Global Epidemiology最新文献

{"title":"Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers","authors":"Md. Siddikur Rahman,&nbsp;Md. Abu Bokkor Shiddik","doi":"10.1016/j.gloepi.2025.100210","DOIUrl":"10.1016/j.gloepi.2025.100210","url":null,"abstract":"<div><h3>Background</h3><div>Dengue represents a significant public health threat in Bangladesh, characterized by its complex ecological transmission dynamics. To improve dengue prevention and control efforts, firstly, we employ state-of-the-art artificial intelligence (AI) methods to identify the roles of eco-climatic factors in predicting dengue outbreaks in Bangladesh.</div></div><div><h3>Methods</h3><div>We utilize high-performance machine learning (ML) models, XGBoost and LightGBM, combined with explainable AI (XAI) methodologies to evaluate the predictive performance and impact of various dengue determinants in Bangladesh from 2000 to 2023. The LightGBM and XGBoost models were also utilized to predict dengue cases and early warning trends from 2024 to 2030. Climatic, socio-demographic, and landscape features were used to train these models; SHapley Additive Explanations (SHAP) values and LIME (Local Interpretable Model-agnostic Explanations) were used to interpret the results.</div></div><div><h3>Findings</h3><div>Between 2000 and 2023, Bangladesh experienced the highest number of dengue cases in August, while November saw the most fatalities. The XGBoost model excelled in predicting dengue outbreaks, achieving an AUC score of 0.89, a Log Loss of 0.64. Key predictors identified by the model include population density, precipitation, temperature, and land-use patterns. Additionally, Local Interpretable Model-agnostic Explanations (LIME) provided insights into the model's predictions, highlighting the significance of population density, relative humidity, and minimum temperature in dengue outbreaks.</div></div><div><h3>Interpretation</h3><div>This study showcases the potential of XAI in uncovering the complexities of dengue outbreaks, providing a robust tool for public health interventions. Our AI-driven framework can be utilized to generate prompt and timely alerts to prevent imminent dengue and other infectious disease outbreaks.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"10 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241899","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":"Artificial intelligence for advancing eye care in resource-poor settings: Assessing the predictive accuracy of an AI-model for diabetic retinopathy screening in India","authors":"Rohan Chawla ,&nbsp;Prachi Karkhanis ,&nbsp;Malay Shah ,&nbsp;Aritra Das ,&nbsp;Rishabh Sharma ,&nbsp;Dhwani Almaula ,&nbsp;Pradeep Venkatesh ,&nbsp;Harsh Vardhan Singh ,&nbsp;Mukul Kumar ,&nbsp;Ramanuj Samanta ,&nbsp;Vinod Kumarl ,&nbsp;Amar Shah ,&nbsp;Bhavin Vadera ,&nbsp;Nakul Jain ,&nbsp;Akanksha Sen ,&nbsp;Shyamsundar Shreedhar ,&nbsp;Vipin Garg ,&nbsp;Soma Dhaval ,&nbsp;Kowshik Ganesh ,&nbsp;Srinivas Rana ,&nbsp;Radhika Tandon","doi":"10.1016/j.gloepi.2025.100209","DOIUrl":"10.1016/j.gloepi.2025.100209","url":null,"abstract":"<div><h3>Background</h3><div>Timely identification and treatment of Diabetic Retinopathy (DR) is critical in avoiding vision loss. DR screening is challenging, especially in resource-limited areas where trained ophthalmologists are scarce. AI solutions show promise in addressing this challenge. In this study, the performance metrics of an AI solution (MadhuNetrAI) developed in India was evaluated for referring and grading DR.</div></div><div><h3>Methods</h3><div>MadhuNetrAI was developed de novo by the All India Institute of Medical Sciences (AIIMS) and Wadhwani AI (WIAI). It was tested on 1078 fundus images (from AIIMS Delhi and an unannotated subset of publicly available EyePACS images) against two ophthalmologists and an adjudicator serving as independent gold-standard annotators, wherein the disease status of the patients remained unknown.</div></div><div><h3>Findings</h3><div>MadhuNetrAI demonstrated high sensitivity (93·2 %; CI: 89·5 %–95·6 %) and specificity (95·3 %; CI: 93·7 %–96·6 %) in detecting referable DR (moderate, severe, proliferative DR). The area-under-the-curve for referring DR against the gold standard was 0·97 (CI: 0·95–0·99) indicating excellent diagnostic performance. The agreement in grading DR severity was high (kappa = 0·89, CI: 0·86–0·91). The model performed comparably in detecting DR too.