadu, while also investigating challenges faced by various stakeholders in the onion supply chain. The various analyses have made use of secondary data that has been gathered from different sources. The NHRDF office in Coimbatore, NHB website and AGMARKNET website are the sources of the monthly data on wholesale market prices and arrivals of small onions f markets of Dindigul, Chennai, Coimbatore and Idukki from 2014 to 2023. Primary data also obtained to fulfill the one of the research objectives by surveying the sample of respondents. Two districts were selected purposefully: Perambalur and Trichy districts, based on the top onion producing districts in Tamil Nadu. The sample size constitutes 180 respondents (80 farmers, 40 retailers, 20 wholesalers and 40 consumers) were randomly interviewed with help of pre structured interview schedule. Trend analysis revealed increasing price trends across all markets, with Idukki experiencing the highest rate of increase. Seasonal index analysis indicated that prices in Tamil Nadu markets are influenced by arrivals, with lower prices during peak arrival periods., while Idukki market in Kerala prices less influenced by arrivals. Farmers struggle primarily with high labor costs (78%) and high cost of fertilizers pesticides and machineries (68.7%), while consumers face problems with price fluctuations (68.8%) and quality concerns (68.6%), retailers face problems with lack of storage facilities (76.27%) and absence of facilities for grading (64.85%), and wholesalers struggle with financial risks (79.2%). The study's findings will aid policymakers to focus on improving market infrastructure, regulating intermediaries, and supporting farmers in managing costs. Leveraging existing labor schemes could help alleviate workforce shortages. Furthermore, customers would gain from initiatives to maintain price stability and raise quality standards.