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It is now 836 days since the first COVID-19 case was reported in Nigeria. As at June 13, 2022 the confirmed cases are 260,623 with 3,144 (1.23%) fatalities, an average of 4 fatalities per day. However, to date, 250,117 or 97.6% were successfully managed and discharged leaving a balance of 3,003 (1.17%) active cases being managed.

Based on equal days forecast, by September 26, 2024, 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 8,370,412 8,169,522 102,956 97,934 198
Upper ARIMA 2,695,334 2,630,646 33,153 31,535 177
Smooth Spline 329,335 321,430 4,051 3,853 198
Semilog 281,892 275,127 3,467 3,298 179
Linear 127,675 124,611 1,570 1,494 396
Essembled based on weight of fit 76,716 74,875 944 898 1.9
Essembled with equal weight 76,118 74,291 936 891 298
Essembled based on weight 15,452 15,081 190 181 415
Essembled based on summed weight 8,109 7,915 100 95 417
Growth 2,315 2,259 28 27 266
Without knots -317,467 -309,848 -3,905 -3,714 397
Quadratic Polynomial -767,717 -749,292 -9,443 -8,982 245
Lower ARIMA -2,648,845 -2,585,272 -32,581 -30,991 172

Constrained forecast of COVID-19 for Nigeria

Model Confirmed cases Recoveries Fatalities Active
Essembled based on weight of fit 95% 3,009,473 2,937,246 37,017 35,211
ARIMA 95% 2,946,264 2,875,554 36,239 34,471
Essembled based on weight 95% 2,903,473 2,833,790 35,713 33,971
Essembled based on summed weight 95% 2,721,161 2,655,853 33,470 31,838
With knots 95% 1,703,791 1,662,900 20,957 19,934
Without knots 95% 1,036,394 1,011,521 12,748 12,126
Essembled with equal weight 95% 606,467 591,912 7,460 7,096
Semilog 281,892 275,127 3,467 3,298
Linear 127,675 124,611 1,570 1,494
Smooth Spline 95% 17,545 17,124 216 205
Essembled based on weight of fit 80% 11,407 11,133 140 133
Smooth Spline 80% 9,458 9,231 116 111
Without knots 80% 4,414 4,308 54 52
Growth 2,315 2,259 28 27
Essembled based on summed weight 80% 358 349 4 4
ARIMA 80% 162 158 2 2
Essembled based on weight 80% 186 182 2 2
With knots 80% 2 2 0 0
Quadratic Polynomial 80% 0 0 0 0
Quadratic Polynomial 95% 0 0 0 0
Essembled with equal weight 80% 6 6 0 0

However, the actual forecast made by the various models on the last day i.e. September 26, 2024 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 June 13, 2022 . That is 17.78% of total or 221 cases per day. The all time cases per day is 342 . The mortality in the omicron wave is 6.107% of the total or 1 deaths per day. The all time deaths per day is 4 .

Fig. 2 Perecentages and previous days differences of COVID-19 in Nigeria Starting from February 29, 2020 to June 13, 2022

Fig. 3 Cases recorded in percentages Starting from February 29, 2020 to June 13, 2022 (legend as Fig. 2)

Fig. 4 Cumulative cases and Forecast of COVID-19 cases in Nigeria Starting from June 14, 2022 to September 26, 2024

Fig. 4a Components of COVID-19 cases in Nigeria between February 29, 2020 and June 13, 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 June 13, 2022 that cases were recorded in the States

Fig. 7a Numbers 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 September 26, 2024

