Published since December 1, 2011

Epidemiologic Methods

ISSN: 2161-962X

Editor-in-chief: Hélène Karcher

Edited by: Rino Bellocco, Lamiae Grimaldi-Bensouda, Li Fan, Mark J. van der Laan, Mohammad Mansournia, Kara Rudolph, Alejandro Schuler

Managing Editor: Rita Moreira da Silva

Submit manuscript

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.

Submit your article

We invite you to contribute to the following special issue:

"Causal inference in comparative effectiveness research"

CALL FOR SUBMISSIONS for the special thematic issue on Causal inference in comparative effectiveness, with Dr. Hélène Karcher Novartis, Switzerland) and Prof. Fan Li (Yale University, US) and as Issue Editors. The issue is scheduled for publication in 2023. The (extended) deadline for submission is August 31, 2023.

The proposed special issue aims to inform and educate a broad community of researchers conducting prospective and/or retrospective observational (real-world) studies or related comparative effectiveness studies using a range of possible causal inference methods. We aim to support these studies with innovative methods and/or practical considerations with thoughtful case study illustrations.

Therefore, we welcome article submissions dealing with the development of novel methods or case studies in the field of causal inference, including, but not limited, to the following topics:

Review of current statistical methods for causal inference in epidemiology and comparative effectiveness

Development of innovative causal inference methods for the design or analysis of observational data

Comprehensive simulation evaluation to compare state-of-the-art causal inference methods in the context of epidemiological studies

Case studies or tutorials on the application of state-of-the-art causal inference methods, with explanations on their implementation, how to improve current practice and possible limitations

In particular, the best paper nominated by the editors will be awarded a full Open Access waiver by the publisher De Gruyter!

More information you can find HERE

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)

Latest Special Issues:

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

Epidemiologic Methods is covered by the following services:

Baidu Scholar
Cabells Journalytics
CNKI Scholar (China National Knowledge Infrastructure)
CNPIEC - cnpLINKer
Dimensions
EBSCO (relevant databases)
EBSCO Discovery Service
EMBASE
Genamics JournalSeek
Google Scholar
Japan Science and Technology Agency (JST)
J-Gate
JournalTOCs
KESLI-NDSL (Korean National Discovery for Science Leaders)
MyScienceWork
Naver Academic
Naviga (Softweco)
Norwegian Register for Scientific Journals, Series and Publishers
Primo Central (ExLibris)
ProQuest (relevant databases)
Publons
QOAM (Quality Open Access Market)
ReadCube
Reaxys
Scilit
SCImago (SJR)
SCOPUS
Semantic Scholar
Sherpa/RoMEO
Summon (ProQuest)
TDNet
Text Mining
Ulrich's Periodicals Directory/ulrichsweb
WanFang Data
WorldCat (OCLC)
X-MOL
Yewno Discover

Supplementary Materials

Topics

External Links

Volume 12 Issue 1

January 2023

Research Articles

Publicly Available May 17, 2023

Identification of time delays in COVID-19 data

Nicola Guglielmi, Elisa Iacomini, Alex Viguerie

Article number: 20220117

More Download PDF

Abstract

Objective COVID-19 data released by public health authorities is subject to inherent time delays. Such delays have many causes, including delays in data reporting and the natural incubation period of the disease. We develop and introduce a numerical procedure to recover the distribution of these delays from data. Methods We extend a previously-introduced compartmental model with a nonlinear, distributed-delay term with a general distribution, obtaining an integrodifferential equation. We show this model can be approximated by a weighted-sum of constant time-delay terms, yielding a linear problem for the distribution weights. Standard optimization can then be used to recover the weights, approximating the distribution of the time delays. We demonstrate the viability of the approach against data from Italy and Austria. Results We find that the delay-distributions for both Italy and Austria follow a Gaussian-like profile, with a mean of around 11 to 14 days. However, we note that the delay does not appear constant across all data types, with infection, recovery, and mortality data showing slightly different trends, suggesting the presence of independent delays in each of these processes. We also found that the recovered delay-distribution is not sensitive to the discretization resolution. Conclusions These results establish the validity of the introduced procedure for the identification of time-delays in COVID-19 data. Our methods are not limited to COVID-19, and may be applied to other types of epidemiological data, or indeed any dynamical system with time-delay effects.

Publicly Available May 12, 2023

A country-specific COVID-19 model

Gunter Meissner, Hong Sherwin

Article number: 20220112

More Download PDF

Abstract

Objectives To dynamically measure COVID-19 transmissibility consistently normalized by population size in each country. Methods A reduced-form model enhanced from the classical SIR is proposed to stochastically represent the Reproduction Number and Mortality Rate, directly measuring the combined effects of viral evolution and population behavioral response functions. Results Evidences are shown that this e(hanced)-SIR model has the power to fit country-specific empirical data, produce interpretable model parameters to be used for generating probabilistic scenarios adapted to the still unfolding pandemic. Conclusions Stochastic processes embedded within compartmental epidemiological models can produce measurables and actionable information for surveillance and planning purposes.

