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Tag: epidemiology

  • Statistics and Machine Learning in Public Health: When to Use What

    If you’re trained in epidemiology or biostatistics, you likely think in terms of models, inference, and evidence. Now, with machine learning entering the scene, you’re probably hearing about algorithms that can “predict” disease, “detect” outbreaks, and “learn” from data. But while ML offers exciting possibilities, it’s important to understand how it differs from classical statistical approaches—especially when public health decisions depend on more than just prediction.

    Let’s explore how statistics and machine learning differ—not just in technique, but in mindset, use case, and the all-important concept of causality.

    How They Think

    Statistics and machine learning begin with different goals.

    Statistics is built to answer questions like: Does exposure X cause outcome Y? It aims to explain relationships, test hypotheses, and estimate effect sizes. It relies on assumptions—like randomness, independence, and model structure—to ensure that findings reflect the real world, not just the sample at hand.

    Machine learning, in contrast, asks: Given this data, what outcome should I predict? It doesn’t aim to explain but to perform—minimising error and maximising predictive accuracy, even if the relationships are complex or difficult to interpret.

    That’s a major shift. While statistics seeks truth about the population, ML seeks performance in unseen data.

    How They Work

    Statistical methods are grounded in probability theory and estimation. They involve fitting models with interpretable parameters: coefficients, confidence intervals, p-values. The analyst usually specifies the form of the model in advance, guided by theory and prior evidence.

    Machine learning models are trained through algorithms, often using large datasets and iterative techniques to optimise performance. Models like decision trees, support vector machines, and random forests find patterns without assuming linearity or distribution. You don’t always know what the model is “looking at”—you just know if it works.

    There are also hybrid approaches—like regularised regression, ensemble models, and causal forests—that blend the logic of both.

    What They Do Well

    Statistics excels in clarity and rigour. It tells you not just whether something matters, but how much, and with what certainty. It’s ideally suited for:

    Identifying risk factors Estimating treatment effects Designing policy interventions Publishing findings with transparent reasoning

    Machine learning is best when:

    Relationships are non-linear or unknown You have many predictors and large datasets You need fast, repeatable predictions (e.g. real-time risk scoring) The goal is performance, not explanation

    In short, statistics helps you understand, ML helps you predict.

    Where They Fall Short

    Statistics can break down when data gets messy—especially when model assumptions are violated or the number of variables overwhelms the number of observations. It also isn’t built to handle unstructured data like images or free text.

    Machine learning’s biggest limitation is often overlooked: it doesn’t care about causality. A model may predict hospitalisation risk with 95% accuracy, but it doesn’t tell you why. It might rely on variables that are associated, not causal. Worse, it might act on misleading proxies that look predictive but don’t offer actionable insight.

    This matters deeply in public health. Predicting who dies is not the same as preventing death. Models that ignore cause can lead to misguided interventions or unjust decisions.

    Another weakness of ML is interpretability. Many powerful algorithms (like gradient boosting or neural networks) are “black boxes”—hard to explain and harder to justify in policy decisions. While newer tools like SHAP can improve transparency, they still fall short of the clarity offered by traditional statistical models.

    When to Use Each

    Use statistics when:

    Your primary goal is inference or explanation You need to estimate effects or support causal conclusions You’re informing policy or making ethical decisions You want results that are interpretable and reportable

    Use machine learning when:

    Your primary goal is prediction or classification You’re handling high-dimensional or complex data You need scalable automation (e.g. early warning systems) You can validate predictions with real-world data

    Most importantly, if causality matters, don’t rely solely on ML—use statistical thinking or causal ML techniques that explicitly model counterfactuals and assumptions.

    What You Should Expect

    From statistics, expect:

    Clear models with interpretable outputs Transparent assumptions The ability to test hypotheses and quantify uncertainty

    From machine learning, expect:

    High performance with minimal assumptions Useful predictions even when mechanisms are unknown Some loss of interpretability (unless addressed deliberately)

    Just remember: good prediction doesn’t imply good understanding. And good models don’t always lead to good decisions—unless we interpret them wisely.

    A Path Forward for Epidemiologists and Biostatisticians

    Here’s the good news: your training in statistics and epidemiology is not a limitation—it’s your greatest asset. You already understand data, confounding, validity, and generalisability. You’re equipped to evaluate models critically and ask: Does this make sense? Is it actionable? Is it ethical?

    Start small. Try ML approaches that are extensions of what you know—like regularised logistic regression, decision trees, or ensemble methods. Explore tools like caret, tidymodels, or scikit-learn. And when you’re ready to dive deeper, look into causal ML methods like:

    • Targeted maximum likelihood estimation (TMLE)
    • Causal forests (grf)
    • Double machine learning (EconML)
    • DoWhy (for structural causal models)

    The best analysts of the future won’t just be statisticians or ML engineers—they’ll be methodologically bilingual, able to switch between explanation and prediction as the question demands.

    Your role isn’t to replace one with the other, but to integrate both—so that public health remains not just data-driven, but wisely so.

  • The Evolution of Causality in Understanding Disease

    Understanding causality has always been central to the quest for knowledge about health and disease. From the philosophical inquiries of Aristotle to the precision of modern causal inference frameworks, our ideas about what causes disease and how to intervene have evolved through centuries of intellectual effort. This article traces that journey, highlighting key contributions from Aristotle, Al-Farabi, Robert Koch, Austin Bradford Hill, Ken Rothman, and Judea Pearl, and connects their ideas to modern medical practice.