</div></div><div><h3>Interpretation</h3><div>MadhuNetrAI's ability to grade DR severity and identify referrable cases could bring DR patients to care much earlier. Further research and clinical trials are needed to ensure its reliability and generalizability across diverse populations and image qualities.</div></div><div><h3>Funding</h3><div>MadhuNetrAI was developed by technical and programmatic teams at WIAI, with inputs and contributions by the clinical team at AIIMS, and funded by USAID. The authors have no financial or non-financial conflicts of interest to disclose.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231531","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":"Epidemiological and histopathological profile of lung Cancer: Insights from a 15-year cross-sectional study at a tertiary care centre in South India","authors":"Asmita A. Mehta ,&nbsp;Keechilat Pavithran ,&nbsp;Prem Kumar Nair ,&nbsp;Vishnu Vazhoor ,&nbsp;Georg Gutjahr ,&nbsp;V.P. Lakshmi Priya","doi":"10.1016/j.gloepi.2025.100208","DOIUrl":"10.1016/j.gloepi.2025.100208","url":null,"abstract":"<div><div>Abstract</div><div>Background</div><div>In India, lung cancer accounts for 5.9 % of all cancers and 8.1 % of all cancer-related deaths, with adenocarcinoma emerging as the most common histopathological subtype in developing countries.</div><div>Aims</div><div>To analyze the shifting trends in the epidemiology and histopathology of lung cancer over 15 years, with a focus on gender differences in the prevalence of adenocarcinoma.</div><div>Method</div><div>This observational, cross-sectional study was conducted at a tertiary care center in Southern India to evaluate the trends in the epidemiology and histopathology of lung cancer over a 15-year period (2008–2022). Data were gathered from patients aged ≥18 diagnosed with primary lung carcinoma. The annual distribution of patients was documented based on age, sex, and tumor histopathology. The analysis was conducted using SPSS software.</div><div>Results</div><div>A total of 4466 newly diagnosed primary lung cancer cases were analyzed over a 15-year period. The median age at diagnosis was 64 years, with a shift in age distribution over time. The proportion of female cases rose from 20.1 % to 28.4 %, while male cases declined from 79 % to 71 %, indicating a significant gender shift. Adenocarcinoma was the most common histopathology subtype, increasing from 22 % to 40 % in men and from 32 % to 55 % in women. Significant associations were observed between histopathology subtype and age group, gender, and year of diagnosis.</div><div>Conclusion</div><div>The study revealed evolving trends in the lung cancer profile over the last 15 years. A significant increase in the prevalence of adenocarcinoma was observed, with a more pronounced rise among women compared to men.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231530","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":"An AI assistant for critically assessing and synthesizing clusters of journal articles","authors":"Louis Anthony Cox Jr.","doi":"10.1016/j.gloepi.2025.100207","DOIUrl":"10.1016/j.gloepi.2025.100207","url":null,"abstract":"<div><div>Current large language models (LLMs) face significant challenges in attempting to synthesize and critically assess conflicting causal claims in scientific literature about exposure-associated health effects. This paper examines the design and performance of AIA2, an experimental AI system (freely available at <span><span>http://cloud.cox-associates.com/</span><svg><path></path></svg></span>) designed to help explore and illustrate potential applications of current AI in assisting analysis of clusters of related scientific articles, focusing on causal claims in complex domains such as epidemiology, toxicology, and risk analysis. Building on an earlier AI assistant, AIA1, which critically reviewed causal claims in individual papers, AIA2 advances the approach by systematically comparing multiple studies to identify areas of agreement and disagreement, suggest explanations for differences in conclusions, flag methodological gaps and inconsistencies, synthesize and summarize well-supported conclusions