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It is now 405 days since the first COVID-19 case was reported in Nigeria. As at April 08, 2021 the confirmed cases are 166,453 with 2,058 (1.26%) fatalities, however, 153,750 (94.04%) have recovered.

Based on equal days forecast, by May 18, 2022, Nigeria’s aggregate confirmed COVID-19 cases are forecast to be:

Model Confirmed cases Recoveries Fatalities RMSE
Without knots 2,209,362 2,077,684 27,838 195
Upper ARIMA 797,199 749,686 10,045 204
Essembled with equal weight 362,391 340,793 4,566 233
Quadratic Polynomial 228,538 214,917 2,880 395
Essembled based on weight of fit 223,421 210,105 2,815 293
Essembled based on weight 114,069 107,271 1,437 175
With knots -123,640 -116,271 -1,558 182
Essembled based on summed weight -170,708 -160,534 -2,151 181
Smooth Spline -204,344 -192,166 -2,575 391
Lower ARIMA -729,764 -686,270 -9,195 204

Refer to Table 2 and Table 3 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 April 08, 2021

Fig. 3 Cases recorded in percentages Starting from February 29, 2020 to April 08, 2021 (legend as Fig. 2)

Fig. 4 Cumulative cases and Forecast of COVID-19 cases in Nigeria Starting from April 09, 2021 to May 18, 2022

Fig. 4a Components of COVID-19 cases in Nigeria between February 29, 2020 and April 08, 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 April 08, 2021 that cases were recorded in the States

Fig. 8 Number of days the last COVID19 case was recorded as at April 08, 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 April 08, 2021 that cases were recorded in the Zones

Fig. 14 Monthly summary of COVID19 cases in the Zones

Table 1 Regression estimates of factors that might have stressed Nigeria’s economy within the period of the COVID-19 pandemic

COVID19PMSInflationTeledensityBirth ratePopulation
(Intercept)-869.514   -6195.410 *  -160.815  1333.586 ** -42.964 ****16.133 ****
(558.454)  (3079.148)   (401.347) (519.529)   (8.744)    (0.262)    
PMSprice-0.124 **        0.051  0.153 ***-0.001     0.001 *   
(0.039)          (0.032) (0.034)   (0.001)    (0.000)    
Inflation0.871 * 4.250          -0.205    -0.020     0.002     
(0.429)  (2.703)         (0.559)   (0.012)    (0.004)    
Teledensity0.645 **4.501 ***-0.072          0.012     -0.005 **  
(0.219)  (1.008)   (0.196)         (0.007)    (0.002)    
`Birth rate`-4.525   -61.953    -11.509  19.713             0.280 ****
(12.408)  (70.698)   (7.070) (11.888)            (0.052)    
Population49.879   362.748 *  12.431  -79.020 ** 2.719 ****         
(35.227)  (195.108)   (24.724) (33.139)   (0.508)             
COVID19       -4.310 ** 0.361 *0.761 ** -0.003     0.004     
       (1.339)   (0.178) (0.259)   (0.009)    (0.003)    
nobs15       15        15      15        15         15         
r.squared0.770   0.849    0.768  0.854    0.884     0.881     
adj.r.squared0.643   0.766    0.640  0.773    0.819     0.814     
sigma1.508   8.884    0.970  1.638    0.040     0.013     
statistic6.038   10.153    5.973  10.534    13.712     13.269     
p.value0.010   0.002    0.010  0.001    0.001     0.001     
df5.000   5.000    5.000  5.000    5.000     5.000     
logLik-23.611   -50.216    -16.999  -24.858    30.753     47.804     
AIC61.221   114.432    47.998  63.716    -47.507     -81.608     
BIC66.178   119.389    52.955  68.673    -42.550     -76.652     
deviance20.456   710.276    8.472  24.157    0.015     0.001     
df.residual9.000   9.000    9.000  9.000    9.000     9.000     
nobs.115.000   15.000    15.000  15.000    15.000     15.000     
**** p < 0.001; *** p < 0.01; ** p < 0.05; * p < 0.1.

