Seasonal Decomposition Analysis Research Articles

An epidemiological and spatiotemporal analysis of visceral leishmaniasis in West Pokot, Kenya, between 2018 and 2022

BackgroundVisceral leishmaniasis (VL) remains a significant public health concern in West Pokot County, Kenya, where a large outbreak between 2020 and 2022 emphasised the need for improved VL control strategies. However, these measures are partially hampered by limited insight into the geographical distribution of cases and localised outbreaks of the disease. This study aimed to describe the epidemiology and spatiotemporal patterns of VL in West Pokot between 2018 and 2022, in order to map the spread of VL transmission and identify regions that should be prioritised for control interventions.MethodsVL patient demographics and village of residence were retrieved from admission records of Kacheliba Sub-County Hospital in West Pokot, Kenya. The temporal trend in VL admissions between 2018 and 2022 was analysed using seasonal decomposition analysis. To describe the spatial distribution of VL cases, geographic coordinates of villages of residence were collected from pre-established databases, and VL incidence was mapped at the sub-location level. Hotspot analysis was performed per study year to identify villages with high VL incidence, and scan statistics were applied to detect spatiotemporal clusters of VL cases during the study period.ResultsA total of 1948 VL patients were reported between 2018 and 2022. The annual number of cases increased from 245 in 2019 to 598 in 2022, and VL admissions were generally higher at the start of the wet seasons. 70% of the VL cases could be georeferenced, and mapping of VL incidence revealed high case rates in the east of West Pokot during the complete study period. The eastern villages Lotongot and Chepaywat were marked as VL hotspots at a 99% confidence level in all study years. In addition, five significant spatiotemporal clusters were detected in the east and north, suggestive of local VL outbreaks in these regions.ConclusionsThe increase in VL hospital admissions during the study period stresses the need for enhanced VL control and outbreak mitigation in West Pokot. These control measures should be focused on the hotspot regions in the east of the county.

Low-Cost Real-Time Water Level Monitoring Network for Falling Water River Watershed: A Case Study

Streamflow monitoring for flood warning and watershed management applications in the United States is a cost-intensive venture, and usually performed by government agencies such as the US Geological Survey (USGS). With reduced resources across the federal agencies towards environmental monitoring, agencies and stakeholders are challenged to respond with cross-cutting, collaborative, and low-cost alternatives for streamflow monitoring. One such alternative is using low-cost environmental sensors and developing a real-time gage/sensor network using IoT (Internet of Things) devices. With this technology, smaller watersheds (e.g., HUC-8 and HUC-10 level) can be equipped with low-cost gages at many locations and a clear picture of the hydrological response can be obtained. This paper presents the development and implementation of a low-cost real-time water monitoring network for the Falling Water River (FWR) watershed in the middle Tennessee region in the US. To develop and implement this gage network, the following three tasks were performed: (i) assemble a low-cost, real-time internet enabled water level gage, (ii) field-test the sensor prototype and, (iii) deploy the sensors and build a network. A collaborative partnership was developed with stakeholders including the Tennessee Department of Environment and Conservation, Tennessee Department of Transportation, Burgess Falls State Park, City of Cookeville, and Friends of Burgess Falls. The performance of the gages in water level estimation was compared with the water levels measured with a nearby USGS streamgage. The comparison was performed for the 2020–2022 time period and at two levels: event-based comparison and a long-term comparison. Nine storm events were selected for the comparison, which showed “Very Good” agreement in terms of Coefficient of Determination (R2), Nash–Suttcliffe Efficiency (NSE), and percent bias (PBIAS) (except for four events). The mean squared error (MSE) ranged between 0.07 and 1.06 while the root mean squared error (RMSE) ranged between 3 inches and 12 inches. A long-term comparison was performed using Wilcoxon Signed-Rank test and Loess Seasonal Decomposition analysis, which showed that the differences between the two datasets is not significant and that they trended well across the two year period. The gages are currently installed along the main channel and tributaries of the Falling Water River, which also include portions of the Window Cliffs State Natural Area. With continued support from the stakeholders, the number of sensors are projected to increase, resulting in a dense sensor network across the watershed. This will over time enable the stakeholders to have a spatially variable hydrological response of the Falling Water River Watershed.

