Precision Public Health: Using Data to Tailor Interventions (www.kiu.ac.ug)

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Precision Public Health: Using Data to Tailor
Interventions
Mugisha Emmanuel K.
Faculty of Science and Technology Kampala International University Uganda
ABSTRACT
Precision Public Health (PPH) is revolutionizing traditional public health strategies by utilizing data-
driven methodologies to tailor interventions at individual and subgroup levels. By integrating
epidemiology, data science, and sociology, PPH enhances health predictions and intervention
effectiveness. The field relies on diverse data sources, including electronic health records, digital health
platforms, and wearable technologies, to improve real-time decision-making. Advanced analytics, such as
machine learning and predictive modeling, further refine health strategies by identifying risk patterns and
optimizing personalized interventions. However, ethical challenges must be addressed to ensure
responsible implementation, including data privacy, informed consent, and health equity concerns. This
paper examines the role of PPH in enhancing public health outcomes while navigating the challenges of
data integration and ethical considerations.
Keywords: Precision Public Health, Data Analytics, Predictive Modeling, Health Disparities, Machine
Learning, Digital Health, Epidemiology.
INTRODUCTION
The term “Precision Public Health” is tailoring public health interventions to individuals or population
subgroups using data. In Precision Public Health, predictions generated from public health data are used
to inform the design of public health interventions most likely to be efficacious for someone. In this new
field, health is measured in more than just traditional terms, and data capable of predicting health
outcomes are utilized. Different scientific disciplines are integrated to examine health and the data,
including but not limited to: sociology, epidemiology, and data science. There is a long history of utilizing
data to design precision interventions in the field of mathematics and education, for example, as seen in
the “Red Belt” example from 2009. However, it was not until the concept was described in The Lancet in
2015 that this idea jumped into contemporary public health purview. As the data and support for the
Precision Public Health framework continue to grow, the more it is hoped it can help to catalyze a
fascinating transformation of public health practice and advance its progress in general. There are several
tenets of the Precision Public Health framework. The Precision Public Health process first describes ways
of conceptualizing health, highlighting the importance of measuring health in multiple ways. It then lays
out a framework for utilizing data in public health practice. This framework suggests public health can
implement its practice to be more experimental, focusing on predicting the health impacts of potential
interventions before testing them. If such potential health impacts can be predicted for a population, then
interventions can be chosen (or designed if they do not exist) with health outcomes in mind that are
predicted from data. This is a change from usual public health practice. Interventions are normally chosen
based on population-level needs or require individuals to self-identify, instead of being more freely
available to those that may need them. The last part of the framework then describes ways of making
interventions more accessible for individuals or subgroups that may benefit from them, integrating public
health intervention design with their usual practice of policy dissemination or other intervention
implementation [1, 2].
EURASIAN EXPERIMENT JOURNAL OF SCIENTIFIC AND APPLIED RESEARCH
(EEJSAR) ISSN: 2992-4146
©EEJSAR Publications Volume 7 Issue 1
2025

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Definition and Conceptual Framework
Current conceptualizations of Precision Public Health focus on delivering appropriate interventions to the
right population at the right time, employing new methods for measuring diseases and health factors. Big
data and informatics are pivotal, with data usage often seen as central to this approach. However, these
ideas connect to a broader framework that involves models of genetics, environment, behavior, and
health, along with strategies for advancing research and policy for tailored interventions. Population
stratification is a key goal, leveraging advanced technologies and big data to promote health through
these tailored methods. The application of technology addresses (1) etiology and mechanistic research, (2)
health policy development and evaluation, and (3) targeted deployment of interventions. Examples
illustrate how this innovative method enhances public health decision-making and influences policy
changes. This evolving paradigm has significant implications for public health initiatives, with literature
from 'omics' fields providing foundational principles and priorities applicable across informatics contexts,
crucial for analyzing specific data sources within Precision Public Health [3, 4].
