Epidemics最新文献_第3页

Supporting LGBTQ+ epidemiologists in the UK during research-related travel and international collaboration 支持英国LGBTQ+流行病学家进行与研究相关的旅行和国际合作。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-11-13 DOI: 10.1016/j.epidem.2025.100871

Joseph T. Hicks , Bethan Cracknell Daniels , Rosie Maddren , Jack Doyle , Travis Mager , Christina J. Atchison , Lucy Okell

Conferences, fieldwork, international positions, and collaborations with international partners are beneficial to any epidemiologist, strengthening relationships with fellow scientists, policymakers, health professionals, and those affected by the studied disease. However, international working can pose unique challenges for minority groups. In the UK, LGBTQ+ scientists have a degree of legal protection against discrimination, and universities often have LGBTQ+ staff-student networks that provide support. By contrast, international work can present barriers that non-LGTBQ+ colleagues may not be aware of, such as stress when travelling to countries with anti-LGBTQ+ laws, policies, or sentiments. Homophobic, biphobic, and transphobic beliefs, policies, and actions fluctuate over time, but persist or are on the rise in many locations across the world, including high-income countries. Without institutional support, work-related travel can present a cognitive burden, threatening both physical safety and mental well-being of LGBTQ+ researchers. At Imperial College London, we have worked to address these challenges by developing resources and training for LGBTQ+ staff, students, and allies. We developed an initiative including the creation of online written resources, integration of these materials into travel safety protocols, and a partnership with a LGBTQ+ mental health organization to offer in-person training. We present our experience developing these resources, describe feedback of training participants, and discuss strategies for institutions to develop their own support resources, fostering greater equity in the research experience for individuals of all identities.

会议、实地考察、国际职位以及与国际伙伴的合作对任何流行病学家都是有益的，可以加强与科学家同行、政策制定者、卫生专业人员以及受所研究疾病影响者的关系。然而，国际工作可能给少数群体带来独特的挑战。在英国，LGBTQ+ 科学家有一定程度的免受歧视的法律保护，大学通常有LGBTQ+ 师生网络提供支持。相比之下，国际工作可能会带来非lgbtq + 同事可能没有意识到的障碍，例如前往有反lgbtq + 法律、政策或情绪的国家旅行时的压力。憎恶同性恋、双性恋和跨性别者的信仰、政策和行动随着时间的推移而波动，但在包括高收入国家在内的世界许多地方持续存在或呈上升趋势。如果没有机构支持，与工作相关的旅行可能会带来认知负担，威胁LGBTQ+ 研究人员的身体安全和心理健康。在伦敦帝国理工学院，我们通过为LGBTQ+ 员工、学生和盟友开发资源和培训，努力应对这些挑战。我们制定了一项倡议，包括创建在线书面资源，将这些材料整合到旅行安全协议中，并与LGBTQ+ 心理健康组织合作，提供面对面的培训。我们介绍了我们开发这些资源的经验，描述了培训参与者的反馈，并讨论了机构开发自己的支持资源的策略，以促进所有身份的个人在研究经验方面的更大公平。

{"title":"Supporting LGBTQ+ epidemiologists in the UK during research-related travel and international collaboration","authors":"Joseph T. Hicks , Bethan Cracknell Daniels , Rosie Maddren , Jack Doyle , Travis Mager , Christina J. Atchison , Lucy Okell","doi":"10.1016/j.epidem.2025.100871","DOIUrl":"10.1016/j.epidem.2025.100871","url":null,"abstract":"<div><div>Conferences, fieldwork, international positions, and collaborations with international partners are beneficial to any epidemiologist, strengthening relationships with fellow scientists, policymakers, health professionals, and those affected by the studied disease. However, international working can pose unique challenges for minority groups. In the UK, LGBTQ+ scientists have a degree of legal protection against discrimination, and universities often have LGBTQ+ staff-student networks that provide support. By contrast, international work can present barriers that non-LGTBQ+ colleagues may not be aware of, such as stress when travelling to countries with anti-LGBTQ+ laws, policies, or sentiments. Homophobic, biphobic, and transphobic beliefs, policies, and actions fluctuate over time, but persist or are on the rise in many locations across the world, including high-income countries. Without institutional support, work-related travel can present a cognitive burden, threatening both physical safety and mental well-being of LGBTQ+ researchers. At Imperial College London, we have worked to address these challenges by developing resources and training for LGBTQ+ staff, students, and allies. We developed an initiative including the creation of online written resources, integration of these materials into travel safety protocols, and a partnership with a LGBTQ+ mental health organization to offer in-person training. We present our experience developing these resources, describe feedback of training participants, and discuss strategies for institutions to develop their own support resources, fostering greater equity in the research experience for individuals of all identities.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100871"},"PeriodicalIF":2.4,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145558112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized geo-hierarchical ensemble model to forecast hospitalizations from respiratory viruses in the United States 一个优化的地理层次集成模型预测美国呼吸道病毒住院率。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-11-13 DOI: 10.1016/j.epidem.2025.100869

Shaochong Xu , Hongru Du , Ensheng Dong , Xianglong Wang , Liyue Zhang , Lauren M. Gardner