Coefficient estimates for number of states reporting COVID-19 daily

LinearSemilogGrowthSpline without knotsLinear splinesSpline with knotsARIMA
(Intercept)0.613 ****16.112 ****2.657 ****9.343 ****2.840 *   -0.883          
(0.068)    (1.286)    (0.044)    (0.772)    (1.537)    (2.357)         
Series-0.002 ****         -0.001 ****                                
(0.000)             (0.000)                                    
log(Series)         -0.637 ***                                          
         (0.225)                                             
bs(Series, knots = NULL)1                           18.126 ****                       
                           (2.229)                           
bs(Series, knots = NULL)2                           -4.665 ***                        
                           (1.413)                           
bs(Series, knots = NULL)3                           -0.735                            
                           (1.219)                           
bs(DDPtable$Series, knots = LBREAKS)1                                    -2.779                   
                                    (2.192)                  
bs(DDPtable$Series, knots = LBREAKS)2                                    13.771 ****              
                                    (1.626)                  
bs(DDPtable$Series, knots = LBREAKS)3                                    22.715 ****              
                                    (1.898)                  
bs(DDPtable$Series, knots = LBREAKS)4                                    -0.747                   
                                    (1.685)                  
bs(DDPtable$Series, knots = LBREAKS)5                                    28.970 ****              
                                    (1.735)                  
bs(DDPtable$Series, knots = LBREAKS)6                                    -3.174 *                 
                                    (1.709)                  
bs(DDPtable$Series, knots = LBREAKS)7                                    3.343 *                 
                                    (1.731)                  
bs(DDPtable$Series, knots = LBREAKS)8                                    19.212 ****              
                                    (1.764)                  
bs(DDPtable$Series, knots = LBREAKS)9                                    0.995                   
                                    (1.772)                  
bs(DDPtable$Series, knots = LBREAKS)10                                    17.151 ****              
                                    (1.927)                  
bs(DDPtable$Series, knots = LBREAKS)11                                    -4.498 **                
                                    (2.054)                  
bs(DDPtable$Series, knots = LBREAKS)12                                    3.524 *                 
                                    (1.833)                  
bs(Series, knots = QBREAKS)1                                             7.501 ***      
                                             (2.902)         
bs(Series, knots = QBREAKS)2                                             22.166 ****     
                                             (2.487)         
bs(Series, knots = QBREAKS)3                                             17.505 ****     
                                             (2.816)         
bs(Series, knots = QBREAKS)4                                             12.702 ****     
                                             (2.511)         
bs(Series, knots = QBREAKS)5                                             9.512 ****     
                                             (2.577)         
bs(Series, knots = QBREAKS)6                                             16.951 ****     
                                             (2.716)         
bs(Series, knots = QBREAKS)7                                             6.826 **       
                                             (2.936)         
bs(Series, knots = QBREAKS)8                                             4.739 *        
                                             (2.696)         
ma1                                                      -0.773 
                                                      (0.022)
nobs759         759         759         759         759         759         758     
r.squared0.125     0.011     0.071     0.238     0.755     0.373          
adj.r.squared0.124     0.009     0.070     0.235     0.751     0.366          
sigma0.936     6.078     0.600     5.342     3.046     4.862     3.128 
statistic108.432     8.037     57.676     78.410     191.564     55.689          
p.value0.000     0.005     0.000     0.000     0.000     0.000          
df1.000     1.000     1.000     3.000     12.000     8.000          
logLik-1025.672     -2445.660     -688.779     -2346.740     -1915.918     -2272.724     -1939.887 
AIC2057.344     4897.319     1383.558     4703.480     3859.836     4565.449     3883.775 
BIC2071.240     4911.215     1397.454     4726.640     3924.684     4611.769     3893.036 
deviance663.029     27960.570     272.896     21544.879     6923.367     17727.251          
df.residual757.000     757.000     757.000     755.000     746.000     750.000          
nobs.1759.000     759.000     759.000     759.000     759.000     759.000     758.000 
**** 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 9500 3900 7700 3000 1700 3400 1600 1800
Absolute Percent Error 760 580 570 360 200 480 180 200
Accuracy 0 0 0 0 0 0 0 0
Adjusted R Square 0.12 0.0092 0.07 0.37 0.75 0.23 0 0
Akaike’s Information Criterion AIC 2100 4900 1400 4600 3900 4700 0 3900
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 13 0.00000000000000016 10 0.00000000000000051 -0.000000000000000051 -0.00000000000000019 0.000000000000002 0.019
Brier score 200 40 100 20 9 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 -400 -400 -400 -400 -400 -400 -400
Mean Absolute Error 13 5.1 10 4 2.3 4.5 2.1 2.3
Mean Absolute Percent Error 1 0.77 0.75 0.47 0.27 0.63 0.23 0.26
Mean Average Precision at k 0 0 0 0 0 0 0 0
Mean Absolute Scaled Error 4.4 1.8 3.6 1.4 0.81 1.6 0.72 0.83
Median Absolute Error 12 4.8 9.9 3.5 1.9 4.4 1.6 1.9
Mean Squared Error 190 37 140 23 9.1 28 7.6 9.8
Mean Squared Log Error 6.8 0.31 1.9 0.18 0.077 0.24 0.059 0.075
Model turning point error 451 451 451 432 423 428 422 564
Negative Predictive Value 0 0 0 0 0 0 0 0
Percent Bias 1 -0.51 0.72 -0.24 -0.1 -0.38 -0.09 -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 0.011 0.071 0.37 0.75 0.24 0 0
Relative Absolute Error 2.4 0.98 2 0.76 0.44 0.87 0.4 0.45
Recall 0.86 1 1 0.71 1 1 1 1
Root Mean Squared Error 14 6.1 12 4.8 3 5.3 2.8 3.1
Root Mean Squared Log Error 2.6 0.56 1.4 0.43 0.28 0.49 0.24 0.27
Root Relative Squared Error 2.3 0.99 1.9 0.79 0.49 0.87 0.45 0.51
Relative Squared Error 5.2 0.99 3.7 0.63 0.25 0.76 0.21 0.26
Schwarz’s Bayesian criterion BIC 2100 4900 1400 4600 3900 4700 0 3900
Sensitivity 0 0 0 0 0 0 0 0
specificity 0 0 0 0 0 0 0 0
Squared Error 150000 28000 110000 18000 6900 22000 5800 7400
Squared Log Error 5200 230 1400 140 59 180 45 57
Symmetric Mean Absolute Percentage Error 1.9 0.44 1.2 0.35 0.23 0.4 0.2 0.22
Sum of Squared Errors 150000 28000 110000 18000 6900 22000 5800 7400
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 June 13, 2022

