Abstract:
The problems that arise during a pandemic outbreak include: loss of track of trends by health practitioners, lack of resources to contain the pandemic, poor decision-making, and inability to track the effect of the pandemic on 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 the COVID-19 pandemic. The models were implemented using the 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 the COVID-19 pandemic for seven days, and the results show a step-up trend in the spread of COVID-19 in Nigeria within the selected time frame.
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. A 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, the coronavirus disease of 2019 (COVID-19), and the like." Among all known ribonucleic acid (RNA) viruses, coronaviruses have the biggest genomes, which gives the virus more flexibility in accepting and changing genes.
