+++++++++++++++++++***************************+++++++++++++++++++++++++++

It is now 937 days since the first COVID-19 case was reported in Nigeria. As at September 22, 2022 the confirmed cases are 267,668 with 3,157 (1.18%) fatalities, an average of 3 fatalities per day. However, to date, 261,077 or 97.54% were successfully managed and discharged leaving a balance of 3,392 (1.28%) active cases being managed.

Based on equal days forecast, by April 16, 2025, Nigeria’s aggregate confirmed COVID-19 cases are forecast to be:

Unconstrained forecast of COVID-19 for Nigeria

Model Confirmed cases Recoveries Fatalities Active RMSE
With knots 3,407,149 3,323,333 40,204 43,612 180
Upper ARIMA 2,545,118 2,482,509 30,032 32,578 157
Semilog 258,652 252,289 3,052 3,311 158
Without knots 98,719 96,291 1,165 1,264 367
Essembled with equal weight 26,254 25,608 310 336 270
Linear 22,694 22,135 268 290 362
Essembled based on weight of fit 21,004 20,487 248 269 2
Growth 1,155 1,127 14 15 217
Essembled based on summed weight -81,426 -79,423 -961 -1,042 390
Essembled based on weight -99,065 -96,628 -1,169 -1,268 382
Quadratic Polynomial -877,774 -856,181 -10,358 -11,236 218
Smooth Spline -2,362,705 -2,304,582 -27,880 -30,243 180
Lower ARIMA -2,499,159 -2,437,680 -29,490 -31,989 152

Constrained forecast of COVID-19 for Nigeria

Model Confirmed cases Recoveries Fatalities Active
Essembled based on weight 95% 3,371,063 3,288,135 39,779 43,150
ARIMA 95% 2,955,866 2,883,152 34,879 37,835
Essembled with equal weight 95% 2,558,297 2,495,363 30,188 32,746
Essembled based on summed weight 95% 2,420,818 2,361,266 28,566 30,986
Essembled based on weight of fit 95% 1,521,196 1,483,775 17,950 19,471
Without knots 95% 946,885 923,592 11,173 12,120
Semilog 258,652 252,289 3,052 3,311
Smooth Spline 95% 75,137 73,289 887 962
Smooth Spline 80% 53,108 51,801 627 680
Linear 22,694 22,135 268 290
Essembled with equal weight 80% 1,934 1,887 23 25
Essembled based on weight 80% 1,753 1,710 21 22
Essembled based on weight of fit 80% 1,715 1,673 20 22
Growth 1,155 1,127 14 15
Without knots 80% 1,072 1,046 13 14
Essembled based on summed weight 80% 300 292 4 4
ARIMA 80% 76 74 1 1
With knots 80% 0 0 0 0
Quadratic Polynomial 80% 0 0 0 0
Quadratic Polynomial 95% 0 0 0 0
With knots 95% NA NA NA NA

However, the actual forecast made by the various models on the last day i.e. April 16, 2025 is shown below:

Unconstrained forecasts on the last day

Constrained forecasts on the last day

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. 1b COVID-19 cases and deaths in the omicron wave in Nigeria

Notes COVID-19 omicron wave - between November 16 and September 22, 2022 . That is 19.92% of total or 171 cases per day. The all time cases per day is 342 . The mortality in the omicron wave is 6.747% of the total or 1 deaths per day. The all time deaths per day is 3 .

Fig. 2 Perecentages and previous days differences of COVID-19 in Nigeria Starting from February 29, 2020 to September 22, 2022

Fig. 3 Cases recorded in percentages Starting from February 29, 2020 to September 22, 2022 (legend as Fig. 2)

Fig. 4 Cumulative cases and Forecast of COVID-19 cases in Nigeria Starting from September 23, 2022 to April 16, 2025

Fig. 4a Components of COVID-19 cases in Nigeria between February 29, 2020 and September 22, 2022

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 September 22, 2022 that cases were recorded in the States

Fig. 7a Number of States that COVID-19 cases were reported on daily basis

Fig. 7b Forecast of Numbers of States that may still be COVID-19 affected by April 16, 2025