Accurate forecasting of infectious diseases is crucial for timely public health response. Ensemble frameworks have shown promising outcomes in short-term forecasting of COVID-19, among other respiratory viruses, however, there is a need to further improve these frameworks. Here, we propose the generalized Optimized Geo-Hierarchical Ensemble Model (OGEM), a novel forecasting machine learning framework to forecast state-level hospitalizations of influenza, COVID-19, and RSV in the U.S. independently. This framework is multi-resolution: it integrates state, regionally-trained, and nationally-trained models through an ensemble layer and applies various optimization methods to parameterize the model weights and enhance overall predictive accuracy. This proposed framework builds on existing forecasting literature by 1) employing an ensemble of three spatially hierarchical models with state-level forecasts as the output; 2) incorporating four distinct weight optimization methods to generate the ensemble; 3) utilizing clustering methods to dynamically identify multi-state regions as a function of short-term and long-term hospitalization trends for the regionally-trained model; and 4) providing a generalized framework to forecast the expected near-term hospitalizations from Influenza, RSV and COVID-19. Results demonstrate OGEM is a robust framework with relatively high performance. Extensive experimentation using historical data highlights the predictive power of our framework compared to existing ensemble approaches. Its robust performance underscores the framework's effectiveness and potential for improving and broadening infectious disease forecasting.

传染病的准确预测对于及时作出公共卫生反应至关重要。集成框架在COVID-19和其他呼吸道病毒的短期预测中显示出有希望的结果，但是，需要进一步改进这些框架。在这里，我们提出了广义优化地理层次集成模型（OGEM），这是一种新的预测机器学习框架，可以独立预测美国流感、COVID-19和RSV的州一级住院情况。该框架是多分辨率的：它通过集成层集成了状态、区域和国家训练的模型，并应用各种优化方法来参数化模型权重，提高整体预测精度。该框架建立在现有预测文献的基础上：1)采用三个空间层次模型的集合，以国家级预测作为输出；2)结合四种不同的权重优化方法生成集成；3)利用聚类方法动态识别多状态区域，作为区域训练模型短期和长期住院趋势的函数；4)提供一个通用框架来预测流感、RSV和COVID-19的近期预期住院情况。结果表明，OGEM是一个具有较高性能的鲁棒框架。与现有的集成方法相比，使用历史数据的大量实验突出了我们框架的预测能力。它的强劲表现突出了该框架在改进和扩大传染病预测方面的有效性和潜力。

{"title":"An optimized geo-hierarchical ensemble model to forecast hospitalizations from respiratory viruses in the United States","authors":"Shaochong Xu , Hongru Du , Ensheng Dong , Xianglong Wang , Liyue Zhang , Lauren M. Gardner","doi":"10.1016/j.epidem.2025.100869","DOIUrl":"10.1016/j.epidem.2025.100869","url":null,"abstract":"<div><div>Accurate forecasting of infectious diseases is crucial for timely public health response. Ensemble frameworks have shown promising outcomes in short-term forecasting of COVID-19, among other respiratory viruses, however, there is a need to further improve these frameworks. Here, we propose the generalized Optimized Geo-Hierarchical Ensemble Model (OGEM), a novel forecasting machine learning framework to forecast state-level hospitalizations of influenza, COVID-19, and RSV in the U.S. independently. This framework is multi-resolution: it integrates state, regionally-trained, and nationally-trained models through an ensemble layer and applies various optimization methods to parameterize the model weights and enhance overall predictive accuracy. This proposed framework builds on existing forecasting literature by 1) employing an ensemble of three spatially hierarchical models with state-level forecasts as the output; 2) incorporating four distinct weight optimization methods to generate the ensemble; 3) utilizing clustering methods to dynamically identify multi-state regions as a function of short-term and long-term hospitalization trends for the regionally-trained model; and 4) providing a generalized framework to forecast the expected near-term hospitalizations from Influenza, RSV and COVID-19. Results demonstrate OGEM is a robust framework with relatively high performance. Extensive experimentation using historical data highlights the predictive power of our framework compared to existing ensemble approaches. Its robust performance underscores the framework's effectiveness and potential for improving and broadening infectious disease forecasting.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100869"},"PeriodicalIF":2.4,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145573965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A pilot study to correlate wastewater and clinical surveillance for hepatitis A in New York state 一项在纽约州将废水和临床监测与甲型肝炎相关联的初步研究。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-11-11 DOI: 10.1016/j.epidem.2025.100867

Maxwell D. Weidmann , Patrick W. Bryant , Lynsey Schoultz , Aishwarya Jadhav , Lindsey Rickerman , Dustin T. Hill , Daryl M. Lamson , David A. Larsen , Kirsten St. George

The COVID-19 pandemic prompted a rapid expansion of wastewater-based surveillance in New York State (NYS). Pilot studies were initiated in 2023 to assess the use of this system for the surveillance of hepatitis A virus (HAV) and other pathogens of public health interest. A known cause of outbreaks in the US associated with contaminated food products and transmission between injection drug users, HAV is present in feces for weeks before the onset of symptoms. However, the use of wastewater surveillance as an early warning system has not been assessed outside of the outbreak setting. We compare clinical HAV surveillance with quantitative testing of wastewater samples for HAV RNA from four counties in NYS between September 2022 and November 2023, a period of relatively low HAV incidence. There was a significantly higher mean concentration of HAV RNA in wastewater from sewersheds in districts with reported HAV cases, relative to those without (267 vs. 21 gene copies per microliter, p < 0.05). For 91 % of HAV cases, HAV RNA was detected in the wastewater from the same county between HAV exposure onset and diagnosis, and new HAV RNA detection in wastewater occurred, on average, 41 days before case diagnosis. Our findings demonstrate that wastewater surveillance may provide early warning of case clusters at the county level in low-incidence settings and may allow for detection of otherwise missed asymptomatic or mild illness. Expansion of testing to include all sewersheds in each county may further improve the sensitivity for identifying locations for targeted HAV intervention.