despite conflicts, and propose recommendations to help resolve knowledge gaps. We illustrate these capabilities with a case study of formaldehyde exposure and leukemia using a cluster of four papers that feature very different approaches and partly conflicting conclusions. AIA2 successfully identifies major points of agreement and contention, discusses the robustness of the evidence for causal claims, and recommends future research directions to address current uncertainties. AIA2's outputs suggest that current AI can offer a promising, practicable approach to AI-assisted review of clusters of papers, promoting methodological rigor, thoroughness, and transparency in review and synthesis, notwithstanding current limitations of LLMs. We discuss the implications of AI-assisted literature review systems for improving evidence-based decision-making, resolving conflicting scientific claims, and promoting rigor and reproducibility in causal research and health risk analysis.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"10 ","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241900","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":"Detecting spatial clusters of Crimean Congo hemorrhagic fever in Iraq in 2023","authors":"Hanan Abdulghafoor Khaleel ,&nbsp;Riyadh Abdulameer Alhilfi ,&nbsp;Sabrina Brown","doi":"10.1016/j.gloepi.2025.100205","DOIUrl":"10.1016/j.gloepi.2025.100205","url":null,"abstract":"<div><h3>Background</h3><div>Since the start of the first large outbreak of Crimean Congo Hemorrhagic Fever (CCHF) in Iraq in 2022, there has been no assessment of clustering of cases by district. The aim of this study is to identify clusters of high and low incidences of human CCHF to guide preventive and control measures, and distribute limited resources.</div></div><div><h3>Methods</h3><div>This is a cross-sectional study of reported and confirmed CCHF cases in Iraq from January 1, 2023 to December 11, 2023. We used a retrospective purely spatial Poisson scan statistic model to identify clusters of high and low incidences of CCHF at the district level (<em>p</em> &lt; 0.05).</div></div><div><h3>Findings</h3><div>There were 580 confirmed CCHF cases, distributed in 149 districts. The incidence was 1.3 per 100,000. There were eight statistically significant clusters (three high-incidence and five low-incidence). The three high-incidence clusters were in the southeast while the five low-incidence clusters were mostly in the north and middle-east Iraq.</div></div><div><h3>Interpretation</h3><div>There is evidence of CCHF clustering in 40 districts in six governorates in south and mid-east Iraq. Additionally, there is evidence of low-incidence clustering of CCHF in 17 governorates, in north and central Iraq, and a risk for future outbreaks. Identifying clusters allows for focused preventive activities, such as insecticide spraying to reduce the tick population, controlling the spread of ticks by treating animals with repellents and other chemicals, and modifying landscapes. Distributing educational materials about handling meat and livestock products and engaging the community can help reduce exposure to ticks and the spread of disease.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069407","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 the use of natural language processing to implement the target trial framework using unstructured data from the electronic health record","authors":"Nicole Rafalko ,&nbsp;Milena Gianfrancesco ,&nbsp;Neal D. Goldstein","doi":"10.1016/j.gloepi.2025.100204","DOIUrl":"10.1016/j.gloepi.2025.100204","url":null,"abstract":"<div><div>The increasing availability and accessibility of electronic health record (EHR) data has made it a rich secondary source to conduct comparative effectiveness studies. To perform such studies, many researchers are turning to the target trial framework (TTF) to emulate the hypothetical randomized clinical trial. The quality of this emulation depends, in part, on the availability and accessibility of data for each component of the TTF. Yet one overarching challenge with using EHR data is that unstructured fields, such as clinical encounter notes, contain copious details on the patient yet require additional steps to extract if needed in the conduct of the study. Natural language processing (NLP) represents a spectrum of methods to assist with automating this extraction, from simpler rule-based methods to machine learning and artificial intelligence approaches that can handle complex language structures. What follows is a discussion on how NLP methods can augment information and data for researchers looking to estimate a treatment effect using EHR data via the TTF to emulate the hypothetical clinical trial. We conclude with recommendations for researchers interested in using NLP methods to obtain data stored in the free text of the EHR as well as considerations regarding the quality and validity of this data for the TTF.