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 June 13, 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)-308.712     -1669.510    -652.694 *** 663.526     -35.889 ****15.857 ****
(341.997)    (4154.751)   (214.316)    (502.191)    (6.238)    (0.190)    
PMSprice-0.044 ***         0.033 *** 0.065 *** 0.000     0.000     
(0.015)            (0.011)    (0.022)    (0.000)    (0.000)    
Inflation0.546 **  8.799 ***         -0.561     -0.024 ****0.007 *** 
(0.262)    (2.891)            (0.411)    (0.006)    (0.002)    
Teledensity0.429 ****4.194 ***-0.133              0.003     -0.001     
(0.107)    (1.422)   (0.098)             (0.004)    (0.001)    
`Birth rate`1.588     25.306    -16.292 ****8.328              0.270 ****
(7.441)    (89.079)   (4.241)    (11.013)             (0.043)    
Population15.992     74.287    43.438 *** -35.749     2.339 ****         
(21.655)    (262.043)   (13.170)    (31.938)    (0.372)             
COVID19         -6.386 ***0.291 **  0.961 ****0.001     0.001     
         (2.114)   (0.140)    (0.239)    (0.006)    (0.002)    
nobs29         29        29         29         29         29         
r.squared0.536     0.529    0.720     0.503     0.699     0.726     
adj.r.squared0.436     0.426    0.659     0.395     0.634     0.667     
sigma1.551     18.578    1.131     2.321     0.043     0.015     
statistic5.324     5.159    11.834     4.661     10.702     12.194     
p.value0.002     0.003    0.000     0.004     0.000     0.000     
df5.000     5.000    5.000     5.000     5.000     5.000     
logLik-50.510     -122.526    -41.365     -62.208     53.186     84.476     
AIC115.021     259.052    96.730     138.415     -92.371     -154.952     
BIC124.592     268.623    106.301     147.986     -82.800     -145.381     
deviance55.307     7938.561    29.435     123.915     0.043     0.005     
df.residual23.000     23.000    23.000     23.000     23.000     23.000     
nobs.129.000     29.000    29.000     29.000     29.000     29.000     
**** 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)391.360 ****214.223 **5.601 ****47.082     -3.032          122.138 *** 23.638 0.029     
(28.767)    (85.511)  (0.130)    (54.700)    (102.529)         (41.395)    (67.975)(16.097)    
Day-0.190 ***        -0.002 ****                       1.737 ****              
(0.060)           (0.000)                           (0.228)                  
log(Day)         17.009                                                          
         (14.699)                                                         
bs(Day, knots = NULL)1                         790.373 ****                                     
                         (158.000)                                         
bs(Day, knots = NULL)2                         274.837 ***                                      
                         (100.152)                                         
bs(Day, knots = NULL)3                         -5.257                                          
                         (86.411)                                         
bs(Day, knots = BREAKS)1                                  3.985                                 
                                  (198.208)                                
bs(Day, knots = BREAKS)2                                  14.162                                 
                                  (128.395)                                
bs(Day, knots = BREAKS)3                                  841.902 ****                            
                                  (147.182)                                
bs(Day, knots = BREAKS)4                                  172.938                                 
                                  (125.034)                                
bs(Day, knots = BREAKS)5                                  196.110                                 
                                  (136.887)                                
bs(Day, knots = BREAKS)6                                  -72.214                                 
                                  (128.649)                                
bs(Day, knots = BREAKS)7                                  2018.559 ****                            
                                  (127.440)                                
bs(Day, knots = BREAKS)8                                  -238.541 *                               
                                  (126.188)                                
bs(Day, knots = BREAKS)9                                  -66.110                                 
                                  (126.861)                                
bs(Day, knots = BREAKS)10                                  763.380 ****                            
                                  (127.411)                                
bs(Day, knots = BREAKS)11                                  13.071                                 
                                  (120.350)                                
bs(Day, knots = BREAKS)12                                  1121.104 ****                            
                                  (136.854)                                
bs(Day, knots = BREAKS)13                                  -1002.618 ****                            
                                  (154.634)                                
bs(Day, knots = BREAKS)14                                  341.069 ***                             
                                  (125.803)                                
ar1                                           0.680                        
                                           (0.192)                       
ar2                                           -0.888                        
                                           (0.155)                       
ar3                                           -0.100                        
                                           (0.126)                       
ar4                                           -0.098                        
                                           (0.060)                       
ar5                                           -0.281                        
                                           (0.057)                       
ma1                                           -1.409                        
                                           (0.191)                       
ma2                                           1.656                        
                                           (0.288)                       
ma3                                           -0.891                        
                                           (0.271)                       
ma4                                           0.353                        
                                           (0.128)                       
