Published since December 1, 2011

Epidemiologic Methods

ISSN: 2161-962X

Editors-in-chief: Hélène Karcher, Li Fan

Edited by: Rino Bellocco, Mark J. van der Laan, Haidong Lu, Mohammad Mansournia, Kara Rudolph, Alejandro Schuler, Shirley Wang, Paul Zivich

Call for Papers

About this journal

Objective

Epidemiologic Methods aims at filling the gap for a journal to publish innovative methodology for epidemiologic research. Most social and applied science fields have one or more journals devoted exclusively to methodology. Epidemiologic Methods provides such a journal for the field of epidemiology.

In 2020-2022 we have invited less methodology-oriented epidemiology papers to support in part dissemination of knowledge on the COVID pandemic.

From January 1^st, 2023 onwards, we are re-centering the journal on methodological publications. We seek contributions on innovative methodology for epidemiologic research, real-world evidence, and evidence generation in a broader sense, and welcome submissions that provide a comprehensive evaluation or comparison of state-of-the-art methods in the context of epidemiologic research (via simulation studies or analyses of real data sets). We invite papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate the methodology are strongly encouraged.

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Your Benefits

Useful forum for in-depth articles on epidemiologic methods
Digital repository of methodologic contributions, case studies and tutorials　
Close cooperation between epidemiologists and statisticians　
High quality peer-review led by our top-notch editors (list below)

Special Issue open for submissions:

2024: Special issue: Leveraging data from multiple sources in epidemiologic research: transportability, dynamic borrowing, external controls, and beyond
Edited by Dr. Hélène Karcher or Prof. Kara Rudolph

Latest Special Issues:

2023: Special issue: Causal inference in comparative effectiveness research
2022: Covid-19: lessons learned in the use of epidemiologic methods
2021: Covid-19: Modelling the impact of vaccination campaigns, containment measures, mutations and geographical spread on the pandemic
2020: Covid-19 dynamics and containment: findings from epidemiologic modeling experts

Abstracting and Indexing

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Supplementary Materials

Topics

External Links

Volume 13 (2024)

Issue 1

Volume 12 (2023)

Volume 11 (2022)

Volume 10 (2021)

Volume 9 (2020)

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Recent Special Issues

Special Issue on Causal Inference [Issue s1, Volume 12 (2023)]

CiteScore	1.8	2022, Scopus (Elsevier B.V., 2023)
SCImago Journal Rank	0.318	2022, SJR (Scimago Lab, 2023; Data Source: Scopus)
Source Normalized Impact per Paper	0.403	2022, CWTS Journal Indicators (CWTS B.V., 2023; Data Source: Scopus)

More information about journal rankings

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Submission

You can easily submit your manuscript online. Simply go to...
http://mc.manuscriptcentral.com/dgem
...and you will be guided through the whole peer-reviewing and publishing process.

Your benefits of publishing with us

Rapid online ahead-of-print publication with short turnaround times
High quality manuscript processing
Optional open access publication
Accepted papers will be published online first as DOI-citable, forward-linked articles for quickest possible visibility for the scientific community
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
Secure archiving by De Gruyter and the independent archiving service Portico

ARTICLE FORMATS

1. Original Research Article, including

Epidemiologic Method: describing novel statistical approaches for design and analysis of epidemiology studies, ideally with an illustrative epidemiologic data example
Epidemiology Case Study: reporting an epidemiology study with a robust design and analysis supported by state-of-the-art statistical methods
Abstract for Original Research Articles: Objectives, Methods, Results, and Conclusions.

2. Tutorial

This is a new category aimed at providing epidemiologists with the means to understand and implement an advanced epidemiologic method for study design and/or data analysis.
We welcome tutorial articles that introduce cutting-edge epidemiologic methods with a focus on the following aspects:

Clarification on the use or misuse of the method, and descriptions of purpose and typical applications for which the method is most appropriate.
Discussion on the advantages and limitations of the new method as compared standard epidemiologic method used in current practice.
Tools or software code to implement the cutting-edge method with a comprehensive and accessible case study.
Abstract for Tutorial: Method description, Advantages & Limitation, Case Example.

