From data science to public health: how the next generation is shaping smarter, fairer, and more resilient health systems

In this Infectious Thoughts interview, we are delighted to feature Cynthiah Lang’at, a rising voice at the intersection of public health and data science. From her early inspiration during the COVID-19 pandemic to hands-on experience in sanitation surveys and WASH data analysis in Kenya, Cynthiah’s journey highlights the transformative power of evidence-driven decision-making. She shares how trust-building with communities, real-time data tools, and spatial analysis can accelerate impact, and reflects on the future of public health analytics — from predictive modelling to equitable, inclusive outcomes. Cynthiah's story is not only one of personal growth but also a compelling illustration of how the next generation of public health professionals are shaping smarter, fairer, and more resilient health systems and the research and technology gaps which urgently need addressing.

Early Motivation - Can you share a little about your background and what first inspired you to pursue a career at the intersection of public health and data analytics?

My interest in public health was first sparked in high school after attending a career talk that introduced me to the field’s community and global impact. However, it was the COVID-19 pandemic in 2020 that truly solidified my commitment. Witnessing the global disruption and realizing that more informed, data-driven decisions could have mitigated the crisis deepened my resolve to contribute meaningfully to public health. As I began my undergraduate studies, I discovered data analytics and quickly recognized its critical role in shaping effective public health interventions. I became fascinated by how accurate, well-structured, and ethically handled data can guide policy and ultimately save lives. This intersection of public health and data analytics became the foundation of my career aspirations, driven by a desire to improve outcomes through evidence-based decision-making.

Internship Experience - During your attachment at Makadara Sub-County Public Health Office, what was the most impactful lesson you learned from conducting door-to-door sanitation surveys and WASH data analysis? Were there any tools which you would like to see developed or more easily accessible to facilitate some of the challenges of this task?

During my attachment one of the most impactful lessons I learnt was the importance of cultivating trust within the community. Effective collaboration hinges on the public’s understanding of their role in promoting a healthy environment. Through door-to-door sanitation surveys, I saw first-hand how community engagement and education are foundational to successful public health implementation. From a data perspective, I also recognized the challenges of delayed reporting. The time lag between data collection and integration often slowed down decision-making and response efforts. This experience highlighted the need for tools that support real-time data capture and analysis especially in fast-moving public health contexts. Having more accessible mobile-based platforms or integrated dashboards would greatly enhance the speed and accuracy of interventions.

Evidence to Action - Can you share an example where your data analysis directly influenced a public health decision or intervention in Kenya?

During my involvement in public health data analysis, I contributed to the periodic review of WASH (Water, Sanitation, and Hygiene) indicators across 13 units in Viwandani ward. These indicators included methods of sewage disposal, the number of latrines per household, water treatment practices, and sources of water supply. Through this analysis, the WASH department in the sub-county identified gaps and trends that informed the implementation of targeted sanitation dialogues. These dialogues were designed to monitor and accelerate progress in Urban Community-Led Total Sanitation (CLTS). As a result, the number of Open Defecation Free (ODF) units increased from 7 to 10 within just five weeks. Over the same period, Viwandani ward recorded a 16% reduction in water-related illnesses; demonstrating the tangible impact of data-driven public health interventions.

Mapping - How do you see mapping and spatial analysis transforming the way we understand and respond to public health challenges like sanitation, nutrition, or vector-borne diseases? What would be the priority public health challenges faced in Kenya that could benefit from this approach? 

Mapping and spatial analysis are revolutionizing public health by uncovering how geography, environment, and socio-economic conditions shape disease patterns and access to care. Through GIS, researchers can identify hidden disease hotspots, link health outcomes with determinants like sanitation, rainfall, and poverty, and target interventions more efficiently In Kenya, these tools are especially valuable for tackling challenges like vector-borne diseases, poor sanitation, and maternal health disparities. GIS platforms such as QGIS allow for predictive modelling; identifying malaria-prone zones, forecasting cholera risks based on rainfall and infrastructure, and guiding the strategic placement of health facilities. By visualizing data in context, spatial analysis strengthens early warning systems, enhances resource allocation, and empowers communities through clearer, more actionable insights.