I(Day^2)                                                -0.002 ****              
                                                (0.000)                  
fitf                                                         0.131 -0.057     
                                                         (0.937)(0.039)    
fit0f                                                         0.352 -0.003     
                                                         (0.951)(0.211)    
fit1f                                                         -3.943 0.829 ****
                                                         (4.567)(0.062)    
fitpif                                                         -0.310 0.003     
                                                         (0.418)(0.215)    
fitaf                                                         0.446 0.228 ****
                                                         (2.815)(0.053)    
fitf:fit0f                                                         -0.004          
                                                         (0.015)         
fitf:fit1f                                                         0.009          
                                                         (0.011)         
fit0f:fit1f                                                         0.024          
                                                         (0.027)         
fitf:fitpif                                                         0.003          
                                                         (0.007)         
fit0f:fitpif                                                         -0.000          
                                                         (0.002)         
fit1f:fitpif                                                         0.008          
                                                         (0.023)         
fitf:fitaf                                                         0.006          
                                                         (0.009)         
fit0f:fitaf                                                         -0.016          
                                                         (0.021)         
fit1f:fitaf                                                         0.010          
                                                         (0.015)         
fitpif:fitaf                                                         0.008          
                                                         (0.015)         
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)         
nobs836         836       836         836         836         835     836         836     836         
r.squared0.012     0.002   0.078     0.100     0.593          0.095     0.830 0.821     
adj.r.squared0.011     0.000   0.077     0.096     0.586          0.092     0.823 0.820     
sigma415.510     417.709   1.877     397.169     268.755     199.267 398.010     175.720 177.309     
statistic10.205     1.339   70.424     30.658     85.493          43.538     126.332 761.216     
p.value0.001     0.248   0.000     0.000     0.000          0.000     0.000 0.000     
df1.000     1.000   1.000     3.000     14.000          2.000     31.000 5.000     
logLik-6225.899     -6230.312   -1711.808     -6187.155     -5855.081     -5602.070 -6189.424     -5491.111 -5511.940     
AIC12457.798     12466.624   3429.616     12384.309     11742.161     11224.140 12386.848     11048.222 11037.880     
BIC12471.984     12480.810   3443.801     12407.952     11817.819     11271.415 12405.762     11204.267 11070.980     
deviance143988895.460     145517147.937   2939.473     131242427.493     59300086.579          131956858.854     24825474.957 26093852.534     
df.residual834.000     834.000   834.000     832.000     821.000          833.000     804.000 830.000     
nobs.1836.000     836.000   836.000     836.000     836.000     835.000 836.000     836.000 836.000     
**** 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 230000 240000 260000 120000 220000 66000 79000 220000 260000 67000 67000 260000
Absolute Percent Error Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf
Accuracy 0 0 0 0 0 0 0 0 0.092 0 0 0.092
Adjusted R Square 0.011 0.00041 0.077 0.59 0.096 0 0 0.092 0 0.82 0.82 0
Akaike’s Information Criterion AIC 12000 12000 3400 12000 12000 0 11000 12000 0 11000 11000 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 0.0000000000000073 -0.000000000000038 310 0.000000000000015 -0.000000000000045 -0.000000000000067 0.077 -0.0000000000000029 310 0.00000000000000022 0.00000000000000041 310
Brier score 200000 200000 300000 70000 200000 30000 40000 200000 6000000 30000 30000 300000
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 0.1 -0.1 0.1 0.9 0.5 1 0.9 0.4 1 1 1 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 -11000 -11000 -11000 -10000 -11000 -11000 -11000 -11000 NaN -11000 -11000 NaN
Mean Absolute Error 280 290 310 150 260 79 95 260 430 81 80 310
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 2.5 2.6 2.8 1.3 2.3 0.71 0.85 2.3 2.8 0.72 0.72 2.8
Median Absolute Error 220 250 150 100 190 32 43 190 100 36 31 110
Mean Squared Error 170000 170000 270000 71000 160000 32000 39000 160000 6300000 30000 31000 270000
Mean Squared Log Error 4.8 5 12 NaN 3.5 0.77 NaN NaN 25 NaN 0.85 25
Model turning point error 438 437 438 422 418 408 466 418 466 424 455 460
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 NaN NaN NaN -Inf
Positive Predictive Value 0 0 0 0 0 0 0 0 0 0 0 0
Precision 0.025 0.025 0.025 0 0.025 0 0.017 0.029 0 0.015 0.015 0
R Square 0.012 0.0016 0.078 0.59 0.1 0 0 0.095 0 0.83 0.82 0
Relative Absolute Error 0.98 1 1.1 0.51 0.9 0.28 0.33 0.9 0.48 0.28 0.28 0.58
Recall 1 1 1 0 1 0 0.5 1 0 0.5 0.5 0
Root Mean Squared Error 420 420 520 270 400 180 200 400 2500 170 180 520
Root Mean Squared Log Error 2.2 2.2 3.5 NaN 1.9 0.88 NaN NaN 5 NaN 0.92 5
Root Relative Squared Error 0.99 1 1.2 0.64 0.95 0.43 0.47 0.95 1.2 0.41 0.42 1.2
Relative Squared Error 0.99 1 1.5 0.41 0.9 0.18 0.23 0.91 1.6 0.17 0.18 1.6
Schwarz’s Bayesian criterion BIC 12000 12000 3400 12000 12000 0 11000 12000 0 11000 11000 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 150000000 220000000 59000000 130000000 27000000 33000000 130000000 230000000 25000000 26000000 230000000
Squared Log Error 4000 4200 10000 NaN 2900 650 NaN NaN 21000 NaN 710 21000
Symmetric Mean Absolute Percentage Error 1 1 1.8 0.8 0.97 0.48 0.55 0.98 0.74 0.52 0.49 0.78
Sum of Squared Errors 140000000 150000000 220000000 59000000 130000000 27000000 33000000 130000000 230000000 25000000 26000000 230000000
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 June 14, 2022 to September 26, 2024 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 June 14, 2022 to September 26, 2024 using a more advaced plotting method

