Mathematical model of the spread of COVID-19 in Plateau State, Nigeria

Abstract

In this research, a mathematical model consisting of non-pharmaceutical control measures is formulated. The developed model helps to examine the transmission of COVID-19 infection in Plateau State, Nigeria, using face masks$$c_{f}$$cfand social distancing$$c_{d}$$cdas control measures. Data used for the simulation of the developed model were obtained from Nigeria Centre for Disease Control which was fitted to the system of ordinary differential equations using nonlinear least squares method. Results at baseline values$$c_{f} = 0.1$$cf=0.1and$$c_{d} = 0.2$$cd=0.2of control measures indicate 2.3 estimation as basic reproduction number which suggests that COVID-19 in Plateau State tends towards endemic state. However, above about 40% in the use of face masks in the population and corresponding above 50% adherence to social distancing could as well bring down the basic reproduction number to a value below 1 necessary for disease eradication. The results at baseline values further indicate that the peak of the COVID-19 had been reached in less than 250 days from the first detection date after about 476,455 undetected asymptomatic individuals, 92,168 undetected symptomatic individuals and 83,801 detected quarantined individuals have been fully infectious. Therefore, the policymakers in Plateau State have the possibility of eradicating the disease with further strict non-pharmaceutical control measures provided that the present conditions of analysis remain fairly the same.

Investigation of a Suitable Hybrid Time Series Model for Predicting Clove Price

“…Five (5) of these articles were judged to be unrelated to the issue of crop prediction, while three (3) articles were found to be duplicates. As tabulated in Table 2, eleven (11) articles were found to be relevant for the identification of the hybrid time series models used to predict crops in general. Chi, (2021) [30] In the fifth stage, the study created a dataset (Appendix A) for hybrid time series models to predict crops.…”

“…The narrative review has revealed eleven (11) articles that have used the hybrid models to predict crops in general. Out of these articles, the study found seven (7) hybrid models, namely ARIMA-ANN, ARIMA-SVM, ARIMA-TDNN, ARIMA-NLSVR, ARIMAX-SVM, ARIMAX-ANN, and SARIMA-NARNN.…”

“…Linear models using linear functions, such as Autoregressive Integrated Moving Average (ARIMA), have been the most popular in the prediction of linear components in time series data [10]. Later, Seasonal Autoregressive Integrated Moving Average (SARIMA) was introduced to incorporate ARIMA and seasonal variation [11], while the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) was introduced to include the long-run effect of external variables in the ARIMA model [12]. However, these linear models are insufficient to predict nonlinear components and the uncertainty and volatility of time series, leading to several errors [13].…”

“…However, these linear models are insufficient to predict nonlinear components and the uncertainty and volatility of time series, leading to several errors [13]. Nonlinear time series models which use nonlinear functions in prediction, such as Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN), have been used to handle nonlinear components of time series data [11,12]. However, the prediction accuracy of these models is challenged by the fact that time series data rarely consist of pure nonlinear patterns alone, sometimes comprising both linear and nonlinear components [14].…”