艰难梭菌感染和定植在医疗机构中传播动力学的数学建模：系统综述

数据集获取（Figshare）

• 源站标题：Mathematical Modeling of the Transmission Dynamics of <i>Clostridium difficile</i> Infection and Colonization in Healthcare Settings: A Systematic Review
• DOI：10.1371/journal.pone.0163880
• 发布日期：2016-09-30
• 文件数：4 个
• 文件总体积：2.57 MB
• 源站页面：https://plos.figshare.com/articles/dataset/Mathematical_Modeling_of_the_Transmission_Dynamics_of_i_Clostridium_difficile_i_Infection_and_Colonization_in_Healthcare_Settings_A_Systematic_Review/3977259

命令行下载

curl -L -o "S1 Table.PDF" "https://ndownloader.figshare.com/files/6215955"
curl -L -o "S1 Appendix.PDF" "https://ndownloader.figshare.com/files/6215970"
curl -L -o "S2 Appendix.PDF" "https://ndownloader.figshare.com/files/6215979"
curl -L -o "S3 Appendix.PDF" "https://ndownloader.figshare.com/files/6215985"

文件清单

• S1 Table.PDF（64.40 KB）
• S1 Appendix.PDF（62.02 KB）
• S2 Appendix.PDF（174.37 KB）
• S3 Appendix.PDF（2.26 MB）

文件总体积 2.57 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Background
We conducted a systematic review of mathematical models of transmission dynamic of Clostridium difficile infection (CDI) in healthcare settings, to provide an overview of existing models and their assessment of different CDI control strategies.
Methods
We searched MEDLINE, EMBASE and Web of Science up to February 3, 2016 for transmission-dynamic models of Clostridium difficile in healthcare settings. The models were compared based on their natural history representation of Clostridium difficile, which could include health states (S-E-A-I-R-D: Susceptible-Exposed-Asymptomatic-Infectious-Resistant-Deceased) and the possibility to include healthcare workers and visitors (vectors of transmission). Effectiveness of interventions was compared using the relative reduction (compared to no intervention or current practice) in outcomes such as incidence of colonization, CDI, CDI recurrence, CDI mortality, and length of stay.
Results
Nine studies describing six different models met the inclusion criteria. Over time, the models have generally increased in complexity in terms of natural history and transmission dynamics and number/complexity of interventions/bundles of interventions examined. The models were categorized into four groups with respect to their natural history representation: S-A-I-R, S-E-A-I, S-A-I, and S-E-A-I-R-D. Seven studies examined the impact of CDI control strategies. Interventions aimed at controlling the transmission, lowering CDI vulnerability and reducing the risk of recurrence/mortality were predicted to reduce CDI incidence by 3–49%, 5–43% and 5–29%, respectively. Bundles of interventions were predicted to reduce CDI incidence by 14–84%.
Conclusions
Although CDI is a major public health problem, there are very few published transmission-dynamic models of Clostridium difficile. Published models vary substantially in the interventions examined, the outcome measures used and the representation of the natural history of Clostridium difficile, which make it difficult to synthesize results and provide a clear picture of optimal intervention strategies. Future modeling efforts should pay specific attention to calibration, structural uncertain

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0163880（source: https://plos.figshare.com/articles/dataset/Mathematical_Modeling_of_the_Transmission_Dynamics_of_i_Clostridium_difficile_i_Infection_and_Colonization_in_Healthcare_Settings_A_Systematic_Review/3977259）。