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It is now 710 days since the first COVID-19 case was reported in Nigeria. As at February 07, 2022 the confirmed cases are 257,906 with 3,139 (0%) fatalities, an average of 4 fatalities per day. However, to date, 230,145 or 90.69% were successfully managed and discharged leaving a balance of 20,496 (9.31%) active cases being managed.

Based on equal days forecast, by January 18, 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
Linear 360,324 326,778 0 33,546 415
Semilog 313,240 284,077 0 29,163 194
Growth 4,339 3,935 0 404 262
Without knots 1,354,816 1,228,682 0 126,133 428
Smooth Spline -1,776,545 -1,611,149 0 -165,396 225
With knots -20,980,816 -19,027,502 0 -1,953,314 225
Quadratic Polynomial 13,587 12,322 0 1,265 263
Lower ARIMA -370,856 -336,329 0 -34,527 186
Upper ARIMA 813,408 737,680 0 75,728 193
Essembled with equal weight -357,636 -324,341 0 -33,296 317
Essembled based on weight 39,981 36,259 0 3,722 431
Essembled based on summed weight 45,104 40,905 0 4,199 426
Essembled based on weight of fit -2,651,385 -2,404,541 0 -246,844 1.5

Constrained forecast of COVID-19 for Nigeria

Model Confirmed cases Recoveries Fatalities Active
Linear 360,324 326,778 0 33,546
Semilog 313,240 284,077 0 29,163
Growth 4,339 3,935 0 404
Without knots 80% 706 640 0 66
Without knots 95% 2,000,768 1,814,497 0 186,272
Smooth Spline 80% 190,598 172,854 0 17,745
Smooth Spline 95% 1,233,990 1,119,105 0 114,884
With knots 80% 7 6 0 1
With knots 95% 552,919 501,442 0 51,477
Quadratic Polynomial 80% 92,542 83,926 0 8,616
Quadratic Polynomial 95% 109,070 98,915 0 10,154
ARIMA 80% 2,837 2,572 0 264
ARIMA 95% 2,526,814 2,291,567 0 235,246
Essembled with equal weight 80% 10,487 9,510 0 976
Essembled with equal weight 95% 1,224,852 1,110,819 0 114,034
Essembled based on weight 80% 2,519 2,285 0 235
Essembled based on weight 95% 2,253,094 2,043,331 0 209,763
Essembled based on summed weight 80% 2,388 2,166 0 222
Essembled based on summed weight 95% 2,554,877 2,317,018 0 237,859
Essembled based on weight of fit 80% 27,635 25,062 0 2,573
Essembled based on weight of fit 95% 840,127 761,911 0 78,216

However, the actual forecast made by the various models on the last day i.e. January 18, 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 - November 16 : February 07, 2022 . That is 16.91% of total or 519 cases per day. The all time cases per day is 342 . The mortality in the omicron wave is 5.957% of the total or 2 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 February 07, 2022

Fig. 3 Cases recorded in percentages Starting from February 29, 2020 to February 07, 2022 (legend as Fig. 2)

Fig. 4 Cumulative cases and Forecast of COVID-19 cases in Nigeria Starting from February 08, 2022 to January 18, 2024

Fig. 4a Components of COVID-19 cases in Nigeria between February 29, 2020 and February 07, 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 February 07, 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 January 18, 2024

