Time Series Analysis Research Articles

Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia.

This paper presents a dataset related to the physical activity behavior of 206 adolescents (107 females and 99 males) from 11 to 16 years old and 25 adults (13 females and 12 males) living in rural (77 adolescents and 15 adults) and urban (129 adolescents and 10 adults) parts of New Caledonia, an archipelago of the South Pacific. Physical behavior was assessed through 60-Hz triaxial GENEActiv accelerometers worn for 5 to 7 consecutive days between July 2018 and April 2019. Participants were randomly recruited at school and trained staff fitted the devices on the nondominant wrist, at which time all were reminded of the expectations while wearing the device. The dataset contains restricted raw .bin accelerometer data provided by the GENEActiv software and open 1-second epoch .csv accelerometer data derived from the .bin files with an R script, provided in the repositories as well. Similarly, there is open information (age range and sex) and restricted information (age in months, place of living, declared cultural community and socioeconomic status) about the participants. The paper describes the file contents, describes the participants involved when collecting this dataset, and gives examples of how the data can be processed to assess the physical behavior of these participants. This high-quality dataset contains diverse sociodemographic information about the participants, which will enable the determination of correlations with their physical behavior. Moreover, since raw data with high granularity and continuous information is provided, studies interested in data processing and testing new methods on time series analyses can use the dataset.

Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series

BackgroundAnomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decision-making, helping to identify issues like operational inefficiencies, fraud and emerging health complications. ObjectivesThis study presents a novel method for detecting both collective and individual anomalies in healthcare data through time series analysis using unsupervised machine learning. The dual-strategy approach leverages two methodologies: a ’practice centre-based approach’ which monitors changes across different practice centres and a ’process-based approach’ which focuses on identifying anomalies within individual centres. The former allows for early detection of systemic issues, while the latter highlights specific irregularities within a centre’s operations. MethodsThe study utilised a dataset over 500,000 medical records from multiple GP practice centres in the UK collected between 2018–2023. Data are clustered using DBSCAN to identify collective anomalies from deviations from linear trends in consecutive two-month scatterplots. Individual anomalies are identified by examining the SOM-clustered time series of various medical processes within a specific practice centre, where graphs show deviation from the typical pattern. FindingsOur approach addresses some challenges posed by the complexity and sensitivity of healthcare data by not requiring personal information. The method offers accurate visual representations making the data accessible and interpretable for non-technical users. Unlike traditional methods focusing solely on subsequence anomalies, our technique analyses the collective behaviour across multiple time series providing a more comprehensive perspective. ConclusionThis study underscores the importance of integrating unsupervised anomaly detection with clinical expertise to ensure that statistically anomalous patterns align with clinical relevance. The dual-strategy clustering method holds significant potential for enabling timely interventions, proactively identifying potential crises, and ultimately contributing to better decision-making and operational efficiency within the healthcare sector.

Comparing voluntary and government-mandated management measures for meeting sustainable fishing targets.

Management of natural resources and environmental systems has often involved top-down approaches in which government agencies set and enforce regulations on extractive activities. More recently, market-driven approaches were introduced to incentivise producers to voluntarily engage in practices that align with management objectives and support regulations. For the first time, we compare government and voluntary approaches within fisheries management systems and quantify their relative influences on the sustainability status of fish populations. Voluntary measures include eco-certification against the Marine Stewardship Council's (MSC) Fisheries Standard and Fishery Improvement Projects (FIP). Government-mandated measures are implemented for individual populations, or else at national and international levels. Using a hierarchical time series analysis, we treated each of these measures as independent interventions potentially affecting trends in fishing pressure and biomass of nearly 300 populations. Supporting earlier findings, we confirmed a strong effect of government rebuilding plans in sharply reducing fishing pressure and allowing population biomass to recover. Other government-mandated measures further contributed to reducing fishing pressure. While simultaneously accounting for government measures, we found that biomass increases were associated with stronger incentives generated by voluntary measures. This influence was attributed to the opening of conditions of certification or suspension of certification for MSC fisheries, while no clear influence was attributed to FIPs. MSC certification was rarely observed in the absence of strong government-mandated measures, however, suggesting that sustainability-related incentives associated with voluntary measures can promote more desirable environmental outcomes for target stocks if used in parallel with more conventional approaches to management of natural resources.

