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  1. 2021
    “…The influence of meteorological determinants on tuberculosis (TB) incidence remains severely under-discussed, especially through the perspective of time series analysis. In the current study, we used a distributed lag nonlinear model (DLNM) to analyze a 10-year series of consecutive surveillance data. …”
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    The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015
  2. 2022
    “…In particular, we extracted a neuronal apoptosis network that characterized the cell death execution phase at E8/E9 and apoptotic cell clearance at E14 by combining the gene time series analysis with network synthesis from the chicken interactome. …”
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    Bid Expression Network Controls Neuronal Cell Fate During Avian Ciliary Ganglion Development
  3. 2023
    “…In this work, we leverage information theory and time series analysis to propose a novel and simple method for dissecting changes in causal influence. …”
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    Detecting intermittent switching leadership in coupled dynamical systems
  4. 2024
    “…METHODS: This is a cross-sectional study with a time series analysis. Neonatal deaths that contained neonatal sepsis records in any field of the death certificate between the years 2000 and 2013 were studied. …”
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    NEONATAL SEPSIS: MORTALITY IN A MUNICIPALITY IN SOUTHERN BRAZIL, 2000 TO 2013
  5. 2025
    “…In order to test the research hypothesis time-series analysis was conducted. RESULTS: Increasing trends in LE and in the older age (>65 or >80 years) groups’ share and declining trends in TFR were recorded. …”
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    Trends of Cardiovascular Disease Mortality in Relation to Population Aging in Greece (1956 - 2015)
  6. 2026
    “…INTRODUCTION: Using Interrupted Time Series Analysis and generalized estimating equations, this study identifies factors that influence the size and significance of Hurricane Sandy’s estimated impact on HIV testing in 90 core-based statistical areas from January 1, 2011 to December 31, 2013. …”
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    Vulnerabilities Associated with Post-disaster Declines in HIV-testing: Decomposing the Impact of Hurricane Sandy
  7. 2027
    “…Time trend plots were constructed to assess the correlation between Google search trends and disease notification under the IDSP RESULTS: Temporal correlation was observed between the IDSP reporting and Google search trends. Time series analysis of the Google Trends showed strong correlation with the IDSP data with a lag of −2 to −3 weeks for chikungunya and dengue fever in Chandigarh (r > 0.80) and Haryana (r > 0.70). …”
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    Google Search Trends Predicting Disease Outbreaks: An Analysis from India
  8. 2028
    “…The datasets generated offer an insight into the transcriptomic profile of the host–pathogen interaction and can be used to investigate the expression of a subset of transcripts or targeted genes prior to designing cost-intensive RNA sequencing experiments, that would be best further explored with replication and a time series analysis.…”
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    Exploration of wheat and pathogen transcriptomes during tan spot infection
  9. 2029
    “…Here we quantify the abundance and distribution of three primary PRDs using time-series analysis of 30-m resolution Landsat imagery from 1999 to 2014. …”
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    Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic
  10. 2030
    “…Over 95% of the collected Anopheles spp. mosquitoes were An. albimanus. Using time series analysis techniques, we found that population dynamics of larvae and adults were not significantly correlated with each other at any time lag, though correlations were highest at one month lag between larvae and adults and four months lag between adults and larvae. …”
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    Population Dynamics of Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a Village in a Region Targeted for Malaria Elimination in Panamá
  11. 2031
    “…OBJECTIVE: This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. METHODS: The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. …”
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    Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment
  12. 2032
    “…Three different synchrony conditions with varying levels of complexity (naturally embedded, naturally isolated, and artificial) were generated for time series analysis with windowed cross-lagged correlation/ -regression (WCLC, WCLR). …”
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    Identification of movement synchrony: Validation of windowed cross-lagged correlation and -regression with peak-picking algorithm
  13. 2033
    “…Further studies are necessary to search for ways to explain the varying results in the Korea Health Panel data, as well as to consider a time series analysis of the mediating effect. The results of this study present the clear implication that even though it is crucial to address the unmet needs, but it is not enough to tackle the income related health inequalities.…”
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    Poor People and Poor Health: Examining the Mediating Effect of Unmet Healthcare Needs in Korea
  14. 2034
    “…Here, we present a methodologic framework to estimate future health impacts under climate change scenarios based on a defined set of assumptions and advanced statistical techniques developed in time-series analysis in environmental epidemiology. The proposed methodology is illustrated through a step-by-step hands-on tutorial structured in well-defined sections that cover the main methodological steps and essential elements. …”
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    Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health
  15. 2035
    “…Our method enables the visualization, quantitative comparison, and time-series analysis for formations in different time scales by focusing on transitions between clusters at each hierarchy. …”
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    Clustering algorithm for formations in football games
  16. 2036
    “…METHODS: we performed a cross-sectional study with a time series analysis based on data from the Hospital Information System of Brazil from 1998 to 2016. …”
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    Rheumatic Fever: A Disease without Color
  17. 2037
    “…Methods: Seasonal-trend decomposition of time series analysis was used to explore the seasonal distribution and trend analysis. …”
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    Spatiotemporal Distribution of Tuberculosis during Urbanization in the New Urban Area of Nanchang City, China, 2010–2018
  18. 2038
    “…This dataset can be used to develop new classification methods based on time-series analysis using deep learning, and to monitor and predict crop succession.…”
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    Time-series spectral dataset for croplands in France (2006–2017)
  19. 2039
    “…This was subsequently corroborated by an analysis of their neural data. Neural time series analysis revealed no significant difference in decision-making using social cues in the early stages, unlike neural expectation studies with predictive cues. …”
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    How Our Perception and Confidence Are Altered Using Decision Cues
  20. 2040
    “…We use a transcriptional time series analysis to identify genes that are associated with key shifts in seed development, including genes associated with secondary cell wall synthesis, mitotic cell cycle, chromatin organization, auxin synthesis, fatty acid metabolism, and seed maturation. …”
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    Developmental Analysis of Mimulus Seed Transcriptomes Reveals Functional Gene Expression Clusters and Four Imprinted, Endosperm-Expressed Genes
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