May 3, 2023

Incidence and trend of leishmaniasis and its related factors in Golestan province, northeastern Iran: time series analysis

Mostafa Majidnia, Ali Hosseinzadeh, Ahmad Khosravi

Article number: 20220124

Abstract

Objectives Leishmaniasis is a parasitic disease whose transmission depends on climatic conditions and is more important in northeast Iran. This study aimed to investigate the time trend of leishmaniasis and present a prediction model using meteorological variables in Golestan province. Methods The 10-year data on leishmaniasis (2010–2019) were collected from the portal of the Ministry of Health and the National Meteorological Organization. First, the disease incidence (per 100,000 population) in different cities of the Golestan province was estimated. Then, the geographical distribution and disease clusters map were prepared at the province level. Finally, by using the Jenkins box model time series analysis method, the disease incidence was predicted for the period 2020 to 2023 of the total province. Results From 2010 to 2019, 8,871 patients with leishmaniasis were identified. The mean age of patients was 21.0 ± 18.4 years. The highest mean annual incidence was in Maravah-Tappeh city (188 per 100,000 population). The highest and lowest annual incidence was in 2018 and 2017, respectively. The average 10-year incidence was 48 per 100,000 population. The daily meteorological variables like monthly average wind speed, sunshine, temperature, and mean soil temperature at depth of 50 cm were significantly associated with the incidence of the disease. The estimated threshold for an epidemic was 40.3 per 100,000 population. Conclusions According to the results, leishmaniasis incidence cases apears in July and with a peak in November. The incidence rate was highest in Maravah-Tapeh and Gonbad-Kavous, and lowest in Kordkoy counties. The study showed that there were two peaks in 2010 and 2018 and also identified a downward trend in the disease between 2010 and 2013 with a clear decrease in the incidence. Climate conditions had an important effect on leishmaniasis incidence. These climate and epidemiological factors such as migration and overcrowding could provide important input to monitor and predict disease for control strategies.

April 17, 2023

Application of machine learning tools for feature selection in the identification of prognostic markers in COVID-19

Sprockel Diaz Johm Jaime, Hector Fabio Restrepo Guerrero, Juan Jose Diaztagle Fernandez

Article number: 20220132

Abstract

Objective To identify prognostic markers by applying machine learning strategies to the feature selection. Methods An observational, retrospective, multi-center study that included hospitalized patients with a confirmed diagnosis of COVID-19 in three hospitals in Colombia. Eight strategies were applied to select prognostic-related characteristics. Eight logistic regression models were built from each set of variables and the predictive ability of the outcome was evaluated. The primary endpoint was transfer to intensive care or in-hospital death. Results The database consisted of 969 patients of which 486 had complete data. The main outcome occurred in 169 cases. The development database included 220 patients, 137 (62.3%) were men with a median age of 58.2, 39 (17.7%) were diabetic, 62 (28.2%) had high blood pressure, and 32 (14.5%) had chronic lung disease. Thirty-three variables were identified as prognostic markers, and those selected most frequently were: LDH, PaO2/FIO2 ratio, CRP, age, neutrophil and lymphocyte counts, respiratory rate, oxygen saturation, ferritin, and HCO3. The eight logistic regressions developed were validated on 266 patients in whom similar results were reached (accuracy: 65.8–72.9%). Conclusions The combined use of strategies for selecting characteristics through machine learning techniques makes it possible to identify a broad set of prognostic markers in patients hospitalized for COVID-19 for death or hospitalization in intensive care.

January 26, 2023

A study of the impact of policy interventions on daily COVID scenario in India using interrupted time series analysis

Subhankar Chattopadhyay, Debika Ghosh, Raju Maiti, Samarjit Das, Atanu Biswas, Bibhas Chakraborty

Article number: 20220113

Abstract

Objectives The rapid increase both in daily cases and daily deaths made the second wave of COVID-19 pandemic in India more lethal than the first wave. Record number of infections and casualties were reported all over India during this period. Delhi and Maharashtra are the two most affected places in India during the second wave. So in response to this, the Indian government implemented strict intervention policies (“lockdowns”, “social distancing” and “vaccination drive”) in every state during this period to prohibit the spread of this virus. The objective of this article is to conduct an interrupted time series (ITS) analysis to study the impact of the interventions on the daily cases and deaths. Methods We collect daily data for Delhi and Maharashtra before and after the intervention points with a 14-day (incubation period of COVID-19) observation window. A segmented linear regression analysis is done to study the post-intervention slopes as well as whether there were any immediate changes after the interventions or not. We also add the counterfactuals and delayed time effects in the analysis to investigate the significance of our ITS design. Results Here, we observe the post-intervention trends to be statistically significant and negative for both the daily cases and the daily deaths. We also find that there is no immediate change in trend after the start of intervention, and hence we study some delayed time effects which display how changes in the trends happened over time. And from the Counterfactuals in our study, we can have an idea what would have happened to the COVID scenario had the interventions not been implemented. Conclusions We statistically try to figure out different circumstances of COVID scenario for both Delhi and Maharashtra by exploring all possible ingredients of ITS design in our analysis in order to present a feasible design to show the importance of implementation of proper intervention policies for tackling this type of pandemic which can have various highly contagious variants.