2019冠状病毒病大流行促使纽约州迅速扩大了基于废水的监测。2023年启动了试点研究，以评估该系统在监测甲型肝炎病毒（HAV）和其他与公共卫生有关的病原体方面的使用情况。甲肝病毒是美国爆发甲肝疫情的已知原因，与受污染的食品和注射吸毒者之间的传播有关，甲肝病毒在出现症状前数周就存在于粪便中。然而，废水监测作为早期预警系统的使用尚未在疫情环境之外进行评估。我们比较了2022年9月至2023年11月期间纽约州四个县的临床甲肝病毒监测与甲肝病毒RNA定量检测废水样本，这是甲肝病毒发病率相对较低的时期。在报告有甲型肝炎病例的地区，下水道废水中甲型肝炎RNA的平均浓度明显高于没有报告甲型肝炎病例的地区（267对21基因拷贝/微升）

{"title":"A pilot study to correlate wastewater and clinical surveillance for hepatitis A in New York state","authors":"Maxwell D. Weidmann , Patrick W. Bryant , Lynsey Schoultz , Aishwarya Jadhav , Lindsey Rickerman , Dustin T. Hill , Daryl M. Lamson , David A. Larsen , Kirsten St. George","doi":"10.1016/j.epidem.2025.100867","DOIUrl":"10.1016/j.epidem.2025.100867","url":null,"abstract":"<div><div>The COVID-19 pandemic prompted a rapid expansion of wastewater-based surveillance in New York State (NYS). Pilot studies were initiated in 2023 to assess the use of this system for the surveillance of hepatitis A virus (HAV) and other pathogens of public health interest. A known cause of outbreaks in the US associated with contaminated food products and transmission between injection drug users, HAV is present in feces for weeks before the onset of symptoms. However, the use of wastewater surveillance as an early warning system has not been assessed outside of the outbreak setting. We compare clinical HAV surveillance with quantitative testing of wastewater samples for HAV RNA from four counties in NYS between September 2022 and November 2023, a period of relatively low HAV incidence. There was a significantly higher mean concentration of HAV RNA in wastewater from sewersheds in districts with reported HAV cases, relative to those without (267 vs. 21 gene copies per microliter, p < 0.05). For 91 % of HAV cases, HAV RNA was detected in the wastewater from the same county between HAV exposure onset and diagnosis, and new HAV RNA detection in wastewater occurred, on average, 41 days before case diagnosis. Our findings demonstrate that wastewater surveillance may provide early warning of case clusters at the county level in low-incidence settings and may allow for detection of otherwise missed asymptomatic or mild illness. Expansion of testing to include all sewersheds in each county may further improve the sensitivity for identifying locations for targeted HAV intervention.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100867"},"PeriodicalIF":2.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence for health security in Africa: Benefits, risks and opportunities 人工智能促进非洲卫生安全：利益、风险和机遇。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-11-11 DOI: 10.1016/j.epidem.2025.100870

Claire J. Standley , J. Gabrielle Breugelmans , Amol Chaudhari , Neil Cherian , Sabrina Chwalek , Arminder Deol , Janan Dietrich , Lora du Moulin , Geoffrey Otim , Wilmot James , Stefan Kloth , Sana Masmoudi , Nicaise Ndembi , Nqobile Ndlovu , Danny Scarponi , Franz Schnetzinger , Molly Shapiro , Andrew Hebbeler

Artificial intelligence (AI) provides paradigm-shifting opportunities to accelerate epidemic preparedness and response and ensure health security. Such benefits may be particularly applicable to countries in Africa, which have to date struggled to meet compliance obligations under international health security frameworks. Here, we build on discussions that took place at the March 2024 Health Security Partnership for Africa workshop in Addis Ababa, Ethiopia, to describe potential applications of AI-enabled approaches to accelerate activities throughout the preparedness ecosystem, with a particular focus on the rapid development and deployment of novel vaccines in support of the 100 Days Mission, focusing on Africa. We also consider the risks and barriers that may challenge successful deployment of AI for health security in African settings, and opportunities to elevate African leadership on governance and implementation.

人工智能（AI）为加快流行病防范和应对并确保卫生安全提供了转变范式的机会。这种福利可能特别适用于非洲国家，这些国家迄今一直在努力履行国际卫生安全框架规定的遵守义务。在此，我们以2024年3月在埃塞俄比亚亚的斯亚贝巴举行的非洲卫生安全伙伴关系研讨会上进行的讨论为基础，描述人工智能方法的潜在应用，以加速整个防备生态系统的活动，特别侧重于快速开发和部署新型疫苗，以支持以非洲为重点的100天任务。我们还考虑了可能挑战在非洲环境中成功部署人工智能促进卫生安全的风险和障碍，以及提升非洲在治理和实施方面领导地位的机会。