Impact of COVID-19 pandemic on critically ill patients with colon cancer: A population-level study.

297 Background: The COVID-19 pandemic strained healthcare systems and disrupted pre-pandemic levels and trends in the short-term mortality of colon cancer hospitalizations. Colon cancer is the fourth most diagnosed cancer in the US and Texas. The impact on short term mortality in Texas in the years since the COVID-19 pandemic have not been explored. Methods: We used publicly available, deidentified, and state-wide data to conduct a population-base cohort study of hospitalizations aged ≥ 18 years admitted to intensive care units at acute care hospitals in Texas during 2016-2022 with a diagnosis of colon cancer. Hospitalizations transferred to another acute care hospital were excluded from the study. Colon cancer was identified using ICD-10-CM codes C18x and C20 selected from Clinical Classification Software Refined category NEO015: Gastrointestinal cancers - colorectal. Short-term mortality was defined as in-hospital mortality or discharge to a hospice. The response variables are pre-pandemic time trend, level change, change in time trend, and final time trend. The primary analysis method was interrupted time series analysis (ITSA) based on multilevel-multivariable logistic regression with results reported as average marginal effects per quarter (AME) and 95% confidence intervals (95% CI) in changes to the absolute probability of mortality. The matched pairs t-test was applied to ITSA and counterfactual predicted probabilities to further elucidate the impact of the COVID-19 pandemic. Sensitivity analyses was performed for hospitalizations aged ≥ 65 years and for hospitalizations without a diagnosis of COVID-19. Results: A total of 49,007 hospitalizations were included in the study of which 21,995 (44.9%) occurred after the beginning of the pandemic. Unadjusted short-term mortality was lower in the pre-pandemic period compared to the post-pandemic period (18.1% vs 20.1%). On adjusted analysis, the post-pandemic period remained associated with higher mortality rates. The pre-pandemic short-term mortality time trend was negative (AME -0.19%/quarter, 95% CI [-0.28% to -0.10%], p = 0.0001). The change in time trend was positive (AME 0.39%/quarter, 95% CI [0.23% to 0.55%], p < 0.0001) and the change in level was also positive (AME 1.25%, 95% CI [0.0.04% to 2.47%], p = 0.0436). The final time trend was significant and positive (AME 0.20%/quarter, 95% CI [0.07% to 0.33%], p = 0.0023). The point estimate and 95% CI for the difference between ITSA predicted and counterfactual predicted probabilities of mortality was 3.45% (3.41% to 3.48%). Similar results were observed on sensitivity analyses. Conclusions: Since the onset of the pandemic there was a 2% increase in the absolute probability of short-term mortality and a change from negative to positive time trend in short-term mortality. The average impact of the COVID-19 pandemic on the absolute probability of short-term mortality is estimated at over 3%.

African Swine Fever: Spread and seasonal patterns worldwide.

African Swine Fever (ASF) is a viral disease affecting both wild and domestic swine, with the potential for major lethality rates. In addition to direct losses for producers, its notification in a free country or zone leads to international trade restrictions. The disease has spread globally at concerning levels, with outbreaks reported in recent years across five continents. Time series analysis of ASF outbreak notifications indicates an increasing trend in Europe and Asia. For Europe, including both domestic pigs and wild boar, seasonality was pronounced in the summer and autumn (July, August, and October). Wild boar represented 78.00 % (25,017) of the reported outbreaks in the continent, with pronounced seasonality in winter (December, January and February) and a peak in summer (July). In domestic pigs, seasonality was pronounced mainly in the summer (July and August) and autumn (October). Poland and Romania were the countries with the highest number of reported outbreaks on the continent, representing 35.34 % and 22.50 % of the total in Europe, respectively. In Asia, analysis including both domestic pigs and wild boar showed pronounced seasonality in February and March. For domestic animals, a higher number of outbreaks occur in the early months of the year (mainly February, and March), in the third quarter and early fourth (August, September, October and November), with a decrease in the middle (July) and at the end of the year (December). In China, the notifications are predominantly in domestic swine, with 97.21 % (209) of the reported outbreaks in the country. For wild boars, South Korea accounts for 96.46 % (1690) of the notifications in Asia. Seasonality in Europe may be related to increased human movement during these periods and wild boar behavior. In Asia, seasonality coincides with the period immediately following the Chinese New Year, probably related to the increased national demand for pork and the movement of people and by-products in the country. Recent notifications in 2021 and 2022 in the Caribbean region have raised concerns across the Americas.

