Predicting local COVID-19 emergences: A time-series classification approach and value of data from social media, search engines, and neighbouring regions [0.03%]
预测局部COVID-19爆发：时间序列分类方法及社交网络、搜索引擎和相邻区域数据的价值
Erin E Rees,Mani Sotoodeh,José Denis-Robichaud et al.
Erin E Rees et al.
Background: Early warning for known infectious disease threats use methods that focus on detection of outbreaks, often at large geographical scales. However, earlier warning, specifically at the onset of disease emergence...
A robust compartmental modeling framework for infectious disease monitoring and analysis via fractional differential equations [0.03%]
基于分数阶微分方程的传染病监测与分析的鲁棒隔室模型框架
Farrukh A Chishtie,John Drozd,X Li et al.
Farrukh A Chishtie et al.
This study presents a comprehensive framework for infectious disease monitoring using fractional differential equations, specifically developing the SEIQRDP (Susceptible, Exposed, Infected, Quarantined, Recovered, Deceased, Protected) model...
Estimating influenza transmission parameters: Comparing two study designs, 2023-2024 [0.03%]
估计流感传播参数：比较两种研究设计（2023-2024）
Jessica E Biddle,Stacey House,Jennie H Kwon et al.
Jessica E Biddle et al.
Household studies play a critical role in estimating influenza transmission parameters, which are essential for real-time modeling of epidemic and pandemic dynamics to inform influenza control strategies. We compared two approaches for esti...
Social contact patterns derived from an epidemiological survey and GPS-based co-location data - A systematic comparison using parallel data collections during the COVID-19 pandemic in Germany [0.03%]
基于传染病调查和GPS协同定位数据的社会接触模式——在德国新冠肺炎疫情期间平行收集的数据的系统比较
Huynh Thi Phuong,Janik Suer,Vitaly Belik et al.
Huynh Thi Phuong et al.
The parametrisation of contact behaviour is crucial for infectious disease transmission models. Contact information derived from self-reported surveys and from co-location in space and time (GPS-based) may reflect different dimensions of co...
Machine learning-based short-term forecasting of COVID-19 hospital admissions using routine hospital patient data [0.03%]
基于机器学习的COVID-19住院病人短期预测方法研究
Martin S Wohlfender,Judith A Bouman,Olga Endrich et al.
Martin S Wohlfender et al.
During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmission dynamics and anticipate potential shortages of critical resources such as hospital cap...
Whose knowledge counts? Equity, epistemic justice, and reforming infectious disease research culture [0.03%]
何人的知识才重要？公平性、认识论公正是如何改革传染病研究文化的
Hanna-Tina Fischer,Augustina Koduah
Hanna-Tina Fischer
Infectious disease epidemiology is shaped by engrained research cultures that privilege biomedical and quantitative knowledge systems, systematically marginalizing qualitative, contextual, and locally informed approaches. These hierarchies ...
Evaluating mobility restrictions through spatiotemporal effective reproduction number analysis in a multi-patch model with complex mobility data [0.03%]
基于复杂移动数据的多区模型通过时空有效再生数分析评估流动性限制措施的效果
Byul Nim Kim,Minchan Choi,Hyosun Lee et al.
Byul Nim Kim et al.
Understanding the spatial and temporal dynamics of infectious disease transmission is critical for effective epidemic preparedness and response. COVID-19 transmission is influenced by mobility patterns, regional connectivity, and evolving p...
Augmenting community-driven vector surveillance with automated image classification: Lessons from the Artificial Intelligence Mosquito Alert (AIMA) system [0.03%]
基于社区的蚊虫媒介病原体监测项目中图像识别技术的应用及挑战——AI蚊虫警报系统(AIMA)的经验与教训
Monika Falk,Joan Garriga,Roger Eritja et al.
Monika Falk et al.
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 t...
UnMuted: Defining SARS-CoV-2 lineages according to temporally consistent mutation clusters in wastewater samples [0.03%]
基于时间上一致的废水样本突变簇定义SARS-CoV-2谱系
Devan Becker
Devan Becker
SARS-CoV-2 lineages are defined according to placement in a phylogenetic tree, but approximated by a list of mutations based on sequences collected from clinical sampling. Wastewater lineage abundance is generally found under the assumption...
Bayesian spatio-temporal modelling for infectious disease outbreak detection [0.03%]
传染病爆发的时空 Bayesian 模型检测方法研究
Matthew Adeoye,Xavier Didelot,Simon E F Spencer
Matthew Adeoye
The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the structured population. Here we present new generally app...