    Aristotle and the origins of causal thinking

    Aristotle (384–322 BCE) introduced what is arguably the first formal framework for understanding causation. He proposed that to fully explain why something exists or happens, one must consider four types of causes: material, formal, efficient, and final causes.

    The material cause is what something is made of. In medicine, this could refer to the tissues, cells, or substances involved in disease. The formal cause is the design or pattern that gives a thing its structure, comparable to the organisation of cells or the genetic blueprint of the body. The efficient cause is the agent or force that produces change. In health, this might be an infectious agent, injury, or environmental exposure. The final cause represents the purpose or goal. For Aristotle, everything in nature had an end or purpose, and in medical terms, this could be metaphorically linked to the goal of health or survival.

    Aristotle’s framework laid the foundation for causal reasoning not only in natural science but also in ethics, politics, and medicine. His approach encouraged generations of thinkers to seek deep, structured explanations for the phenomena they observed.

    Al-Farabi and the integration of causality into Islamic philosophy

    Al-Farabi (872–950 CE), often called the Second Teacher after Aristotle, engaged deeply with Aristotle’s ideas and reinterpreted them through the lens of Islamic philosophy. Al-Farabi did not discard the Four Causes, but he gave them new meaning within a framework that aligned with tawhid, the concept of divine unity.

    Al-Farabi argued that all efficient causes ultimately trace back to the First Cause, God. He extended Aristotle’s final cause beyond natural purposes to include divine wisdom and the moral purpose of human life. In Al-Farabi’s philosophy, causality was not simply a mechanical chain of events but a reflection of divine order and purpose.

    His famous idea that human beings are madani bi al-tab‘i (social by nature) linked causality to the collective pursuit of well-being. In his vision of the Virtuous City (al-Madinah al-Fadilah), knowledge of causes guided not just individual health, but the health of the community and the moral responsibility to promote the common good.

    Robert Koch and the birth of scientific causality in medicine

    The modern scientific study of disease causation began with the work of Robert Koch (1843–1910). Koch introduced formal criteria, known as Koch’s postulates, for identifying the causal relationship between a microorganism and a disease.

    Koch’s postulates required that the microorganism be found in every case of the disease, be isolatable in pure culture, cause the disease when introduced into a healthy host, and be re-isolated from the experimentally infected host. This approach transformed causality in medicine, especially in infectious diseases, from speculative reasoning to testable science.

    Koch’s work exemplified the search for necessary causes in disease. His criteria worked well for infections like tuberculosis but less so for complex diseases that result from multiple interacting factors.

    Austin Bradford Hill and the rise of multifactorial causality

    By the mid-20th century, it had become clear that many diseases did not have single necessary causes. Chronic diseases like cancer, heart disease, and diabetes involved numerous risk factors. Austin Bradford Hill (1897–1991) addressed this complexity by proposing a set of considerations, now known as the Bradford Hill criteria, to help scientists judge whether an observed association is likely to be causal.

    The criteria include strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence, experimental evidence, and analogy. These considerations reflect the complexity of disease causation and guide researchers in interpreting epidemiological data.

    Hill’s approach helped move the focus from single necessary causes to component causes that contribute to sufficient causal mechanisms. This shift set the stage for modern causal models.

    Ken Rothman and the component cause model

    Ken Rothman (born 1945) further refined the understanding of disease causation by introducing the component cause model, often visualised as the causal pie model. This model describes how a disease can result from different combinations of factors, where each combination forms a sufficient cause.

    In Rothman’s model, component causes represent individual factors (such as smoking, genetic susceptibility, or environmental exposure) that combine to complete a causal mechanism. No single component cause needs to be necessary or sufficient on its own. The model illustrates why many diseases cannot be attributed to a single factor and why prevention strategies must target multiple risk factors.

    Rothman’s work has influenced generations of epidemiologists and public health professionals, providing a practical and visual tool to understand and teach multifactorial causation.

    Judea Pearl and the ladder of causation

    The most recent revolution in causality comes from Judea Pearl (born 1936), whose work has transformed causal inference into a formal, mathematical science. Pearl introduced causal diagrams, known as directed acyclic graphs (DAGs), and structural causal models to make causal relationships explicit and testable in data.

    One of Pearl’s key contributions is the concept of the Ladder of Causation. The ladder describes three levels of causal reasoning. The first level is association, where one observes patterns in data. The second level is intervention, where one reasons about what happens if something is changed or manipulated. The third level is counterfactuals, where one asks what would have happened under different circumstances.

    Pearl’s framework allows researchers to distinguish between mere correlation and true causation and to address complex issues such as confounding, mediation, and effect modification. His work is now central to fields ranging from epidemiology to artificial intelligence.

    Causality in modern medicine

    Understanding causality has practical implications in modern medicine. Few diseases today are thought to have single necessary and sufficient causes. Instead, most conditions arise from combinations of component causes that form sufficient causal mechanisms.

    Take lung cancer as an example. Smoking is neither a necessary cause (because lung cancer can occur in non-smokers) nor a sufficient cause (because not all smokers develop lung cancer). However, smoking is a major component cause that contributes to sufficient causal mechanisms. Interventions that reduce smoking prevalence can prevent many cases of lung cancer, even if they do not eliminate the disease entirely.

    Similarly, understanding that hypertension, high cholesterol, and physical inactivity are component causes of cardiovascular disease guides interventions that target these factors. The insights from causal reasoning help shape prevention strategies, clinical decisions, and public health policies.