Fig. 15 COVID-19, inflation, PMS price, Teledensity and birth rate in Nigeria

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

Table 2 The coefficient estimates of the various models for forecasting of COVID-19 for Nigeria1

Spline with knotsSpline without knotsARIMAQuardratic polynomial
(Intercept)140.033 *   -3.991          -15.260     
(77.292)    (75.175)         (59.375)    
bs(niz[, 1], knots = NULL)1-175.498                            
(223.399)                           
bs(niz[, 1], knots = NULL)2949.413 ****                       
(141.944)                           
bs(niz[, 1], knots = NULL)3311.081 **                         
(121.984)                           
bs(niz[, 1], knots = BREAKS)1         7.494                   
         (145.338)                  
bs(niz[, 1], knots = BREAKS)2         10.759                   
         (94.160)                  
bs(niz[, 1], knots = BREAKS)3         847.875 ****              
         (107.972)                  
bs(niz[, 1], knots = BREAKS)4         165.261 *                 
         (91.792)                  
bs(niz[, 1], knots = BREAKS)5         213.707 **                
         (100.955)                  
bs(niz[, 1], knots = BREAKS)6         -99.529                   
         (96.022)                  
bs(niz[, 1], knots = BREAKS)7         2662.969 ****              
         (115.934)                  
bs(niz[, 1], knots = BREAKS)8         -492.339 ****              
         (118.043)                  
bs(niz[, 1], knots = BREAKS)9         220.144 **                
         (104.454)                  
ar1                  1.005          
                  (0.105)         
ar2                  -0.303          
                  (0.060)         
ma1                  -1.652          
                  (0.096)         
ma2                  0.754          
                  (0.077)         
Day                       3.250 ****
                       (0.675)    
I(Day^2)                       -0.004 *** 
                       (0.002)    
nobs405         405         404     405         
r.squared0.199     0.801          0.181     
adj.r.squared0.193     0.796          0.177     
sigma392.475     197.046     204.814 396.334     
statistic33.128     176.682          44.343     
p.value0.000     0.000          0.000     
df3.000     9.000          2.000     
logLik-2991.511     -2709.400     -2721.911 -2995.979     
AIC5993.023     5440.799     5453.823 5999.957     
BIC6013.042     5484.842     5473.830 6015.973     
deviance61768632.053     15336735.436          63146414.466     
df.residual401.000     395.000          402.000     
nobs.1405.000     405.000     404.000 405.000     
**** p < 0.001; *** p < 0.01; ** p < 0.05; * p < 0.1.

Table 3 Various selection criteria for the estimated models2
Spline with knots Spline without knots Smooth spline ARIMA Quardratic polynomial
Absolute Error 120966 41553 37299 42806 115880
Absolute Percent Error Inf Inf Inf Inf Inf
Accuracy 0 0 0 0 0
Adjusted R Square 0.19 0.8 0 0 0.18
Akaike’s Information Criterion AIC 5993 5441 0 5454 6000
Allen’s Prediction Sum-Of-Squares (PRESS, P-Square) 0 0 0 0 0.17
Area under the ROC curve (AUC) 0.12 0.01 0.01 0.02 0.01
Average Precision at k 0 0 0 0 0
Bias 0 0 0 0.59 0
Brier score 152515 37868 33250 41431 155917
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 0
kappa statistic 0 0 0 0 0
Log Loss Inf Inf Inf Inf Inf
Mallow’s cp 4 10 0 0 3
Matthews Correlation Coefficient 0 0 0 0 0
Mean Log Loss Inf Inf Inf Inf Inf
Mean Absolute Error 299 103 92 106 286
Mean Absolute Percent Error Inf Inf Inf Inf Inf
Mean Average Precision at k 0 0 0 0 0
Mean Absolute Scaled Error 2.4 0.84 0.75 0.86 2.3
Median Absolute Error 275 55 42 54 248
Mean Squared Error 152515 37868 33250 41431 155917
Mean Squared Log Error 2.3 NaN 0.18 0.19 NaN
Model turning point error 202 195 190 265 200
Negative Predictive Value 0 0 0 0 0
Percent Bias -Inf NaN NaN NaN NaN
Positive Predictive Value 0 0 0 0 0
Precision NaN NaN NaN NaN NaN
R Square 0.2 0.8 0 0 0.18
Relative Absolute Error 0.94 0.32 0.29 0.33 0.9
Recall 135 -2.3 -0.16 0.5 4
Root Mean Squared Error 391 195 182 204 395
Root Mean Squared Log Error 1.5 NaN 0.42 0.43 NaN
Root Relative Squared Error 0.9 0.45 0.42 0.47 0.91
Relative Squared Error 0.8 0.2 0.17 0.22 0.82
Schwarz’s Bayesian criterion BIC 6013 5485 0 5474 6016
Sensitivity 0 0 0 0 0
specificity 0 0 0 0 0
Squared Error 61768632 15336735 13466064 16779477 63146414
Squared Log Error 928 NaN 72 75 NaN
Symmetric Mean Absolute Percentage Error 0.85 0.4 0.34 0.39 0.81
Sum of Squared Errors 61768632 15336735 13466064 16779477 63146414
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 April 09, 2021 to May 18, 2022 using a more advaced plotting method