Projections of future heat-related emergency hospitalizations for asthma under climate and demographic change scenarios: a Japanese nationwide time-series analysis

There is growing concern about climate impacts on human health. However, empirical evidence is lacking regarding future projections of heat-related asthma hospitalizations. This study aimed to project excess emergency hospitalizations for heat-related asthma exacerbation in Japan. Using Japanese nationwide administrative data from 2011 to 2019, we conducted an ecological time-series quasi-Poisson regression analysis to estimate the heat-related relative risk of emergency hospitalization for asthma over a lag of 0-3 days during the warm season (June to September). Heat exposure was defined as the region-specific daily mean temperature exceeding the locally defined minimum morbidity temperature percentile (MMP). Heat-related excess hospitalizations for asthma were projected under future climate and demographic change scenarios based on Shared Socioeconomic Pathways (SSPs). We identified 75,829 emergency hospitalizations for asthma. The heat-related relative risk of hospitalization was 1.22 (95% confidence interval (CI): 1.12-1.33) at the 99th percentile temperature relative to the MMP, with the highest estimates for cases aged 0-14 years. Heat-related excess hospitalizations were projected to increase by 6.78 (95%CI: 5.84-7.67) times in 2091-2099 versus 2011-2019 along SSP5-8.5 when constant population structure was assumed. The increasing trend persisted even when the future population decline was considered (4.19 (95%CI: 3.53-4.85) times in 2091-2099 versus 2011-2019 under SSP5-8.5). Future heat-related impacts on asthma exacerbation are expected to increase in Japan toward the end of this century, even when the future demographic change is considered. Our projections will contribute to resilient health systems adapting to ongoing climate change.

Virological characterization of Parvovirus B19 isolated during the atypical 2023-2024 outbreak in France.

A Parvovirus B19 (B19V) outbreak has been reported in Europe in 2023-2024. The aims of this study were 1) to describe the incidence of primary cases from 2012 to 2024 in one French hospital 2) to analyze the genome of 2023 strains 3) to identify virological profiles according to the clinical presentations of B19V infection. The incidence of B19V primary cases was studied through an interrupted time-series analysis. Genomes of 2023 strains were sequenced in the NS1-VP1u region. Blood viral loads, IgG and IgM levels were analyzed in 158 cases according to clinical manifestations with Kruskal-Wallis test and a machine learning approach based on k-nearest neighbors. During the 2023-2024 B19V outbreak, there was an 8-time increase in the incidence of B19V infections compared with pre-pandemic levels (8.25 (95%CI: 5.79-11.76)). The 2023 strains belonged to genotype 1a and were closely related to pre-2019 strains. Blood viral loads were significantly different between clinical presentations (p<0.0001). Machine learning allowed us to classify 68.8% (95% CI: 60.9-75.9) patients into the correct clinical group. The 2023-24 epidemic is probably due to the reemergence of the pre-2019 strain. The virological profiles highlighted in this study could assist in accurately interpreting virology results.

Diabetes disparities in diabetes health care access and outcomes during the COVID-19 pandemic in the United States

ABSTRACT Objectives To investigate the impact of COVID-19 on hospitalization and consequent diabetes-related complications in patients with type 2 diabetes mellitus (diabetes). Methods We conducted a retrospective cohort study of patients with diabetes. Interrupted time series analysis (ITS) was used to analyze the monthly trends in diabetes-related hospitalization rates, including short- and long-term complications, 1-year before and after onset of COVID-19. Results Persons with diabetes experienced a significant (p < 0.001) rapid drop in monthly hospital admission rates at onset of COVID-19, then rose significantly (p = 0.003) to higher than pre-COVID-19 levels. Older age, lower education, and income levels were associated with higher base-level monthly hospital admission rates and a greater rate reduction at COVID-19 onset. ITS analysis showed monthly hospital admission rates from short-term complications surged to higher level 6 months after COVID-19 onset. Hospital admissions due to long-term complications decreased immediately post-COVID-19, but rose significantly (p < 0.001) to higher than pre-COVID levels, with patients experiencing higher nephropathy, angiography, and dermatological complications post-COVID-19. Conclusion COVID-19 had a negative impact on diabetes-related hospitalization access, resulting in increased short- and long-term complications. Long-term effects of deferred care due to COVID-19 on diabetes-related complications may persist, emphasizing the need for continued education toward improved diabetes self-management.

