1877-5845
Published by: Elsevier
https://www.journals.elsevier.com/spatial-and-spatio-temporal-epidemiology
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Social Sciences and Humanities | 486 | 26 | 24 | 9 |
The foci of Spatial and Spatio-temporal Epidemiology are Statistics, Bayesian probability, Environmental health, Incidence (epidemiology) and Public health. The featured Statistics works encompass concepts such as Covariate, Bayes' theorem and Spatial analysis and examines them in conjunction with Random effects model. The majority of Bayes' theorem studies presented zero in on Bayesian hierarchical modeling.
Spatial analysis study tackled is connected to the field of Spatial ecology. It connects research in Bayesian probability with the related topic of Data mining. The Data mining study featured in it draws connections with the study of Geographic information system.
Spatial and Spatio-temporal Epidemiology facilitates discussions on Environmental health that incorporate concepts from other fields like Air pollution and Epidemiology.
The journal papers generally zeroe in on subjects such as Geographic information system, Data mining, Statistics, Bayes' theorem and Spatial epidemiology. The journal publications facilitate discussions in Covariate as part of the larger field of Statistics, however, they also tackle fields such as Index (economics). The discussions in the journal publications emphasized the topic of Bayes' theorem in an attempt to further explore the field of Bayesian probability.
The main research concerns discussed in the journal are Coronavirus disease 2019 (COVID-19), Public health, Socioeconomic status, Bayesian probability and Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the journal, Humidity, Bayes' theorem and Economic geography are investigated in conjunction with one another to address concerns in Coronavirus disease 2019 (COVID-19) research. The study of Public health encompasses disciplines such as Epidemiology, as well as fields such as Disease monitoring, Urbanization, Transfusion dependence and Transmission (medicine), all of which overlap with one another.
The study of Statistics serves as the foundation of the Bayesian probability research discussed in the journal. Spatial and Spatio-temporal Epidemiology deals with Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in conjunction with other fields like
2019-20 coronavirus outbreak which intersects with area such as Bayesian paradigm, Health data, Relevance (information retrieval) and Bayesian hierarchical modeling,
Spatial analysis, Hiv prevalence and Hiv incidence most often made with reference to Incidence (epidemiology).. The journal goes beyond the discussion of Bayesian inference as it connects it with closely related disciplines like
Laplace's method that connect with fields like Covariate,
Econometrics most often made with reference to Measure (data warehouse)..
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Spatial and Spatio-temporal Epidemiology (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Spatial and Spatio-temporal Epidemiology (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 4.65% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 14.63% were posted by at least one author from the top 10 institutions publishing in the journal. Another 19.51% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 9.76% of all publications and 56.10% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Another critical aspect to consider when delving into the field of Spatial and Spatio-temporal Epidemiology is exploring potential careers and specific roles related to these specializations. One of the prominent career paths in this area is becoming a substance abuse counselor. Substance abuse counselors use knowledge from different dimensions including public health, statistics, and data mining to understand the patterns and risk factors associated with substance abuse and addiction. This role often involves working directly with patients, providing recovery assistance tailored to individual needs, which requires a compassionate understanding of the impacts of substance abuse on personal health and societal relations. To further illustrate, consider the pathway of becoming a substance abuse counselor in Nebraska. This role has specific education, licensing requirements, and practical training procedures. For a more in-depth exploration of this career, you may find help in our reference page: How to become a Substance Abuse Counselor in Nebraska Our aim is to provide comprehensive guidance to those interested in contributing to public health and well-being through their skills and knowledge in Spatial and Spatio-temporal Epidemiology. Becoming a substance abuse counselor is just one example of the impactful careers available in this evolving field.
Jack Cordes;Marcia C. Castro
(2020)Fahui Wang;Changzhen Wang;Yujie Hu;Julie Weiss
(2020)Priyanka Vyas;Hugh Sturrock;Pamela M Ling
(2020)Christopher N. Morrison;Christopher N. Morrison;Andrew G. Rundle;Charles C. Branas;Stanford Chihuri
(2020)Kimberly A. Clevenger;Sue C. Grady;Karl Erickson;Karin A. Pfeiffer
(2020)Dustin T. Duncan;Seann D. Regan;Su Hyun Park;William C. Goedel
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