{"title":"Artificial intelligence for health security in Africa: Benefits, risks and opportunities","authors":"Claire J. Standley , J. Gabrielle Breugelmans , Amol Chaudhari , Neil Cherian , Sabrina Chwalek , Arminder Deol , Janan Dietrich , Lora du Moulin , Geoffrey Otim , Wilmot James , Stefan Kloth , Sana Masmoudi , Nicaise Ndembi , Nqobile Ndlovu , Danny Scarponi , Franz Schnetzinger , Molly Shapiro , Andrew Hebbeler","doi":"10.1016/j.epidem.2025.100870","DOIUrl":"10.1016/j.epidem.2025.100870","url":null,"abstract":"<div><div>Artificial intelligence (AI) provides paradigm-shifting opportunities to accelerate epidemic preparedness and response and ensure health security. Such benefits may be particularly applicable to countries in Africa, which have to date struggled to meet compliance obligations under international health security frameworks. Here, we build on discussions that took place at the March 2024 Health Security Partnership for Africa workshop in Addis Ababa, Ethiopia, to describe potential applications of AI-enabled approaches to accelerate activities throughout the preparedness ecosystem, with a particular focus on the rapid development and deployment of novel vaccines in support of the 100 Days Mission, focusing on Africa. We also consider the risks and barriers that may challenge successful deployment of AI for health security in African settings, and opportunities to elevate African leadership on governance and implementation.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100870"},"PeriodicalIF":2.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145574407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A binary prototype for time-series surveillance and intervention 时间序列监视和干预的二进制原型

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-11-11 DOI: 10.1016/j.epidem.2025.100866

Jason Olejarz , Till Hoffmann , Alex Zapf , Douaa Mugahid , Ross Molinaro , Chadwick Brown , Artem Boltyenkov , Taras Dudykevych , Ankit Gupta , Marc Lipsitch , Rifat Atun , Jukka-Pekka Onnela , Sarah Fortune , Rangarajan Sampath , Yonatan H. Grad

Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost,

c

, whenever the system is abnormal and no action is taken, or an immediate cost,

k

, with action, where

k < c

. If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.

尽管对从监测数据中早期发现异常进行了大量研究，但缺乏对这些信号采取适当行动的系统框架。我们通过制定一个用于时间序列监视的隐马尔可夫式模型来解决这一差距，其中系统状态、观测数据和决策规则都是二进制的。当系统异常且不采取任何行动时，我们会产生延迟成本c，或者产生立即成本k，其中k<；c。如果行动成本太高，那么监督是有害的，干预就不应该发生。如果行动成本足够低，那么监督是有害的，应该始终进行干预。只有当行动成本处于中间水平，监督成本足够低时，监督才有益。我们的公式提供了一个框架，用于评估哪种方法可以在一系列情况下适用，如果有必要进行监测，则有助于对干预策略进行系统分类。因此，我们的模型为设计现实世界的公共卫生监测系统提供了概念基础。

{"title":"A binary prototype for time-series surveillance and intervention","authors":"Jason Olejarz , Till Hoffmann , Alex Zapf , Douaa Mugahid , Ross Molinaro , Chadwick Brown , Artem Boltyenkov , Taras Dudykevych , Ankit Gupta , Marc Lipsitch , Rifat Atun , Jukka-Pekka Onnela , Sarah Fortune , Rangarajan Sampath , Yonatan H. Grad","doi":"10.1016/j.epidem.2025.100866","DOIUrl":"10.1016/j.epidem.2025.100866","url":null,"abstract":"<div><div>Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, <span><math><mi>c</mi></math></span>, whenever the system is abnormal and no action is taken, or an immediate cost, <span><math><mi>k</mi></math></span>, with action, where <span><math><mrow><mi>k</mi><mo><</mo><mi>c</mi></mrow></math></span>. If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100866"},"PeriodicalIF":2.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecasting 增强流行病预测的深度学习方法：用于传染病预测的变压器神经网络和多源数据融合的回顾性评估。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-11-01 DOI: 10.1016/j.epidem.2025.100865

Jiande Wu , Shakhawat Tanim , MinJae Woo , Tanvir Ahammed , Amanda Marie Bleichrodt , Lior Rennert

This paper introduces a deep learning model for county-level Covid-19 forecasting, presenting it as a retrospective case study. We utilize a transformer neural network with multi-source data fusion, incorporating historical case data, death data, and social media sentiment to capture complex temporal and spatial dynamics. Additionally, we develop multi-level and multi-scale attention mechanisms for adaptive time-frequency analysis. In a retrospective evaluation across three Omicron variant waves (December 2021 through February 2023), the model demonstrated strong performance in predicting county-level Covid-19 cases and deaths, with median county agreement accuracy ranging from 74.0 % to 82.6 % for one-week case forecasts and 68.7–79.6 % for 5-week case forecasts. While these historical results are promising, prospective validation is needed to assess the model's utility under live, evolving data conditions. Median county agreement accuracy for deaths ranged from 83.2 % to 86.3 % for one-week forecasts and 84.3–87.2 % for five-week forecasts. Incorporating social media data yielded mild to moderate improvement in forecasting accuracy. Overall, the proposed model yielded substantial improvements compared to a baseline persistence model utilizing the last observation carried forward. By integrating real-time data and capturing complex pandemic dynamics, this approach surpasses traditional methods. The results demonstrate the model's strong performance in a retrospective setting, highlighting the utility of multi-source data fusion and attention mechanisms for fine-grained epidemiological forecasting. This work serves as a case study on the application of advanced deep learning techniques to local-level pandemic data, offering a methodological framework for future research.