3. Perspective and Counterpoint Perspective Articles

Articles that do not fall into the Original Research Article or Tutorial may be considered for this category. This category typically considers papers that provide a critical review of epidemiologic evidence or discuss a unique perspective on how a method has been implemented in the current epidemiology literature.
Abstract for Perspective and Counterpoint Perspective Articles: Context, Methods, Findings, Perspective

Topics
• epidemiology
• genetic epidemiology
• infectious disease
• pharmaco-epidemiology
• ecologic studies
• environmental exposures
• screening
• surveillance
• social networks
• comparative effectiveness
• safety studies
• statistical modeling
• causal inference
• measurement error
• machine learning
• artificial intelligence
• study design
• cohort profile
• meta-analysis
• real-world evidence
• methodology
Submission process

Before preparing your manuscript please have a look at our Instructions for Authors
Submission of your paper via our submission management tool http://mc.manuscriptcentral.com/dgem
Peer review process (you will be guided through every step)
Decision on your paper
If accepted: you have the option to publish it open access
Publication online only

Please note:

Manuscripts must be written in clear and concise English
Methodologic contributions should be clearly relevant to the theory and practice of epidemiology
The provision of sample code is strongly suggested. With more technical contributions, authors are also encouraged to include a section or appendix on "Epidemiologic Practice" which summarizes, in a broadly accessible manner, how the methodology proposed (or a special case thereof) may be used in practice
Before submitting your article please have a look at our Ethical Guidelines and our Copyright Transfer Agreement
The Template for Ethical and Legal Declarations needs to be customized by the submitting author on behalf of all others and uploaded as a separate doc-file at the time of the original submission. Instructions on how to make declarations can be found here.
Once your article is accepted you have the option to publish it open access
Our Repository Policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
If you have any general questions please visit our FAQ page for authors

We look forward to receiving your manuscript!

Editorial

Editors-in-Chief
Hélène Karcher, PhD, Novartis, Basel, Switzerland (Editor in Chief)

Fan Li, Yale University School of Public Health, New Haven, CT, USA

Editorial Board
Rino Bellocco, Università degli Studi di Milano-Bicocca, Milano, Italia

Mohammad Ali Mansournia, Tehran University of Medical Sciences, Tehran, Iran

Mark van der Laan, University of California, Berkeley, CA,USA

Haidong Lu, Yale University School of Medicine, New Haven, CT, United States

Kara Rudolph, Columbia University Mailman School of Public Health, New York, NY, USA

Alejandro Schuler, University of California, Berkeley, CA, USA

Shirley Wang, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Paul Zivich, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Founding Editors
Eric J. Tchetgen Tchetgen, Harvard University, Boston, MA, USA
Tyler J. VanderWeele, Harvard University, Boston, MA, USA

Journal Coordinator
Paulina Żarnecka
paulina.zarnecka@degruyter.com

Volume 13 Issue 1

January 2024

Research Articles

April 24, 2024

A study of a deterministic model for meningitis epidemic

Samaila Jackson Yaga, Funmilayo W. O. Saporu

Article number: 20230023

Abstract

Objectives A compartmental deterministic model that allows immunity from two stages of infection and carriage, and disease induced death, is used in studying the dynamics of meningitis epidemic process, in a closed population. It allows for difference in the transmission rate of infection to a susceptible by a carrier and an infective. It is generalized to allow a proportion of those susceptible infected to progress directly to infectives in stage I. Methods Two models are used in this study; the generalized model when these proportions are greater than zero (called model 1) and a special case when the proportion is zero (called model 2). The threshold conditions for the spread of carrier and infectives in stage I are derived for both models. Sensitivity analysis is performed on the reproductive number derived from the next generation matrix. The case-carrier ratio profile for various parameters and threshold values are shown. So also, are the graphs of the total number ever infected as influenced by the odds in favor of a carrier over an infective, in transmitting an infection to a susceptible, and the proportion of those susceptible that can progress directly to the infected stage. Results The infection transmission rate, these odds in favor of a carrier, and the carrier conversion rate to an infective in stage I, are identified as key parameters that should be subject of attention for any control intervention strategy. The case-carrier ratio profiles provide evidence of a critical case-carrier ratio attained before the number of reported cases grow to an epidemic level. They also provide visual evidence of epidemiological context, in this case, epidemic incidence (in later part of dry season) and endemic incidence (during rainy season). Conclusions Results from the total proportion ever infected suggest that the special case (model 2) can adequately represent, in essence, the generalized model (model 1) for this study.

