April 11, 2024
Updated May 20, 2025
20 minute read
A Comprehensive Guide to a Career as a Public Health Scientist
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Find a path to becoming a Public Health Scientist. Learn more at:
OpenCourser.com/career/qgn4wu/public
Reading list
We've selected some books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Public Health Scientist.
Universally recognized as the doctoral-level bible of epidemiologic methodology, the fourth edition by Lash, VanderWeele, Haneuse, and Rothman formalizes counterfactual causal inference, directed acyclic graphs (DAGs), target trial emulation, and quantitative bias analysis.
Measuring long-term human exposures plagued by recall error, biological variation, and collinearity requires specialized validation study designs and regression calibration. Willett's landmark treatise teaches measurement-error correction applicable across all chronic-disease epidemiology.
Distilling seventy years of Epidemic Intelligence Service (EIS) outbreak investigations, this operational field guide walks public health responders through case definition, epidemic curve construction, line-list interviewing, and emergency containment protocols.
Comprehensive guide to managing diabetes and preventing its complications. Bernstein physician and a pioneer in the field of diabetes care.
Healthcare-associated infections, multidrug-resistant pathogen surveillance, antimicrobial stewardship, and nosocomial outbreak control demand rigorous clinical epidemiology. Weber and Talbot provide evidence-based protocols for hospital epidemiologists.
A comprehensive and widely used textbook covering linear statistical models, this book is excellent for developing a deep understanding of the subject. It is often used in undergraduate and graduate programs and serves as a valuable reference for professionals. It covers regression, analysis of variance, and experimental design.
Considered a more advanced counterpart to 'An Introduction to Statistical Learning,' this book delves deeper into the theoretical underpinnings of statistical learning. It's a valuable reference for those looking to deepen their understanding and is highly regarded in both academic and industry settings. It covers a wide range of topics relevant to statistical modeling and machine learning.
Comprehensive guide to diabetes prevention for health professionals. Ryan professor of nursing at the University of California, San Francisco, and the director of the UCSF Diabetes Prevention Program.
Practical guide to diabetes prevention in clinical practice. Collins professor of medicine at the University of North Carolina at Chapel Hill and the director of the UNC Diabetes Prevention Program.
Beloved by medical and MPH students for its visual clarity and step-by-step calculations of incidence, prevalence, sensitivity/specificity, relative risk, and odds ratios, Celentano and Szklo update Leon Gordis's classic with COVID-19 pandemic case lessons.
Misinterpreting Simpson's paradox, Lord's paradox, collinearity, or baseline adjustment in observational regression models regularly leads to spurious public health claims. Tu and Gilthorpe dissect the statistical pitfalls lurking inside epidemiologic data.
Bridges the gap between statistical modeling and machine learning, focusing on the practical aspects of building predictive models. It covers various techniques and provides guidance on the entire modeling process. It's highly relevant for those interested in applying statistical modeling for prediction.
Hands-on compartmental modeling (SIR, SEIR, stochastic branching, and age-structured contact matrices) enables epidemiologists to estimate the basic reproduction number R0, herd immunity thresholds, and vaccination campaign impacts.
Comprehensive guide to prediabetes, a condition that can lead to type 2 diabetes if not treated. Barnard clinical professor of medicine at the George Washington University School of Medicine and Health Sciences and the president of the Physicians Committee for Responsible Medicine.
Connecting cohort, case-control, and cross-sectional study designs directly to community health policy, environmental toxicology, and social determinants of health, Friis and Sellers provide a comprehensive foundation for public health practitioners.
Is written for the layperson with a focus on the practical steps one can actually do to prevent diabetes. It gives a comprehensive overview and covers everything from diet and exercise to sleep and stress management. Spero serves as clinical assistant professor of medicine at the Center for Metabolism and Endocrine Disease of the University of Pennsylvania's Perelman School of Medicine.
Focusing on practical applications rather than just theory, this book provides numerous examples to illustrate regression analysis techniques. It's a good resource for understanding how to apply regression in real-world scenarios and is suitable for those with an elementary statistics background. The book covers diagnostics, transformations, and various regression types.