Historical Context and Evolution
Precision Public Health represents both a modern focus and an evolution of public health concepts,
tracing its roots to traditional public health ideas. Over time, the approach shifted from viewing health as
a public good to personalized interventions that consider genetic factors, lifestyle, and environmental
influences. Key milestones in PPH are marked by a transition from generic, population-wide prevention
strategies to tailored, individualized health interventions based on specific risk profiles. The evolution
reflects advancements in population monitoring technologies, from ancient boundary markers to
contemporary biosensors and IoT devices. Significant public health milestones over the last century
highlight changing collective health interventions and population monitoring methods. These
technological advancements have altered how health interventions are developed and executed.
Influential case studies provide insight into public health history and disease control approaches,
showcasing methods and techniques that have been influenced by broader socio-economic
transformations. These shifts in public health reflect an increasing recognition of the diverse health needs
of various population subgroups, shaped by their social, environmental, and occupational contexts. This
awareness links historical developments in health strategies with the current challenges faced in
implementing PPH, illustrating the significance of understanding the interplay between environmental
risk factors and health outcomes in modern public health discourse [5, 6].
Data Sources in Precision Public Health
Precision public health is a data-driven approach aimed at enhancing population health through tailored,
context-specific interventions informed by data. To succeed, it must harness rapidly growing data
streams and advanced technologies capable of processing and interpreting this data. Critical data sources
for PPH involve traditional elements like health records, vital statistics, drug prescription trends,
surveys, syndromic surveillance, spatial data, and census data, which offer estimations of population and
sociodemographic health determinants. However, these sources lag behind real-world advancements in
both the datasets and analysis methods, particularly amid challenges such as climate change, pandemics,
and personalized health care, limiting their effectiveness in addressing urgent public health issues.
Emerging data streams fall into two categories: novel, innovative sources that may provide proxy signals
of disease and common data streams utilized in creative new ways. The latter, especially when combined,
can deliver transformative insights. Nonetheless, ethical concerns must be addressed, particularly
regarding privacy and data ownership, and it remains uncertain how these diverse data types can be
integrated into a comprehensive framework. The variety in data formats necessitates significant
preprocessing for exploratory analysis, warranting a review of PPH integration methods in epidemiology
literature to emphasize interoperability as a crucial objective. Lastly, ensuring the quality of data sources
is essential, with ongoing discussions about managing bias and establishing metrics to guarantee data
quality in public health initiatives [7, 8].
Traditional Sources
Public health has heavily relied on systematic data collection, primarily from hospital records, health
surveys, and government databases. These structured, reliable data sets are vital for monitoring health
outcomes and disease spread, forming the basis for epidemiological insights on populations. Timeliness
and structure make this data crucial for health planners, especially during crises when waiting for
comprehensive data is impractical. While concerns about data quality have arisen, the refinement of data
reliability has accompanied its growth. The retrospective nature of data limits the comprehensive

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understanding of events, often misaligning with the needs of target populations. The variability in data
quality complicates public health initiatives. However, a valuable contemporary approach combines older
and newer data sets, particularly concerning individualized risk profiles and environmental exposures. In
the context of suicide, this strategy reveals significantly higher overdose death rates, showcasing gender
disparities previously underestimated. Community-public health collectives emerging outside official
departments exhibit strong capabilities in creating rigorous data methodologies that rival traditional
public health efforts [9, 10].