    From philosophy to practice

    Tracing the journey of causality thinking from Aristotle to Pearl shows the progression from philosophical reflection to scientific precision. Aristotle’s Four Causes encouraged us to look for deeper reasons behind events. Al-Farabi integrated these ideas with a moral and social vision, reminding us that understanding causes should serve the common good. Koch’s postulates gave us tools to prove necessary causes in infectious diseases. Bradford Hill’s criteria helped navigate the complexity of chronic disease causation. Rothman’s component cause model illustrated the multifactorial nature of disease. Pearl’s ladder of causation and causal models now give us the tools to analyse and act on causal relationships in complex systems.

    Together, these frameworks have helped medicine move beyond treating symptoms to addressing root causes. They also remind us that understanding causality is not only about explaining disease but also about guiding interventions that promote health and well-being.

    References

    Hill, A. B. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.

    Koch, R. (1884). Die aetiologie der tuberkulose. Berliner Klinische Wochenschrift, 21, 221–230.

    Pearl, J. (2018). The book of why: The new science of cause and effect. Basic Books.

    Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology (3rd ed.). Lippincott Williams & Wilkins.

    VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.

  • Training Critical Thinking and Logical Thinking in the Age of AI for Biostatistics and Epidemiology

    The arrival of generative AI tools like ChatGPT is changing the way we teach and practise biostatistics and epidemiology. Tasks that once took hours, like coding analyses or searching for information, can now be completed within minutes by simply asking the right questions. This development brings many opportunities, but it also brings new challenges. One of the biggest risks is that students may rely too much on AI without properly questioning what it produces.

    In this new environment, our responsibility as educators must shift. It is no longer enough to teach students how to use AI. We must now teach them how to think critically about AI outputs. We must train them to question, verify and improve what AI generates, not simply accept it as correct.

    Why critical thinking is important

    AI produces answers that often sound very convincing. However, sounding convincing is not the same as being right. AI tools are trained to predict the most likely words and patterns based on large amounts of data. They do not understand the meaning behind the information they provide. In biostatistics and epidemiology, where careful thinking about study design, assumptions and interpretation is vital, careless use of AI could easily lead to wrong conclusions.

    This is why students must develop a critical and questioning attitude. Every output must be seen as something to be checked, not something to be believed blindly.

    Recent academic work also supports this direction. Researchers have pointed out that users must develop what is now called “critical AI literacy”, meaning the ability to question and verify AI outputs rather than accept them passively (Ng, 2023; Mocanu, Grzyb, & Liotta, 2023). Although the terms differ, the message is the same: critical thinking remains essential when working with AI.

    How to train critical thinking when using AI

    Build a sceptical mindset

    Students should be taught from the beginning that AI is only a tool. It is not a source of truth. It should be seen like a junior intern: helpful and fast, but not always right. They should learn to ask questions such as:

    What assumptions are hidden in this output? Are the methods suggested suitable for the data and research question? Is anything important missing?

    Simple exercises, like showing students examples of AI outputs with clear mistakes, can help build this habit.

    Teach structured critical appraisal

    To help students evaluate AI outputs properly, it is useful to give them a structured way of thinking. A good framework involves five main points:

    First, methodological appropriateness

    Students must check whether the AI suggested the correct statistical method or study design. For example, if the outcome is time to death, suggesting logistic regression instead of survival analysis would be wrong.

    Second, assumptions and preconditions

    Every method has assumptions. Students must identify whether these assumptions are mentioned and whether they make sense. If assumptions are not stated, students must learn to recognise them and decide whether they are acceptable.

    Third, completeness and relevance

    Students should check whether the AI output missed important steps, variables or checks. For instance, has the AI forgotten to adjust for confounding factors? Is stratification by key variables missing?

    Fourth, logical and statistical coherence

    The reasoning must be checked for soundness. Are the conclusions supported by the results? Is there any step that does not follow logically?

    Fifth, source validation and evidence support

    Students should verify any references or evidence provided. AI sometimes produces references that do not exist or that are outdated. Cross-checking with real sources is necessary.

    By using these five points, students can build a habit of structured checking, instead of relying on their instincts alone.

    Encourage comparison and cross-verification

    Students should not depend on one AI output. They should learn to ask the same question in different ways and compare the answers. They should also check against textbooks, lectures, or real research papers.

    Practise reverse engineering

    One effective exercise is to give students an AI-generated answer with hidden mistakes and ask them to find and correct the errors. This strengthens their ability to read carefully and think independently.

    Make students teach back to AI

    Another good practice is to ask students to correct the AI. After finding an error, they should write a prompt that explains the mistake to the AI and asks for a better answer. Being able to explain an error clearly shows true understanding.

    Why logical thinking in coding and analysis planning remains essential

    Although AI can now generate codes and suggest analysis steps, it does not replace the need for human logical thinking. Writing good analysis plans and coding correctly require structured reasoning. Without this ability, students will not know how to guide AI properly, how to spot mistakes, or how to build reliable results from raw data.

    Logical thinking in analysis means asking and answering step-by-step questions such as:

    What is the research question? What are the variables and their roles? What is the right type of analysis for this question? What assumptions need to be checked? What is the correct order of steps?

    If students lose this skill and depend only on AI, they will not be able to detect when AI suggests inappropriate methods, forgets a critical step, or builds a wrong model. Therefore, teaching logical thinking in data analysis planning and coding must stay an important part of the curriculum.

    Logical planning and good coding are not simply technical skills. They reflect the student’s ability to reason clearly, to see the structure behind the problem, and to create a defensible path from data to answer. These are skills that no AI can replace.