Coefficient estimates for number of states reporting COVID-19 daily

LinearSemilogGrowthSpline without knotsLinear splinesSpline with knotsARIMA
(Intercept)0.634 ****16.625 ****2.653 ****9.335 ****2.838 *   -0.804          
(0.066)    (1.257)    (0.042)    (0.747)    (1.576)    (2.359)         
Series-0.002 ****         -0.001 ****                                
(0.000)             (0.000)                                    
log(Series)         -0.743 ****                                         
         (0.218)                                             
bs(Series, knots = NULL)1                           18.796 ****                       
                           (2.159)                           
bs(Series, knots = NULL)2                           -6.320 ****                       
                           (1.369)                           
bs(Series, knots = NULL)3                           -0.192                            
                           (1.181)                           
bs(DDPtable$Series, knots = LBREAKS)1                                    -2.773                   
                                    (2.248)                  
bs(DDPtable$Series, knots = LBREAKS)2                                    13.757 ****              
                                    (1.667)                  
bs(DDPtable$Series, knots = LBREAKS)3                                    22.789 ****              
                                    (1.946)                  
bs(DDPtable$Series, knots = LBREAKS)4                                    -1.023                   
                                    (1.728)                  
bs(DDPtable$Series, knots = LBREAKS)5                                    29.070 ****              
                                    (1.779)                  
bs(DDPtable$Series, knots = LBREAKS)6                                    -2.549                   
                                    (1.753)                  
bs(DDPtable$Series, knots = LBREAKS)7                                    3.248 *                 
                                    (1.775)                  
bs(DDPtable$Series, knots = LBREAKS)8                                    19.418 ****              
                                    (1.807)                  
bs(DDPtable$Series, knots = LBREAKS)9                                    0.553                   
                                    (1.811)                  
bs(DDPtable$Series, knots = LBREAKS)10                                    16.765 ****              
                                    (1.858)                  
bs(DDPtable$Series, knots = LBREAKS)11                                    -7.918 ****              
                                    (2.056)                  
bs(DDPtable$Series, knots = LBREAKS)12                                    9.857 ****              
                                    (2.000)                  
bs(DDPtable$Series, knots = LBREAKS)13                                    4.562 **                
                                    (2.020)                  
bs(Series, knots = QBREAKS)1                                             7.280 **       
                                             (2.904)         
bs(Series, knots = QBREAKS)2                                             22.473 ****     
                                             (2.488)         
bs(Series, knots = QBREAKS)3                                             16.615 ****     
                                             (2.814)         
bs(Series, knots = QBREAKS)4                                             13.579 ****     
                                             (2.509)         
bs(Series, knots = QBREAKS)5                                             8.871 ****     
                                             (2.562)         
bs(Series, knots = QBREAKS)6                                             18.999 ****     
                                             (2.709)         
bs(Series, knots = QBREAKS)7                                             -0.076          
                                             (2.913)         
bs(Series, knots = QBREAKS)8                                             11.044 ****     
                                             (2.649)         
ma1                                                      -0.724 
                                                      (0.035)
ma2                                                      -0.050 
                                                      (0.033)
nobs794         794         794         794         794         794         793     
r.squared0.134     0.014     0.074     0.244     0.740     0.364          
adj.r.squared0.133     0.013     0.073     0.241     0.735     0.358          
sigma0.931     6.031     0.592     5.290     3.124     4.866     3.201 
statistic122.344     11.630     63.580     84.884     170.367     56.185          
p.value0.000     0.001     0.000     0.000     0.000     0.000          
df1.000     1.000     1.000     3.000     13.000     8.000          
logLik-1069.110     -2552.418     -709.793     -2447.281     -2024.110     -2378.476     -2047.402 
AIC2144.219     5110.836     1425.587     4904.562     4078.219     4776.951     4100.803 
BIC2158.250     5124.867     1439.618     4927.947     4148.375     4823.722     4114.831 
deviance686.892     28809.790     277.855     22106.836     7613.812     18589.070          
df.residual792.000     792.000     792.000     790.000     780.000     785.000          
nobs.1794.000     794.000     794.000     794.000     794.000     794.000     793.000 
**** p < 0.001; *** p < 0.01; ** p < 0.05; * p < 0.1.