New job, new habits? A multilevel interrupted time series analysis of changes in diet, physical activity and sleep among young adults starting work for the first time

BackgroundThe workplace is an important determinant of health that people are exposed to for the first-time during adolescence or early adulthood. This study investigates how diet, physical activity, and sleep change as people aged 16–30 years transition into work and whether this varies for different individuals and job types.MethodsMultilevel linear regression models assessed changes in fruit and vegetable intake, sleep duration, and physical activity among 3,302 UK Household Longitudinal Study (UKHLS) participants aged 16–30 years, who started work for the first time between 2015 and 2023. In line with interrupted time series analysis, models assessed behavioural trends in the period before starting work, the immediate effect of starting work, and changes in behaviour over time after employment. Stratified analyses examined differences by selected individual and job characteristics, adjusted for covariates. All analyses were conducted in R v.4.3.2.ResultsSleep duration was stable over the years before and after starting work, but starting work was associated with an immediate reduction in sleep duration (β-9.74 [95% CI:-17.32 to -2.17 min/night). Physical activity, measured in Metabolic Equivalent Tasks (METs), increased immediately after starting work (β = 113.3, [95% CI: 80.49 to 146.11] MET-min/day), but subsequently decreased over time after starting work (β= -26.7, [95% CI: -40.75 to -12.66] MET-min/day/year). The increase in physical activity was greater among males, among those with no degree and among those starting lower socioeconomic classification jobs. Starting a “work from home” job had an immediate negative effect on physical activity (β= -126.42 [95% CI: -264.45 to 11.61] MET-min/day), whereas those who worked at their employer’s premises showed an initial increase (β = 128.81 [95% CI: 89.46 to 168.16] MET-min/day). Starting work had little influence on fruit and vegetable consumption.ConclusionsThis is the first study to examine how diet, physical activity, and sleep in young adults change as they start employment in the UK. Starting work is associated with decreased sleep time and increased physical activity, with differences based on sociodemographic and job characteristics. Future research should consider these potential influences of the work environment when developing interventions to promote healthy behaviour in the workplace.

Global Impact of Mass Vaccination Campaigns on Circulating Type 2 Vaccine-Derived Poliovirus Outbreaks: An Interrupted Time-Series Analysis.

Between 2016 and 2023, 3248 cases of circulating vaccine-derived type 2 poliomyelitis (cVDPV2) were reported globally and supplementary immunization activities (SIAs) with monovalent type 2 oral poliovirus vaccine (mOPV2) and novel type 2 oral poliovirus vaccine (nOPV2) targeted an estimated 356 and 525 million children, respectively. This analysis estimates the community-level impact of nOPV2 relative to mOPV2 SIAs. We fitted interrupted time-series regressions to surveillance data between January 2016 and November 2023 to estimate the impact of nOPV2 and mOPV2 SIAs on cVDPV2 poliomyelitis incidence and prevalence in environmental surveillance across 37 countries, directly comparing the impact of SIAs in 13 countries where both vaccines were used. We did not find any statistically significant differences between nOPV2 and mOPV2 SIA impact except for in the Democratic Republic of Congo (DRC), where nOPV2 SIAs had lower impact (adjusted relative risk [aRR] for cVDPV2 poliomyelitis incidence per nOPV2 SIA, 0.505; 95% confidence interval [CI], .409-.623) compared to mOPV2 (aRR, 0.193; 95% CI, .137-.272); P value for difference in RRs = 3e-6. We find variation in OPV2 SIA impacts globally, with greater certainty about Nigeria and DRC, where large outbreaks provided an opportunity to assess impact at scale. In most countries, we find no significant difference between nOPV2 and mOPV2 SIA impact. We are unable to identify the reason for the significant difference in DRC, which could include differential SIA coverage, timing, vaccine effectiveness, or outbreak dynamics.