    Ethical use of generative AI and the need for transparency

    Along with critical and logical thinking, students must also be trained to use generative AI tools ethically. They must understand that using AI does not remove their professional responsibility. If they rely on AI outputs for any part of their work, they must check it, improve it where needed, and take ownership of the final product.

    Students should also be taught about data privacy. Sensitive or identifiable information must never be shared with AI platforms, even during casual exploration or practice. Responsibility for patient confidentiality, research ethics, and academic honesty remains with the human user.

    Another important point is transparency. Whenever AI tools are used to assist in study design, data analysis, writing or summarising, this use should be openly declared. Whether in academic assignments, published articles or professional reports, readers have the right to know how AI was involved in shaping the content. Full and honest declaration supports academic integrity, maintains trust, and shows respect for the standards of research and publication.

    Students should be guided to include a simple statement such as:

    “An AI tool was used to assist with [describe briefly], and the final content has been reviewed and verified by the author.”

    By practising transparency from the beginning, students learn that AI is not something to hide, but something to use responsibly and openly.

    Building a modern curriculum

    To prepare students for this new reality, we must design courses that combine:

    Training in critical thinking when using AI outputs Training in logical thinking for building analysis plans and writing codes Training in ethical use and transparent declaration of AI assistance

    Students should be given real-world tasks where they must plan analyses from scratch, use AI as a helper but not as a leader, check every output carefully, and justify every step they take. They should also be trained to reflect on the choices they make, and on how to improve AI suggestions if they find them weak or incorrect.

    By doing this, we can prepare future biostatisticians and epidemiologists who are not only technically skilled but also intellectually strong and ethically responsible.

    A new way forward

    Teaching students to use AI critically is not just a good idea. It is essential for the future. In biostatistics and epidemiology, where errors can affect public health and policy, we must prepare a new generation who can use AI wisely without losing their own judgement.

    The best users of AI will not be those who follow it blindly, but those who can guide it with intelligence, knowledge and ethical care. Our role as teachers is to help students become leaders in the AI age, not followers.

    References

    Ng, W. (2023). Critical AI literacy: Toward empowering agency in an AI world. AI and Ethics, 3(1), 137–146. https://doi.org/10.1007/s43681-021-00065-5

    Mocanu, E., Grzyb, B., & Liotta, A. (2023). Critical thinking in AI-assisted decision-making: Challenges and opportunities. Frontiers in Artificial Intelligence, 6, Article 1052289. https://doi.org/10.3389/frai.2023.1052289

    Disclaimer

    This article discusses the responsible use of generative AI tools in education and research. It is based on current understanding and practices as of 2025. Readers are encouraged to apply critical judgement, stay updated with evolving guidelines, and ensure compliance with their institutional, professional, and ethical standards.

  • Epidemiology and Biostatistics in the Light of Divine Unity

    In the Islamic worldview, knowledge is not categorised into ‘Islamic’ and ‘secular.’ There is only one knowledge — al-‘ilm — bestowed by Allah, whether discovered through divine revelation (wahy) or human reason (‘aql). All beneficial knowledge should ultimately draw us closer to Allah, the All-Knowing. This article explores the field of epidemiology and biostatistics through this lens of divine unity, affirming that scientific inquiry and statistical reasoning are not merely technical disciplines, but pathways to understanding the patterns and wisdom embedded in Allah’s creation.

    John M. Last (1988) defined epidemiology as “the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems.” This definition highlights three core components: distribution, determinants, and application. Distribution refers to patterns — who is affected, where, and when. Determinants delve into the causes, risk factors, and protective factors. Application demands action — the use of findings to prevent and control diseases.

    In Islam, observation of patterns in nature and society is encouraged. The Qur’an repeatedly invites reflection (tadabbur) on signs (ayat) in the universe and within ourselves. Understanding patterns of disease aligns with this call to contemplation and action. Epidemiology, therefore, becomes a means of fulfilling the Islamic obligation to protect life (hifz al-nafs), one of the five higher objectives of Shariah (maqasid al-shariah).

    Sir Austin Bradford Hill (1965) introduced a set of principles to guide causal inference in epidemiology. His criteria — strength, consistency, temporality, biological gradient, plausibility, coherence, experiment, specificity, and analogy — serve as guides rather than strict rules.

    Yet, we must recognise the epistemological humility within our methods. In regression models, confidence intervals, and Hill’s criteria, there is always an element of uncertainty. This aligns with the Islamic view that human knowledge is inherently limited. As Allah reminds us: “And you (O mankind) have not been given of knowledge except a little.” (Qur’an, Al-Isra’, 17:85)

    Hence, we strive to understand cause and effect through careful observation and reasoning, but ultimately, we acknowledge that true causality is known only to Allah. Our frameworks are approximations — tools to aid, not final truths.

    Historical accounts during the time of the Prophet Muhammad ﷺ and his companions demonstrate the application of outbreak control principles. One notable example is the plague (ṭā‘ūn) during the rule of Caliph Umar ibn al-Khattab. When the plague broke out in Syria, Umar decided not to enter the area, and advised others not to leave or enter — an early form of quarantine.

    The Prophet ﷺ said: “If you hear of a plague in a land, do not enter it; and if it breaks out in a land where you are, do not leave it.” (Sahih al-Bukhari, Hadith 5728; Sahih Muslim, Hadith 2219)

    This hadith reflects core outbreak control principles such as isolation, movement restriction, and collective responsibility — key strategies in modern epidemiology.

