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Where to organise clinical trials for Leprosy in Nigeria

Updated: Oct 19, 2023


Accurately estimating the burden of disease and identifying risk factors are vital for efficient resource management in the ongoing fight against infectious diseases. This holds particularly true for leprosy, an ancient ailment that has been under surveillance since as far back as 1400 BC. EPCON is proud to have participated in a nationwide epidemiological pilot conducted in Nigeria. The objective of this initiative was to estimate the prevalence of leprosy at the community level to support the decision making process of setting up clinical trials. This endeavour was initiated in response to a request from the Johnson & Johnson Foundation, which sought to conduct pilot studies in modelling the burden of disease for leprosy.


Leprosy, a chronic infectious disease caused by a type of bacteria, Myobacterium leprae, occurs in over 120 countries, with more than 200,000 cases reported annually. Even though the disease can cause progressive and permanent disabilities, it is curable with multidrug therapy. Sadly, there has been a resurgence of this debilitating disease in Nigeria, perhaps the consequence of competing health priorities in HIV / AIDS, tuberculosis and malaria. In fact, Nigeria ranks among the top five countries with the highest burden of leprosy in Africa, with more than 3,000 cases reported per year, of which a fair number are children.


Unleashing the Power of Predictive Modelling: A Game-Changer in Disease Management


To counteract the continuous threat posed by infectious diseases, it’s vitally important to estimate the disease burden and assess the associated risks in terms of location, timing, and intensity. The disease can then be managed effectively. By understanding the influence of various risk factors such as sociodemographic and environmental characteristics, intervention planning and resource allocation is significantly enhanced.


EPCON’s promising solution of predictive modelling uses real-world data to overcome the limitations of programmatic data. These models can uncover causal relationships and identify influential factors, enabling the prediction of disease emergence in areas with high-risk factors even before an outbreak occurs. This proactive approach empowers local authorities to mobilise their efforts and allocate resources in the right direction, maximising the effectiveness of disease control measures.


Scaling Up for Comprehensive Impact


EPCON’s pilot model in Nigeria leveraged the existing platform and utilised two main components to build its country-wide model, namely:

  1. Contextual data - including but not limited to population density, vaccination coverage, access to clean water, sanitation facilities, literacy rates, child mortality rate (under five), night-time lights, etc.

  2. Leprosy specific data sources: case notification data and the available care network.


EPCON's AI model

However, the geospatial representation of predicted leprosy cases could now be shown at a ward, LGA and state level:



The geoportal also provided visualisation allowing users to interact with the relevant maps and prioritise screening areas. The burden in cities was generally lower than high-risk regions in North and Central Nigeria. Interestingly, the best predictors of disease were vaccination coverage (which is a proxy for access to health care), literacy, percentage of stunted children, poverty and clean water access.


As EPCON’s pioneering efforts continue to unfold, the power of predictive modelling in disease management becomes increasingly evident. By accurately estimating disease burden, identifying influential factors, and providing actionable insights, this transformative approach holds the potential to revolutionise disease control strategies not only in Nigeria but also in other regions grappling with infectious diseases.


From data to action, EPCON’s endeavours exemplify the remarkable impact that predictive modelling can have on public health, paving the way for a healthier and more resilient future.


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