Spatiotemporal dynamics of hemorrhagic fever with renal syndrome in Jiangxi province, China

Historically, Jiangxi province has had the largest HFRS burden in China. However, thus far, the comprehensive understanding of the spatiotemporal distributions of HFRS is limited in Jiangxi. In this study, seasonal decomposition analysis, spatial autocorrelation analysis, and space–time scan statistic analyses were performed to detect the spatiotemporal dynamics distribution of HFRS cases from 2005 to 2018 in Jiangxi at the county scale. The epidemic of HFRS showed the characteristic of bi-peak seasonality, the primary peak in winter (November to January) and the second peak in early summer (May to June), and the amplitude and the magnitude of HFRS outbreaks have been increasing. The results of global and local spatial autocorrelation analysis showed that the HFRS epidemic exhibited the characteristic of highly spatially heterogeneous, and Anyi, Fengxin, Yifeng, Shanggao, Jing’an and Gao’an county were hot spots areas. A most likely cluster, and two secondary likely clusters were detected in 14-years duration. The higher risk areas of the HFRS outbreak were mainly located in Jiangxi northern hilly state, spreading to Wuyi mountain hilly state as time advanced. This study provided valuable information for local public health authorities to design and implement effective measures for the control and prevention of HFRS.

High relative humidity might trigger the occurrence of the second seasonal peak of dengue in the Philippines

BackgroundDengue in some regions has a bimodal seasonal pattern, with a first big seasonal peak followed by a second small seasonal peak. The factors associated with the second small seasonal peak remain unclear. MethodsMonthly data on dengue cases in the Philippines and its 17 regions from 2008 to 2017 were collected and underwent a time series seasonal decomposition analysis. The associations of monthly average mean temperature, average relative humidity, and total rainfall with dengue in 19 provinces were assessed with a generalized additive model. Logistic regression and a classification and regression tree (CART) model were used to identify the factors associated with the second seasonal peak of dengue. ResultsDengue incidence rate in the Philippines increased substantially in the period 2013–2017, particularly for the regions in south Philippines. Dengue peaks in south Philippines predominantly occurred in August, with the peak in the national capital region (NCR) (i.e., Metropolitan Manila) occurring in September. The association between mean temperature and dengue appeared J-shaped or upside-down-V-shaped, and the association between relative humidity (or rainfall) and dengue was heterogeneous across different provinces (e.g., J shape, reverse J shape, or upside-down V shape, etc). Relative humidity was the only factor associated with the second seasonal peak of dengue (odds ratio: 1.144; 95% confidence interval: 1.023–1.279; threshold: 77%). ConclusionsDengue control and prevention resources are increasingly required in regions beyond the NCR, and relative humidity can be used as a predictor of the second seasonal peak of dengue in the Philippines.

A descriptive analysis of the Spatio-temporal distribution of intestinal infectious diseases in China