Novel Data Streams
The ethical dimension of technological developments must be central to stakeholders’ actions as
clinicians, academics, public health communities, and regulators engage with emerging digital data. New
sources of data from online platforms and smart technology offer public health innovative ways to
leverage non-traditional information streams. This evolution allows for advanced capabilities in data
capture, analysis, and evaluation. Precision Public Health is a forward-looking vision aiming to provide
timely, targeted interventions based on individual and environmental health determinants, with insights
gained from diverse data analytics. The conversation around data perspectives is ongoing, extending
beyond remote monitoring and machine-generated datasets toward encompassing broad public discourse
as mobile apps and wearables proliferate. Healthy data can be captured from both beneficial and harmful
platforms that track health indicators. Machine learning transforms vast amounts of cyberspace into
actionable health information, capturing experiences often overlooked by traditional health datasets. This
presents an opportunity to change perspectives on health issues by gathering user-reported data, but it
also risks excluding those without digital access and introducing bias. Concerns about data quality,
privacy, and ethical usage of health information are increasingly voiced among healthcare practitioners
and researchers as innovation advances. To effectively incorporate new data streams into public health
strategies, robust frameworks are necessary. The following examples illustrate the modern data sources
shaping the goals of Precision Public Health [11, 12].
Data Analytics and Machine Learning in Precision Public Health
Data analytics and machine learning technologies have become essential to modern health practice and
research, particularly in the transition from genome-based precision medicine to data-diverse precision
public health. These technologies promise the discovery of actionable knowledge and insights from vast
data resources that can guide the development and evaluation of a myriad of innovative interventions to
address the enormous heterogeneity of goals and responsibilities in health. The objectives of precision
public health also require the identification of large numbers of novel and unexpected targets for
surveillance and intervention that are present and sometimes blatantly obvious in the infrequent variant
subgroups and have been missed by the widespread application of univariate analyses. Keeping sight of
the ultimate objectives of the technologies can result in very precise and highly actionable insights that
justify the extensive resources required for their development and application. Ongoing evidence and
demonstration of these insights could help in gaining public trust, investment, and regulatory
permissions for the further development and extension of precision public health applications. This paper
reviews the current capabilities and limitations of big data analytics and machine learning technologies
for precision health and outlines some of the research strategies for their advancement and application in
precision public health to achieve the impacts intended by the application of these widely hyped
technologies to the broader goals of health and the novel targets of precision public health. Basic terms
and the gestation fail safety paradigm are used to clarify critical distinctions between data and actionable
knowledge and between the true and naive objectives of big data analytics, predictive modeling, and
computer simulation technologies. These technologies are indispensable for exploring the vastly
multidimensional search space of data-driven analyses and interventions, but their embodiments and
application to data-driven public health can only provide accounts and explanations of the causal
structures and mechanisms of biobehavioral systems required for effective intervention [13, 14].
Descriptive Analytics
Descriptive analytics is essential in precision public health, offering a way to aggregate historical data to
identify relevant trends and patterns. Its primary aim is to derive data-driven insights across various
levels, from individual incidents to broader populations within specific geographical and temporal
contexts. The outcomes of descriptive analysis support recommendations and help visualize results,
facilitating clearer communication with stakeholders who may lack expertise. Visualization tools aid
public health practitioners in quicker risk identification and understanding of health determinants.

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Focused infographics summarize key indicators like incidence and prevalence rates over time, while
epidemiological surveys utilize visual aids to present distributions and determinants of health conditions,
guiding public health recommendations. However, descriptive analytics cannot establish causality or
predict future outcomes; its role extends to evaluation and formulating strategies for public crafting.
Ultimately, descriptive analytics contributes to informed scientific decision-making, illustrated by several
practical examples demonstrating its effectiveness in enhancing understanding and supporting evidence-
based approaches [15, 16].
Predictive Analytics
Local health departments are consuming and producing volumes of data as never before. Through the
vast networks of interconnected electronic health records, integrated case management systems, and
electronic lab reporting, information on the health of communities flows rapidly. These sources are being
joined by others, such as social media posts, search engine queries, and even sensors on people and in
homes and public spaces that are joining the incoming data deluge. Precision health is intended to ensure
that the right preventive or therapeutic interventions can be employed at the moment of greatest effect.