Coefficient estimates for number of states reporting COVID-19 daily

LinearSemilogGrowthSpline without knotsLinear splinesSpline with knotsARIMA
(Intercept)0.378 ****12.971 ****2.547 ****7.138 ****2.841 *   -0.893          
(0.074)    (1.297)    (0.045)    (0.791)    (1.573)    (2.447)         
Series-0.001 ****         -0.000 ***                                 
(0.000)             (0.000)                                    
log(Series)         0.042                                              
         (0.230)                                             
bs(Series, knots = NULL)1                           27.156 ****                       
                           (2.286)                           
bs(Series, knots = NULL)2                           -10.355 ****                       
                           (1.450)                           
bs(Series, knots = NULL)3                           7.474 ****                       
                           (1.250)                           
bs(DDPtable$Series, knots = LBREAKS)1                                    -2.783                   
                                    (2.243)                  
bs(DDPtable$Series, knots = LBREAKS)2                                    13.774 ****              
                                    (1.664)                  
bs(DDPtable$Series, knots = LBREAKS)3                                    22.706 ****              
                                    (1.942)                  
bs(DDPtable$Series, knots = LBREAKS)4                                    -0.738                   
                                    (1.724)                  
bs(DDPtable$Series, knots = LBREAKS)5                                    28.954 ****              
                                    (1.775)                  
bs(DDPtable$Series, knots = LBREAKS)6                                    -3.144 *                 
                                    (1.749)                  
bs(DDPtable$Series, knots = LBREAKS)7                                    3.284 *                 
                                    (1.772)                  
bs(DDPtable$Series, knots = LBREAKS)8                                    19.334 ****              
                                    (1.808)                  
bs(DDPtable$Series, knots = LBREAKS)9                                    -3.188                   
                                    (2.018)                  
bs(DDPtable$Series, knots = LBREAKS)10                                    17.104 ****              
                                    (2.023)                  
bs(DDPtable$Series, knots = LBREAKS)11                                    4.120 **                
                                    (1.912)                  
bs(Series, knots = QBREAKS)1                                             7.529 **       
                                             (3.014)         
bs(Series, knots = QBREAKS)2                                             22.135 ****     
                                             (2.584)         
bs(Series, knots = QBREAKS)3                                             17.581 ****     
                                             (2.934)         
bs(Series, knots = QBREAKS)4                                             12.631 ****     
                                             (2.618)         
bs(Series, knots = QBREAKS)5                                             9.651 ****     
                                             (2.723)         
bs(Series, knots = QBREAKS)6                                             15.315 ****     
                                             (2.837)         
bs(Series, knots = QBREAKS)7                                             11.057 ****     
                                             (3.094)         
bs(Series, knots = QBREAKS)8                                             11.725 ****     
                                             (2.986)         
ma1                                                      -0.771 
                                                      (0.023)
nobs692         692         692         692         692         692         691     
r.squared0.048     0.000     0.010     0.226     0.728     0.285          
adj.r.squared0.046     -0.001     0.009     0.223     0.724     0.277          
sigma0.977     5.939     0.588     5.232     3.117     5.047     3.216 
statistic34.466     0.033     7.295     67.039     165.838     34.033          
p.value0.000     0.856     0.007     0.000     0.000     0.000          
df1.000     1.000     1.000     3.000     11.000     8.000          
logLik-964.540     -2213.696     -613.815     -2124.984     -1762.653     -2097.632     -1787.520 
AIC1935.080     4433.393     1233.630     4259.969     3551.306     4215.264     3579.041 
BIC1948.699     4447.011     1247.248     4282.667     3610.321     4260.660     3588.117 
deviance658.126     24335.103     238.827     18831.423     6608.304     17400.067          
df.residual690.000     690.000     690.000     688.000     680.000     683.000          
nobs.1692.000     692.000     692.000     692.000     692.000     692.000     691.000 