Properties of the estimated coefficients

Linear Semilog Growth Spline with knots Linear splines Spline without knots Smooth spline ARIMA
Absolute Error 9800 4000 8000 3100 1800 3500 1600 1900
Absolute Percent Error 800 600 600 370 210 490 180 210
Accuracy 0 0 0 0 0 0 0 0
Adjusted R Square 0.13 0.013 0.073 0.36 0.74 0.24 0 0
Akaike’s Information Criterion AIC 2100 5100 1400 4800 4100 4900 0 4100
Allen’s Prediction Sum-Of-Squares (PRESS, P-Square) 0 0 0 0 0 0 0 0
Area under the ROC curve (AUC) 0 0 0 0 0 0 0 0
Average Precision at k 0 0 0 0 0 0 0 0
Bias 12 0.0000000000000007 10 0.000000000000000079 0.000000000000000062 0.00000000000000053 -0.0000000000000015 0.036
Brier score 200 40 100 20 10 30 8 10
Classification Error 1 1 1 1 1 1 1 1
F1 Score 0 0 0 0 0 0 0 0
fScore 0 0 0 0 0 0 0 0
GINI Coefficient 0.4 0.4 0.4 0.6 0.9 0.5 0.9 0.9
kappa statistic 0 0 0 0 0 0 0 0
Log Loss Inf Inf Inf Inf Inf Inf Inf Inf
Mallow’s cp 4 4 4 4 4 4 NaN 4
Matthews Correlation Coefficient 0 0 0 0 0 0 0 0
Mean Log Loss 170 -390 -390 -390 -390 -390 -390 -390
Mean Absolute Error 12 5 10 3.9 2.3 4.4 2.1 2.4
Mean Absolute Percent Error 1 0.76 0.75 0.47 0.27 0.61 0.23 0.26
Mean Average Precision at k 0 0 0 0 0 0 0 0
Mean Absolute Scaled Error 4.3 1.7 3.5 1.4 0.81 1.5 0.72 0.83
Median Absolute Error 12 4.7 9.8 3.4 1.8 4.2 1.7 1.9
Mean Squared Error 190 36 140 23 9.6 28 8 10
Mean Squared Log Error 6.8 0.3 1.9 0.18 0.08 0.24 0.061 0.078
Model turning point error 469 469 469 450 439 448 440 600
Negative Predictive Value 0 0 0 0 0 0 0 0
Percent Bias 1 -0.5 0.72 -0.25 -0.11 -0.37 -0.092 -0.09
Positive Predictive Value 0 0 0 0 0 0 0 0
Precision 1 1 1 1 1 1 1 1
R Square 0.13 0.014 0.074 0.36 0.74 0.24 0 0
Relative Absolute Error 2.4 0.98 2 0.77 0.45 0.86 0.4 0.46
Recall 0.86 1 1 0.71 1 1 1 1
Root Mean Squared Error 14 6 12 4.8 3.1 5.3 2.8 3.2
Root Mean Squared Log Error 2.6 0.55 1.4 0.42 0.28 0.49 0.25 0.28
Root Relative Squared Error 2.3 0.99 1.9 0.8 0.51 0.87 0.46 0.53
Relative Squared Error 5.1 0.99 3.7 0.64 0.26 0.76 0.22 0.28
Schwarz’s Bayesian criterion BIC 2200 5100 1400 4800 4100 4900 0 4100
Sensitivity 0 0 0 0 0 0 0 0
specificity 0 0 0 0 0 0 0 0
Squared Error 150000 29000 110000 19000 7600 22000 6300 8100
Squared Log Error 5400 240 1500 140 64 190 48 62
Symmetric Mean Absolute Percentage Error 1.9 0.44 1.2 0.35 0.23 0.39 0.2 0.23
Sum of Squared Errors 150000 29000 110000 19000 7600 22000 6300 8100
True negative rate 0 0 0 0 0 0 0 0
True positive rate 0 0 0 0 0 0 0 0