本文介绍了一种县级Covid-19预测的深度学习模型，并以回顾性案例研究的形式进行了介绍。我们利用具有多源数据融合的变压器神经网络，结合历史案例数据、死亡数据和社交媒体情绪来捕捉复杂的时空动态。此外，我们还开发了多层次和多尺度的自适应时频分析注意机制。在对三个Omicron变异波（2021年12月至2023年2月）的回顾性评估中，该模型在预测县级Covid-19病例和死亡方面表现出色，一周病例预测的中位县一致性准确率为74.0 %至82.6 %，五周病例预测的中位县一致性准确率为68.7- 79.6% %。虽然这些历史结果很有希望，但需要进行前瞻性验证，以评估模型在实时、不断变化的数据条件下的效用。对于一周预测，死亡的中位数县协议准确率为83.2 %至86.3 %，对于五周预测为84.3-87.2 %。结合社交媒体数据在预测准确性方面产生了轻微到中度的改善。总的来说，与利用最后一次观测的基线持久性模型相比，所提出的模型产生了实质性的改进。通过整合实时数据和捕捉复杂的大流行动态，这种方法超越了传统方法。结果表明，该模型在回顾性设置中具有强大的性能，突出了多源数据融合和注意机制在细粒度流行病学预测中的实用性。这项工作是将先进的深度学习技术应用于地方一级大流行数据的案例研究，为未来的研究提供了方法框架。

{"title":"A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecasting","authors":"Jiande Wu , Shakhawat Tanim , MinJae Woo , Tanvir Ahammed , Amanda Marie Bleichrodt , Lior Rennert","doi":"10.1016/j.epidem.2025.100865","DOIUrl":"10.1016/j.epidem.2025.100865","url":null,"abstract":"<div><div>This paper introduces a deep learning model for county-level Covid-19 forecasting, presenting it as a retrospective case study. We utilize a transformer neural network with multi-source data fusion, incorporating historical case data, death data, and social media sentiment to capture complex temporal and spatial dynamics. Additionally, we develop multi-level and multi-scale attention mechanisms for adaptive time-frequency analysis. In a retrospective evaluation across three Omicron variant waves (December 2021 through February 2023), the model demonstrated strong performance in predicting county-level Covid-19 cases and deaths, with median county agreement accuracy ranging from 74.0 % to 82.6 % for one-week case forecasts and 68.7–79.6 % for 5-week case forecasts. While these historical results are promising, prospective validation is needed to assess the model's utility under live, evolving data conditions. Median county agreement accuracy for deaths ranged from 83.2 % to 86.3 % for one-week forecasts and 84.3–87.2 % for five-week forecasts. Incorporating social media data yielded mild to moderate improvement in forecasting accuracy. Overall, the proposed model yielded substantial improvements compared to a baseline persistence model utilizing the last observation carried forward. By integrating real-time data and capturing complex pandemic dynamics, this approach surpasses traditional methods. The results demonstrate the model's strong performance in a retrospective setting, highlighting the utility of multi-source data fusion and attention mechanisms for fine-grained epidemiological forecasting. This work serves as a case study on the application of advanced deep learning techniques to local-level pandemic data, offering a methodological framework for future research.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100865"},"PeriodicalIF":2.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145460189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating the impact of non-pharmaceutical interventions (NPIs) on post-pandemic Respiratory Syncytial Virus (RSV) hospitalisations and seasonality in Wales, UK 调查非药物干预措施（npi）对大流行后呼吸道合胞病毒（RSV）住院治疗和季节性的影响

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-10-28 DOI: 10.1016/j.epidem.2025.100860

Gabriella Santiago , Carla White , Brendan Collins , Simon Cottrell , Chris Williams , Biagio Lucini , Mike B. Gravenor

Introduction:

Respiratory Syncytial Virus (RSV) is a single-stranded RNA virus and a major cause of hospitalisations in paediatric and geriatric populations. In the Northern Hemisphere, the RSV season is typically between October and March. Following the introduction of Non-pharmaceutical Interventions (NPIs), in response to the COVID-19 pandemic, disruptions in seasonality have been observed.

Methods:

We used an age-structured, deterministic SE₂I₂R model with time-dependent contact rates to study RSV hospitalisations and seasonality in the context of specific NPIs in Wales. The transmission process was linked to a clinical events model, to allow comparison to paediatric admissions data from Public Health Wales. The model was calibrated using Welsh demographics, social contact surveys and a severity index of Welsh NPI impact.

Results:

Admissions data revealed three out-of-season outbreaks (Autumn 2020, Autumn 2021 and Summer 2022). A surge of admissions in Winter 2022-23 and Winter 2023-24 were forecasted, with peak timings correctly predicted, despite a more protracted outbreak observed in the data. Approximately, 90% of RSV admissions in Wales from 2016-22 were in infants under 1 year old; with the greatest shift in admissions age-structure in 2-4 year olds (quintupling in 2021). The model predicted a rapid return to pre-pandemic patterns after disruptions.

Discussion/Conclusions:

Out-of-season peaks chiefly coincided with NPI relaxation. The post-pandemic response of RSV, in terms of timings, magnitude and age-structure shift, were all broadly consistent with simple interruptions in population exposure during the pandemic and the build up of immune naïve cohorts. Our model forms the basis of medium-term projections for paediatric RSV admissions in Wales.