Effects of a Procrastination Intervention for Japanese University Students With Attention-Deficit/Hyperactivity Disorder Tendencies: An Interrupted Time Series Analysis.

University students, especially those with attention-deficit/hyperactivity disorder (ADHD), experience distress due to procrastination. However, the existing treatment for adult ADHD does not adequately address procrastination. A brief procrastination-focused cognitive behavioral therapy program was developed for the current study, and its effects on procrastination, depression, and life satisfaction were assessed. Using a single-case AB design, procrastination behaviors and mood during the baseline and intervention periods were recorded. Data from 24 students with ADHD symptoms (mean age = 20.42 years, SD = 1.50) were analyzed using interrupted time series and counterfactual analyses. The effects of the program on the depressive symptoms and life satisfaction were also examined. The intervention changed the trajectory of the students' procrastination behaviors, significantly reducing them compared with baseline. They also reported improved life satisfaction following the intervention. This novel intervention appears effective in reducing procrastination among university students with ADHD symptoms.

Long-Term Effects of Direct-Acting Antivirals on Hepatitis C: Trends in Liver Disease-Related Hospitalisations in Italy.

This study aimed to evaluate the effectiveness of direct-acting antivirals (DAAs) on hepatitis C virus (HCV) hospitalisation trends in Italy, the country with not only the highest burden of HCV-related disease but also the highest number of patients treated for chronic HCV infection in Europe. Incident hospital discharge records in Italy from 2012 to 2019 that included a liver cirrhosis diagnosis without mention of alcohol, hepatocellular carcinoma (HCC), HCV and liver cirrhosis without mention of alcohol and/or HCC, cirrhosis with mention of alcohol, as defined by the International Classification of Diseases (ICD-9-CM) were reviewed. An interrupted time series analysis compared the incidence of cirrhosis and HCC before and after the introduction of DAAs (Year 2015). Overall, non-alcoholic cirrhosis significantly decreased after the introduction of DAAs (β3 = 0.03) and for those 40-59 years of age (β3 = 0.025). HCV with cirrhosis and/or HCC significantly reduced overall for those aged 40-59 and older than 60 ( ). HCC-related hospitalisation rates significantly decreased in patients younger than 60 ( ). Cirrhosis-related hospitalisations with mention of alcohol did not differ during the study period before and after the year 2015 ( ). There was a significant reduction in HCV-related hospitalisations throughout Italy after introducing DAAs.

Correlation of PM2.5 pollution and daily mortality rate of cardiovascular and cerebrovascular diseases in Northeast China through PM2.5 sources analysis

Abstract Objective This study aims to evaluate the relationship between PM2.5 concentration and daily mortality rate from cardiovascular and cerebrovascular diseases (CCVD) in cold regions of northern China, as well as to identify the primary sources of PM2.5. Methods A time series analysis model was used to calculate the exposure-response relationship between PM2.5 and CCVD mortality in Harbin. Positive matrix factorization (PMF) was employed to analyze the sources of PM2.5. Results After adjusting for multiple pollutant combinations, the maximum excess risk of cardio-cerebrovascular death on the day of PM2.5 exposure was 0.42% (95% CI: 0.15%-0.70%). Stratification by gender and age revealed that the elderly individuals and men were particularly susceptible to PM2.5 exposure. The source analysis identified seven major pollution sources contributing to PM2.5 in Harbin. Conclusion Our findings strengthen the evidence that PM2.5 is an independent risk factor for daily CCVD mortality, identify vulnerable populations that require special attention, and pinpoint the primary sources of PM2.5 in Harbin. These findings provide reference points for effectively reducing the health risks associated with PM2.5 exposure.

