Prediction of Cumulative Death Cases in Nigeria Due to COVID-19 Using Mathematical Models

Abstract

In this paper, we present various growth models such as Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in fitting and evaluating the COVID-19 epidemic pattern as of 15 July 2020 in the form of the total number of SARS-CoV-2 deaths in Nigeria. The MMF model was found to be the best model having the highest adjusted R2 value and lowest RMSE value. The values for the Accuracy and Bias Factors were near unity (1.0). The parameters derived from the MMF model include maximum growth rate (log) of 0.02 (95% CI from 0.02 to 0.03), curve constant (d) that affects the infection point of 1.61 (95% CI from 1.42 to 1.79) and maximal total number of death cases (Ymax) of 1,717 (95% CI from 1,428 to 2,123). The model estimated that the total number of death cases for Nigeria on the coming 15th of August and 15th of September 2020 were 940 (95% CI of 847 to 1,043) and 1,101 (95% CI of 968 to 1,252), respectively. The predictive ability of the model employed in this study is a powerful tool for epidemiologist to monitor and assess the severity of COVID-19 in Nigeria in months to come. However, like any other model, these values need to be taken with caution because of the COVID-19 uncertainty situation locally and globally.

Runs Test for the Residuals of The Morgan-Mercer-Flodin (MMF) Model Used for Modelling the Total Number of Covid-19 Cases for Brazil

“…Organisms growth including viral infection cases over time usually exhibit a sigmoidal growth profile that exhibits lag time (λ), acceleration to a maximal value (µm) and a final phase where the rate decreases and eventually reaches zero or an asymptote (A) is observed [19]. The sigmoidal curve can be fitted by different mathematical functions, such as Logistic [19,20], modified Gompertz [19,21], Richards [19,22], Schnute [19,23], Baranyi-Roberts [24], Von Bertalanffy [19,[25][26][27], Buchanan three-phase [28,29], Huang [30][31][32][33] and Morgan-Mercer-Flodin (MMF) [34][35][36][37][38][39][40][41][42][43][43][44][45][46][47]. For the analysis of the COVID-19 pandemic [8], strong predictive ability was employed models, such as updated Gompertz and Bertalanffy and logistics.…”

“…For the analysis of the COVID-19 pandemic [8], strong predictive ability was employed models, such as updated Gompertz and Bertalanffy and logistics. The total infection case of SARS-CoV-2 in Brazil as of 15 th of July 2020 to the 20 th of December 2020 was modelled using several primary growth models with the MMF models found to be the best [41,42,44,46,[48][49][50][51].…”