Fig. 8 Number of days the last COVID19 case was recorded as at September 22, 2022

Fig. 8a

Fig. 8b

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 September 22, 2022 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)-566.847 *   -2264.532     -570.013 **  823.128 *   -33.994 ****16.027 ****
(289.261)    (3486.932)    (274.790)    (420.805)    (6.589)    (0.184)    
PMSprice-0.039 **           0.052 ****0.061 **  0.000     0.000     
(0.016)             (0.013)    (0.022)    (0.001)    (0.000)    
Inflation0.266     7.310 ****         -0.360     -0.015 **  0.004 **  
(0.198)    (1.829)             (0.289)    (0.005)    (0.002)    
Teledensity0.427 ****3.725 **  -0.156              0.005     -0.002 *   
(0.106)    (1.344)    (0.125)             (0.004)    (0.001)    
`Birth rate`-3.991     5.465     -14.976 **  12.277              0.249 ****
(6.436)    (73.538)    (5.468)    (9.117)             (0.044)    
Population32.715 *   117.110     38.233 **  -46.163 *   2.206 ****         
(18.204)    (217.735)    (16.888)    (26.573)    (0.392)             
COVID19         -4.988 **  0.244     0.903 ****-0.004     0.003 *   
         (1.998)    (0.182)    (0.224)    (0.006)    (0.002)    
nobs32         32         32         32         32         32         
r.squared0.473     0.567     0.649     0.482     0.612     0.655     
adj.r.squared0.372     0.484     0.581     0.382     0.538     0.589     
sigma1.533     17.386     1.467     2.229     0.046     0.016     
statistic4.675     6.812     9.600     4.840     8.217     9.881     
p.value0.004     0.000     0.000     0.003     0.000     0.000     
df5.000     5.000     5.000     5.000     5.000     5.000     
logLik-55.749     -133.465     -54.353     -67.737     56.198     91.107     
AIC125.498     280.930     122.706     149.473     -98.395     -168.213     
BIC135.758     291.190     132.966     159.733     -88.135     -157.953     
deviance61.080     7858.959     55.976     129.204     0.056     0.006     
df.residual26.000     26.000     26.000     26.000     26.000     26.000     
nobs.132.000     32.000     32.000     32.000     32.000     32.000     
**** 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