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100204"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935152","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":"Calculating preventable risk fractions for exposure-reducing interventions","authors":"Louis Anthony Cox Jr.","doi":"10.1016/j.gloepi.2025.100206","DOIUrl":"10.1016/j.gloepi.2025.100206","url":null,"abstract":"<div><div>How can causal models be used to quantify the fractions of risk associated with environmental and occupational exposures that would be prevented by reducing exposures by different amounts? This paper provides a constructive answer. It introduces three key metrics — Interventional Probability of Causation (IPoC), Causal Assigned Shares (CAS), and Preventable Risk Fraction (PRF) curves — to help overcome the limitations of traditional association-based metrics, such as Population Attributable Fractions (PAFs), which are sometimes misused to answer interventional causal questions. The tools introduced here provide scenario-specific, individual-level predictions of risk reductions grounded in mechanistic causality rather than associations. Using case studies of benzene exposure and acute myeloid leukemia (AML), smoking and lung cancer, and blood lead levels and mortality, we demonstrate how PRF curves quantify the potential risk-reduction benefits caused by exposure reductions at both the individual and population levels, even under uncertainty or heterogeneity. Monte Carlo simulations capture inter-individual variability, and scenario analyses identify practical thresholds where additional exposure reductions yield minimal added benefit. These methods can provide evidence-based assessments of how specific exposure reductions affect risk. By shifting the focus from attribution to prevention of harm, this framework can potentially advance risk assessment, policy development, and legal decision-making. It offers a simple, easily visualized, transparent, and scientifically rigorous approach to identifying causally effective interventions and quantifying risk-reduction benefits.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106091","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":"Talc, ovarian cancer, and recall bias in the sister study","authors":"Julie E. Goodman ,&nbsp;Denali Boon ,&nbsp;Robyn L. Prueitt","doi":"10.1016/j.gloepi.2025.100203","DOIUrl":"10.1016/j.gloepi.2025.100203","url":null,"abstract":"<div><div>O'Brien et al. [<span><span>6</span></span>] reported on the patterns and reliability of self-reported talc use in the Sister Study, a US-based prospective cohort study of women aged 35–74 who had a sister with a history of breast cancer. They found that among certain groups of women, reported use of talc was different at baseline and follow-up. O'Brien et al. [<span><span>7</span></span>] evaluated the association between talc and ovarian cancer in this cohort and conducted a quantitative bias analysis (QBA), reporting evidence for differential recall of talc use at baseline and follow-up, which likely increased the magnitude of risk estimates based on recall at follow-up. Additional analyses (e.g., using distributions of recall bias rather than fixed point estimates) may allow for a more complete characterization of the potential impact of recall bias, including a better characterization of the uncertainty around the bias-corrected effect estimates. Future analyses that evaluate recall and other biases more comprehensively, particularly with respect to more fully addressing uncertainty, will contribute to a better understanding of the magnitude of the impact of differential recall on estimated risks.