呼吸道合胞病毒（RSV）是一种单链RNA病毒，是儿童和老年人群住院的主要原因。在北半球，RSV的流行季节通常在10月到3月之间。在为应对COVID-19大流行而采取非药物干预措施之后，已观察到季节性中断。方法：我们使用年龄结构，确定性SE2I2R模型与时间相关的接触率来研究威尔士特定npi背景下RSV住院和季节性。传播过程与临床事件模型相关联，以便与威尔士公共卫生部门的儿科入院数据进行比较。该模型使用威尔士人口统计，社会接触调查和威尔士NPI影响的严重程度指数进行校准。结果：入院数据显示了三次淡季疫情（2020年秋季、2021年秋季和2022年夏季）。预测2022-23年冬季和2023-24年冬季的入院人数激增，并正确预测了峰值时间，尽管数据中观察到的爆发时间更长。2016-22年间，威尔士约90%的RSV入院患者是1岁以下的婴儿；入学年龄结构变化最大的是2-4岁儿童（2021年翻了五倍）。该模型预测，在中断之后，将迅速恢复到大流行前的模式。讨论/结论：淡季高峰主要与NPI松弛一致。RSV大流行后的反应，就时间、程度和年龄结构变化而言，都与大流行期间人群暴露的简单中断和免疫naïve队列的建立大致一致。我们的模型构成了威尔士儿科RSV入院中期预测的基础。

{"title":"Investigating the impact of non-pharmaceutical interventions (NPIs) on post-pandemic Respiratory Syncytial Virus (RSV) hospitalisations and seasonality in Wales, UK","authors":"Gabriella Santiago , Carla White , Brendan Collins , Simon Cottrell , Chris Williams , Biagio Lucini , Mike B. Gravenor","doi":"10.1016/j.epidem.2025.100860","DOIUrl":"10.1016/j.epidem.2025.100860","url":null,"abstract":"<div><h3>Introduction:</h3><div>Respiratory Syncytial Virus (RSV) is a single-stranded RNA virus and a major cause of hospitalisations in paediatric and geriatric populations. In the Northern Hemisphere, the RSV season is typically between October and March. Following the introduction of Non-pharmaceutical Interventions (NPIs), in response to the COVID-19 pandemic, disruptions in seasonality have been observed.</div></div><div><h3>Methods:</h3><div>We used an age-structured, deterministic SE<sub>2</sub>I<sub>2</sub>R model with time-dependent contact rates to study RSV hospitalisations and seasonality in the context of specific NPIs in Wales. The transmission process was linked to a clinical events model, to allow comparison to paediatric admissions data from Public Health Wales. The model was calibrated using Welsh demographics, social contact surveys and a severity index of Welsh NPI impact.</div></div><div><h3>Results:</h3><div>Admissions data revealed three out-of-season outbreaks (Autumn 2020, Autumn 2021 and Summer 2022). A surge of admissions in Winter 2022-23 and Winter 2023-24 were forecasted, with peak timings correctly predicted, despite a more protracted outbreak observed in the data. Approximately, 90% of RSV admissions in Wales from 2016-22 were in infants under 1 year old; with the greatest shift in admissions age-structure in 2-4 year olds (quintupling in 2021). The model predicted a rapid return to pre-pandemic patterns after disruptions.</div></div><div><h3>Discussion/Conclusions:</h3><div>Out-of-season peaks chiefly coincided with NPI relaxation. The post-pandemic response of RSV, in terms of timings, magnitude and age-structure shift, were all broadly consistent with simple interruptions in population exposure during the pandemic and the build up of immune naïve cohorts. Our model forms the basis of medium-term projections for paediatric RSV admissions in Wales.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100860"},"PeriodicalIF":2.4,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The bridge between two worlds: Global South researchers' journeys through Global North academic training and beyond 两个世界之间的桥梁：全球南方研究人员的旅程，通过全球北方的学术培训和超越。

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-10-24 DOI: 10.1016/j.epidem.2025.100864

Bimandra A. Djaafara , Mumbua Mutunga , Obiora A. Eneanya , Alpha Forna , Zulma M. Cucunubá

International training of Global South researchers represents a strategic investment that yields substantial returns, rather than the traditional “brain drain” framing. This perspective synthesises the experiences of infectious disease epidemiologists from Colombia, Indonesia, Kenya, Nigeria, and Sierra Leone who completed training in Global North institutions between 2015 and 2024. Despite facing challenges, language barriers, and representational pressures, we demonstrate how Global South researchers transform these obstacles into unique strengths that enhance local research capabilities. Our experiences also show that Global South researchers serve as vital bridges between academic worlds, contributing irreplaceable contextual knowledge while building collaborative networks that advance infectious disease epidemiology research regardless of geographic location. We provide four strategic recommendations for better infectious disease epidemiology research ecosystems: 1) creating supportive institutional environments in Global North institutions, 2) building sustainable partnerships that strengthen home institutions, 3) embracing individual agency and responsibility, and 4) strengthening regional collaborations while adapting to evolving global contexts. Our narrative progresses from challenges to empowerment, demonstrating that Global South researchers are valuable contributors essential to advancing infectious disease epidemiology research.

