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Empowering Life Science with Data-Driven Insights


Data-driven insights are a key for unlocking better public healthcare provision in low and middle-income countries (LMICs). These regions often face significant challenges due to limited resources, underdeveloped healthcare infrastructure, and high disease burdens. In addition, pharmaceutical companies working in these regions are frequently hampered by poor health-related insights, non-targeted interventions and suboptimal resource allocation. Data, combined with predictive modelling, can provide transformative solutions, helping bridge the gap between wealthy and poorer countries. At EPCON we are committed to playing a pivotal role in aiding pharmaceutical companies in LMICs.



Providing accurate insights on disease burden

We obtain accurate insights into disease burden by integrating various population-level data sets into advanced AI engines, such as Bayesian network models. These models are capable of identifying complex cause-effect patterns within diverse data sets, including:


  1. Health-related data: Encompassing health outcomes, associations, risk factors, and genotype prevalence, this data helps pharmaceutical companies understand specific health challenges in different regions.

  2. Socio-demographic data: With data including population estimates, household numbers, and population growth trends, we can tailor health interventions to the demographic characteristics of each area.

  3. Health systems data: Information on healthcare access and facility infrastructure enables companies to identify gaps and optimise resource allocation and priority areas.

  4. Vulnerability factors: Data on poverty, nutritional status, and literacy rates provide insights into the socio-economic conditions affecting health outcomes, helping locate at-risk population groups requiring specific interventions.

  5. Environmental data: Factors such as land use, air pollution, and residential areas are analysed to understand their impact on health.

  6. Spatial data: Administrative boundaries, road networks, and development indicators are crucial for planning and implementing health interventions at a granular level.


Population health studies based on these data sources are useful for pharmaceutical companies. They offer an innovative way to assess disease epidemiology and contributing factors, especially in contexts with limited data or when exploring new geographical areas. 

In these projects, findings require validation. One way to achieve this is to purposefully omit data from the model to see whether it can “discover” the unseen data.


Overcoming challengesHealth studies involving wide-ranging predictions are not without challenges. For instance: 

  • Insights tend to be suited for more high-level research questions or exploratory analyses aimed at the population level.

  • External stakeholders need to understand and support the methodologies used. 


Despite these challenges, we believe our AI models can transform healthcare outcomes. Pharmaceutical companies and other healthcare providers can leverage the potential of big data and predictive analytics to make well-informed decisions in the public health arena. By unlocking these capabilities, we hope to drive significant improvements in healthcare efficiency and effectiveness in low and middle-income countries. 

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