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The Epi-control platform is a software, accessible via a browser, it consists of 4 different modules.

Our platform empowers companies to conduct more data-driven, efficient, and targeted epidemiological studies.


By leveraging socio-demographic and contextual data, we can enhance decision-making, resource allocation, and the overall effectiveness of public health initiatives, ultimately contributing to improved health outcomes and the development of pharmaceutical solutions that better meet the needs of diverse populations.

4 modules of Epi-control platform

Disease burden: Epidemiological modeling

We employ AI algorithms and data analytics to analyze a wide range of data sources, including public health databases, open source data and literature studies. By doing so, we are able to estimate the current and future disease burden associated with specific indications or therapeutic areas. These estimates provide crucial insights into the prevalence, incidence, severity, and impact of diseases enabling life science companies to understand the market potential for their current and future products.


Pharmaceutical companies use a our data-driven approach for clinical trial site selection, demand forecasting:

  • Demand Forecasting: Pharmaceutical companies can use our AI to estimate the demand for healthcare services, medications, and vaccines in specific regions based on socio-demographic and contextual factors. This ensures that supply chains and distribution are optimized.

  • Clinical Trial Site Selection: Our tool supports in selecting optimal locations for clinical trials. It can consider factors such as the availability of suitable patient populations, disease prevalence and contextual variables like access to healthcare. We provide AI computational expertise that can make the clinical trials faster, more predictable and thereby more successful.  

Network Optimization

Network optimization is a systematic approach to improving care delivery. It seeks to address challenges such as delays in diagnosis, inadequate treatment, and suboptimal patient outcomes, with the goal of improving overall patient health. 

We support the process of network optimization at the level of various aspects:

  • Resource allocation: Giving insights in how to optimize the distribution of medication and/or diagnostics to ensure that resources are being used effectively and efficiently.

  • Service delivery: Improving the accessibility and quality of health care services, with a focus on reducing barriers to care and ensuring that patients receive accurate diagnoses, effective treatments, and proper follow-up care.

Ultimately, network optimization aims to reduce the burden and the spread of a disease and improve health outcomes for patients. 

Treatment Outcome

Predictive modeling can tell us "who" is most likely to use/not use health services, benefit the most from a specific intervention like financial incentive or nutritional support, suffer adverse events, default on treatment or  drop out of trials and "where" they are located.

Epcon Dashboard



A few reference cases

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