Home Latest News Uniosun launches Online Forecasting Model for COVID-19

Uniosun launches Online Forecasting Model for COVID-19

The Osun State University, Osogbo has launched an online forecasting modelling in order to provide academic thrust to guide the policymakers in the deployment of intervention strategies to combat Coronavirus.

The institution in conjunction with some specialists in the field, predicted that COVID-19 confirmed cases could raise to 150 – 158 on April 1.
Though as at the time of filing this report, the Nigeria Centre for Disease Control, NCDC, confirmed 12 new cases of COVID-19; 9 in Osun, 2 in Edo and 1 in Ekiti State which make all a total 151 cases.
The Public Relations Officer of the institution, Ademola Adesoji, said the forecast was part of the school contributions to complement the effort of the government in avoiding the spread of Coronavirus disease.
According to him, the online predictive model was developed by Kabir Abdulmajeed, a First-Class graduate of Electrical and Electronic Engineering at Osun State University and currently a Sensors and Algorithms R&D Specialist in the Oil and Gas Sector at the United Arab Emirates and facilitated by Monsuru Adeleke, an Associate Professor of Public Health Entomology and Parasitology at the institution.
Ademola said the predictive model was not intended to create panic but to relate possibility of upsurge or decrease in the episodes of COVID-19 on a daily basis as the government makes frantic efforts to control the infection.
“The daily forecast is currently available on the UNIOSUN website and will be updated on a weekly basis.
“This algorithm includes a set of online models, automatically adapting its parameters and updating its predictions by streaming data from reliable information sources on the COVID-19 situation in Nigeria.
“The hope is that we may be able to predict an envelope on the number of cases daily by observing the trend in NCDC’s releases.
“Obviously, it is difficult to model Covid-19 (in fact, any real-life scenario is difficult to model) – the number of tests, randomness, interventions, stay-at-home compliance, curfew, epidemiological realities, and many other factors contribute to the difficulty of forecast models in this case. This is for academic purposes.
“Predictive models can help guide our policymakers in the deployment of intervention strategies. This is what we intend to achieve,” he added.

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