全球南方研究人员的国际培训代表了一项产生可观回报的战略投资，而不是传统的“人才流失”框架。这一观点综合了来自哥伦比亚、印度尼西亚、肯尼亚、尼日利亚和塞拉利昂的传染病流行病学家的经验，他们在2015年至2024年期间在全球北方机构完成了培训。尽管面临着挑战、语言障碍和代表性压力，我们展示了全球南方的研究人员如何将这些障碍转化为独特的优势，从而提高了当地的研究能力。我们的经验还表明，全球南方的研究人员是学术界之间的重要桥梁，他们贡献了不可替代的背景知识，同时建立了协作网络，推动了传染病流行病学研究，而不受地理位置的限制。我们为改善传染病流行病学研究生态系统提供了四项战略建议：1)在全球北方机构中创造支持性的制度环境；2)建立可持续的伙伴关系，加强国内机构；3)拥抱个人机构和责任；4)在适应不断变化的全球环境的同时加强区域合作。我们的叙述从挑战到赋权，表明全球南方的研究人员是推动传染病流行病学研究的重要贡献者。

{"title":"The bridge between two worlds: Global South researchers' journeys through Global North academic training and beyond","authors":"Bimandra A. Djaafara , Mumbua Mutunga , Obiora A. Eneanya , Alpha Forna , Zulma M. Cucunubá","doi":"10.1016/j.epidem.2025.100864","DOIUrl":"10.1016/j.epidem.2025.100864","url":null,"abstract":"<div><div>International training of Global South researchers represents a strategic investment that yields substantial returns, rather than the traditional “brain drain” framing. This perspective synthesises the experiences of infectious disease epidemiologists from Colombia, Indonesia, Kenya, Nigeria, and Sierra Leone who completed training in Global North institutions between 2015 and 2024. Despite facing challenges, language barriers, and representational pressures, we demonstrate how Global South researchers transform these obstacles into unique strengths that enhance local research capabilities. Our experiences also show that Global South researchers serve as vital bridges between academic worlds, contributing irreplaceable contextual knowledge while building collaborative networks that advance infectious disease epidemiology research regardless of geographic location. We provide four strategic recommendations for better infectious disease epidemiology research ecosystems: 1) creating supportive institutional environments in Global North institutions, 2) building sustainable partnerships that strengthen home institutions, 3) embracing individual agency and responsibility, and 4) strengthening regional collaborations while adapting to evolving global contexts. Our narrative progresses from challenges to empowerment, demonstrating that Global South researchers are valuable contributors essential to advancing infectious disease epidemiology research.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100864"},"PeriodicalIF":2.4,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145410676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Random Forest of epidemiological models for Influenza forecasting 流感预测流行病学模型的随机森林

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-10-21 DOI: 10.1016/j.epidem.2025.100862

Majd Al Aawar, Ajitesh Srivastava

Forecasting the hospitalizations caused by the Influenza virus is vital for public health planning so hospitals can be better prepared for an influx of patients. Many forecasting methods have been used in real-time during the Influenza seasons and submitted to the CDC for public communication. We hypothesize that we can improve forecasting by using multiple mechanistic models to produce potential trajectories and use machine learning to learn how to combine those trajectories into an improved forecast. We propose a Tree Ensemble model design that utilizes the individual predictors of our baseline model SIkJalpha to improve its performance. Each predictor is generated by changing a set of hyperparameters. We compare our prospective forecasts deployed for the FluSight challenge (seasons ending in 2022, 2023, and 2024) to all the other submitted approaches. Our approach is fully automated and does not require any manual tuning. Our submissions remained in the top 33% of the models in all seasons. We demonstrate that our Random Forest-based approach is able to improve upon the forecasts of the individual predictors in terms of mean absolute error, coverage, and weighted interval score. Our method retrospectively outperformed all other models in terms of the mean absolute error and the weighted interval score based on the mean across all weekly submissions of the 2021–22 season.

预测流感病毒引起的住院人数对公共卫生规划至关重要，这样医院才能更好地为大量患者的涌入做好准备。许多预测方法已经在流感季节实时使用，并提交给疾病预防控制中心进行公众沟通。我们假设我们可以通过使用多种机制模型来产生潜在的轨迹，并使用机器学习来学习如何将这些轨迹组合成改进的预测来改进预测。我们提出了一个树集成模型设计，利用我们的基线模型sikalpha的单个预测因子来提高其性能。每个预测器都是通过更改一组超参数生成的。我们将我们为flightchallenge（2022年、2023年和2024年结束的季节）部署的预期预测与所有其他提交的方法进行了比较。我们的方法是完全自动化的，不需要任何手动调优。我们的作品在所有季节都保持在前33%。我们证明了基于随机森林的方法能够在平均绝对误差、覆盖率和加权区间得分方面改进单个预测器的预测。我们的方法回顾性地在平均绝对误差和加权间隔分数方面优于所有其他模型，加权间隔分数基于2021-22赛季所有每周提交的平均值。