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905947","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":"Comparison of access to stroke diagnostics, treatment, rehabilitation, and outcome between men and women","authors":"Josefine Grundtvig ,&nbsp;Katrine Sværke ,&nbsp;Mathilde Preskou ,&nbsp;Louisa Marguerite Christensen ,&nbsp;Thorsten Steiner ,&nbsp;Hanne Christensen","doi":"10.1016/j.gloepi.2025.100201","DOIUrl":"10.1016/j.gloepi.2025.100201","url":null,"abstract":"<div><h3>Background</h3><div>We aimed to compare access to diagnostics, treatment, rehabilitation, and outcome in women and men with stroke.</div></div><div><h3>Methods</h3><div>In this observational study we used routinely-collected, aggregate health data from all patients admitted with a stroke in the Capital Region and Region Zealand, Denmark from May 2016 until October 2022.</div></div><div><h3>Results</h3><div>Among 28,855 stroke patients (55 % men, 45 % women), women were older (53 % women vs. 47 % men with age &gt; 75 years). Overall, women had reduced access to: magnetic resonance imaging (MRI; 40 % vs. 43 %, CI 95 %: 1.06–1.16), computer tomography (CT)-angiography (25 % vs. 28 %, CI95%: 1.07–1.19), carotid ultrasound (48 % vs. 52 %, CI95%: 1.12–1.23), thrombolysis (16 % vs. 18 %, CI95%: 1.13–1.28), and neuropsychological assessment (9 % vs. 16 %, CI95%: 1.70–1.97). Home discharge rates were lower for women (45 % vs. 47 %, CI95%: 1.04–1.15), while in-hospital mortality was higher (8 % vs. 6 %, CI95%: 0.59–0.71). For patients &gt;75 years, women had less access to MRI (34 % vs. 35 %, CI95%: 1–1.16), carotid ultrasound (46 % vs. 51 %, CI95%: 1.13–1.30), thrombolysis (15 % vs. 16 %, CI95%: 1.02–1.24), and neuropsychological assessment (2 % vs. 4 %, CI95%: 1.60–2.42). Women's in-hospital mortality remained higher also in patients &gt;75 years (12 % vs. 8 %, CI95%: 0.62–0.79).</div></div><div><h3>Conclusions</h3><div>Women had reduced access to testing, treatment, and rehabilitation across all age groups and stroke types. This disparity was most notable in interventions not included in the national quality assessment program.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900122","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":"Prevalence of Chikungunya, Dengue, and West Nile arboviruses in Iran based on enzyme-linked immunosorbent assay (ELISA): A systematic review and meta-analysis","authors":"Ebrahim Abbasi ,&nbsp;Mohammad Djaefar Moemenbellah-Fard","doi":"10.1016/j.gloepi.2025.100202","DOIUrl":"10.1016/j.gloepi.2025.100202","url":null,"abstract":"<div><h3>Introduction</h3><div>Arboviruses, including Chikungunya (CHIKV), Dengue (DENV), and West Nile (WNV) viruses, are significant viral threats that affect numerous people globally each year. This report explores the prevalence of these viruses in Iran through a systematic review and meta-analysis.</div></div><div><h3>Methods</h3><div>The present survey was performed using a systematic review and meta-analysis method on the seroprevalence of WNV, CHIKV, and DENV using the ELISA test. Accordingly, by searching Web of Science, PubMed, Scopus, Cochrane Library, Science Direct, and Google Scholar scientific databases, all relevant published papers were sorted out and reviewed. Power ratification of data was conducted with a random effects model in meta-analysis, meta-regression, <em>I</em>^<em>2</em> index, and Egger test.</div></div><div><h3>Results</h3><div>This meta-analysis report embodies twelve published papers between 2000 and 2024. The seroprevalence of positive ELISA tests for WNV in Iran was estimated at 12.9 % (CI = 95 %: 7.4–18.4) and for CHIKV at 6.2 % (CI = 95 %: 0.6–11.8). Regarding DENV, only two studies were conducted, with a zero prevalence in one study and a seroprevalence of 5.6 % in another study.</div></div><div><h3>Conclusion</h3><div>According to these data, WNV, CHIKV, and DENV fevers have been detected in Iran using the ELISA test. Considering the seropositivity of WNV and CHIKV and their detection in several provinces, it can be assumed that these viruses are ubiquitous, while DENV fever remains sporadic in Iran.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":"9 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895591","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}