The full capacity and promise of precision health will not be realized without precision public health, a
complementary approach that scales these interventions to populations and underpins them with open
systems that generate the data needed for their accurate prediction. Both precision health and the
precision public health enterprises depend on technologies that recently arrived in the domains of health
and human services generally, and especially so in the realm of public health. Key among the
transformative tools of these two new approaches are data analytics and machine learning. These
computational methods together provide a potent analytical toolkit capable of describing events, making
predictions, and suggesting optimal courses of action for a wide range of real-world problems of relevance
to human health [17, 18].
Prescriptive Analytics
Predictive analytics links outcomes with individual characteristics, while prescriptive analytics explores
treatment options, recommending those that may prompt behavior change, regardless of predictive bases.
It generates personalized, evidence-informed insights that promote health improvements and support
decision-making in public health programs. A new stochastic machine learning model for dose-response
health benefits aims to refine physical activity guidelines. Given individual differences in guidelines,
prescriptive analytics focuses on describing behavior change effectively. In binary machine learning
models, sensitive individuals’ behavior change isn't captured, and median predictive performance is
inadequate. To detail health benefit guidelines, a statistical stochastic machine learning framework called
classification distribution regression is employed. This model utilizes the sensitive curve to represent
individual probabilities for necessary doses, capturing the health benefit probability distribution as a
percentage of weekly doses, thus defining the dose needed for specific benefit thresholds [19, 20].
Ethical Considerations in Precision Public Health
Precision Public Health employs innovative strategies to deliver effective health solutions for individuals
and communities by using refined models to uncover trends and disparities linked to social determinants
of health. For instance, it can reveal areas with high lead poisoning incidences, allowing for timely
preventative measures. Such initiatives are essential for fostering an inclusive health system, but
implementing them without community input may exacerbate existing disparities. Additionally, ethical
dilemmas arise regarding data privacy and informed consent; maintaining individual data rights against
group benefits poses challenges. Public trust in privacy must be prioritized, as data misuse can lead to
trust erosion and severe consequences, such as insurance premium hikes due to ‘pre-existing conditions’
classifications. Medical data could potentially be exploited for commercial studies, risking personal harm
to participants. Thus, it is vital to ensure effective anonymization of collected data and to critically assess
any attempts to link datasets to prevent privacy violations [21, 22].
Case Studies in Precision Public Health
A fundamental goal in public health is to pursue evidence-based interventions for improving community
health and promoting equity. The emergence of Precision Public Health is shifting focus toward tailored
applications of diverse data sources to create individualized interventions. This has led to innovative
public health programs, policies, and technologies that utilize sensor data on behaviors and exposures,
machine learning algorithms, and rapid response capabilities. Case studies illustrate the successful
adaptation of real-world interventions to new data streams, particularly in monitoring infectious diseases
through real-time data on outbreaks, which allows timely detection in populations often underserved by

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healthcare providers, like those relying on telemedicine. One system optimizes response strategies using
data-driven simulations, proving effective containment. Additionally, MDAM-based methods can be
adapted for chronic disease management, allowing personalized intervention plans based on biomarker
measures. Health policies can also benefit from analyzing insurance claims data to improve cardiovascular
prevention, offering clear advantages for low-income participants. The program targeting diabetic
patients highlights the potential of integrating various data sources, such as medical records and lifestyle
data, to develop personalized risk assessments and messaging campaigns. However, regulations can
hinder the implementation of innovative health data systems, marking a significant evolution in best
practices regarding data management. These case studies exemplify a range of strategies for integrating
Big Data into public health, addressing multiple diseases with innovative approaches, and offering
valuable insights for future Precision Public Health initiatives [23, 24].
Infectious Disease Surveillance
Infectious disease surveillance illustrates how data-driven approaches aid public health responses.