Fig. 20a Lagged forecasts (1-14 days) of COVID-19 cases in Nigeria

Table 4 Summary of the dynamic COVID-19 forecasts
Model Mar 26, 21 - Apr 20, 22 Apr 02, 21 - May 04, 22 Apr 05, 21 - May 10, 22 Apr 06, 21 - May 12, 22 Apr 07, 21 - May 14, 22 Apr 08, 21 - May 16, 22
Without knots 375724 71778 -56135 -91647 -132054 -167621
Smooth Spline 366323 1423396 1720020 1877880 1968619 2101462
With knots -850383 -52396 -276215 -127031 -203146 -120747
Quadratic Polynomial 437422 332310 285418 271988 256649 242893
Lower ARIMA -701860 -698596 -717105 -715273 -725629 -724915
Upper ARIMA 776911 804303 795387 801120 793884 798542
Essembled with equal weight -71302 604258 250269 295414 360679 321173
Essembled based on weight 229536 238885 243541 246568 247092 247557
Essembled based on summed weight 222938 181287 161696 154860 149687 141759
Essembled based on weight of fit 39812 420181 193295 213039 242000 208327
Table 3 RMSE of the models in the dynamic forecasts
Model Mar 26, 21 - Apr 20, 22 Apr 02, 21 - May 04, 22 Apr 05, 21 - May 10, 22 Apr 06, 21 - May 12, 22 Apr 07, 21 - May 14, 22 Apr 08, 21 - May 16, 22
Without knots 385.94 389.62 390.51 390.49 390.62 390.57
Smooth Spline 197.19 195.62 195.19 194.97 194.88 194.71
With knots 185.36 183.64 183.21 182.94 182.64 182.45
Quadratic Polynomial 386.03 391.25 393.27 393.6 394.15 394.48
Lower ARIMA 206.93 205.27 204.52 204.27 204.04 203.79
Upper ARIMA 206.93 205.27 204.52 204.27 204.04 203.79
Essembled with equal weight 234.03 233.78 233.72 233.54 233.48 233.33
Essembled based on weight 457.98 455.45 454.68 454.32 454.09 453.77
Essembled based on summed weight 457.98 455.19 454.11 453.63 453.26 452.79
Essembled based on weight of fit 223.05 215.43 213.11 212.5 211.91 211.4

Fig. 21 Percentage of COVID19 cases that resulted into casualty per State as at April 08, 2021

Fig. 22 Percentage of COVID19 cases that recovered per State as at April 08, 2021

Fig. 23 Percentage of recoveries and deaths from COVID19 cases per Zone as at April 08, 2021

Fig. 24 Distribution of COVID19 in the States as at April 08, 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↩︎