Fig. 20c Constrained forecast of COVID-19 cases in Nigeria Starting from June 14, 2022 to September 26, 2024 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 May 31, 22 - Aug 29, 24 Jun 07, 22 - Sep 12, 24 Jun 10, 22 - Sep 18, 24 Jun 11, 22 - Sep 20, 24 Jun 12, 22 - Sep 22, 24 Jun 13, 22 - Sep 24, 24
Linear 149372 138507 133570 132462 130741 129024
Semilog 278809 276750 275843 283206 275504 274985
Growth 2511 2413 2360 2365 2339 2313
Without knots -279174 -300037 -311038 -308783 -313102 -317302
Smooth Spline 9155624 8785606 8570219 8574714 8488322 8403383
With knots -49128 156540 -30139 222056 64063 -277856
Quadratic Polynomial -716064 -742369 -754476 -756418 -760702 -764952
Lower ARIMA -2621175 -2607809 -2647968 -2623712 -2633213 -2652338
Upper ARIMA 2625004 2682997 2662098 2692978 2698016 2685649
Essembled with equal weight 85551 71429 74651 74342 70069 78563
Essembled based on weight 616539 551446 652625 579487 609093 693991
Essembled based on summed weight 500668 825960 532066 858929 865194 878082
Essembled based on weight of fit 87517 79549 79547 78296 72578 80418
Table 4a Summary of the dynamic COVID-19 constrained forecasts
Model May 31, 22 - Aug 29, 24 Jun 07, 22 - Sep 12, 24 Jun 10, 22 - Sep 18, 24 Jun 11, 22 - Sep 20, 24 Jun 12, 22 - Sep 22, 24 Jun 13, 22 - Sep 24, 24
Linear 149372 138507 133570 132462 130741 129024
Semilog 278809 276750 275843 283206 275504 274985
Growth 2511 2413 2360 2365 2339 2313
Without knots 80% 81 5650 195 6526 4288 1574
Without knots 95% 183547 2083029 3035387 2097688 1041049 1161496
Smooth Spline 80% 1 3 3 3 2 2
Smooth Spline 95% NaN 1827695 1817653 1755849 2377552 2121707
With knots 80% 24220 16198 15023 12174 10720 1655
With knots 95% 38378 27345 24555 21737 19685 2090
Quadratic Polynomial 80% 0 0 1 0 1 1
Quadratic Polynomial 95% 1 0 1 0 1 1
ARIMA 80% 160 161 161 161 161 162
ARIMA 95% 2890012 2916941 2928750 2932707 2938582 2942051
Essembled with equal weight 80% 1 0 1 2 3 4
Essembled with equal weight 95% 299 1 1339 224941 226122 119432
Essembled based on weight 80% 40813 35546 39354 33587 37368 40691
Essembled based on weight 95% 3109670 3117201 3175391 3164625 3166029 3202405
Essembled based on summed weight 80% 362657 382340 382073 451392 391035 395544
Essembled based on summed weight 95% 804085 819493 862671 2008718 853537 859847
Essembled based on weight of fit 80% 10682 10561 10928 10650 41564 12302
Essembled based on weight of fit 95% 2995840 2625632 2388295 2363744 3200688 3020962

