Spatial and Spatio-Temporal Epidemiology

Scope & Guideline

Innovating Approaches to Spatio-Temporal Challenges

Introduction

Explore the comprehensive scope of Spatial and Spatio-Temporal Epidemiology through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore Spatial and Spatio-Temporal Epidemiology in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN1877-5845
PublisherELSEVIER SCI LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 2009 to 2024
AbbreviationSPAT SPATIO-TEMPORAL / Spat. Spatio-Temporal Epidemiol.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address125 London Wall, London EC2Y 5AS, ENGLAND

Aims and Scopes

The journal 'Spatial and Spatio-Temporal Epidemiology' focuses on the integration of spatial and temporal analysis in epidemiological research, aiming to provide insights into the distribution and determinants of health-related states and events in specified populations.
  1. Spatial Epidemiology:
    The journal emphasizes the study of how geographical, environmental, and social factors influence the distribution of diseases and health outcomes.
  2. Methodological Advancements:
    It highlights innovative statistical and computational methods, including Bayesian modeling, machine learning, and geostatistical techniques, for analyzing complex epidemiological data.
  3. Public Health Applications:
    Research published in the journal often seeks to inform public health strategies and interventions through the analysis of spatial patterns and trends in disease incidence.
  4. Socioeconomic Determinants:
    A consistent focus on understanding how socioeconomic factors correlate with health disparities across different geographic regions.
  5. Infectious Disease Dynamics:
    The journal frequently explores the temporal and spatial dynamics of infectious diseases, particularly in the context of emerging global health threats.
Recent publications in 'Spatial and Spatio-Temporal Epidemiology' indicate several emerging themes that reflect the evolving landscape of epidemiological research, particularly in response to global health challenges.
  1. COVID-19 Research:
    A significant increase in studies related to COVID-19, focusing on its spatial and temporal dynamics, highlighting the pandemic's impact on public health and the importance of real-time data analysis.
  2. Climate Change and Health:
    Emerging studies examining the effects of climate change on disease patterns, particularly vector-borne diseases, illustrate a growing concern for environmental factors influencing public health.
  3. Social Determinants of Health:
    An increasing trend towards exploring the influence of social determinants, such as race, poverty, and education, on health outcomes, reflecting a deeper understanding of health inequities.
  4. Machine Learning Applications:
    The application of machine learning techniques in epidemiological modeling is on the rise, showcasing a shift towards more sophisticated analytical methods capable of managing complex datasets.
  5. Geospatial and Temporal Data Integration:
    A trend towards integrating diverse datasets, including historical and real-time data, to enhance the understanding of health trends and inform policy decisions.

Declining or Waning

While the journal maintains a strong focus on several core areas, there are certain themes that appear to be declining in frequency or prominence in recent publications.
  1. Non-Infectious Disease Analysis:
    There seems to be a decreasing emphasis on non-infectious diseases, such as chronic conditions, as the journal increasingly prioritizes infectious disease modeling, especially in the context of the COVID-19 pandemic.
  2. Traditional Statistical Methods:
    The reliance on traditional statistical methods appears to be waning, with a noticeable shift towards machine learning and advanced Bayesian approaches, indicating that simpler methodologies are becoming less favored.
  3. Geographical Accessibility Studies:
    Research focusing on geographical accessibility to healthcare services, while still relevant, is less frequently featured compared to studies directly linking spatial data to disease prevalence and transmission.

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