    Islam strongly advocates prevention. The Prophet ﷺ advised moderation in eating: “The son of Adam does not fill any vessel worse than his stomach. It is sufficient for the son of Adam to eat a few mouthfuls to keep him going. If he must do that (fill his stomach), then let him fill one-third with food, one-third with drink, and one-third with air.” (Sunan Ibn Majah, Hadith 3349)

    This guidance is preventive in nature and closely aligns with public health nutrition. Islam connects overindulgence and lack of restraint to the whispers of Shayṭān. Preventive health, therefore, is not just a matter of science, but a matter of spiritual discipline.

    Islamic rituals incorporate hygiene into acts of worship. Ablution (wudu’), performed five times daily before prayer, involves washing the hands, mouth, nose, face, arms, head, and feet — the very areas associated with microbial transmission.

    The Prophet ﷺ also instructed: “Cover your utensils and tie your water skins, for there is a night in the year when plague descends, and it does not pass an uncovered utensil or untied water skin without some of that plague descending into it.” (Sahih Muslim, Hadith 2014)

    These teachings reflect divine wisdom in infection prevention, centuries before the discovery of microbes and germ theory.

    Biostatistics provides us with essential tools to summarise data and draw meaningful inferences about populations from sample observations. Among its most powerful techniques is regression analysis, which allows us to explore and quantify the relationship between an outcome (dependent variable) and one or more explanatory (independent) variables.

    The general form of a multiple linear regression model is:

    y = β₀ + β₁x₁ + β₂x₂ + … + βₖxₖ + ε

    In this equation:

    • y represents the outcome or response variable we aim to predict or explain,

    • x₁ to xₖ are the predictor variables that we believe influence the outcome,

    • β₀ is the intercept, the expected value of y when all predictors are zero,

    • β₁ to βₖ are the regression coefficients that quantify the effect of each predictor on the outcome, and

    • ε is the error term, capturing the variability in y that the model cannot explain.

    This error term is more than just a technical component; it is a profound acknowledgment of the limits of human understanding. Even with the most refined models and abundant data, there will always be elements of unpredictability — due to omitted variables, imprecise measurements, biological variation, or other unknown factors. The presence of this uncertainty is a built-in reminder that our knowledge is partial and conditional.

    From an Islamic perspective, this aligns beautifully with the concept of epistemic humility. As Allah states in the Qur’an: “And you (O mankind) have not been given of knowledge except a little.” (Qur’an, Al-Isra’, 17:85)

    Thus, while biostatistics helps us make informed decisions and uncover meaningful relationships, it also teaches us to recognise the boundaries of what we can know. The error term symbolises the divine reality — that ultimate knowledge lies only with Allah. It invites us to pursue knowledge responsibly, with sincerity, but never with arrogance.

    This concept is further reinforced in the Qur’an: “And above every possessor of knowledge is one [more] knowing.” (Qur’an, Yusuf, 12:76)

    Every estimate, statistical model, and inference must be grounded in this awareness. We can model, measure, and approximate, but only Allah knows the unseen, the future, and the full complexity of creation. Biostatistics, therefore, is not only a scientific tool but also a spiritual exercise in recognising our role as seekers of knowledge, always dependent on the One who knows all.

    Epidemiology and biostatistics, when viewed through the Islamic perspective of tawḥīd (oneness of Allah), are not detached from faith but are deeply connected to it. These sciences offer not just understanding but also tools to protect life, serve society, and fulfil the trust placed upon us as khalifah (stewards) on Earth. By unifying rational inquiry with spiritual awareness, we find that knowledge — whether derived from revelation or observation — is ultimately from the same source. Through this lens, our pursuit of health knowledge becomes a journey toward Allah.

    References
    1. Last, J. M. (1988). A Dictionary of Epidemiology (2nd ed.). Oxford University Press.
    2. Hill, A. B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.
    3. The Noble Qur’an, Surah Al-Isra’ (17:85), Surah Yusuf (12:76).
    4. Sahih al-Bukhari, Book 76, Hadith 5728.
    5. Sahih Muslim, Book 39, Hadith 2219; Book 23, Hadith 2014.
    6. Sunan Ibn Majah, Book 29, Hadith 3349.
    7. Al-Ghazali, I. H. Ihya Ulum al-Din – On the virtues of knowledge and its relation to action and worship.
    8. Nasr, S. H. (1992). Science and Civilization in Islam. Harvard University Press.

  • A Decade Too Soon: Uniting Tawhid and Public Health for Malaysia’s Future

    Jamalludin Ab Rahman

    Malaysia is facing a silent but accelerating epidemic. Cardiovascular disease (CVD) is not only the leading cause of death in the country, but it is also affecting Malaysians a decade earlier than in advanced nations (APAC CVD Alliance, 2024). Nearly one in four CVD patients was under the age of 50 in 2019, and the largest increase in stroke incidence occurred among those aged 35 to 39. Ischaemic heart disease is 1.6 times more prevalent in men, while stroke affects more women—showing no demographic is spared. Malaysia now records one of the highest rates of heart failure in Southeast Asia, with hospitalisation rates of 10 percent and 30-day readmission rates reaching 25 percent. Worse, heart failure in Malaysia is diagnosed six to ten years earlier than in other countries.

    Behind these clinical realities lies a lifestyle in crisis. Nearly 50 percent of adults are overweight or obese, with women slightly more affected (54.7 percent). Three in ten Malaysians suffer from hypertension, and one in five has diabetes—often without knowing it. Salt intake remains well above the WHO recommended limit, while the intake of fats and sugars has increased by 80 percent and 33 percent respectively over the last 45 years. The consequences are severe: Malaysia incurs USD 1.68 billion annually in direct and indirect costs from premature CVD mortality and disability (APAC CVD Alliance, 2024).