BackgroundIntestinal infectious diseases (IIDs) have caused numerous deaths worldwide, particularly among children. In China, eight IIDs are listed as notifiable infectious diseases, including cholera, poliomyelitis, dysentery, typhoid and paratyphoid (TAP), viral Hepatitis A, viral Hepatitis E, hand-foot-mouth disease (HFMD) and other infectious diarrhoeal diseases (OIDDs). The aim of the study is to analyse the spatio-temporal distribution of IIDs from 2006 to 2016.MethodsData on the incidence of IIDs from 2006 to 2016 were collected from the public health science data centre issued by the Chinese Center for Disease Control and Prevention. This study applied seasonal decomposition analysis, spatial autocorrelation analysis and space-time scan analysis. Plots and maps were constructed to visualize the spatio-temporal distribution of IIDs.ResultsRegarding temporal analysis, the incidence of HFMD and Hepatitis E showed a distinct increasing trend, while the incidence of TAP, dysentery, and Hepatitis A presented decreasing trends over the last decade. The incidence of OIID remained steady. Summer is the season with the greatest number of cases of different IIDs. Regarding the spatial distribution, approximately all p values for the global Moran’s I from 2006 to 2016 were less than 0.05, indicating that the incidences of the epidemics were unevenly distributed throughout the country. The high-risk areas for HFMD and OIDD were located in the Beijing-Tianjin-Tangshan (BTT) region and south China. The high-risk areas for TAP were located in some parts of southwest China. A higher incidence rates for dysentery and Hepatitis A were observed in the BTT region and some west provincial units. The high-risk areas for Hepatitis E were the BTT region and the Yangtze River Delta area.ConclusionsBased on our temporal and spatial analysis of IIDs, we identified the high-risk periods and clusters of regions for the diseases. HFMD and OIDD exhibited high incidence rates, which reflected the negligence of Class C diseases by the government. At the same time, the incidence rate of Hepatitis E gradually surpassed Hepatitis A. The authorities should pay more attention to Class C diseases and Hepatitis E. Regardless of the various distribution patterns of IIDs, disease-specific, location-specific, and disease-combined interventions should be established.

Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance

BackgroundThe recent situation of dengue infection in Cirebon district is concerning due to an upsurge trend since the year 2010. The largest dengue outbreak was reported in 2016 which has affected more than 1600 children. A study was conducted to explore the temporal variability of dengue outbreak in Cirebon’s child population in during 2011–2017, and to assess the short-term effects of climatic and environmental factor on dengue incidence. In addition, the spatial pattern of dengue incidence in children and high-risk villages were investigated.MethodsA total of 4597 confirmed dengue cases in children notified from January 2011 to December 2017 were analysed. Seasonal decomposition analysis was carried out to examine the annual seasonality. A generalized linear model (GLM) was applied to assess the short-term effect of climate and normalized difference vegetation index (NDVI) on dengue incidence. The incidence rate ratio (IRR) of the final model was reported. Spatial analyses were conducted by using Moran’s I and local indicator of spatial association (LISA) analyses to explore geographical clustering in incidence and to identify high-risk villages for dengue, respectively.ResultsAn annual dengue epidemic period was observed with peaks occurring every January/February. Based on the GLM, temperature at a lag 4 months (IRR = 1.27; 95% confidence interval, 95% CI: 1.22–1.31, P < 0.001), rainfall at a lag 2 months (IRR = 0.99, 95% CI: 0.99–0.99, P < 0.001), humidity at lag 0 month (IRR = 1.05, 95% CI: 1.04–1.06, P < 0.001) and NDVI at a lag 1 month (IRR = 3.07, 95% CI: 1.94–4.86, P < 0.001) were associated with dengue incidence in children. The dengue incidence in children was spatially varied and clustered at the village level across Cirebon. During 2011–2017, a total of 38 high-risk villages for dengue were identified, which were mainly located in the northern part of Cirebon.ConclusionsSeasonal patterns of dengue incidence in children in Cirebon were strongly associated with rainfall, temperature, humidity and NDVI variability, suggesting that climatic and environmental data could be used to help predict dengue outbreaks. Our spatial analysis revealed a clustered pattern in dengue incidence and high-risk villages for dengue across Cirebon, suggesting that effective interventions such as vector surveillance and school-based campaigns should be prioritized around the identified high-risk villages. Temporal and spatial analytical tools could be utilized to support local health authorities to apply timely and targeted public health interventions and help better planning and decision-making in order to minimize the impact of dengue outbreaks.