Open Access March 22, 2024

Bounds for selection bias using outcome probabilities

Stina Zetterstrom

Article number: 20230033

More Download PDF

Abstract

Objectives Determining the causal relationship between exposure and outcome is the goal of many observational studies. However, the selection of subjects into the study population, either voluntary or involuntary, may result in estimates that suffer from selection bias. To assess the robustness of the estimates as well as the magnitude of the bias, bounds for the bias can be calculated. Previous bounds for selection bias often require the specification of unknown relative risks, which might be difficult to provide. Here, alternative bounds based on observed data and unknown outcome probabilities are proposed. These unknown probabilities may be easier to specify than unknown relative risks. Methods I derive alternative bounds from the definitions of the causal estimands using the potential outcomes framework, under specific assumptions. The bounds are expressed using observed data and unobserved outcome probabilities. The bounds are compared to previously reported bounds in a simulation study. Furthermore, a study of perinatal risk factors for type 1 diabetes is provided as a motivating example. Results I show that the proposed bounds are often informative when the exposure and outcome are sufficiently common, especially for the risk difference in the total population. It is also noted that the proposed bounds can be uninformative when the exposure and outcome are rare. Furthermore, it is noted that previously proposed assumption-free bounds are special cases of the new bounds when the sensitivity parameters are set to their most conservative values. Conclusions Depending on the data generating process and causal estimand of interest, the proposed bounds can be tighter or wider than the reference bounds. Importantly, in cases with sufficiently common outcome and exposure, the proposed bounds are often informative, especially for the risk difference in the total population. It is also noted that, in some cases, the new bounds can be wider than the reference bounds. However, the proposed bounds based on unobserved probabilities may in some cases be easier to specify than the reference bounds based on unknown relative risks.

February 1, 2024

Population dynamic study of two prey one predator system with disease in first prey using fuzzy impulsive control

Khushbu Singh, Kaladhar Kolla

Article number: 20230037

Abstract

Objectives The prey-predator model provides a mathematical framework for understanding the population dynamics of interacting species, highlighting the delicate balance between predator and prey populations in ecological systems. The four-species predator-prey model extends the Lotka-Volterra framework to explore the dynamics of ecosystems with multiple interacting species. It provides a theoretical foundation for understanding how the populations of multiple prey and predator species influence each other over time. Apart from the traditional methods like direct approach for solving the non-linear system of equations, recent Fuzzy method approaches have been developed. The solution of non-linear systems using classical methods is not easy due to its non-linearity, analytical complexity, chaotic behavior, etc. and the T-S method is very much effective to analyze the non-linear models. Methods In this study, we considered an eco-epidemic model with two populations of prey and one population of predator, with the only infectious disease infecting the first prey population. The four-dimensional Lotka-Volterra predator-prey system’s model stability has been examined using the Takagi-Sugeno (T-S) impulsive control model and the Fuzzy impulsive control model. Following the formulation of the model, the global stability and the Fuzzy solution are carried out through numerical simulations and graphical representations with appropriate discussion for a better understanding the dynamics of our proposed model. Results The Takagi-Sugeno method has diverse applications in modeling, control, pattern recognition, and decision-making in systems where uncertainty and non-linearity play a significant role. Its ability to combine fuzzy logic with traditional mathematical models provides a powerful tool for addressing complex real-world problems. Conclusions The impulse control approach, what is considered within the foundation of fuzzy systems established on T-S model, is found to be suitable for extremely complex and non-linear systems with impulse effects.

(Deutsch)

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Online ISSN: 2161-962X
Type: Journal
Language: English
Publisher: De Gruyter
First published: December 1, 2011
Publication Frequency: 1 Issue per Year
Audience: researchers and professionals in the field of epidemiology