    These are not just numbers—they are warnings. And the root cause is not simply medical, but spiritual and behavioural. The overconsumption of food, physical inactivity, and dependence on chemical cures without lifestyle transformation are symptoms of deeper imbalance. It is in this light that Islamic teachings and ethical models of care must reclaim their place—not only in public health planning, but in the consultation room, the community, and the curriculum.

    Islam offers profound guidance on eating and health. The Prophet Muhammad (peace be upon him) said, “The son of Adam does not fill any vessel worse than his stomach. It is sufficient for him to eat a few bites to keep his back straight. But if he must, then one-third for his food, one-third for his drink, and one-third for his breath” (al-Tirmidhi, Hadith 2380). Likewise, the Qur’an instructs, “Eat and drink, but do not be excessive. Indeed, He does not like those who commit excess” (Qur’an 7:31). These teachings embed moderation, gratitude, and accountability within the act of eating—turning what we consume into a reflection of our spiritual consciousness.

    Public health models, such as the Health Belief Model (HBM), help explain why people change or fail to change behaviour. The model shows that individuals are more likely to adopt preventive actions when they perceive a personal risk, understand the severity of the disease, believe in the benefits of change, and encounter minimal barriers (Becker, 1974; Champion & Skinner, 2008). In Malaysia, however, these elements must be delivered within local, spiritual, and cultural frameworks. That means moving beyond posters and pamphlets to engaging communities through trusted voices—especially doctors, religious leaders, and educators.

    Doctors have a unique and sacred role. They are not only healers but also leaders, educators, and examples. Every consultation is an opportunity not just to prescribe medication, but to prescribe a lifestyle. Patients with hypertension, diabetes, or obesity must be advised on dietary change, physical activity, spiritual discipline, and fasting—not merely given chemical interventions. Lifestyle prescriptions must become part of routine clinical practice, not optional or secondary. Hospitals and clinics must transform from treatment centres into wellness institutions.

    This responsibility begins with doctors themselves. Their credibility is strengthened when they live the lifestyle they promote. A doctor who fasts regularly, avoids gluttony, walks or cycles, and maintains balance in diet and conduct offers a silent but powerful form of da’wah. Islamic hospitals and medical faculties should reinforce this vision, ensuring that doctors are seen as moral exemplars and not merely technical experts. Their example can shift norms and inspire communities to follow a path of moderation.

    To make this sustainable, preventive health education must be strengthened at the foundation. Medical schools should embed modules that combine behavioural science, nutrition, spiritual wellness, and Islamic ethics. Students should be trained to give khutbahs, lead community dialogues, and understand the social determinants of health from a tawhidic worldview.

    Tawhidic epistemology gives this approach its moral clarity. It asserts that all knowledge—whether biomedical or behavioural—must lead to Allah. The body is a trust (amanah), and health is a blessing that demands stewardship. Healing, therefore, is not limited to the removal of symptoms but must also serve to realign the human being with divine balance (mizan). As articulated by Bakar (2021, 2025), tawhid integrates rational and revealed knowledge to ensure that science and healthcare are spiritually accountable. By embracing tawhid, we move from seeing the patient as a consumer of treatment to a servant of the Creator, responsible for preserving his or her own body and influencing society.

    Malaysia’s battle against early-onset CVD will not be won in hospitals and pharmacies alone. It will be won in the hearts, homes, and habits of the people. By combining the insight of the Health Belief Model with the moral depth of tawhidic epistemology—and empowering doctors to lead through both words and example—we can return to the prophetic path: to eat moderately, to live purposefully, and to heal with meaning.

    References

    APAC CVD Alliance. (2024). Malaysia: A call for cohesive action—Redefining cardiovascular care in the Asia-Pacific. https://apac-cvd.org/publications/

    al-Tirmidhi, M. I. (n.d.). Jamiʿ at-Tirmidhi (Hadith 2380)

    Bakar, O. (2021). Tawhid and science: Essays on the history and philosophy of Islamic science (2nd ed.). UBD Press.

    Bakar, O. (2025). Defining the core identity of a 21st-century Islamic university. In The Muslim 500: The World’s 500 Most Influential Muslims (2025 Edition) (pp. 70–73). The Royal Islamic Strategic Studies Centre.

    Becker, M. H. (1974). The Health Belief Model and personal health behavior. Health Education Monographs, 2, 324–473.

    Champion, V. L., & Skinner, C. S. (2008). The Health Belief Model. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior and health education: Theory, research, and practice (4th ed., pp. 45–65). Jossey-Bass.

    The Qur’an. (n.d.). Surah al-Aʿraf, 7:31

  • The Silent Toll of Excess Mortality During the COVID-19 Pandemic

    Introduction

    The COVID-19 pandemic has reshaped global health systems, revealing vulnerabilities in healthcare and public health infrastructure. While official COVID-19 death counts capture the immediate impact, excess mortality estimates uncover the pandemic’s broader effects, including indirect deaths caused by disrupted healthcare services and societal changes. This study examines global and regional excess mortality data and emphasises the role of Malaysia’s White Health Paper in preparing for future pandemics.

    Global Excess Mortality Estimates

    Globally, the World Health Organization (WHO) reported approximately 14.9 million excess deaths between January 2020 and December 2021, nearly three times the officially recorded COVID-19 deaths (World Health Organization, 2022). Similarly, the Institute for Health Metrics and Evaluation (IHME) estimated approximately 18.3 million excess deaths during the same period (Wang et al., 2022). These figures underscore the extensive direct and indirect impacts of the pandemic.