Data Science Transition - You’re expanding into data science — what advanced analytical tools or methods are you most excited to apply in public health practice?

As I expand into data science, I’m especially excited to deepen my skills in programming with R and Python, tools that offer immense potential for public health analytics. I’m eager to apply them in real-world contexts, from cleaning and visualizing health data to building predictive models that inform interventions. Additionally, I’m keen to master platforms like Epi Info and DHIS2, which are central to Kenya’s national health information systems. These tools not only support robust statistical analysis but also play a crucial role in streamlining data collection and reporting for timely, evidence-based decision-making.

Equity & Access - How do you ensure that data-driven insights translate into equitable outcomes, especially for underserved or vulnerable communities?

To ensure data-driven insights lead to equitable outcomes, I prioritize disaggregating data by key variables such as gender, income, and geographic location. This approach helps uncover disparities that might otherwise remain hidden, allowing for more targeted and inclusive interventions. By identifying which groups are most vulnerable, decision-makers can allocate resources more effectively and design programs that directly address the unique needs of underserved populations, ultimately increasing the impact and fairness of public health initiatives.

Interdisciplinary Collaboration - Public health often requires bridging technical and policy worlds. How do you approach communicating complex data insights to non-technical stakeholders such as policymakers or community leaders? How important are data visualizations and what are some of the common mistakes or challenges to avoid? 

Communicating complex data insights to non-technical stakeholders requires clarity, empathy, and strategic visualization. I prioritize using simplified charts, infographics, and real-world imagery to make abstract concepts tangible and relatable. These visuals help policymakers and community leaders quickly grasp the significance of the data and its implications for action. However, one common pitfall is overcomplicating visuals verbose labels, cluttered layouts, or inappropriate chart types. To avoid this, I tailor each visualization to the audience’s level of familiarity and decision-making needs, ensuring the message is not just seen but understood. Ultimately, effective communication bridges the gap between data and impact.

Future of Public Health Analytics - What trends in epidemiology and data science do you think will most shape the future of global health systems in the next decade?

In the near future, the fusion of data science and epidemiology will revolutionize global health systems. Advancements in digital tools will enhance how we measure health indicators and collect data in real time. The integration of GIS will allow for precise mapping of disease drivers and hotspots, enabling faster, location-specific responses. Cloud technologies will streamline secure data storage and retrieval, improving access and collaboration across regions. Most notably, machine learning will empower predictive modelling for forecasting outbreaks, identifying at-risk populations, and guiding resource allocation. These innovations will strengthen the agility and effectiveness of public health systems worldwide.

Personal Growth - Looking back at your journey so far, what skills or experiences have been most transformative in shaping you as both a public health professional and an aspiring data scientist?

One of the most transformative skills I’ve developed in my journey is a deep attention to detail. In public health, this has been essential for ensuring the quality, integrity, and reliability of data; elements that directly influence the effectiveness of decisions and interventions. As I’ve grown into data analysis, I’ve learned to approach datasets with intentionality: asking what question the data should answer, identifying the key components of that answer, and considering who the insights are meant to serve. This mindset has shaped how I navigate the entire data lifecycle from collection and cleaning to interpretation and communication.

Advice to Peers - What advice would you give to other young professionals who want to combine data science with public health to drive meaningful change?

I believe this integration is not just a trend, it’s a transformative shift in how we approach health promotion and disease prevention. Data science offers powerful tools to uncover patterns, predict outcomes, and optimize interventions. When applied thoughtfully, it strengthens the bond between healthcare and technology, enabling more responsive, equitable, and evidence-based solutions. My advice is to focus on cultivating both technical skills and contextual understanding. The success of public health interventions depends not only on algorithms, but on empathy, ethics, and collaboration. By bridging these worlds, we can drive meaningful change that truly improves lives.