January 17, 2023

Measuring COVID-19 spreading speed through the mean time between infections indicator

Gabriel Pena, Verónica Moreno, Nestor Ruben Barraza

Article number: 20220106

Abstract

Objectives To introduce a novel way of measuring the spreading speed of an epidemic. Methods We propose to use the mean time between infections (MTBI) metric obtained from a recently introduced nonhomogeneous Markov stochastic model. Different types of parameter calibration are performed. We estimate the MTBI using data from different time windows and from the whole stage history and compare the results. In order to detect waves and stages in the input data, a preprocessing filtering technique is applied. Results The results of applying this indicator to the COVID-19 reported data of infections from Argentina, Germany and the United States are shown. We find that the MTBI behaves similarly with respect to the different data inputs, whereas the model parameters completely change their behaviour. Evolution over time of the parameters and the MTBI indicator is also shown. Conclusions We show evidence to support the claim that the MTBI is a rather good indicator in order to measure the spreading speed of an epidemic, having similar values whatever the input data size.

January 12, 2023

Performance evaluation of ResNet model for classification of tomato plant disease

Sachin Kumar, Saurabh Pal, Vijendra Pratap Singh, Priya Jaiswal

Article number: 20210044

Abstract

Objectives The plant tomato (Solanum Lycopersicum) is vastly infected by various diseases. Exact diagnosis on time contributes a significant job to the good production of tomato crops. The key objective of this article is to recognize the infection in tomato leaves with better accuracy and in less time. Methods Nowadays deep convolutional neural networks have attained surprising outcomes in several applications, together with the categorization of tomato leaves infected with several diseases. Our work is based on deep CNN with different residual networks. Finally; we have performed tomato leaves disease classification by using pre-trained deep CNN with the residual network using MATLAB available on the cloud. Results We have used a dataset of tomato leaves for the experiments which contain six different types of diseases with one healthy tomato leaf class. We have collected 6,594 tomato leaves dataset from Plant Village and we did not collect actual tomato leaves for testing. The outcome obtained by ResNet-50 shows a significant result with 96.35% accuracy for 50% training and 50% testing data and if we focus on time consumption for the outcome then ResNet-18 consumes 12.46 min for 70% training and 30% testing. Conclusions After observation of several outcomes, we have concluded that ResNet-50 shows a better accuracy for 50% training and 50% testing of data and ResNet-18 shows better efficiency for 70% training and 30% testing of data for the same dataset on the cloud.

January 4, 2023

Energy- efficient model “Inception V3 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients

Sachin Kumar, Saurabh Pal, Vijendra Pratap Singh, Priya Jaiswal

Article number: 20210046

Abstract

Objectives COVID-19 is frightening the health of billions of persons and speedily scattering worldwide. Medical studies have revealed that the majority of COVID-19 patients. X-ray of COVID-19 is extensively used because of their noticeably lower price than CT. This research article aims to spot the COVID-19 virus in the X-ray of the chest in less time and with better accuracy. Methods We have used the inception-v3 available on the cloud platform transfer learning model to classify COVID-19 infection. The online Inception v3 model can be reliable and efficient for COVID-19 disease recognition. In this experiment, we collected images of COVID-19-infected patients, then applied the online inception-v3 model to automatically extract features, and used a softmax classifier to classify the COVID-19 images. Finally, the experiment shows inception v3 is significant for COVID-19 image classification. Results Our results demonstrate that our proposed inception v3 model available on the cloud platform can detect 99.41% of COVID-19 cases between COVID-19 and Lung Mask diseases in 44 min only. We have also taken images of the normal chest for better outcomes. To estimate the computation power of the model, we collected 6018 COVID-19, Lung Masks, & Normal Chest images for experimentation. Our projected model offered a trustworthy COVID-19 classification by using chest X-rays. Conclusions In this research paper, the inception v3 model available on the cloud platform is used to categorize COVID-19 infection by X-ray images. The Inception v3 model available on the cloud platform is helpful to clinical experts to examine the enormous quantity of human chest X-ray images. Scientific and clinical experiments will be the subsequent objective of this paper.

Volume 12 (2023)

Issue 1

Volume 11 (2022)

Volume 10 (2021)

Volume 9 (2020)

CiteScore	1.2
SCImago Journal Rank	0.478
Source Normalized Impact per Paper	0.532

Submit

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