Epidemiologic Methods最新文献

{"title":"Energy- efficient model “Inception V3 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients","authors":"Sachin Kumar, S. Pal, Vijendra Pratap Singh, Priya Jaiswal","doi":"10.1515/em-2021-0046","DOIUrl":"https://doi.org/10.1515/em-2021-0046","url":null,"abstract":"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.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74365959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incidence and trend of leishmaniasis and its related factors in Golestan province, northeastern Iran: time series analysis","authors":"M. Majidnia, A. Hosseinzadeh, Ahmad Khosravi","doi":"10.1515/em-2022-0124","DOIUrl":"https://doi.org/10.1515/em-2022-0124","url":null,"abstract":"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.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87085901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A country-specific COVID-19 model","authors":"G. Meissner, Hong Sherwin","doi":"10.2139/ssrn.4043977","DOIUrl":"https://doi.org/10.2139/ssrn.4043977","url":null,"abstract":"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.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89060519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and application of an evidence-based directed acyclic graph to evaluate the associations between metal mixtures and cardiometabolic outcomes.","authors":"Emily Riseberg,&nbsp;Rachel D Melamed,&nbsp;Katherine A James,&nbsp;Tanya L Alderete,&nbsp;Laura Corlin","doi":"10.1515/em-2022-0133","DOIUrl":"https://doi.org/10.1515/em-2022-0133","url":null,"abstract":"<p><strong>Objectives: </strong>Specifying causal models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph (DAG) diagraming metal mixture exposure and cardiometabolic outcomes.</p><p><strong>Methods: </strong>We conducted a literature search to develop the DAG of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We calculated the proportion of statements supported by the data and compared this to the proportion of conditional independence statements supported by 1,000 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes (i.e., cardiovascular disease, fasting glucose, and systolic blood pressure). We applied them to the SLVDS using Bayesian kernel machine regression, linear mixed effects, and Cox proportional hazards models.</p><p><strong>Results: </strong>From the 42 articles included in the review, we developed an evidence-based DAG with 74 testable conditional independence statements (43 % supported by SLVDS data). We observed evidence for an association between As and Mn and fasting glucose.</p><p><strong>Conclusions: </strong>We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.</p>","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"12 1","pages":"20220133"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10292771/pdf/em-12-1-em-2022-0133.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10352001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient model “DenseNet201 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients","authors":"Sachin Kumar, Vijendra Pratap Singh, S. Pal, Priya Jaiswal","doi":"10.1515/em-2021-0047","DOIUrl":"https://doi.org/10.1515/em-2021-0047","url":null,"abstract":"Abstract Objective The outbreak of the coronavirus caused major problems in more than 151 countries around the world. An important step in the fight against coronavirus is the search for infected people. The goal of this article is to predict COVID-19 infectious patients. Methods We implemented DenseNet201, available on cloud platform, as a learning network. DenseNet201 is a 201-layer networkthat. is trained on ImageNet. The input size of pre-trained DenseNet201 images is 224 × 224 × 3. Results Implementation of DenseNet201 was effectively performed based on 80 % of the training X-rays and 20 % of the X-rays of the test phases, respectively. DenseNet201 shows a good experimental result with an accuracy of 99.24 % in 7.47 min. To measure the computational efficiency of the proposed model, we collected more than 6,000 noise-free data infected by tuberculosis, COVID-19, and uninfected healthy chests for implementation. Conclusions DenseNet201 available on the cloud platform has been used for the classification of COVID-19-infected patients. The goal of this article is to demonstrate how to achieve faster results.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"752 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76887951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On some pitfalls of the log-linear modeling framework for capture-recapture studies in disease surveillance","authors":"Yuzi Zhang, Lin Ge, Lance A. Waller, Robert H. Lyles","doi":"10.1515/em-2023-0019","DOIUrl":"https://doi.org/10.1515/em-2023-0019","url":null,"abstract":"Abstract In epidemiological studies, the capture-recapture (CRC) method is a powerful tool that can be used to estimate the number of diseased cases or potentially disease prevalence based on data from overlapping surveillance systems. Estimators derived from log-linear models are widely applied by epidemiologists when analyzing CRC data. The popularity of the log-linear model framework is largely associated with its accessibility and the fact that interaction terms can allow for certain types of dependency among data streams. In this work, we shed new light on significant pitfalls associated with the log-linear model framework in the context of CRC using real data examples and simulation studies. First, we demonstrate that the log-linear model paradigm is highly exclusionary. That is, it can exclude, by design, many possible estimates that are potentially consistent with the observed data. Second, we clarify the ways in which regularly used model selection metrics (e.g., information criteria) are fundamentally deceiving in the effort to select a “best” model in this setting. By focusing attention on these important cautionary points and on the fundamental untestable dependency assumption made when fitting a log-linear model to CRC data, we hope to improve the quality of and transparency associated with subsequent surveillance-based CRC estimates of case counts.