Ogbomoso North Advisory

Ogbomoso North Advisory is an open-source, GIS-enabled, scaleable Online Advisory that predicts future pandemics by aggregating and interpreting datasets on health risks at the level of the Local Government.

Baba Oladeji

The Problem: Because of distance and bureaucracy, federal governments struggle to respond rapidly pandemics the way a local government within 10 miles can. In Nigeria, for instance, the local government is typically the home of primary data such as births and deaths and our area of focus: Ogbomosho North, with 297,400 people is a link town between Nigeria's two most populous regions: the North and South with a population of over 150 million between them. Thus, it is important to highlight such an underreported location as a guidepost to track and predict the earliest signs of a future pandemic, at the level of the local government in the most populous black nation in the world.

Public health practitioners in Africa typically have little to zero primary data to prosecute research and cannot generate data-driven insights for policy adoption. While they can sense trends through hospital records; they lack data to articulate policy positions.

We are empowering public health practitioners with data to interface with government and with the general populace. 

We plan to engage them through training in digital data collection. As more public health practitioners get trained in digital data collection, they gradually clear up the invisibility of other informal, underreported and under-documented communities in Africa.

Our solution is an open-source model. The original source code used to build it will be made freely available and may be redistributed and modified by others. We expect that this will be easily adopted and adapted by Nigeria's energetic tech scene.

Also, we are sourcing primary and hitherto non-existent data for public health practitioners, policymakers, data scientists and even citizens to use. All of this we will do free of charge.

Clearer path to policy adoption with data driven insights that enables public health practitioners articulate their research policymakers.

Scalability of open source platform to other 773 local governments in Nigeria and other countries requiring similar solutions.

Increased visibility for underserved Communities such that developmental opportunities never pass them by.

Deepened citizenship due to public accountability that will come from the transparency of the data shared.

Capacity building for participating public health practitioners who will get trained in digital collation of data.

In 18 months, we hope to have built-in AI features into our solution to make sure the systems are independent of human input.

This frees the team to harness the data and results collected to extend the solution to other localities where they are sorely needed, within and outside Nigeria, explore potential beneficial solutions derived from them and create a network of connected multilateral localities with efficient and resilient systems.

With our solution, up and running, adoption in other communities becomes easier and quicker.

We plan to measure outcomes through:

1. The number of (public) health practitioners we are able to train in the community to carry out the work of digital data collection

2. The number of unique visitors to our online advisory coupled with their requests for datasets 

3. The number of impressions from the dissemination of information across media platforms.

Massachusetts Institute of Technology MIT

Ministry of Architecture, Nigeria