LinearSemilogGrowthSpline without knotsSpline with knotsARIMAQuardratic polynomialEssembled based on weightEssembled based on summed weight
(Intercept)416.527 ****341.974 ****5.916 ****27.774     -2.059          175.118 ****43.032   -0.007     
(25.022)    (76.744)    (0.132)    (47.269)    (83.564)         (36.082)    (86.834)  (12.323)    
Day-0.279 ****         -0.003 ****                       1.264 ****                
(0.046)             (0.000)                           (0.178)                    
log(Day)         -9.630                                                              
         (12.943)                                                             
bs(Day, knots = NULL)1                           989.211 ****                                       
                           (136.526)                                           
bs(Day, knots = NULL)2                           7.335                                            
                           (86.519)                                           
bs(Day, knots = NULL)3                           36.046                                            
                           (74.679)                                           
bs(Day, knots = BREAKS)1                                    0.423                                   
                                    (161.545)                                  
bs(Day, knots = BREAKS)2                                    17.623                                   
                                    (104.646)                                  
bs(Day, knots = BREAKS)3                                    835.817 ****                              
                                    (119.958)                                  
bs(Day, knots = BREAKS)4                                    180.911 *                                 
                                    (101.907)                                  
bs(Day, knots = BREAKS)5                                    176.797                                   
                                    (111.571)                                  
bs(Day, knots = BREAKS)6                                    -49.929                                   
                                    (104.863)                                  
bs(Day, knots = BREAKS)7                                    1978.395 ****                              
                                    (103.906)                                  
bs(Day, knots = BREAKS)8                                    -164.839                                   
                                    (102.986)                                  
bs(Day, knots = BREAKS)9                                    -196.700 *                                 
                                    (103.881)                                  
bs(Day, knots = BREAKS)10                                    993.777 ****                              
                                    (105.361)                                  
bs(Day, knots = BREAKS)11                                    -349.511 ****                              
                                    (102.074)                                  
bs(Day, knots = BREAKS)12                                    1102.447 ****                              
                                    (96.555)                                  
bs(Day, knots = BREAKS)13                                    -718.116 ****                              
                                    (109.079)                                  
bs(Day, knots = BREAKS)14                                    553.096 ****                              
                                    (120.366)                                  
bs(Day, knots = BREAKS)15                                    -88.987                                   
                                    (109.860)                                  
bs(Day, knots = BREAKS)16                                    121.402                                   
                                    (111.364)                                  
ar1                                             0.352                          
                                             (0.129)                         
ar2                                             -0.531                          
                                             (0.045)                         
ar3                                             -0.309                          
                                             (0.042)                         
ar4                                             -0.201                          
                                             (0.037)                         
ar5                                             -0.215                          
                                             (0.050)                         
ma1                                             -0.950                          
                                             (0.131)                         
ma2                                             0.651                          
                                             (0.080)                         
I(Day^2)                                                  -0.002 ****                
                                                  (0.000)                    
fitf                                                           -0.872   -0.071 *   
                                                           (0.956)  (0.041)    
fit0f                                                           0.078   -0.001     
                                                           (0.750)  (0.093)    
fit1f                                                           1.412   1.019 ****
                                                           (1.980)  (0.065)    
fitpif                                                           -0.293   0.000     
                                                           (0.376)  (0.099)    