Gilded Connections: Unraveling the Relationship between Gold Prices and the Indian Stock Market

This study, "Gilded Connections: Unraveling the Relationship Between Gold Prices and the Indian Stock Market," delves into the dynamic relationship between the Nifty 50 index and Gold Futures over a 15-year period (April 2008 – June 2023). It explores whether gold functions purely as a safe-haven asset in times of stock market turbulence or if a deeper predictive link exists between these two financial indicators. Through a combination of stationarity tests, descriptive statistics, and time-series analysis techniques such as Vector Autoregression (VAR), this study uncovers key patterns in the interplay between gold price movements and Nifty 50 fluctuations. The results reveal a weak inverse correlation between the two, with Nifty exhibiting higher volatility and Gold offering stability during periods of economic uncertainty. The findings offer valuable insights into portfolio diversification, risk management, and the potential for gold to hedge against equity market risks. Overall, the study contributes to both theoretical understanding and practical applications for investors, traders, and policymakers in navigating market dynamics.

Exploring Crop Production Strategies to Mitigate Greenhouse Gas Emissions Based on Scenario Analysis

In the context of global climate change and carbon neutrality goals, agriculture has emerged as a major source of greenhouse gas (GHG) emissions, and faces the critical challenge of reducing emissions while ensuring food security. However, existing research has rarely focused on dynamic simulation and scenario-based analysis of optimised agricultural layouts and their impact on GHG emissions. Taking the three northeastern provinces (Heilongjiang, Jilin, and Liaoning) of China as the study area, this study quantifies GHG emissions from major grain crops and employs time-series analysis and machine learning methods to conduct a scenario analysis, including three scenarios (Business as Usual, Sustainable Optimisation, and Ecological Priority). Specific policy implications are proposed for optimising agricultural layouts and mitigating GHG emissions. The results indicate that GHG emissions in Northeast China primarily stem from methane emissions in rice cultivation and nitrous oxide emissions from fertiliser use. A scenario analysis reveals that the “Sustainable Optimisation” scenario reduces GHG emissions by 22.0% through optimised planting layouts while maintaining stable crop production. The “Ecological Priority” scenario further enhances emission reductions to 25.2% by increasing the share of low-emission crops, such as corn, and reducing high-emission crops, such as rice. The study provides a practical reference for promoting the low carbonisation of agriculture, and demonstrates that optimising planting layouts and production structures can simultaneously achieve food security and climate change mitigation.

A Methodological Approach to Measuring the Impact of TAK-003 for the Prevention of Dengue in Dourados, Brazil: Optimizing Strategies for Public Health

Background/Objectives: Takeda’s tetravalent dengue vaccine TAK-003 has been approved by the Brazilian regulatory agency ANVISA for dengue disease prevention in individuals aged 4 to 60 years. Dourados, in the state of Mato Grosso do Sul, became the world’s first city to implement a mass vaccination campaign targeting approximately 120,000 individuals. An ongoing collaborative, observational, population-based study using national surveillance and vaccination data was planned to measure the impact of the vaccine on the reduction in dengue incidence. Methods: In this manuscript, the study’s methodology, including its programmatic steps and public health relevance, is described. A collaborative assessment with multidisciplinary researchers in Brazil was conducted to identify key programmatic areas for the successful implementation of the study. These areas included feasibility and site selection assessment, methodology selection, vaccination program implementation, and public health importance. Results/Conclusions: Identification of the public health problem and understanding the disease burden, local healthcare infrastructure, and strategic partnerships were critical for a robust feasibility assessment. One of the feasibility criteria identified was the ability of the Dourados Municipal Health Secretary and the principal investigator to conduct an active vaccination campaign, utilizing extramural activities and diverse communication channels to increase vaccine acceptance and coverage. The selection of analytical methods, such as time series analysis, was dependent on the national and local structures of the databases and data availability.

Causality Test between Foreign Aid and Economic Growth: The Case of Afghanistan

This study examines all available data to determine if ODA has a long-term beneficial impact on GDP, identify the significant issues with ODA expenditure and aid dependency in Afghanistan, and improve economic growth prospects in Afghanistan. Database for the period 2002-2021 is s considered for this study. The study uses time series analysis using the EViews 12 software. These tests were conducted using ADF and Phillips-Perron (PP), the Johansen cointegration test, and the Granger causality test. Johansen's cointegration test revealed the presence of two cointegration relationships between GDP and ODA variables. This suggests that GDP and ODA move together in the long run, indicating a mutual relationship or interdependence between the two variables. Moreover, Granger causality test results suggest that neither ODA nor GDP Granger causes the other.