**** 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 9100 3400 7500 2900 1600 3000 1500 1700
Absolute Percent Error 690 480 530 330 180 380 150 170
Accuracy 0 0 0 0 0 0 0 0
Adjusted R Square 0.046 -0.0014 0.009 0.28 0.72 0.22 0 0
Akaike’s Information Criterion AIC 1900 4400 1200 4200 3600 4300 0 3600
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 0.0000000000000011 11 -0.000000000000000084 -0.00000000000000016 0.00000000000000026 0.0000000000000015 0.048
Brier score 200 40 200 30 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 0.2 -0.2 0.2 0.5 0.9 0.4 0.9 0.8
kappa statistic 0 0 0 0 0 0 0 0
Log Loss Inf Inf Inf Inf Inf Inf Inf Inf
Matthews Correlation Coefficient 0 0 0 0 0 0 0 0
Mean Log Loss 200 -420 -420 -420 -420 -420 -420 -420
Mean Absolute Error 13 5 11 4.2 2.4 4.4 2.1 2.4
Mean Absolute Percent Error 1 0.7 0.77 0.48 0.25 0.55 0.22 0.25
Mean Average Precision at k 0 0 0 0 0 0 0.12 0
Mean Absolute Scaled Error 4.5 1.7 3.7 1.4 0.8 1.5 0.72 0.83
Median Absolute Error 13 4.8 11 3.8 1.9 4.3 1.7 2
Mean Squared Error 210 35 150 25 9.5 27 8.1 10
Mean Squared Log Error 6.9 0.28 2 0.19 0.073 0.21 0.056 0.071
Model turning point error 411 378 411 395 384 397 385 512
Negative Predictive Value 0 0 0 0 0 0 0 0
Percent Bias 1 -0.45 0.73 -0.25 -0.096 -0.32 -0.084 -0.078
Positive Predictive Value 0 0 0 0 0 0 0 0
Precision 0 1 1 1 1 1 1 1
R Square 0.048 0.000048 0.01 0.29 0.73 0.23 0 0
Relative Absolute Error 2.7 1 2.2 0.84 0.48 0.89 0.43 0.49
Recall 0 1 1 0.71 1 1 1 1
Root Mean Squared Error 14 5.9 12 5 3.1 5.2 2.8 3.2
Root Mean Squared Log Error 2.6 0.52 1.4 0.43 0.27 0.46 0.24 0.27
Root Relative Squared Error 2.4 1 2.1 0.85 0.52 0.88 0.48 0.54
Relative Squared Error 5.9 1 4.3 0.71 0.27 0.77 0.23 0.29
Schwarz’s Bayesian criterion BIC 1900 4400 1200 4300 3600 4300 0 3600
Sensitivity 0 0 0 0 0 0 0 0
specificity 0 0 0 0 0 0 0 0
Squared Error 140000 24000 100000 17000 6600 19000 5600 7100
Squared Log Error 4800 190 1400 130 51 140 39 49
Symmetric Mean Absolute Percentage Error 2 0.41 1.3 0.36 0.22 0.37 0.19 0.22
Sum of Squared Errors 140000 24000 100000 17000 6600 19000 5600 7100
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 February 07, 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 February 07, 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)-314.572     -1049.380     -126.010     472.492     -34.582 ****15.944 ****
(267.255)    (1444.189)    (159.453)    (350.145)    (7.217)    (0.286)    
PMSprice-0.138 ****         0.097 ****0.208 ****0.001     0.000     
(0.030)             (0.012)    (0.032)    (0.002)    (0.001)    
Inflation1.201 ****7.890 ****         -1.659 ****-0.031 **  0.005     
(0.279)    (1.010)             (0.353)    (0.013)    (0.005)    
Teledensity0.622 ****3.324 ****-0.324 ****         -0.000     -0.002     
(0.098)    (0.508)    (0.069)             (0.007)    (0.002)    
`Birth rate`2.423     21.376     -7.108 **  -0.006              0.266 ****
(5.895)    (30.932)    (3.059)    (7.840)             (0.049)    
Population15.277     42.558     10.675     -23.147     2.275 ****         
(16.951)    (91.028)    (9.833)    (22.296)    (0.421)             
COVID19         -3.858 ****0.412 ****1.090 ****0.004     0.003     
         (0.830)    (0.096)    (0.172)    (0.009)    (0.003)    
nobs25         25         25         25         25         25         
r.squared0.736     0.885     0.910     0.811     0.713     0.741     
adj.r.squared0.667     0.855     0.886     0.761     0.638     0.673     
sigma1.191     6.300     0.697     1.577     0.046     0.016     
statistic10.619     29.378     38.209     16.256     9.451     10.873     
p.value0.000     0.000     0.000     0.000     0.000     0.000     
df5.000     5.000     5.000     5.000     5.000     5.000     
logLik-36.417     -78.058     -23.031     -43.435     44.853     71.670     
AIC86.835     170.116     60.061     100.871     -75.705     -129.340     
BIC95.367     178.648     68.593     109.403     -67.173     -120.808     
deviance26.961     754.171     9.239     47.267     0.040     0.005     
df.residual19.000     19.000     19.000     19.000     19.000     19.000     
nobs.125.000     25.000     25.000     25.000     25.000     25.000     
**** 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)291.021 ****-71.056     4.672 ****-99.823     -2.304          168.154 ****171.352   0.295     
(32.436)    (92.416)    (0.111)    (62.160)    (100.821)         (48.379)    (207.303)  (39.153)    
Day0.203 **           0.001 ****                       1.239 ****                
(0.079)             (0.000)                           (0.314)                    
log(Day)         77.955 ****                                                         
         (16.337)                                                             
bs(Day, knots = NULL)1                           1372.692 ****                                       
                           (179.567)                                           
bs(Day, knots = NULL)2                           -202.196 *                                          
                           (113.868)                                           
bs(Day, knots = NULL)3                           682.476 ****                                       
                           (98.180)                                           
