Path Catalog
正在从 AllPath API 加载 Path Catalog…
ALLPATHFoundationALLPATHFoundationPath Catalog
正在从 AllPath API 加载 Path Catalog…
Path Catalog
列表数据实时取自 GET /api/v1/paths，仅包含存在已发布版本的 Path。
当前筛选：Learning · 清除筛选
共 7800 条 Path · 第 162 / 780 页
This path equips public health and emergency management students with the epidemiological methods needed to assess health impacts, conduct rapid needs assessments, and establish surveillance during disasters. It covers data management, mental health, vulnerable populations, and recovery monitoring, building on foundational epidemiology and surveillance concepts.
This learning path introduces the concepts, methods, and applications of the Global Burden of Disease study. It covers the core metrics (DALYs, YLLs, YLDs, QALYs), cause-of-death estimation, and the interpretation of burden estimates for health policy. Designed for public health and epidemiology students, the path builds from basic epidemiological measures to advanced applications.
This advanced learning path equips epidemiology and public health students with the skills to apply advanced modeling techniques for outbreak prediction and response. Learners will progress from foundational epidemiological concepts through statistical and mechanistic modeling to real-time analytics, forecasting, and decision support, culminating in practical applications for public health.
This advanced learning path equips public health and health administration students with the skills to apply epidemiological methods to health services research and quality improvement. It covers study designs, use of administrative data, outcome measurement, and the translation of evidence into practice. Learners will develop the expertise to evaluate healthcare delivery, measure quality, and address disparities.
This learning path equips epidemiology and research students with advanced meta-analysis skills, covering model selection, heterogeneity assessment, publication bias detection, and advanced methods like meta-regression and network meta-analysis. It also includes the GRADE approach for evaluating evidence quality, building on foundational systematic review and biostatistics knowledge.
This advanced learning path equips epidemiology and public health students with the skills to construct, analyze, and apply mathematical models of infectious diseases. Starting from foundational epidemiology and differential equations, learners progress through compartmental models, threshold dynamics, and advanced topics like age structure and agent-based models. The path culminates in practical applications including parameter estimation, vaccination strategies, and COVID-19 case studies.
This advanced learning path equips epidemiology and geography students with the skills to analyze spatial patterns of disease, detect clusters, and build spatial regression and Bayesian models. It progresses from foundational GIS and epidemiological concepts through spatial statistics to advanced modeling and surveillance applications.
This path guides epidemiology and biostatistics students from foundational causal concepts to advanced analytical methods for estimating causal effects from observational data. It covers directed acyclic graphs (DAGs), propensity score methods, marginal structural models, G-computation, instrumental variables, and sensitivity analysis, emphasizing when and how to apply each approach.
This learning path equips epidemiology and biostatistics students with the knowledge and skills to analyze time-to-event data. Starting from foundational biostatistics and epidemiology concepts, it covers descriptive methods like Kaplan-Meier curves, inferential tests like the log-rank test, and advanced modeling techniques including Cox proportional hazards, time-varying covariates, and competing risks. The path culminates in practical interpretation of survival analysis results.
This learning path equips epidemiology and biostatistics students with the skills to apply advanced regression models—linear, logistic, Poisson, and Cox proportional hazards—to epidemiological data. It covers confounding adjustment, model building strategies, and diagnostic assessment, building on foundational biostatistics and epidemiology.