Considered a classic, this book offers a deep and intuitive look at statistical models, particularly in the linear setting. Written by a highly respected statistician, it provides valuable insights into the principles of model building. It's suitable for those with a solid statistical background looking for a foundational perspective.
Standard reference for analyzing categorical data using statistical models like logistic regression and log-linear models. It's essential for researchers and practitioners who work with data that fall into categories. It provides comprehensive coverage of methods and examples.
Zoonotic spillover surveillance, One Health herd-immunity modeling, cluster sampling across livestock farms, and diagnostic test evaluation without a gold standard make Thrusfield's fourth edition essential for infectious disease and zoonosis epidemiologists.
Integrating clinical epidemiology, biostatistical hypothesis testing, and USPSTF preventive screening guidelines into a unified curriculum, Jekel and Katz equip clinicians to appraise medical literature and population risk critically.
Moyses Szklo and Catherine Lesko update the classic intermediate methods text with modern causal inference frameworks, selection bias diagnostics, and effect modification analysis for rigorous observational study design.
Investigating workplace carcinogen exposure, standardized mortality ratios (SMRs), latency windows, and the healthy-worker survivor bias requires specialized cohort techniques that Monson developed and codified.
Moving beyond qualitative hand-waving in discussion sections, Fox, MacLehose, and Lash provide deterministic and probabilistic Monte Carlo bias-modeling recipes in R and SAS to quantify unmeasured confounding, selection bias, and misclassification.
Covers statistical modeling from a computational perspective, which is increasingly important in modern statistics. It delves into computational methods relevant to fitting and analyzing statistical models. It's suitable for graduate students and researchers interested in the computational aspects.
Offers an innovative approach to statistical modeling, emphasizing the use of models to understand and quantify variation in data. It introduces statistical reasoning with a focus on applying concepts using R. It's suitable for building a solid foundation and understanding the logic behind statistical inference.
Ajith R bridges epidemiologic theory with reproducible R programming, walking practitioners through data wrangling, survival analysis, logistic regression, and outbreak visualization using modern tidyverse workflows.
Tracing epidemiology's intellectual shifts from nineteenth-century sanitary statistics and germ-theory contagionism to chronic-disease risk-factor 'black boxes' and modern eco-epidemiology, Susser and Stein illuminate the philosophy of population science.
Focuses specifically on linear models and their implementation in R. It's a practical guide for understanding and applying linear regression and ANOVA, commonly used statistical modeling techniques. It's suitable for students and practitioners who want to work with linear models in R.
Harvard epidemiologist Miguel Hernan and biostatistician James Robins revolutionize how observational studies are analyzed through inverse probability weighting, g-computation, marginal structural models, and time-varying confounding adjustment.
Provides a holistic approach to diabetes prevention and reversal, with a focus on diet, exercise, and stress management. Christianson naturopathic doctor and the author of several books on natural health.
Classic text on probability and statistics, providing a strong theoretical foundation. A solid understanding of probability and statistical inference is crucial for comprehending statistical modeling. It's suitable for advanced undergraduate and graduate students.
While using S (the predecessor to R), this book covers a wide range of modern statistical methods and their application. It's a comprehensive reference for applied statistics and statistical modeling. Many of the concepts and approaches are directly applicable to R users.
Recounting how physician John Snow and curate Henry Whitehead mapped the 1854 Broad Street cholera outbreak house by house to refute miasma theory, Johnson brings the birth of shoe-leather spatial epidemiology to vivid life.
Provides the necessary mathematical background for understanding many statistical modeling and machine learning techniques. While not solely focused on statistical modeling, a strong grasp of the mathematical concepts presented is highly beneficial for deeper understanding and developing new models.
Provides an introduction to the principles of statistical modeling, focusing on the process of building and evaluating models. It offers a good overview of the fundamental ideas behind using models to explain data. It's suitable for undergraduate students.
For more information about how these books relate to this course, visit:
OpenCourser.com/career/qgn4wu/public
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