    Regional Variations in Excess Mortality

    Excess mortality varied significantly across regions. In Malaysia, a study in The Lancet Regional Health – Western Pacificreported an 8.5% increase in mortality from January 2020 to December 2021, reflecting indirect effects such as healthcare system disruptions and delayed treatments (The Lancet Regional Health – Western Pacific, 2022). In contrast, India reported a 20% increase in excess deaths, highlighting challenges in healthcare access and reporting (The Lancet, 2022). Other countries, such as Brazil and the United States, also faced substantial increases in excess mortality, further demonstrating regional disparities (Faust et al., 2021).

    Indirect Effects of the Pandemic

    Beyond direct COVID-19 fatalities, excess mortality includes deaths exacerbated by the pandemic. Delayed medical treatments due to overwhelmed healthcare systems led to increased deaths from chronic diseases, including cancer and cardiovascular conditions (Maringe et al., 2020). Mental health crises and substance abuse also contributed to rising mortality, particularly among younger populations (Faust et al., 2021).

    The Role of Public Health Specialists and Policymakers in Malaysia

    The pandemic has emphasised the importance of proactive public health leadership. In Malaysia, the White Health Paper provides a comprehensive framework for strengthening healthcare systems and preparing for future pandemics. Key recommendations include:

    1. Strengthening Public Health Infrastructure

    Investments in healthcare infrastructure and workforce capacity are critical. Public health specialists must advocate for equitable healthcare access and improved resource allocation.

    2. Enhancing Surveillance and Data Systems

    Surveillance systems must be upgraded to enable real-time detection and response. Leveraging digital health technologies, such as artificial intelligence and machine learning, is essential for improving data collection and analysis.

    3. Developing Comprehensive Pandemic Preparedness Plans

    Establishing a national pandemic preparedness plan that includes protocols for outbreak management, resource allocation, and community engagement is crucial. This plan should align with the White Health Paper’s strategic vision.

    4. Community Engagement and Health Literacy

    Public health specialists must prioritise health literacy and foster community participation in public health initiatives to ensure compliance during emergencies.

    5. Sustained Investments in Health Systems

    Policymakers must allocate adequate budgets for public health and encourage research in infectious diseases and healthcare innovation.

    Conclusion

    Excess mortality data highlight the devastating effects of the COVID-19 pandemic and the importance of strengthening healthcare systems to mitigate future public health crises. Public health specialists and policymakers in Malaysia must align their efforts with the White Health Paper’s recommendations to ensure preparedness and resilience. By addressing healthcare disparities, improving data systems, and fostering community engagement, Malaysia can build a robust framework for future pandemic responses.

    References

    Faust, J. S., Du, C., Mayes, K. D., et al. (2021). Mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicides during the pandemic in the United States. JAMA, 326(1), 84–86. https://doi.org/10.1001/jama.2021.8012

    Maringe, C., Spicer, J., Morris, M., et al. (2020). The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: A national, population-based, modelling study. The Lancet Oncology, 21(8), 1023–1034. https://doi.org/10.1016/S1470-2045(20)30388-0

    The Lancet. (2022). Estimating excess mortality due to the COVID-19 pandemic: A systematic analysis of COVID-19-related mortality, 2020–21. The Lancet. Retrieved from https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)02796-3/fulltext

    The Lancet Regional Health – Western Pacific. (2022). Excess mortality in Malaysia during the COVID-19 pandemic. The Lancet Regional Health – Western Pacific. Retrieved from https://www.thelancet.com/journals/lanwpc/article/PIIS2666-6065(22)00071-2/fulltext

    Wang, H., Paulson, K. R., et al. (2022). Estimating global excess mortality associated with the COVID-19 pandemic. The Lancet, 399(10334), 1513–1536. https://doi.org/10.1016/S0140-6736(21)02796-3

    World Health Organization. (2022). 14.9 million excess deaths were associated with the COVID-19 pandemic in 2020 and 2021. Retrieved from https://www.who.int/news/item/05-05-2022-14.9-million-excess-deaths-were-associated-with-the-covid-19-pandemic-in-2020-and-2021

  • The Alignment of Malaysia’s Health White Paper with the WHO Pathogen Prioritisation Framework for Infectious Disease Preparedness

    Abstract

    Amid rising global threats from infectious diseases, Malaysia’s Health White Paper (MHW) outlines a national strategy for health security and preparedness, aligning with the WHO’s pathogen prioritisation framework for epidemic and pandemic response. This paper examines the MHW’s focus on pathogen family-based research, surveillance, and international collaboration and assesses its alignment with the WHO’s framework, highlighting key pathogens relevant to Malaysia. Through targeted alignment, Malaysia can enhance its health security infrastructure and capacity to manage emerging and endemic infectious disease threats, thus advancing both national resilience and global health security.

    Introduction

    The increasing frequency of global infectious disease outbreaks has heightened the urgency for robust national health security frameworks. Malaysia’s Health White Paper (MHW) sets forth a comprehensive strategy, focused on enhancing surveillance, rapid response capabilities, and public health infrastructure to tackle these challenges. The WHO’s pathogen prioritisation framework complements this strategy, advocating for a pathogen family-based approach that emphasises adaptability and international collaboration to fortify preparedness. This paper examines the alignment between Malaysia’s strategic goals and the WHO framework, illustrating how family-based prioritisation, regional contextualisation, and proactive research strengthen Malaysia’s capacity to address significant infectious disease threats.