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135053775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of machine learning tools for feature selection in the identification of prognostic markers in COVID-19","authors":"Sprockel Diaz Johm Jaime, Hector Fabio Restrepo Guerrero, J. J. Fernández","doi":"10.1515/em-2022-0132","DOIUrl":"https://doi.org/10.1515/em-2022-0132","url":null,"abstract":"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.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86505772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using repeated antibody testing to minimize bias in estimates of prevalence and incidence of SARS-CoV-2 infection","authors":"Michele Santacatterina, B. Burke, Mihili Gunaratne, W. Weintraub, M. Espeland, Adolfo Correa, DeAnna J. Friedman-Klabanoff, M. Gibbs, David M. Herrington, Kristen Miller, J. Sanders, A. Seals, D. Uschner, T. Wierzba, Morgana Mongraw-Chaffin","doi":"10.1515/em-2023-0012","DOIUrl":"https://doi.org/10.1515/em-2023-0012","url":null,"abstract":"Abstract Objectives The prevalence and incidence of SARS-CoV-2, the virus which causes COVID-19, at any given time remains controversial, and is an essential piece in understanding the dynamics of the epidemic. Cross-sectional studies and single time point testing approaches continue to struggle with appropriate adjustment methods for the high false positive rates in low prevalence settings or high false negative rates in high prevalence settings, and post-hoc adjustment at the group level does not fully address this issue for incidence even at the population level. Methods In this study, we use seroprevalence as an illustrative example of the benefits of using a case definition using a combined parallel and serial testing framework to confirm antibody-positive status. In a simulation study, we show that our proposed approach reduces bias and improves positive and negative predictive value across the range of prevalence compared with cross-sectional testing even with gold standard tests and post-hoc adjustment. Using data from the North Carolina COVID-19 Community Research Partnership, we applied the proposed case definition to the estimation of SARS-CoV-2 seroprevalence and incidence early in the pandemic. Results The proposed approach is not always feasible given the cost and time required to administer repeated tests; however, it reduces bias in both low and high prevalence settings and addresses misclassification at the individual level. This approach can be applied to almost all testing contexts and platforms. Conclusions This systematic approach offers better estimation of both prevalence and incidence, which is important to improve understanding and facilitate controlling the pandemic.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"253 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83681676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing substantial covariate imbalance with propensity score stratification and balancing weights: connections and recommendations","authors":"Laine E. Thomas, Steven M. Thomas, Fan Li, Roland A. Matsouaka","doi":"10.1515/em-2022-0131","DOIUrl":"https://doi.org/10.1515/em-2022-0131","url":null,"abstract":"Abstract Objectives Propensity score (PS) weighting methods are commonly used to adjust for confounding in observational treatment comparisons. However, in the setting of substantial covariate imbalance, PS values may approach 0 and 1, yielding extreme weights and inflated variance of the estimated treatment effect. Adaptations of the standard inverse probability of treatment weights (IPTW) can reduce the influence of extremes, including trimming methods that exclude people with PS values near 0 or 1. Alternatively, overlap weighting (OW) optimizes criteria related to bias and variance, and performs well compared to other PS weighting and matching methods. However, it has not been compared to propensity score stratification (PSS). PSS has some of the same potential advantages; being insensitive extreme values. We sought to compare these methods in the setting of substantial covariate imbalance to generate practical recommendations. Methods Analytical derivations were used to establish connections between methods, and simulation studies were conducted to assess bias and variance of alternative methods. Results We find that OW is generally superior, particularly as covariate imbalance increases. In addition, a common method for implementing PSS based on Mantel–Haenszel weights (PSS-MH) is equivalent to a coarsened version of OW and can perform nearly as well. Finally, trimming methods increase bias across methods (IPTW, PSS and PSS-MH) unless the PS model is re-fit to the trimmed sample and weights or strata are re-derived. After trimming with re-fitting, all methods perform similarly to OW. Conclusions These results may guide the selection, implementation and reporting of PS methods for observational studies with substantial covariate imbalance.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135604264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance evaluation of ResNet model for classification of tomato plant disease","authors":"Sachin Kumar, S. Pal, Vijendra Pratap Singh, P. Jaiswal","doi":"10.1515/em-2021-0044","DOIUrl":"https://doi.org/10.1515/em-2021-0044","url":null,"abstract":"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.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74304998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}