Understanding the complex seasonality of seasonal influenza A and B virus transmission: Evidence from six years of surveillance data in Shanghai, China

ObjectivesUnderstanding the complexity of influenza subtype seasonality is critical to promoting a suitable vaccination program. The aim of this study was to identify and compare the seasonality and epidemiological features of seasonal influenza subtypes after the 2009 A/H1N1 pandemic and to lay a foundation for further investigation into the social and environmental factors affecting seasonal influenza virus transmission. MethodsInfluenza-like illness (ILI) case surveillance was conducted in two sentinel hospitals in Pudong New Area, Shanghai between 2012 and 2018. Weekly data on ILI cases were analyzed. A time-series seasonal decomposition analysis was used to reveal the seasonality of influenza and epidemiological features among different subtypes. ResultsIn total, 10977 ILI patients were enrolled of whom 2385 (21.7%) had laboratory-confirmed influenza. Compared to influenza A (16.3%), influenza B (5.4%) was less frequently detected among the ILI patients (p<0.001). Semiannual epidemic peaks were identified in four of the years during the 6-year study period, while only one annual epidemic peak was found in the other two years. An epidemic peak occurred in each winter season, and a secondary peak also occasionally occurred in summer or spring. A/H3N2 predominated in both summer and winter, while A/H1N1, B/Yamagata, and B/Victoria circulated almost exclusively in winter or spring. Two lineages of influenza B seemed to predominate in alternating years. ConclusionsThis study highlights the complexity of seasonal influenza virus activity in a subtropical region of China, presenting both semiannual and annual epidemic peaks in different years. The results of this study may provide further insight into possible improvements in the timing of influenza vaccination in Shanghai, China.

Comparing the similarity and difference of three influenza surveillance systems in China

Three main surveillance systems (laboratory-confirmed, influenza-like illness (ILI) and nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS)) have been used for influenza surveillance in China. However, it is unclear which surveillance system is more reliable in developing influenza early warning system based on surveillance data. This study aims to evaluate the similarity and difference of the three surveillance systems and provide practical knowledge for improving the effectiveness of influenza surveillance. Weekly influenza data for the three systems were obtained from March 2010 to February 2015. Spearman correlation and time series seasonal decomposition were used to assess the relationship between the three surveillance systems and to explore seasonal patterns and characteristics of influenza epidemics in Gansu, China. Our results showed influenza epidemics appeared a single-peak around January in all three surveillance systems. Time series seasonal decomposition analysis demonstrated a similar seasonal pattern in the three systems, while long-term trends were observed to be different. Our research suggested that a combination of the NIDRIS together with ILI and laboratory-confirmed surveillance is an informative, comprehensive way to monitor influenza transmission in Gansu, China. These results will provide a useful information for developing influenza early warning systems based on influenza surveillance data.

Monitoring Pertussis Infections Using Internet Search Queries

This study aims to assess the utility of internet search query analysis in pertussis surveillance. This study uses an empirical time series model based on internet search metrics to detect the pertussis incidence in Australia. Our research demonstrates a clear seasonal pattern of both pertussis infections and Google Trends (GT) with specific search terms in time series seasonal decomposition analysis. The cross-correlation function showed significant correlations between GT and pertussis incidences in Australia and each state at the lag of 0 and 1 months, with the variation of correlations between 0.17 and 0.76 (p < 0.05). A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed to track pertussis epidemics pattern using GT data. Reflected values for this model were generally consistent with the observed values. The inclusion of GT metrics improved detective performance of the model (β = 0.058, p < 0.001). The validation analysis indicated that the overall agreement was 81% (sensitivity: 77% and specificity: 83%). This study demonstrates the feasibility of using internet search metrics for the detection of pertussis epidemics in real-time, which can be considered as a pre-requisite for constructing early warning systems for pertussis surveillance using internet search metrics.

Is the economic crisis affecting birth outcome in Spain? Evaluation of temporal trend in underweight at birth (2003–2012)

Background: There is growing evidence of the impact of the current European economic crisis on health. In Spain, since 2008, there have been increasing levels of impoverishment and inequality, and important cuts in social services.Aim: The objective is to evaluate the impact of the economic crisis on underweight at birth in Spain.Method: Trends in underweight at birth were examined between 2003 and 2012. Underweight at birth is defined as a singleton, term neonatal weight lesser than -2 SD from the median weight at birth for each sex estimated by the WHO Standard Growth Reference. Using data from the Statistical Bulletin of Childbirth, 2 933 485 live births born to Spanish mothers have been analysed. Descriptive analysis, seasonal decomposition analysis and crude and adjusted logistic regression including individual maternal and foetal variables as well as exogenous economic indicators have been performed.Results and conclusions: Results demonstrate a significant increase in the prevalence of underweight at birth from 2008. All maternal-foetal categories were affected, including those showing the lowest prevalence before the crisis. In the full adjusted logistic regression, year-on-year GDP per capita remains predictive on underweight at birth risk. Previous trends in maternal socio-demographic profiles and a direct impact of the crisis are discussed to explain the trends described.