bs(Day, knots = BREAKS)1                                    1.320                                   
                                    (194.907)                                  
bs(Day, knots = BREAKS)2                                    16.747                                   
                                    (126.257)                                  
bs(Day, knots = BREAKS)3                                    837.363 ****                              
                                    (144.731)                                  
bs(Day, knots = BREAKS)4                                    178.798                                   
                                    (122.952)                                  
bs(Day, knots = BREAKS)5                                    182.470                                   
                                    (134.613)                                  
bs(Day, knots = BREAKS)6                                    -50.023                                   
                                    (126.521)                                  
bs(Day, knots = BREAKS)7                                    1976.318 ****                              
                                    (125.373)                                  
bs(Day, knots = BREAKS)8                                    -159.422                                   
                                    (124.283)                                  
bs(Day, knots = BREAKS)9                                    -214.703 *                                 
                                    (125.433)                                  
bs(Day, knots = BREAKS)10                                    1001.468 ****                              
                                    (125.608)                                  
bs(Day, knots = BREAKS)11                                    -684.504 ****                              
                                    (138.163)                                  
bs(Day, knots = BREAKS)12                                    1796.828 ****                              
                                    (140.083)                                  
bs(Day, knots = BREAKS)13                                    -454.960 ****                              
                                    (136.084)                                  
ar1                                             0.846                          
                                             (0.085)                         
ar2                                             0.229                          
                                             (0.055)                         
ar3                                             -0.223                          
                                             (0.056)                         
ar4                                             -0.011                          
                                             (0.049)                         
ar5                                             0.132                          
                                             (0.040)                         
ma1                                             -0.539                          
                                             (0.079)                         
intercept                                             327.557                          
                                             (131.253)                         
I(Day^2)                                                  -0.001 ****                
                                                  (0.000)                    
fitf                                                           9.932 * -0.088 *   
                                                           (5.105)  (0.050)    
fit0f                                                           -1.282   0.000     
                                                           (1.340)  (0.074)    
fit1f                                                           -8.762   1.107 ****
                                                           (5.917)  (0.076)    
fitpif                                                           -0.867   -0.000     
                                                           (1.040)  (0.127)    
fitaf                                                           -1.137   -0.020     
                                                           (2.547)  (0.069)    
fitf:fit0f                                                           -0.023 *          
                                                           (0.012)           
fitf:fit1f                                                           -0.035 *          
                                                           (0.020)           
fit0f:fit1f                                                           0.045 **         
                                                           (0.021)           
fitf:fitpif                                                           -0.025 *          
                                                           (0.013)           
fit0f:fitpif                                                           0.004            
