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It is now 331 days since the first COVID-19 case was reported in Nigeria. As at January 24, 2021 the confirmed cases are 124,501 with 1,497 fatalities, however, 96,501 have recovered.

Based on equal days forecast, by December 21, 2021, Nigeria’s aggregate confirmed COVID-19 cases are forecast to be:

2,157,567 from grafted (spline) polynomial model without knots

1,609,704 from grafted (spline) polynomial model with knots

401,657 from smooth spline model

387,140 from ARIMA model (95% lower confidence band)

1,165,750 from ARIMA model (95% upper confidence band)

661,277 from quadratic polynomial model

1,421,165 from essembled forcast with equal weight to all the models

314,517 from essembled forcast based on weight of each model

1,422,938 from essembled forcast based on weight of fit of each model

Refer to Table 1 and Table 2 as well as Fig. 18-20 for more details on how the estimates and forecasts were obtained.

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The visuals below supports this facts, take a look!

Fig. 1a Daily observed cases of COVID-19 in Nigeria

Fig. 2 Perecentages and previous days differences of COVID-19 in Nigeria Starting from February 29, 2020 to January 24, 2021

Fig. 3 Cases recorded in percentages Starting from February 29, 2020 to January 24, 2021 (legend as Fig. 2)

Fig. 4 Cumulative cases and Forecast of COVID-19 cases in Nigeria Starting from January 25, 2021 to December 21, 2021

Fig. 4a Components of COVID-19 cases in Nigeria between February 29, 2020 and January 24, 2021

Fig. 5 Number of days since average recorded cases exceeded one in each State

Fig. 6 Daily confirmed Coronavirus cases, by number of the days since 1 first daily case recorded

Fig. 7 Number of days from February 29, 2020 to January 24, 2021 that cases were recorded in the States

Fig. 8 Number of days the last COVID19 case was recorded as at January 24, 2021

Fig. 9 Diverging Bars of COVID-19 cases in the States (normalised)

Fig. 10 Monthly summary of COVID19 cases in the States

Fig. 11 Daily recorded cases of COVID19 in the States

Fig. 12 Number of days since average recorded cases exceeded one in each geo-political zone

Fig. 13 Number of days from February 29, 2020 to January 24, 2021 that cases were recorded in the Zones

Fig. 14 Monthly summary of COVID19 cases in the Zones

Table 3 Regression estimates of the effect of price of PMS and rate of inflation on COVID-19 and vice versa

COVID19PMSInflation
(Intercept)4.615 -38.774     5.332 *** 
(10.176)(31.726)    (1.459)    
PMSprice0.142          0.058 ****
(0.088)         (0.011)    
Inflation-1.301 12.926 ****         
(1.427)(2.468)             
log(MonthCase)     1.573     -0.065     
     (0.978)    (0.071)    
nobs12     12         12         
r.squared0.281 0.806     0.771     
adj.r.squared0.121 0.763     0.720     
sigma2.704 9.002     0.604     
statistic1.759 18.696     15.177     
p.value0.227 0.001     0.001     
df2.000 2.000     2.000     
logLik-27.239 -41.670     -9.257     
AIC62.478 91.341     26.513     
BIC64.418 93.280     28.453     
deviance65.819 729.298     3.286     
df.residual9.000 9.000     9.000     
nobs.112.000 12.000     12.000     
**** p < 0.001; *** p < 0.01; ** p < 0.05; * p < 0.1.

Fig. 15 Relationship between COVID-19, inflation and PMS price in Nigeria

Fig. 16 Relationship between PMS price, inflation and COVID-19 in Nigeria

Fig. 17 Relationship between Inflation, PMS price and COVID-19 in Nigeria Table 1 The coefficient estimates of the various models for forecasting of COVID-19 for Nigeria1

Spline with knotsSpline without knotsARIMAQuardratic polynomial
(Intercept)-417.370 ****0.050          226.794 ****
(50.518)    (70.480)         (56.387)    
bs(niz[, 1], knots = NULL)12396.704 ****                       
(146.054)                           
bs(niz[, 1], knots = NULL)2-1250.222 ****                       
(92.896)                           
bs(niz[, 1], knots = NULL)32024.878 ****                       
(79.696)                           
bs(niz[, 1], knots = BREAKS)1         -7.297                   
         (136.338)                  
bs(niz[, 1], knots = BREAKS)2         25.120                   
         (88.413)                  
bs(niz[, 1], knots = BREAKS)3         822.641 ****              
         (101.618)                  
bs(niz[, 1], knots = BREAKS)4         198.093 **                
         (86.851)                  
bs(niz[, 1], knots = BREAKS)5         135.096                   
         (98.693)                  
bs(niz[, 1], knots = BREAKS)6         42.487                   
         (105.628)                  
bs(niz[, 1], knots = BREAKS)7         1311.564 ****              
         (115.575)                  
bs(niz[, 1], knots = BREAKS)8         1670.940 ****              
         (103.713)                  
ma1                  -0.621          
                  (0.054)         
ma2                  -0.141          
                  (0.051)         
drift                  4.766          
                  (2.558)         
Day                       -1.774 **  
                       (0.784)    
I(Day^2)                       0.012 ****
                       (0.002)    
nobs331         331         330     331         
r.squared0.686     0.805          0.327     
adj.r.squared0.684     0.800          0.323     
sigma232.382     184.702     194.014 339.892     
statistic238.665     166.124          79.767     
p.value0.000     0.000          0.000     
df3.000     8.000          2.000     
logLik-2271.071     -2192.510     -2205.550 -2397.438     
AIC4552.142     4405.020     4419.099 4802.875     
BIC4571.152     4443.041     4434.296 4818.084     
deviance17658422.207     10984949.407          37892700.413     
df.residual327.000     322.000          328.000     
nobs.1331.000     331.000     330.000 331.000     
**** p < 0.001; *** p < 0.01; ** p < 0.05; * p < 0.1.