Selected examples showcase situations where significant epidemiologic work provided crucial
information, even when preparedness was lacking. On April 20, Peru's MOH’s TeleSalud noted a rise in
acute gastrointestinal illness in La Oroya, where a philanthropic hospital operated. An investigation
revealed the likely exposure occurred from hospital water. A subsequent case-control study indicated a
high odds ratio of 27 for illness linked to this water source. Testing showed contamination from
Campylobacter jejuni in samples from both the hospital and a nearby cistern. As a result, the hospital
ceased water service, effectively ending the outbreak and leading to its closure. Developing skills with
new data sources can enhance information gathering, decreasing the chances of missing essential details
in real-time events. Evidence stems from case studies and other relevant occurrences outside the project’s
timeline.
Implementation Challenges and Solutions
Overcoming ethical and social stigma to mitigate effects on outcomes requires creative, context-specific
solutions. With community partners having competing priorities, it’s vital to clarify how big data
techniques enhance population health and emphasize community engagement to avoid mistrust and
negative experiences for newcomers. Precision public health programs necessitate community
involvement from the outset; building interest and trust fosters program uptake and improves outcomes.
Risk can be mitigated through plain language and training for those securing informed consent in big
data projects to address common questions. When data is drawn only from clinic visits, some
communities may develop ways to evade health department tracking, leading to untreated individuals
amplifying risky behaviors. Those overlooked by outreach workers are costly to locate yet most in need of
effective programs reliant on public health surveillance data. Implementing programs solely for those
who accessed care is stigmatizing, especially when government responses focus only on previously
identified individuals. To increase effectiveness and lower stigma, it’s crucial to identify the extent of
missed cases, presenting a challenge that can be partially tackled using public data creatively. Public
health experts must innovate to find and utilize effective data sources for big data methods. In the digital
age, precision public health approaches are more likely to succeed when supported by technologies that
enable community engagement, while ensuring big data use does not reinforce societal inequities [25, 26,
27].
Future Directions and Emerging Technologies
Precision Public Health leverages new technologies and data sources to tackle public health issues.
Research and practitioner communities are increasingly using advancements in artificial intelligence, big
data, and machine learning to transform public health methodologies. These tools signal a shift in how
public health is understood and practiced. Predictive modeling, which estimates outcomes based on data
features, is becoming prevalent in areas like epidemic forecasting and analyzing social determinants of
health. The growth of diverse data sources—like satellite networks, social media, and probiotics—
enhances predictive modeling by incorporating unconventional public health data. This fosters research
output and democratizes tool development for data-driven public health decision-making. As research
methods evolve, future practitioners should critically assess sleek proprietary platforms claiming
comprehensive predictive capabilities. Instead, they are encouraged to engage deeply with available
resources, software, frameworks, and libraries since human inquiry aligns better with tailored problem
contexts. Generally, predictive inquiries important for effective public health intervention fall within
health research and epidemiology; however, significant exploration opportunities remain. Potential areas

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of focus include disease etiology, epidemic control, access to preventive healthcare, and evaluating large-
scale public health interventions like tobacco regulations. Additional interventions may involve assessing
community factors like local food environments in food security studies or exploring place characteristics
that may predict police violence. Ultimately, developing innovative, data-centric estimates for testing and
collaboration with relevant disease-specific communities is crucial [13, 28, 29, 30].
CONCLUSION
Precision Public Health represents a transformative approach to improving public health outcomes by
utilizing data-driven insights to design targeted interventions. By integrating traditional and digital data
sources with advanced analytics, PPH enhances predictive capabilities, optimizes healthcare resources,
and supports personalized health strategies. However, ethical considerations such as data privacy, equity,
and informed consent must be carefully managed to prevent unintended consequences. The future of PPH
lies in balancing technological advancements with ethical responsibility, ensuring that data-driven
interventions promote equitable and effective public health solutions.
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.
CITE AS: Mugisha Emmanuel K. (2025). Precision Public Health:
Using Data to Tailor Interventions. EURASIAN EXPERIMENT
JOURNAL OF SCIENTIFIC AND APPLIED RESEARCH, 7(1): 78-85.

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