dc.contributor.author |
Ikuomola, Aderonke J. |
|
dc.date.accessioned |
2024-10-10T12:13:29Z |
|
dc.date.available |
2024-10-10T12:13:29Z |
|
dc.date.issued |
2023-11-13 |
|
dc.identifier.issn |
3027-0650 |
|
dc.identifier.uri |
http://hdl.handle.net/123456789/649 |
|
dc.description |
Forecasting is the process of predicting future events or occurrences based on previous or present experiences. Alternatively, forecasting uses historical data as the primary input to determine the direction of future trends. Pandemic is "an epidemic occurring globally, or over a very wide area, crossing international boundaries, and typically affecting a large number of people; examples include Ebola, Lassa fever, cholera, swine flu, Coronavirus disease of 2019 (COVID-19), and the like." Among all known ribonucleic acid (RNA) viruses, coronaviruses have the biggest genomes, which provides the virus more flexibility in accepting and changing genes. |
en_US |
dc.description.abstract |
The problems that arises during pandemic outbreak includes: loss of track of trends by the health practitioners, lack of resources to contain the pandemic, poor decision-making, and inability to track the effect of pandemic on the society. As COVID-19 is a threat to human lives, it is important to study the trends and see into the future on damages and ways to either prevent or contain it. In this work, auto-regression integrated moving average (ARIMA) and seasonal auto-regression integrated moving average (SARIMA) Time series techniques were used to predict the future trend of COVID-19pandemic. The model was implemented using Python programming language. The ARIMA (2,2,0) and (0,2,0) models were finally selected based on the parameter test. It was also used to predict the spread of Covid-19 pandemic for seven-days and the results shows a step upward trend of the spread of COVID-19 in Nigeria within the selected time frame. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
[Olusegun Agagu University of Science and Technology, Okitipupa, Ondo State, Nigeria] |
en_US |
dc.relation.ispartofseries |
American University of Nigeria, 1st International Conference Proceeding; |
|
dc.title |
Prediction of Covid-19 Pandemic in Nigeria using Time SeriesModels Approach |
en_US |
dc.type |
Article |
en_US |