Jason Olejarz,Till Hoffmann,Alex Zapf et al.
Jason Olejarz et al.
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 surv...
A deep learning approach for enhancing pandemic prediction: A retrospective evaluation of transformer neural networks and multi-source data fusion for infectious disease forecasting [0.03%]
一种增强大流行预测的深度学习方法：变压器神经网络和多源数据融合在传染病预测中的回顾性评估
Jiande Wu,Shakhawat Tanim,MinJae Woo et al.
Jiande Wu et al.
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...
Investigating the impact of non-pharmaceutical interventions (NPIs) on post-pandemic Respiratory Syncytial Virus (RSV) hospitalisations and seasonality in Wales, UK [0.03%]
调查非药物干预措施(NPIs)对英国威尔士地区新冠肺炎后呼吸道合胞病毒(RSV)住院率和季节性的影响
Gabriella Santiago,Carla White,Brendan Collins et al.
Gabriella Santiago et al.
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...
The bridge between two worlds: Global South researchers' journeys through Global North academic training and beyond [0.03%]
连接两个世界之间的桥梁：全球南方研究人员的全球北方学术训练之旅及其影响
Bimandra A Djaafara,Mumbua Mutunga,Obiora A Eneanya et al.
Bimandra A Djaafara et al.
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 epid...
Majd Al Aawar,Ajitesh Srivastava
Majd Al Aawar
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 seas...
Wastewater-based surveillance for influenza and respiratory syncytial virus: Insights from a 21-month study in Oklahoma [0.03%]
基于污水的流感和呼吸道合胞病毒监测：俄克拉荷马州一项为期21个月的研究启示
Gargi Deshpande,Bijay Rimal,Kristen Shelton et al.
Gargi Deshpande et al.
Upper respiratory infections caused by viruses such as respiratory syncytial virus (RSV) and influenza are major health concerns globally. Traditional surveillance methods of these viruses rely on clinical data, which can miss mild or asymp...
Reimagining the serocatalytic model for infectious diseases: A case study of common coronaviruses [0.03%]
传染病的血清催化模型的新思考：一种常见冠状病毒的案例研究
Soren L Larsen,Junke Yang,Huibin Lv et al.
Soren L Larsen et al.
Despite the increased availability of serological data, understanding serodynamics remains challenging. Serocatalytic models, which describe the rate of seroconversion (gain of antibodies) and seroreversion (loss of antibodies) within a pop...
Forecasting regional COVID-19 hospitalisation in England using ordinal machine learning method [0.03%]
基于序数机器学习方法预测英格兰各地COVID-19住院人数
Haowei Wang,Kin On Kwok,Ruiyun Li et al.
Haowei Wang et al.
Background: The COVID-19 pandemic caused substantial pressure on healthcare, with many systems needing to prepare for and mitigate the consequences of surges in demand caused by multiple overlapping waves of infections. T...
Rtglm: Unifying estimation of the time-varying reproduction number, Rt, under the Generalised Linear and Additive Models [0.03%]
基于广义线性模型和广义加性模型的统一时间变化再生数估计方法研究
Pierre Nouvellet
Pierre Nouvellet
Most current methods to estimate the time-varying reproduction number (Rt), such as EpiEstim, rely on branching processes and the renewal equation. They also require subjective choices to set the level of temporal and spatial heterogeneity ...
Xiahui Li,Fergus Chadwick,Ben Swallow
Xiahui Li
Bayesian inference methods are useful in infectious diseases modeling due to their capability to propagate uncertainty, manage sparse data, incorporate latent structures, and address high-dimensional parameter spaces. However, parameter inf...