{"title":"Random Forest of epidemiological models for Influenza forecasting","authors":"Majd Al Aawar, Ajitesh Srivastava","doi":"10.1016/j.epidem.2025.100862","DOIUrl":"10.1016/j.epidem.2025.100862","url":null,"abstract":"<div><div>Forecasting the hospitalizations caused by the Influenza virus is vital for public health planning so hospitals can be better prepared for an influx of patients. Many forecasting methods have been used in real-time during the Influenza seasons and submitted to the CDC for public communication. We hypothesize that we can improve forecasting by using multiple mechanistic models to produce potential trajectories and use machine learning to learn how to combine those trajectories into an improved forecast. We propose a Tree Ensemble model design that utilizes the individual predictors of our baseline model SIkJalpha to improve its performance. Each predictor is generated by changing a set of hyperparameters. We compare our prospective forecasts deployed for the FluSight challenge (seasons ending in 2022, 2023, and 2024) to all the other submitted approaches. Our approach is fully automated and does not require any manual tuning. Our submissions remained in the top 33% of the models in all seasons. We demonstrate that our Random Forest-based approach is able to improve upon the forecasts of the individual predictors in terms of mean absolute error, coverage, and weighted interval score. Our method retrospectively outperformed all other models in terms of the mean absolute error and the weighted interval score based on the mean across all weekly submissions of the 2021–22 season.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100862"},"PeriodicalIF":2.4,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmenting community-driven vector surveillance with automated image classification: Lessons from the Artificial Intelligence Mosquito Alert (AIMA) system 利用自动图像分类增强社区驱动的病媒监测：来自人工智能蚊子警报（AIMA）系统的经验教训

IF 2.4 3区医学 Q2 INFECTIOUS DISEASES

Epidemics

Pub Date : 2025-10-19 DOI: 10.1016/j.epidem.2025.100863

Monika Falk , Joan Garriga , Roger Eritja , Isis Sanpera-Calbet , Enric Pou , Alex Richter-Boix , John R.B. Palmer , Frederic Bartumeus

The Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public health relevance, contributing to early detection and monitoring efforts. The Artificial Intelligence Mosquito Alert (AIMA) system integrates machine learning image classification within an automated backend pipeline to enable real-time triaging of submissions: critical reports are flagged for expert review, routine cases are classified automatically, and contributors receive immediate feedback fostering participant engagement. By automating routine identifications, the system reduces the burden on experts, allowing them to focus on complex or ambiguous cases that require taxonomic expertise. This study focuses on two AIMA operational periods in 2023 and 2024. We evaluate model updates and performance across these years, highlighting both progress achieved and remaining limitations under real-world citizen science conditions. The most reliably classified species across both models were Aedes albopictus and Culex sp., whereas Aedes aegypti remained difficult to identify. Despite its limitations, AIMA remains central to enabling scalable, responsive, and intelligent mosquito vector surveillance, substantially reducing the time experts must devote to routine identifications. Functioning as an Early Warning System (EWS), MA produces real-time distribution maps of invasive species and rapidly delivers actionable information to public health authorities, facilitating timely responses and intervention.

蚊子警报（MA）平台利用人工智能，通过移动应用程序提交的地理定位图像自动识别蚊子种类，加强社区驱动的蚊子监测。这使公众能够报告与公共卫生相关的本地和入侵蚊子，有助于早期发现和监测工作。人工智能蚊子警报（AIMA）系统将机器学习图像分类集成到自动化后端管道中，以实现提交的实时分类：关键报告被标记供专家审查，常规案例被自动分类，贡献者收到即时反馈，促进参与者参与。通过自动化常规识别，该系统减轻了专家的负担，使他们能够专注于需要分类学专业知识的复杂或模棱两可的案例。本研究的重点是2023年和2024年两个AIMA运营时期。我们评估了这些年来模型的更新和性能，强调了在现实公民科学条件下取得的进展和仍然存在的局限性。两种模式中最可靠的分类物种是白纹伊蚊和库蚊，而埃及伊蚊仍然难以识别。尽管存在局限性，AIMA仍然是实现可扩展、反应灵敏和智能的蚊虫媒介监测的核心，大大减少了专家必须投入常规识别的时间。作为早期预警系统（EWS）， MA生成入侵物种的实时分布图，并迅速向公共卫生当局提供可操作的信息，促进及时的反应和干预。

{"title":"Augmenting community-driven vector surveillance with automated image classification: Lessons from the Artificial Intelligence Mosquito Alert (AIMA) system","authors":"Monika Falk , Joan Garriga , Roger Eritja , Isis Sanpera-Calbet , Enric Pou , Alex Richter-Boix , John R.B. Palmer , Frederic Bartumeus","doi":"10.1016/j.epidem.2025.100863","DOIUrl":"10.1016/j.epidem.2025.100863","url":null,"abstract":"<div><div>The Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public health relevance, contributing to early detection and monitoring efforts. The Artificial Intelligence Mosquito Alert (AIMA) system integrates machine learning image classification within an automated backend pipeline to enable real-time triaging of submissions: critical reports are flagged for expert review, routine cases are classified automatically, and contributors receive immediate feedback fostering participant engagement. By automating routine identifications, the system reduces the burden on experts, allowing them to focus on complex or ambiguous cases that require taxonomic expertise. This study focuses on two AIMA operational periods in 2023 and 2024. We evaluate model updates and performance across these years, highlighting both progress achieved and remaining limitations under real-world citizen science conditions. The most reliably classified species across both models were <em>Aedes albopictus</em> and <em>Culex</em> sp., whereas <em>Aedes aegypti</em> remained difficult to identify. Despite its limitations, AIMA remains central to enabling scalable, responsive, and intelligent mosquito vector surveillance, substantially reducing the time experts must devote to routine identifications. Functioning as an Early Warning System (EWS), MA produces real-time distribution maps of invasive species and rapidly delivers actionable information to public health authorities, facilitating timely responses and intervention.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100863"},"PeriodicalIF":2.4,"publicationDate":"2025-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0