Table 2 Various selection criteria for the estimated models2
Spline with knots Spline without knots Smooth spline ARIMA Quardratic polynomial
Absolute Error 54487 29780 19544 32114 89751
Absolute Percent Error Inf Inf Inf Inf Inf
Accuracy 0 0 0 0 0
Adjusted R Square 0.68 0.8 0 0 0.32
Akaike’s An Information Criterion AIC 4552 4405 0 4419 4803
Allen’s Prediction Sum-Of-Squares (PRESS, P-Square) 0 0 0 0 0.31
Area under the ROC curve (AUC) 0.01 0.03 0.05 0.01 0.41
Average Precision at k 0 0 0 0 0
Bias 0 0 0 -0.16 0
Brier score 53349 33187 13233 37186 114479
Classification Error 1 1 1 1 1
F1 Score 0 0 0 0 0
fScore 0 0 0 0 0
GINI Coefficient 1 1 1 1 1
kappa statistic 0 0 0 0 0
Log Loss Inf Inf Inf Inf Inf
Mallow’s cp 4 9 0 0 3
Matthews Correlation Coefficient 0 0 0 0 0
Mean Log Loss Inf Inf Inf Inf Inf
Mean Absolute Error 165 90 59 97 271
Mean Absolute Percent Error Inf Inf Inf Inf Inf
Mean Average Precision at k 0 0 0 0 0
Mean Absolute Scaled Error 1.5 0.84 0.55 0.9 2.5
Median Absolute Error 146 49 31 51 226
Mean Squared Error 53349 33187 13233 37186 114479
Mean Squared Log Error NaN NaN 0.09 0.62 3
Model turning point error 156 155 140 220 172
Negative Predictive Value 0 0 0 0 0
Percent Bias Inf NaN NaN -Inf -Inf
Positive Predictive Value 0 0 0 0 0
Precision NaN NaN NaN NaN NaN
R Square 0.69 0.8 0 0 0.33
Relative Absolute Error 0.56 0.31 0.2 0.33 0.93
Recall -317 -0.56 0.6 8.9 217
Root Mean Squared Error 231 182 115 193 338
Root Mean Squared Log Error NaN NaN 0.29 0.79 1.7
Root Relative Squared Error 0.56 0.44 0.28 0.47 0.82
Relative Squared Error 0.31 0.2 0.08 0.22 0.67
Schwarz’s Bayesian criterion BIC 4571 4443 0 4434 4818
Sensitivity 0 0 0 0 0
specificity 0 0 0 0 0
Squared Error 17658422 10984949 4380001 12308720 37892700
Squared Log Error NaN NaN 28 206 987
Symmetric Mean Absolute Percentage Error 0.63 0.41 0.3 0.42 0.89
Sum of Squared Errors 17658422 10984949 4380001 12308720 37892700
True negative rate 0 0 0 0 0
True positive rate 0 0 0 0 0

Fig. 18 Models of COVID-19 Cases using ensemble technology

Fig. 19 Equal duration forecast of COVID-19 Cases from the ensemble models using a native plotting method

Fig. 20 Forecast of COVID-19 cases in Nigeria Starting from January 25, 2021 to December 21, 2021 using a more advaced plotting method

Fig. 21 Percentage of COVID19 cases that resulted into casualty per State as at January 24, 2021

Fig. 22 Percentage of COVID19 cases that recovered per State as at January 24, 2021

Fig. 23 Percentage of recoveries and deaths from COVID19 cases per Zone as at January 24, 2021

Fig. 24 Distribution of COVID19 in the States as at January 24, 2021

  1. Note that Smooth spline does not estimate coefficients and other model-characteristic statistics↩︎

  2. Multiclass Area Under the Curve (MAUC) is the mean of the various AUC estimated from the same data↩︎