The data on this page is a prediction based on plotting actual data and using it to extrapolate (forecast/predict) future case numbers for Fiji. I will use the term ‘predict’ for simplicity.

Based on the types of data objects (tables, charts), this page is best viewed on a desktop computer, or if on a mobile device, in landscape (rotated) view.

Updated-Modelled Forecasts

Due to the recent fluctuations in daily new the reported case numbers and the general slowing down of the cumulative case number trend, I have revised my forecasts and present these below in Table 1.1. These are tentative forecasts, so I will wait and see how they compare for now before commenting further. I have not changed anything in the cumulative case numbers yet.

I must add though that the daily case number fluctuations between ~700 one day and double that the next is really distorting predictions. The stats are based on positive cases identified and affected by several factors such as sample delivery, capacity to test the samples etc. It means that the data previously used to construct the predictions are perhaps not as valid going forward. I have therefore amended the source data on which the predictions are based off. Tentatively, I have discarded the June stats and kept the July 1 data onwards for the revised forecast. While I have not shown the graph just yet, I can share the predictions and the fact that the fit of the amended data is linear. Considering how the data trends start to vary unpredictably (pardon the pun), it is prudent to predict for shorter periods, which is why Table 1.1 predictions only extend to August 15, 2021.

The amended predictions are starting from June 29 data, and in Tables 1 and 2, below.

UPDATE: Please note that I have now amended predictions of Cumulative Case Numbers (Table 2) as the error margin had started to exceed the 20% accuracy mark using the previous modelling (Table 4).

DateDay #ActualPredictedError
31-Jul601,251 1,2143.0%
1-Aug61705 1,23943.1% 
2-Aug621,227 1,2632.9% 
3-Aug631,361 1,2885.7% 
4-Aug641,324 1,3120.9% 
5-Aug65 1,0801,33619.2% 
6-Aug66 8391,361 38.4%
7-Aug67 7611,385 45.1%
8-Aug68 7331,410 48.0%
9-Aug69 6731,43453.1% 
10-Aug70295 1,45879.8% 
11-Aug71634 1,48357.3% 
12-Aug72444 2,48070.5% 
13-Aug73 7182,550 53.1%
14-Aug74 3502,621 77.5%
Table 1 Amended Actual and Predicted Daily New Case Numbers
DateDay #ActualPredictedError
4-Aug2137,838 37,1691.8% 
5-Aug2238,919 38,252 1.7%
6-Aug2339,757 39,3341.1% 
7-Aug24 40,51740,417 0.2%
8-Aug25 41,25141,5000.6% 
9-Aug26 41,92342,5831.6% 
10-Aug27 42,21843,6663.4% 
11-Aug2842,851 44,7484.4% 
12-Aug29 43,29645,8315.9% 
13-Aug3044,014 46,9146.6% 
14-Aug31 44,36447,997 8.2%
15-Aug32 44,36449,08010.6% 
16-Aug3345,276 50,16210.8% 
17-Aug3445,934 51,245 11.6%
18-Aug35 52,328 
19-Aug36 53,411 
20-Aug37 54,494 
21-Aug38 55,576 
22-Aug39 56,659 
23-Aug40 57,742 
24-Aug41 58,825 
25-Aug42 59,908 
26-Aug43 60,990 
27-Aug44 62,073 
28-Aug45 63,156 
29-Aug46 64,239 
30-Aug47 65,322 
31-Aug48 66,404 
Table 2 Amended Actual and Predicted Cumulative Case Numbers

Because we are still interested in how the old predictions compare, I have retained them in the tables below.

Previously-Modelled Data

For the interested: The prediction is based on a polynomial fit of the active cases. Depending on when the source data (for preparing the predictions began, there are different values for Day 1 in each table). I predicted the future stats at different times for different metrics. I am watching the unfolding case numbers each day on (which explains why my stats seem a day late), and using their data to predict future case numbers. It is how science works, and while it may yet seem like an imperfect process, it is based on recorded and reported data, and all my reasonings behind the predicted numbers are shared with you. It gives you an insight into my thought process and rationale, and allows you to give me feedback on anything that may be useful that I may have missed. I would appreciate your feedback on these stats and interpretations. Email me here.

I must emphasize that that the predicted stats are unfolding data and as a result, subject to several external factors, all of which are moving parts. These include:

  • The effectiveness of the medical system in Fiji which has recently been boosted with experts from Australia and New Zealand (there could be more coming in which would hopefully help address the spiraling cases);
  • The resistance to vaccination on the ground which slows the response against the virus;
  • The nature (severity) of cases of infection at any time (which affects the rate of recovery); and
  • we are dealing with persons, and each person is different from another in terms of their physiology etc.

The forecast will be regularly updated with the new cases data as it comes.