Epidemiological Characteristics of Renal Colic and Climate-Related Causes in a Continental Area in Spain

Purpose: The aim of this study was to analyze the incidence of renal colic (RC) in a northwestern area of Spain and its relationship with seasonal and meteorological characteristics. Materials and Methods: We examined 700,257 cases that presented at the emergency room (ER) between 2005 and 2013. We reviewed data such as age, gender, arrival at ER, tests performed and destination after ER. Monthly data regarding temperature, humidity and hours of daylight were taken into account. The Student t-test, the Mann-Whitney test and the Chi-square test were used for the statistical analysis. RC visits were correlated with meteorological characteristics using the Pearson correlation coefficients. Results: A total of 9,330 cases were diagnosed as RC episodes (1.41% of total cases presented). The age range was 14-100 years and mean age was 47 years. Prevalence in men was higher (55.6%) than in women (44.4%). After the seasonal decomposition analysis by month, a significant increase in RC incidence was observed in the months of June, July, August and December (107-114%). There is a statistically significant correlation between mean monthly temperature and RC visit rate (R 0.33, p < 0.001). Conclusion: The incidence of RC in our region is slightly higher than the one described in the literature. A significant increase in RCs is observed in the summer months, as well as a significant correlation between incidence and temperature.

Study on the characteristics of temporal distribution and the epidemic trend of autumn-winter type scrub typhus under time series analysis

To explore the characteristics of temporal distribution and epidemic trend of autumn-winter type scrub typhus using the time series analysis. Based on the data of scrub typhus collected from Shandong Diseases Reporting Information System from 2006 to 2011, both spectral analysis and moving average analysis were used to analyze the annual data of scrub typhus while scrub typhus incidence in 2012 - 2014 was forecasted. Seasonal decomposition analysis was applied to analyze the monthly data from January of 2006 to October of 2011, followed by Autoregressive Integrated Moving Average Model (ARIMA) which was constructed to forecast case number in November and December of 2011 and compared to the actual incidence. The results of spectral analysis showed that the prevalence of autumn-winter type scrub typhus had a feature of '3-year-periodicity'. A long-term up-trend was confirmed by method of moving average analysis, with annually case numbers of 310, 337 and another number of 366 forecasted for 2012 to 2014, respectively, with the annual increase rate as 9% per-year. Data from analysis of monthly data of scrub typhus showed that through multiple seasonal decomposition analysis, the results indicated that the prevalence of this disease possessed a typical autumn-winter type. The seasonality indexes for scrub typhus in October and November were 8.454 and 2.230, respectively, while others were less than 1.000. The ARIMA(0,1,1) (0,1,0)(12) model of (1-B)(1-B(12))X(t) = (1-0.811B)u(t) that was used to forecast the prevalence of autumn-winter type scrub typhus and was constructed with the residual error of 16 lags as white noise. The Box-Jenkins test statistic for the model was 3.116, giving a P value of 0.999. The model fitted the data well. Good accordance was achieved between the observed values and the forecasted values of scrub typhus in November and December of 2011 which was produced by the ARIMA model, and all observed values were within the forecasted 95%CI. The prevalence of autumn-winter type scrub typhus showed a 3-year-periodicity, with a long-term up-trend, and the case numbers of 2012 to 2014 were forecasted, rising on the end with an increasing rate of 9% per year, which occurred seasonally with October as the peak time in every year. The ARIMA(0,1,1) (0,1,0)(12) model seemed to be quite appropriate in predicting the autumn-winter type scrub typhus.