fitaf                                                           -0.685   0.052     
                                                           (1.656)  (0.056)    
fitf:fit0f                                                           0.000            
                                                           (0.013)           
fitf:fit1f                                                           0.002            
                                                           (0.008)           
fit0f:fit1f                                                           0.014            
                                                           (0.024)           
fitf:fitpif                                                           0.005 *          
                                                           (0.003)           
fit0f:fitpif                                                           0.000            
                                                           (0.002)           
fit1f:fitpif                                                           -0.005            
                                                           (0.007)           
fitf:fitaf                                                           0.012            
                                                           (0.008)           
fit0f:fitaf                                                           -0.014            
                                                           (0.020)           
fit1f:fitaf                                                           -0.007            
                                                           (0.009)           
fitpif:fitaf                                                           0.004            
                                                           (0.005)           
fitf:fit0f:fit1f                                                           -0.000            
                                                           (0.000)           
fitf:fit0f:fitpif                                                           -0.000            
                                                           (0.000)           
fitf:fit1f:fitpif                                                           -0.000            
                                                           (0.000)           
fit0f:fit1f:fitpif                                                           -0.000            
                                                           (0.000)           
fitf:fit0f:fitaf                                                           -0.000            
                                                           (0.000)           
fitf:fit1f:fitaf                                                           -0.000            
                                                           (0.000)           
fit0f:fit1f:fitaf                                                           0.000            
                                                           (0.000)           
fitf:fitpif:fitaf                                                           -0.000 **         
                                                           (0.000)           
fit0f:fitpif:fitaf                                                           0.000            
                                                           (0.000)           
fit1f:fitpif:fitaf                                                           0.000            
                                                           (0.000)           
fitf:fit0f:fit1f:fitpif                                                           0.000            
                                                           (0.000)           
fitf:fit0f:fit1f:fitaf                                                           0.000            
                                                           (0.000)           
fitf:fit0f:fitpif:fitaf                                                           0.000            
                                                           (0.000)           
fitf:fit1f:fitpif:fitaf                                                           0.000            
                                                           (0.000)           
fit0f:fit1f:fitpif:fitaf                                                           -0.000            
                                                           (0.000)           
fitf:fit0f:fit1f:fitpif:fitaf                                                           -0.000            
                                                           (0.000)           
nobs937         937         937         937         937         936     937         937       937         
r.squared0.038     0.001     0.167     0.135     0.690          0.114     0.847   0.837     
adj.r.squared0.036     -0.000     0.166     0.132     0.684          0.112     0.842   0.836     
sigma382.655     389.927     2.015     363.178     219.042     181.220 367.373     155.060   157.676     
statistic36.451     0.554     187.403     48.480     127.794          59.976     161.648   958.086     
p.value0.000     0.457     0.000     0.000     0.000          0.000     0.000   0.000     
df1.000     1.000     1.000     3.000     16.000          2.000     31.000   5.000     
logLik-6901.008     -6918.648     -1984.867     -6851.056     -6370.707     -6192.067 -6862.319     -6039.318   -6068.263     
AIC13808.016     13843.296     3975.734     13712.112     12777.414     12400.133 13732.637     12144.637   12150.525     
BIC13822.544     13857.824     3990.262     13736.326     12864.582     12438.866 13752.008     12304.445   12184.424     
deviance136906982.331     142160094.647     3795.035     123061134.769     44141026.084          126055312.023     21759485.569   23146204.160     
df.residual935.000     935.000     935.000     933.000     920.000          934.000     905.000   931.000     
nobs.1937.000     937.000     937.000     937.000     937.000     936.000 937.000     937.000   937.000     
**** p < 0.001; *** p < 0.01; ** p < 0.05; * p < 0.1.