DateDay #ActualPredictedError
21-Jul50 1,2171,1912.2%
22-Jul51 1,024  1,23917.4% 
23-Jul52 5221,289 59.5%
24-Jul53 7631,33943.0% 
25-Jul54 6981,39049.8% 
26-Jul55 1,4331,4420.6% 
27-Jul56 7981,49546.7% 
28-Jul571,179 1,54923.9% 
29-Jul58 1,4511,6059.6% 
30-Jul59 1,2971,66121.9% 
31-Jul60 1,2511,71827.2% 
1-Aug61 7051,77660.3% 
2-Aug62 1,2271,83533.1% 
3-Aug63 1,3611,89528.2% 
4-Aug64 1,3241,956 32.3%
5-Aug65 1,0802,01846.5% 
6-Aug66 8392,08159.7% 
7-Aug67 7612,14564.5% 
8-Aug68 7332,21066.8% 
9-Aug69 6732,27670.4% 
10-Aug70 2952,34387.4% 
11-Aug71 6342,41173.7 %
12-Aug72 4442,48082.1% 
13-Aug73 7182,550 71.8%
14-Aug74 3502,621 86.6%
15-Aug75 02,693– 
16-Aug76 9112,76667.0% 
17-Aug77 2,840 
18-Aug78 2,915 
19-Aug79 2,990 
20-Aug80 3,067 
21-Aug81 3,145 
22-Aug82 3,224 
23-Aug83 3,304 
24-Aug84 3,385 
25-Aug85 3,466 
26-Aug86 3,549 
27-Aug87 3,633 
28-Aug88 3,718 
29-Aug89 3,804 
30-Aug90 3,890 
31-Aug91 3,978 
1-Sep92 4,067 
2-Sep93 4,156 
3-Sep94 4,247 
4-Sep95 4,339 
5-Sep96 4,432 
6-Sep97 4,525 
7-Sep98 4,620 
8-Sep99 4,715 
9-Sep100 4,812 
10-Sep101 4,910 
11-Sep102 5,008 
12-Sep103 5,108 
13-Sep104 5,209 
14-Sep105 5,310 
15-Sep106 5,413 
16-Sep107 5,516 
17-Sep108 5,621 
18-Sep109 5,726 
19-Sep110 5,833 
20-Sep111 5,940 
21-Sep112 6,049 
22-Sep113 6,158 
23-Sep114 6,269 
24-Sep115 6,380 
25-Sep116 6,493 
26-Sep117 6,606 
27-Sep118 6,720 
28-Sep119 6,836 
29-Sep120 6,952 
30-Sep121 7,070 
Table 3 Actual and Predicted Daily New Case Numbers
DateDay #ActualPredictedError
21-Jul5022,805 23,248 1.9%
22-Jul51 23,82924,7103.6% 
23-Jul52 24,36126,2377.2% 
24-Jul53 24,11427,8329.8% 
25-Jul54 25,81229,49412.5% 
26-Jul55 27,24531,22712.8% 
27-Jul56 28,04333,03115.1% 
29-Jul5830,673 36,86016.8% 
30-Jul59 31,97138,88717.8% 
31-Jul60 33,22140,99219.0% 
1-Aug61 33,92643,17521.4% 
2-Aug62 35,15345,44022.6% 
3-Aug63 36,51447,78623.6% 
4-Aug64 37,83850,21524.6% 
5-Aug65 38,91952,73026.2% 
6-Aug66 39,75755,330 28.1%
7-Aug67 40,51758,01930.2% 
8-Aug68 41,25160,79732.2% 
9-Aug69 41,92363,66734.2% 
10-Aug70 42,21866,62836.6% 
11-Aug71 42,85169,68438.5% 
12-Aug72 43,29672,83540.6% 
13-Aug73 44,01476,083 42.2%
14-Aug74 44,36479,429 44.1%
15-Aug75 44,36482,87646.5% 
16-Aug76 45,27686,42447.6% 
17-Aug77 45,93490,07549.0% 
18-Aug78 93,830 
19-Aug79 97,691 
20-Aug80 101,660 
21-Aug81 105,738 
22-Aug82 109,926 
23-Aug83 114,226 
24-Aug84 118,640 
25-Aug85 123,168 
26-Aug86 127,813 
27-Aug87 132,577 
28-Aug88 137,459 
29-Aug89 142,463 
30-Aug90 147,589 
31-Aug91 152,839 
1-Sep92 158,215 
2-Sep93 163,717 
3-Sep94 169,348 
4-Sep95 175,110 
5-Sep96 181,002 
6-Sep97 187,028 
7-Sep98 193,188 
8-Sep99 199,484 
9-Sep100 205,918 
10-Sep101 212,491 
Table 4 Actual and Predicted Cumulative Case Numbers

Here is the predictions table (Table 3). Note that this is a recent prediction and it remains to be seen how accurate these predictions are.

The graphed data for cumulative case numbers was started in mid-July, so much more predictions (Table 4) have been available.

Both data sets will be updated regularly as well.

The rest of the information is fairly self-explanatory.

Please note that these are large tables and this page is best viewed on a computer or, at best, a mobile device in rotated (landscape) view.

The predictions would rarely be 100% accurate (or with 0% error). The four factors listed above are some of the causes of this. When the error starts to change in a particular direction (negative or positive), that is called a bias. A bias can be in either direction (increase or decrease). That is usually we revisit the prediction (forecast) and tweak it.

Tweaking a forecasting model can be done in several ways. The first is to inspect whether the trends have changed in the latest data, causing the predictions to be distorted. When that is the case, we re-check the values within the data set that were used to compile the model and produce the predictions. It may require some data to be discarded (usually the oldest), the new data to be included, or both.

The accuracy of the predictions depends on a large extent to how well the data is ‘fit’. As at 22 July 2021, the fit is reasonably good. We can continue to use the models to make predictions as they are.