                                                           (0.004)           
fit1f:fitpif                                                           0.032 *          
                                                           (0.018)           
fitf:fitaf                                                           -0.015            
                                                           (0.024)           
fit0f:fitaf                                                           -0.010            
                                                           (0.012)           
fit1f:fitaf                                                           0.045 *          
                                                           (0.027)           
fitpif:fitaf                                                           0.001            
                                                           (0.011)           
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)           
nobs710         710         710         710         710         710     710         710       710         
r.squared0.009     0.031     0.034     0.081     0.635          0.025     0.815   0.801     
adj.r.squared0.008     0.030     0.032     0.077     0.628          0.022     0.807   0.800     
sigma431.683     426.882     1.480     416.266     264.278     225.867 428.494     190.410   193.971     
statistic6.607     22.769     24.816     20.840     93.128          9.141     96.611   567.069     
p.value0.010     0.000     0.000     0.000     0.000          0.000     0.000   0.000     
df1.000     1.000     1.000     3.000     13.000          2.000     31.000   5.000     
logLik-5314.505     -5306.566     -1285.015     -5287.681     -4960.048     -4852.991 -5308.739     -4717.993   -4744.507     
AIC10635.011     10619.132     2576.030     10585.361     9950.095     9721.982 10625.479     9501.987   9503.013     
BIC10648.706     10632.827     2589.726     10608.187     10018.574     9758.504 10643.740     9652.640   9534.970     
deviance131935734.107     129017758.449     1551.765     122333642.610     48610656.272          129810103.400     24581627.414   26487810.358     
df.residual708.000     708.000     708.000     706.000     696.000          707.000     678.000   704.000     
nobs.1710.000     710.000     710.000     710.000     710.000     710.000 710.000     710.000   710.000     
**** 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 210000 210000 250000 100000 200000 64000 81000 210000 260000 66000 65000 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 0.025 0 0 0.025
Adjusted R Square 0.0078 0.03 0.032 0.63 0.077 0 0 0.022 0 0.81 0.8 0
Akaike’s Information Criterion AIC 11000 11000 2600 10000 11000 0 9700 11000 0 9500 9500 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 -0.000000000000012 -0.000000000000008 360 0.0000000000000056 0.0000000000000002 0.000000000000028 2.1 -0.000000000000034 360 -0.0000000000000005 -0.0000000000000019 360
Brier score 200000 200000 300000 70000 200000 40000 50000 200000 NaN 30000 40000 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 0.3 1 0.9 0.07 0.9 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
Matthews Correlation Coefficient 0 0 0 0 0 0 0 0 NaN 0 0 NaN
Mean Log Loss -13000 -13000 -13000 -12000 -13000 -13000 -13000 -13000 NaN -12000 -13000 NaN
Mean Absolute Error 300 290 360 140 280 90 110 290 NaN 93 91 360
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 0.5 0 0 0 0 0 0
Mean Absolute Scaled Error 2.4 2.3 2.8 1.1 2.2 0.71 0.9 2.3 2.9 0.74 0.72 2.9
Median Absolute Error 260 240 210 90 260 41 53 230 120 45 43 130
Mean Squared Error 190000 180000 320000 68000 170000 38000 51000 180000 NaN 35000 37000 320000
Mean Squared Log Error 2.8 NaN 13 NaN NaN 0.21 0.61 2.6 29 NaN NaN 29
Model turning point error 362 362 362 345 363 335 449 351 421 313 324 415
Negative Predictive Value 0 0 0 0 0 0 0 0 0 0 0 0
Percent Bias -Inf NaN -Inf NaN NaN NaN -Inf -Inf -Inf NaN NaN -Inf
Positive Predictive Value 0 0 0 0 0 0 0 0 0 0 0 0
Precision 0.1 0.056 0.1 0 0 0 0.1 0.1 0 0.067 0 0
R Square 0.0092 0.031 0.034 0.63 0.081 0 0 0.025 0 0.82 0.8 0
Relative Absolute Error 1 0.98 1.2 0.48 0.95 0.3 0.38 0.99 0.52 0.31 0.3 0.62
Recall 1 0.5 1 0 0 0 1 1 0 0.5 0 0
Root Mean Squared Error 430 430 560 260 420 190 220 430 NaN 190 190 570
Root Mean Squared Log Error 1.7 NaN 3.7 NaN NaN 0.46 0.78 1.6 5.4 NaN NaN 5.4
Root Relative Squared Error 1 0.98 1.3 0.6 0.96 0.45 0.52 0.99 1.3 0.43 0.45 1.3
Relative Squared Error 0.99 0.97 1.7 0.37 0.92 0.2 0.27 0.97 1.7 0.18 0.2 1.7
Schwarz’s Bayesian criterion BIC 11000 11000 2600 10000 11000 0 9800 11000 0 9700 9500 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 130000000 130000000 220000000 49000000 120000000 27000000 36000000 130000000 230000000 25000000 26000000 230000000
Squared Log Error 2000 NaN 9500 NaN NaN 150 440 1800 20000 NaN NaN 20000
Symmetric Mean Absolute Percentage Error 0.89 0.89 1.8 0.63 0.86 0.34 0.43 0.89 0.62 0.4 0.36 0.67
Sum of Squared Errors 130000000 130000000 220000000 49000000 120000000 27000000 36000000 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 February 08, 2022 to January 18, 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 February 08, 2022 to January 18, 2024 using a more advaced plotting method