Examining low-intensity surface fires in boreal forests using sentinel-2 time series and bitemporal terrestrial laser scanning

ABSTRACT In Fennoscandia, wildfires are typically low-intensity surface fires that burn vegetation on the forest floor. Multispectral remote sensing, particularly the normalized burn ratio (NBR) index, is widely used to assess burn severity, but its effectiveness in identifying surface fires under dense canopies is less studied. This study examined the characteristics of surface fires across seven one-hectare test sites in Scots pine-dominated boreal forests in Finland. Fire-induced spectral changes, measured as difference normalized burn ratio (dNBR) values from Sentinel-2 data, were compared with structural changes on the forest floor measured using terrestrial laser scanning (TLS). Breakpoint analysis of NBR time series identified most surface fires, with a noticeable decline in NBR values. Fires not identified with the breakpoint analysis had less burned ground vegetation and denser canopy cover. A moderate correlation (r = –0.5) was observed between spectral and volumetric changes, with higher dNBR values corresponding to greater vegetation reduction. However, dense canopies lowered burn severity despite similar ground vegetation volume changes. NBR changes were explained by vegetation volume, canopy cover, and site conditions (R² = 84%). These findings improve understanding of the applicability of multispectral remote sensing in detecting fires, assessing burn area, and estimating burn severity in boreal forests.

Evaluating programmatic reactive focal drug administration impact on malaria incidence in northern Senegal: an interrupted time series analysis

BackgroundThe World Health Organization conditionally recommends reactive drug administration to reduce malaria transmission in settings approaching elimination. However, few studies have evaluated the impact of reactive focal drug administration (rFDA) in sub-Saharan Africa, and none have evaluated it under programmatic conditions. In 2016, Senegal’s national malaria control programme introduced rFDA, the presumptive treatment of compound members of a person with confirmed malaria, and reactive mass focal drug administration (rMFDA), an expanded effort including neighbouring compounds during an outbreak, in 10 low transmission districts in the north of the country. This evaluation sought to measure the impact of rFDA and rMFDA on malaria incidence.MethodsAn interrupted time series analysis was conducted with routine surveillance data on health post-level monthly confirmed malaria case counts from the District Health Information Software (DHIS2). The study evaluated the change in incidence following rFDA and rMFDA rollout (level change), which ranged from August 2016 to November 2019, and monthly thereafter (trend change), using an adjusted negative binomial regression model with data from January 2015 through January 2020. The model was used to estimate the number of cases averted via a counterfactual simulation.ResultsNo incidence rate reductions were estimated immediately following rollout (level change: incidence rate ratio (IRR) = 1.00, 95% credible interval (CI) = 0.76, 1.33). However, rFDA and rMFDA were associated with a 4% monthly decline in incidence relative to the baseline trend (trend change: IRR = 0.96, 95% CI = 0.95, 0.98). Over the study period, RFDA and rMFDA were estimated to avert 2,070 (95% CI = 577, 4,367) of 4,108 (95% CI = 2,620, 6,425) malaria cases.ConclusionsRFDA and rMFDA were associated with reduced malaria incidence in northern Senegal, supporting their use in malaria control in very low transmission areas. However, additional strategies are likely needed to achieve elimination in this setting.

Research on time series analysis of data products in electric power marketing management information platform based on recurrent neural networks

Abstract The power marketing management information platform can promote the energy industry's decision-making ability for power marketing business and improve demand-side management. Due to the large number of power marketing users and heterogeneous data, traditional time series analysis methods are difficult to flexibly deal with this scenario, and deep learning methods based on feedforward neural networks have weak modeling capabilities for the time dimension of data, making it difficult to effectively use these data to support decision-making. To this end, this study proposes a new time series analysis model for the power marketing management information platform, which uses a recurrent neural network (RNN) enhanced by an attention mechanism. By integrating the long-term time dependency modeling capability of RNN and the adaptive extraction capability of the attention mechanism, the model can efficiently represent time series data. Experimental evaluation shows that our method greatly outperforms traditional models and achieves higher prediction accuracy on several key performance indicators.