Primary author: Olivia Boyd
Olivia Boyd, Lily Geidelberg, David Jorgensen, Manon Ragonnet, Igor Siveroni, Erik Volz and the Imperial College COVID-19 Response Team
Report prepared on 2020-06-04
- This is a preliminary analysis based on a small number of genetic sequences sampled in Kinshasa uploaded to gisaid before 2020-05-30.
- We estimate a number of key epidemiological parameters based on the genetic diversity of these samples alongside a set of closely related sequences from elsewhere which act as a global reservoir.
- We estimate an initial R (R0) of 2.18 which falls to 2.15 [CrI 1.77-2.57] by 2020-04-25. Note the credible interval falls below 2 by this point, suggesting R is decreasing over time. As only a small number of sequences are available it is difficult to estimate changes in R(t) over time and these estimates are likely to change with more sequence data.
- We estimate that approximately 0.01% of the Kinshasa population are infected by date of last sample, 2020-04-25.
This is analysis is based on :
- 89 whole genomes sampled from within KS
- 103 whole genomes sampled from outside of KS
- Samples within KS were collected between 2020-03-11 and 2020-04-25
These numbers will differ from the number of uploaded sequence on GISAID as we remove sequences with likely sequencing errors or significant gaps. Sequences were deduplicated prior to analyses. Figure 1 shows the distribution of sequences included in analyses over time, including both endogenous (gold) and exogenous sequences (grey) used in the sample. Due to the lack in metadata linked to sequences, there may be some sampling bias in sampling strategies used to collect sequences that we are unaware of (eg. contact tracing, non-random samples in hospitals). This could mean samples are more closely related than would be expected from a random sample in the population and could lead to bias in our estimates.
Using a phylodynamic model we estimate epidemiological parameters by fitting SARS-CoV 2 sequence data from Kinshasa together with a background set of sequences sampled from the larger internationational viral population to the model developed. The model is explained in detail here. Daily reported confirmed case numbers from Kinshasa for comparison are extracted from corona-stats mobile and CMR covid-19, the multisectoral comitee in response to COVID-19 pandemic in the Democratic Republic of Congo (DRC).These data are used solely for comparison and as such, do not affect any aspects of the analysis described.
Figure 1: Sampling distributions over time of number of sequences included within the region versus sequences included from the international reservoir.
In this preliminary analysis, we estimate 11816 [3472-63734] median [95%CI] cumulative infections at last sample (2020-04-25) by fitting Sars-Cov2 sequence data to our phylogynamic model mentioned prior. The cumulative confirmed infections reported from testing was 429 on the date of last sample. As such, We estimate about 0.01% of the population of Kinshasa to be infected by the date of our last sample (2020-04-25).
Figures 2 and 3 show cumulative and daily estimated infections respectively. Towards the end of the curve the increase in daily number infections each day begins to stabilise. This may be due to the shutting of all borders with neighbouring countries on 2020-03-26 and offical lockdown and ceasing of non-essential activities on 2020-04-06 across most of DRC, including Kinshasa. However, further anlayses are required to assess the impact of non-pharmaceutical interventions on transmission in this region. Unfortunately, case reporting data prior to April 5th in Kinshasa is not available at this time. As such, reported number of infections is not included prior to this date.
Figure 2: Estimated cumulative infections through time represented by solid black line (median) and 95% CrI (ribbon). Black points represent reported cases in KS. The dashed line indicates the date of last sample in KS in this analysis.
Figure 3: Estimated daily infections through time represented by solid black line (median) and 95% CrI (ribbon). Black points represent reported cases in KS. The dashed line indicates the date of last sample in KS in this analysis.
Figure 4 shows estimates of cumulative reporting rate over time. We estimate reporting rate for Kinshasa over time by comparing our model estimates to reported case data. These estimates have high uncertainty due to the small number of local sequences used in this analysis. Additionally, case reporting data prior to April 5th in Kinshasa is not available at this time. As such, we are unable to estimate reporting rate at the start of the outbreak.
Figure 4: Estimated percentage of daily cases reported in KS. Error bars represent the 95% credible interval.
We estimate the reproduction number to remain relatively stable over time, although we see a extremely small decrease following the introduction of lockdown in Kinshasa. The lower bound of the confidence interval drops below 2 by the date of last sample, suggesting the possibility for R to be decreasing at this point. We do expect R to decrease following introduction of lockdown in the region, however it is likely we need later sequence data (greater than two weeks following lockdown) to more accuratly estimate the relationship between lockdown and reduction in transmission.
Figure 5: Reproduction number through time. The black vertical dashed line indicates the date of last sample in KS in this analysis. Orange and red dashed lines indicate dates of school closure and general lockdown in KS, respectively.
Reproduction number at last sample (2020-04-25): 2.15 [1.77-2.57] median [95% CrI]
How quickly has the epidemic in KS grown?
|Quantile||Reproduction number||Growth rate (per day)||Doubling time (days)|
Table 1. Reproduction number, growth rate and doubling times
How has SARS-CoV 2 evolved in KS?
We present here a time scaled phylogeny of SARS-CoV-2 in Kinshasa and the included international reservoir (Figure 6). Kinshasa sequences are relatively well mixed with international sequences, suggesting there is still some cases occuring from new introductions in the community. This is to be expected, as a large number of sequences used in this preliminary analysis are still quite close to first reported case in Kinshasa (2020-03-10) and to when borders were officially closed down in the DRC (2020-03-26). This suggests the sequences used in present analysis may provide a relatively random sample from the population of Kinshasa. However, metadata on sequences collected as well as information on sampling strategies used to collect sequence data is still required to confirm this.
Figure 6: Time scaled phylogeny co-estimated with epidemiological parameters. The colour of the tips corresponds to location sampling; red tips were sampled from within KS, blue tips from outside.
Molecular clock rate of evolution: 0.00102 [0.000865-0.00118] median [95% CrI]
Details on methods and priors can be found here.
Table 2. Effective sample size of model parameters
Model version: seijr0.1.1
Report version: 20200604-124920-d802ce2b
This work was supported by the MRC Centre for Global Infectious Disease Analysis at Imperial College London.