    Pathogen Family-Based Prioritisation and Malaysia’s Health Security Goals

    The WHO’s framework prioritises pathogen families, such as Coronaviridae, Flaviviridae, Filoviridae, and Orthomyxoviridae, allowing nations to address a broad range of epidemic threats within high-risk pathogen families (World Health Organization [WHO], 2024). Malaysia’s Health White Paper Pillar 1—Strengthening Health Security—emphasises preparing for both known and emerging threats, including Nipah virus and seasonal influenza. By adopting the WHO’s family-based focus, Malaysia can fortify its capacity for rapid adaptation to pathogens within these families (Ministry of Health Malaysia [MOH], 2024). Priority organisms include SARS-CoV-2 and MERS-CoV (Coronaviridae), Dengue Virus and Zika Virus (Flaviviridae), Ebola Virus (Filoviridae), and various Alphainfluenzavirus strains (Orthomyxoviridae). These pathogens underscore the need for Malaysia to strengthen surveillance and response across related organisms, ensuring preparedness for potential new threats.

    Regional Surveillance and Contextualised Research

    The WHO framework advocates regional adaptation in surveillance, emphasising pathogen transmission dynamics unique to specific environments and social contexts. This approach is critical in Malaysia, where diseases like dengue, influenced by the tropical climate and high vector presence, pose persistent challenges. Malaysia’s Health White Paper Pillar 2—Strengthening Surveillance and Monitoring—promotes data collection tailored to Malaysia’s specific epidemiology, aligning with WHO’s emphasis on regionalised preparedness (MOH, 2024). Expanding digital health tools and genomic surveillance capabilities will improve Malaysia’s outbreak detection and response, reinforcing the alignment of MHW and WHO goals for region-specific, data-driven preparedness.

    Proactive Pathogen Discovery and Prototype Pathogens

    To foster proactive pathogen discovery, the WHO framework encourages research on prototype pathogens, supporting the development of medical countermeasures (MCMs) that can apply broadly across pathogen families (WHO, 2024). Pillar 4—R&D and Innovation—of the MHW aligns with this strategy, endorsing investments in health research that facilitate scalable MCM development. By prioritising prototype pathogens like Nipah virus, Malaysia can ensure that advancements in research and countermeasures extend across multiple pathogens within the same family, enhancing resilience against both anticipated and unknown threats.

    Closing Knowledge Gaps through Localised Research

    Addressing knowledge gaps in transmission, ecology, and host interactions is essential for effective infectious disease management, particularly in Malaysia’s tropical setting, where vector-borne and zoonotic diseases are prominent. The MHW’s Pillar 5—Addressing Health Disparities—emphasises localised public health interventions, bridging critical knowledge gaps in diseases such as leptospirosis and HFMD. This locally focused research aligns with the WHO’s framework, enhancing Malaysia’s capability to develop context-specific interventions based on accurate, regionally relevant data. Prioritising studies on vector ecology, zoonotic interactions, and seasonal transmission dynamics will inform effective policy and public health strategies, meeting both WHO and MHW goals for contextually adapted health responses.

    Collaborative Networks for Research and Development

    The WHO framework calls for robust international and public-private partnerships to expedite research and preparedness efforts. In the MHW, Pillar 3—Public-Private Partnerships—supports collaborative initiatives within Malaysia’s healthcare ecosystem, facilitating quicker R&D processes and global resource sharing. By engaging in international networks, Malaysia can access critical diagnostic tools, expertise, and resources that align with WHO’s collaborative vision. Establishing networks with regional and global research institutions will enhance Malaysia’s preparedness, supporting WHO’s goals of shared knowledge and resources for infectious disease readiness.

    Rapid Deployment of Medical Countermeasures

    In line with WHO’s recommendation for rapid MCM deployment, Malaysia’s Health White Paper Pillar 6—Emergency Response Capabilities—prioritises swift resource mobilisation during health crises. Developing MCM stockpiles and streamlined distribution processes will allow Malaysia to respond effectively to pathogens like dengue and influenza, whose rapid spread necessitates immediate intervention. Rapid deployment strategies for critical supplies align with WHO’s framework, advancing Malaysia’s ability to manage high-risk pathogens.

    Enhanced Capacity Building and Workforce Development

    Effective pathogen preparedness also depends on building healthcare capacity in diagnostics, surveillance, and outbreak management, as emphasised by WHO. The MHW’s Pillar 7—Health Workforce Development—recognises the importance of a well-trained workforce to manage infectious disease threats. By equipping healthcare professionals with skills in diagnostics, genomic analysis, and rapid response, Malaysia supports WHO’s vision of strengthening national capacity for public health resilience. Expanding training programs and building diagnostic expertise will enable Malaysia to maintain an agile and effective response to public health threats, enhancing preparedness at both local and global levels.

    Conclusion

    The alignment between Malaysia’s Health White Paper and the WHO’s pathogen prioritisation framework establishes a comprehensive foundation for infectious disease preparedness. By incorporating the WHO’s recommendations for pathogen family prioritisation, regional surveillance, and collaborative partnerships, Malaysia’s health system is well-positioned to tackle both current and emerging infectious disease challenges. Through targeted initiatives in surveillance, research, and rapid response, Malaysia strengthens its role in global health security while enhancing national resilience to future public health emergencies.

    Disclaimer: This paper includes insights generated by ChatGPT and should be reviewed and validated by experts before any formal use.

    References

    Ministry of Health Malaysia. (2024). Malaysia Health White Paper: Strategic Directions in Health Security.

    World Health Organization. (2024). Pathogen prioritization framework for epidemic and pandemic preparedness. WHO.