Table 3 Various selection criteria for the estimated models2

Linears Semilogs Growths Spline with knots Spline without knots Smooth spline ARIMA Quardratic polynomial Essembled equal weight Essembled on weight Essembled summed weight Essembled weight of fit
Absolute Error 240000 250000 260000 120000 230000 75000 88000 230000 270000 73000 75000 270000
Absolute Percent Error Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf
Accuracy 0 0 0 0 0 0 0 0 0.15 0 0 0.15
Adjusted R Square 0.036 -0.00048 0.17 0.68 0.13 0 0 0.11 0 0.84 0.84 0
Akaike’s Information Criterion AIC 14000 14000 4000 13000 14000 0 12000 14000 0 12000 12000 0
Allen’s Prediction Sum-Of-Squares (PRESS, P-Square) 0 0 0 0 0 0 0 0 0 0 0 0
Area under the ROC curve (AUC) 0 0 0 0 0 0 0 0 0 0 0 0
Average Precision at k 0 0 0 0 0 0 0 0 0.5 0 0 0.5
Bias -0.000000000000015 -0.000000000000041 280 -0.0000000000000008 0.000000000000014 0.000000000000016 0.069 0.000000000000013 290 -0.00000000000000086 -0.00000000000000035 290
Brier score 100000 200000 200000 50000 100000 20000 30000 100000 1000000 20000 20000 3000000000000000
Classification Error 1 1 1 1 1 1 1 1 1 1 1 1
F1 Score 0 0 0 0 0 0 0 0 0 0 0 0
fScore 0 0 0 0 0 0 0 0 0 0 0 0
GINI Coefficient 0.2 0.2 0.2 0.9 0.5 0.9 0.9 0.4 0.9 1 0.9 0.9
kappa statistic 0 0 0 0 0 0 0 0 0 0 0 0
Log Loss Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf
Mallow’s cp 2 2 2 2 2 NaN 2 2 2 2 2 2
Matthews Correlation Coefficient 0 0 0 0 0 0 0 0 NaN 0 0 NaN
Mean Log Loss -9800 -9800 -9800 -9500 -9800 -9800 -9800 -9600 NaN -9800 -9800 NaN
Mean Absolute Error 260 270 280 130 240 80 94 250 320 78 80 1800000
Mean Absolute Percent Error Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf
Mean Average Precision at k 0 0 0 0 0 0 0 0 0 0 0 0
Mean Absolute Scaled Error 2.3 2.4 2.5 1.2 2.2 0.71 0.84 2.2 2.6 0.7 0.72 2.6
Median Absolute Error 190 230 140 90 160 38 46 200 92 37 39 100
Mean Squared Error 150000 150000 230000 47000 130000 25000 33000 130000 1500000 23000 25000 3000000000000000
Mean Squared Log Error 6 6.5 12 NaN 4.5 2.1 NaN NaN 24 NaN 2.2 24
Model turning point error 503 503 503 482 487 475 638 484 598 414 540 594
Negative Predictive Value 0 0 0 0 0 0 0 0 0 0 0 0
Percent Bias -Inf -Inf -Inf NaN -Inf NaN NaN NaN -Inf NaN NaN -Inf
Positive Predictive Value 0 0 0 0 0 0 0 0 0 0 0 0
Precision 0.014 0.014 0.014 0 0.014 0 0.0079 0.017 0 0 0.0076 0
R Square 0.038 0.00059 0.17 0.69 0.13 0 0 0.11 0 0.85 0.84 0
Relative Absolute Error 0.95 1 1 0.47 0.88 0.29 0.34 0.92 0.48 0.29 0.29 0.59
Recall 1 1 1 0 1 0 0.5 1 0 0 0.5 0
Root Mean Squared Error 380 390 480 220 360 160 180 370 1200 150 160 54000000
Root Mean Squared Log Error 2.4 2.6 3.4 NaN 2.1 1.5 NaN NaN 4.9 NaN 1.5 4.9
Root Relative Squared Error 0.98 1 1.2 0.56 0.93 0.4 0.46 0.94 1.2 0.39 0.4 1.2
Relative Squared Error 0.96 1 1.5 0.31 0.87 0.16 0.21 0.89 1.5 0.15 0.16 1.5
Schwarz’s Bayesian criterion BIC 14000 14000 4000 13000 14000 0 12000 14000 0 12000 12000 0
Sensitivity 0 0 0 0 0 0 0 0 1 0 0 1
specificity 0 0 0 0 0 0 0 0 0 0 0 0
Squared Error 140000000 140000000 220000000 44000000 120000000 23000000 31000000 130000000 220000000 22000000 23000000 220000000
Squared Log Error 5600 6100 11000 NaN 4300 2000 NaN NaN 22000 NaN 2000 22000
Symmetric Mean Absolute Percentage Error 1 1.1 1.8 0.87 1 0.6 0.67 1.1 0.82 0.61 0.61 0.88
Sum of Squared Errors 140000000 140000000 220000000 44000000 120000000 23000000 31000000 130000000 220000000 22000000 23000000 220000000
True negative rate 0 0 0 0 0 0 0 0 0 0 0 0
True positive rate 0 0 0 0 0 0 0 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 Unconstrained forecast of COVID-19 cases in Nigeria Starting from September 23, 2022 to April 16, 2025 using a more advaced plotting method

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

Fig. 20b Constrained forecast of COVID-19 cases in Nigeria Starting from September 23, 2022 to April 16, 2025 using a more advaced plotting method

Fig. 20c Constrained forecast of COVID-19 cases in Nigeria Starting from September 23, 2022 to April 16, 2025 using a more advaced plotting method

