Report 3 Bangladesh

This report is created based on a publicly available data. The data are available on a Google Sheet. Please see the Data Source section for link. Please note that publicly available data are not official and MAY BE UNRELIABLE. I personally did not verify them. I am using them for educational purposes. Use at your discretion.

3.1 Epidemic curve and search for peak

The epidemic curve for Bangladesh and a tentative peak. The red line in the plot indicates a potential peak based on available data. IF the peak (red line) is at the far right, then we may not have reached a peak yet. We must have sustained decrease of incidences to be sure about any peak.

We fit a log linear model to estimate the doubling time (growh phase) and halving time (decline phase, when that happens). The estimated fitted line (solid line) with 95% confidence interval (dotten lines) are superimposed on the epidemic curve below.

The current doubling time as of 2020-09-10 is 9.9 days with a 95% confidence interval of (9.1, 10.9) days.

3.1.1 Estimating the Overall Reproduction Number, \(R_0\)

The log-linear model estimates the \(R_0\) as follows:

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.511   1.567   1.591   1.592   1.612   1.679

3.1.2 Estimating the Effectuve Reproduction Number (\(R_e\))

The estimation of effective reproduction number involves the distribution of serial interval (SI). Based on the literature (REF to be added), a gamma distribution with mean 7.5 and standard deviation 3.5

3.1.3 Simulating the \(R_e\) on Weekly Sliding Window

Now we perform a simulation of different SI distribution to obtain the \(R_e\). For this, we vary the parameters of gamma distribution and obtain the following.

# Recalculating with varying SI distribution parameters 
# we get these results:

config = make_config(list(mean_si = 7.5, std_mean_si = 3.5, 
                                       min_mean_si = 2, max_mean_si =10.0,
                                       std_si = 3.5, std_std_si = 1,
                                       min_std_si = 0.5, max_std_si = 4))
bd_res_uncertain_si <- 
  estimate_R(bd_confirmed_cases$daily_cases, method = "uncertain_si", 
             config = config)

plot_Ri(bd_res_uncertain_si)

Table 3.1: Effective Reproduction Number by Week as of 2020-07-03
t_start t_end Mean(R) Std(R) Quantile.0.025(R) Quantile.0.975(R)
105 106 112 1.046632 0.0169938 1.0189608 1.081899
106 107 113 1.049673 0.0145129 1.0231781 1.079145
107 108 114 1.064205 0.0138124 1.0355469 1.090588
108 109 115 1.067638 0.0154839 1.0303541 1.093207
109 110 116 1.072344 0.0178503 1.0300534 1.099355
110 111 117 1.066645 0.0202760 1.0205370 1.096095
111 112 118 1.027756 0.0209605 0.9843239 1.059694

The effective reproduction number based on the last week’s incidences show an average of 1.03 with a 95% confidence interval (0.98, 1.06).

3.1.4 Simulating \(R_e\) on a Monthly Sliding Window

Table 3.2: Effective Reproduction Number by Month as of 2020-07-03
t_start t_end Mean(R) Std(R) Quantile.0.025(R) Quantile.0.975(R)
59 60 89 1.328111 0.1218987 1.093749 1.527731
60 61 90 1.326367 0.1225633 1.096821 1.529301
61 62 91 1.319118 0.1219674 1.093018 1.523864
62 63 92 1.312845 0.1204491 1.092004 1.517296
63 64 93 1.306606 0.1180362 1.090871 1.508910
64 65 94 1.303426 0.1154575 1.092073 1.502786
65 66 95 1.296084 0.1126439 1.088118 1.490242
66 67 96 1.289401 0.1097843 1.086316 1.478005
67 68 97 1.284089 0.1072704 1.085707 1.467915
68 69 98 1.268620 0.1041213 1.075342 1.446044
69 70 99 1.256148 0.1003377 1.072203 1.427868
70 71 100 1.248356 0.0963948 1.073534 1.414545
71 72 101 1.247968 0.0931521 1.078450 1.409307
72 73 102 1.243734 0.0904911 1.077346 1.399898
73 74 103 1.241699 0.0882930 1.077199 1.392902
74 75 104 1.224717 0.0856263 1.063337 1.370345
75 76 105 1.206741 0.0819941 1.054814 1.345852
76 77 106 1.195827 0.0779376 1.055362 1.329129
77 78 107 1.182595 0.0735190 1.051625 1.310244
78 79 108 1.174473 0.0690322 1.052361 1.295629
79 80 109 1.162138 0.0643547 1.047242 1.275574
80 81 110 1.168266 0.0606673 1.060065 1.275402
81 82 111 1.168308 0.0584652 1.058817 1.269725
82 83 112 1.157930 0.0568349 1.048442 1.254106
83 84 113 1.146390 0.0549897 1.041503 1.238319
84 85 114 1.147418 0.0532516 1.048453 1.236274
85 86 115 1.135368 0.0515691 1.038929 1.221583
86 87 116 1.127179 0.0492675 1.037022 1.210468
87 88 117 1.116696 0.0464521 1.032996 1.196648
88 89 118 1.099904 0.0429453 1.023926 1.175051

The effective reproduction number based on the last months’s incidences show an average of 1.1 with a 95% confidence interval (1.02, 1.18).

3.1.5 Cumulative Deaths (official and unofficial)

Table 3.3: Total number of deaths as of 2020-09-09
country Total Deaths
Bangladesh 4593
Bangladesh(unoff) 1217

3.2 Bangladesh in South Asia

3.2.1 Infection

Table 3.4: Total number of cases as of 2020-09-09
country Total Cases
India 4465863
Bangladesh 331078
Pakistan 300030
Indonesia 203342
Bangladesh(unoff) 156391
Singapore 57166
Nepal 49219
Malaysia 9583
Sri Lanka 3147
Bhutan 234

3.2.2 Deaths

Table 3.5: Total number of deaths as of 2020-09-09
country Total Deaths
India 75062
Indonesia 8336
Pakistan 6365
Bangladesh 4593
Bangladesh(unoff) 1217
Nepal 312
Malaysia 128
Singapore 27
Sri Lanka 12
Bhutan 0

3.2.3 Cases and deaths compared on a specific day

Bangladesh has entered into day 186 since first confirmed case.

Table 3.6: Bangladesh and its peers: total cases and deaths compared on day 186
Country Total cases Total deaths
India 1803695 38135
Indonesia 184268 7750
Pakistan 295053 6283
Bangladesh 331078 4593
Malaysia 8943 124
Nepal 19063 49
Singapore 50369 27
Sri Lanka 2814 11
Bhutan 233 0