Table 3 RMSE of the models in the dynamic forecasts
Model May 31, 22 - Aug 29, 24 Jun 07, 22 - Sep 12, 24 Jun 10, 22 - Sep 18, 24 Jun 11, 22 - Sep 20, 24 Jun 12, 22 - Sep 22, 24 Jun 13, 22 - Sep 24, 24
Linear 417.58 416.3 415.77 415.55 415.38 415.21
Semilog 418.81 418.01 417.7 417.54 417.44 417.35
Growth 522.62 520.43 519.5 519.19 518.88 518.57
Without knots 399.54 397.87 397.16 396.92 396.69 396.45
Smooth Spline 265.15 265.75 266.06 266.06 266.18 266.29
With knots 179.5 179.27 172.53 174.21 178.06 179
Quadratic Polynomial 400.58 398.92 398.21 397.99 397.76 397.52
Lower ARIMA 199.67 198.87 198.52 198.42 198.3 198.19
Upper ARIMA 199.67 198.87 198.52 198.42 198.3 198.19
Essembled with equal weight 246.28 245.75 243.92 244.26 245.13 245.3
Essembled based on weight 500.45 503.74 504.7 505.21 506.1 506.71
Essembled based on summed weight 456.76 460.2 461.95 462.28 462.76 463.29
Essembled based on weight of fit 343.67 349.99 352.58 353.41 354.46 355.41

Fig. 21 Percentage of COVID19 cases that resulted into casualty per State as at June 13, 2022

Fig. 22 Percentage of COVID19 cases that recovered per State as at June 13, 2022

Fig. 23 Percentage of recoveries and deaths from COVID19 cases per Zone as at June 13, 2022

Fig. 24 Distribution of COVID19 in the States as at June 13, 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↩︎