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

Table 4 Summary of the unconstrained dynamic COVID-19 forecasts
Model Sep 09, 22 - Mar 19, 25 Sep 16, 22 - Apr 02, 25 Sep 19, 22 - Apr 08, 25 Sep 20, 22 - Apr 10, 25 Sep 21, 22 - Apr 12, 25 Sep 22, 22 - Apr 14, 25
Linear 37741 30505 27426 26490 25030 24148
Semilog 261178 260009 259503 255279 259079 258943
Growth 1277 1219 1186 1193 1170 1178
Without knots 64408 84636 93486 97439 95755 100323
Smooth Spline 4087527 3799925 3702387 3724762 3528271 3584950
With knots 1548503 -1449791 -2071639 -1496278 -2784031 -91731
Quadratic Polynomial -860752 -868284 -871358 -871995 -874694 -875137
Lower ARIMA -2409560 -2466092 -2463820 -2455249 -2487261 -2473212
Upper ARIMA 2550379 2538980 2555868 2569572 2543975 2564549
Essembled with equal weight 91459 59825 38029 35532 32361 31511
Essembled based on weight 841358 992409 1040323 1025176 1063256 1042542
Essembled based on summed weight 1521205 1563213 1571950 1572856 1598433 1587756
Essembled based on weight of fit 69359 37189 22093 20486 27850 15121
Table 4a Summary of the dynamic COVID-19 constrained forecasts
Model Sep 09, 22 - Mar 19, 25 Sep 16, 22 - Apr 02, 25 Sep 19, 22 - Apr 08, 25 Sep 20, 22 - Apr 10, 25 Sep 21, 22 - Apr 12, 25 Sep 22, 22 - Apr 14, 25
Linear 37741 30505 27426 26490 25030 24148
Semilog 261178 260009 259503 255279 259079 258943
Growth 1277 1219 1186 1193 1170 1178
Without knots 80% 14216 3986 4019 3111 1655 3504
Without knots 95% 1940555 1783602 1140505 1670977 810097 1492581
Smooth Spline 80% 0 0 0 0 0 0
Smooth Spline 95% NaN NaN NaN NaN NaN NaN
With knots 80% 52577 53536 54037 54338 53297 53981
With knots 95% 74269 75587 76112 76731 75751 76494
Quadratic Polynomial 80% 0 1 1 1 0 1
Quadratic Polynomial 95% 0 1 2 1 0 1
ARIMA 80% 77 76 77 77 77 77
ARIMA 95% 2902660 2925958 2939777 2942804 2944241 2948312
Essembled with equal weight 80% 9252 4285 2495 2362 2312 2160
Essembled with equal weight 95% 3190987 2922554 2673906 2630120 2623660 2594928
Essembled based on weight 80% 149980 237866 177123 199878 216218 225022
Essembled based on weight 95% 3164810 3069456 3308384 3239620 3241441 3194398
Essembled based on summed weight 80% 834237 850838 852515 848971 862644 852406
Essembled based on summed weight 95% 2782389 2824574 2838948 2842870 2858994 2856785
Essembled based on weight of fit 80% 8204 4034 2363 2224 2113 1976
Essembled based on weight of fit 95% 2676158 2156297 1710379 1639688 1621075 1583641

Table 3 RMSE of the models in the dynamic forecasts
Model Sep 09, 22 - Mar 19, 25 Sep 16, 22 - Apr 02, 25 Sep 19, 22 - Apr 08, 25 Sep 20, 22 - Apr 10, 25 Sep 21, 22 - Apr 12, 25 Sep 22, 22 - Apr 14, 25
Linear 384.83 383.52 382.98 382.78 382.61 382.42
Semilog 391.39 390.43 390.03 389.87 389.77 389.61
Growth 483.93 482.18 481.44 481.19 480.93 480.68
Without knots 365.03 363.72 363.17 362.97 362.78 362.59
Smooth Spline 218.28 217.66 217.41 217.31 217.23 217.12
With knots 160.67 160.54 160.05 158.61 157.62 157.3
Quadratic Polynomial 369.06 367.94 367.47 367.31 367.13 366.97
Lower ARIMA 181.47 180.98 180.79 180.69 180.62 180.53
Upper ARIMA 181.47 180.98 180.79 180.69 180.62 180.53
Essembled with equal weight 219.82 219.22 218.86 218.4 218.07 217.9
Essembled based on weight 527.25 530.23 531.44 531.84 532.21 532.67
Essembled based on summed weight 492.57 496.32 497.94 498.49 499.18 499.7
Essembled based on weight of fit 418.9 424.03 426.23 426.92 427.74 428.44

Fig. 21 Percentage of COVID19 cases that resulted into casualty per State as at September 22, 2022

Fig. 22 Percentage of COVID19 cases that recovered per State as at September 22, 2022

Fig. 23 Percentage of recoveries and deaths from COVID19 cases per Zone as at September 22, 2022

Fig. 24 Distribution of COVID19 in the States as at September 22, 2022

  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↩︎