Predicted age-specific varicella incidence.

NIAID Data Ecosystem2026-03-10 收录

传染病预测

水痘流行病学

数据链接：

https://figshare.com/articles/dataset/Predicted_age-specific_varicella_incidence_/5019599 数据链接链接失效反馈

官方服务：

资源简介：

Predicted age-specific varicella incidence.

创建时间：

2017-05-19

IDOBE

传染病预测

机器学习

IDOBE是由弗吉尼亚大学生物复杂性研究所主导构建的传染病爆发预测基准数据集，整合了Tycho、JHU-CSSE等四大数据源逾百年的监测数据，涵盖13种疾病的10,799次爆发事件。该数据集通过导数分割算法生成标准化时间序列，包含病例数、住院率等多维度指标，并采用信息熵和排列熵量化流行病学多样性。其核心价值在于为机器学习模型提供跨疾病、跨区域的标准化测试环境，特别针对历史数据有限的新发传染病预测场

arXiv2026-04-21 更新370

Incidence rate ratios of varicella for annual trend, and by season, sex, varicella, temperature and sunny hours, before and after vaccination.

水痘流行病学

疫苗接种效果评估

Incidence rate ratios of varicella for annual trend, and by season, sex, varicella, temperature and sunny hours, before and after vaccination.

NIAID Data Ecosystem60

Compare related work with our research.

传染病预测

序列预测

As of 2022, COVID-19, first reported in Wuhan, China, in November 2019, has become a worldwide epidemic, causing numerous infections and casualties and enormous social and economic damage. To mitigate

NIAID Data Ecosystem40

Additional file 1 of The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study

传染病预测

时间序列分析

Additional file 1. Data on monthly human brucellosis cases from January 2012 to December 2021 in 31 provinces and municipalities in mainland China.

DataCite Commons2024-08-13 更新140

Segmentation is helpful to accurate prediction of the spread of COVID-19.

传染病预测

流行病学细分建模

Segmentation is helpful to accurate prediction of the spread of COVID-19.

Figshare2022-01-12 更新60