Fig. 20c Constrained forecast of COVID-19 cases in Nigeria Starting from February 08, 2022 to January 18, 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 Jan 25, 22 - Dec 21, 23 Feb 01, 22 - Jan 04, 24 Feb 04, 22 - Jan 10, 24 Feb 05, 22 - Jan 12, 24 Feb 06, 22 - Jan 14, 24 Feb 07, 22 - Jan 16, 24
Linear 382570 372529 367780 365683 364360 362280
Semilog 315304 314682 342240 313962 314203 341090
Growth 4347 4352 4353 4346 4349 4343
Without knots 1703598 1534341 1460954 1431428 1410669 1381919
Smooth Spline -20102286 -21533061 -21389219 -21403535 -21163778 -21106805
With knots -133626 -374113 -179398 -281105 29127 -172260
Quadratic Polynomial 118168 69158 46972 37682 31388 22226
Lower ARIMA -351772 -362142 -364806 -369472 -344261 -367963
Upper ARIMA 817054 812056 813062 813716 840317 813573
Essembled with equal weight -4929349 -4547070 -4178436 -4519865 -123922 -3898871
Essembled based on weight 31678 366354 414266 454094 435179 440808
Essembled based on summed weight -20979 -44264 -55485 -56106 -66389 -60868
Essembled based on weight of fit -2823674 -2897206 -2600418 -2840682 -2651528 -38156
Table 4a Summary of the dynamic COVID-19 constrained forecasts
Model Jan 25, 22 - Dec 21, 23 Feb 01, 22 - Jan 04, 24 Feb 04, 22 - Jan 10, 24 Feb 05, 22 - Jan 12, 24 Feb 06, 22 - Jan 14, 24 Feb 07, 22 - Jan 16, 24
Linear 382570 372529 367780 365683 364360 362280
Semilog 315304 314682 342240 313962 314203 341090
Growth 4347 4352 4353 4346 4349 4343
Without knots 80% 7723 645 5961 2610 3130 1274
Without knots 95% 2192032 2306664 1204895 1083163 2347269 2110080
Smooth Spline 80% 37 8 7 7 8 6
Smooth Spline 95% 339935 1327459 1317522 1263393 420209 1201001
With knots 80% 30625 269239 244115 216529 83354 60594
With knots 95% 2636762 924850 1018373 1145854 2154181 2375339
Quadratic Polynomial 80% 145707 118459 107517 103148 100320 96249
Quadratic Polynomial 95% 168561 138476 126165 121215 117978 113332
ARIMA 80% 3907 3018 3321 2208 2473 2803
ARIMA 95% 2517099 2513138 2535182 2484256 2492804 2522015
Essembled with equal weight 80% 22310 14692 13457 12953 11429 8752
Essembled with equal weight 95% 1745309 1443801 1390212 1101387 1355576 1539457
Essembled based on weight 80% 2795 56458 62843 71476 31133 60466
Essembled based on weight 95% 1975366 2228941 2299832 2323433 2617314 2392102
Essembled based on summed weight 80% 2299 60444 109141 73259 102954 109200
Essembled based on summed weight 95% 2763011 1204303 505682 843612 511068 419802
Essembled based on weight of fit 80% 39540 30278 18429 20169 17579 17288
Essembled based on weight of fit 95% 1340600 1122554 1362322 1228372 1241109 1241034
Table 3 RMSE of the models in the dynamic forecasts
Model Jan 25, 22 - Dec 21, 23 Feb 01, 22 - Jan 04, 24 Feb 04, 22 - Jan 10, 24 Feb 05, 22 - Jan 12, 24 Feb 06, 22 - Jan 14, 24 Feb 07, 22 - Jan 16, 24
Linear 432.64 431.73 431.37 431.34 431.18 431.14
Semilog 427.77 426.87 426.54 426.51 426.36 426.33
Growth 567.26 564.64 563.49 563.09 562.72 562.32
Without knots 412.37 413.8 414.31 414.59 414.65 414.9
Smooth Spline 256.17 257.74 259.3 259.73 260.53 261.05
With knots 189.34 189.94 192.8 188.69 186.66 192.66
Quadratic Polynomial 430.54 429.01 428.36 428.2 427.95 427.78
Lower ARIMA 226.77 225.78 225.34 225.19 225.05 224.9
Upper ARIMA 226.77 225.78 225.34 225.19 225.05 224.9
Essembled with equal weight 261.05 261.57 262.57 261.63 261.28 262.91
Essembled based on weight 449.48 450.4 451.71 452.25 452.25 453.1
Essembled based on summed weight 440.06 435.77 433.81 433.23 432.7 431.94
Essembled based on weight of fit 219.92 224.29 226.51 227.18 227.71 228.88

Fig. 21 Percentage of COVID19 cases that resulted into casualty per State as at February 07, 2022

Fig. 22 Percentage of COVID19 cases that recovered per State as at February 07, 2022

Fig. 23 Percentage of recoveries and deaths from COVID19 cases per Zone as at February 07, 2022

Fig. 24 Distribution of COVID19 in the States as at February 07, 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↩︎