Phylodynamic Analysis: San Diego, CA, USA: 2020-04-29
Primary author: Manon Ragonnet
Olivia Boyd, Lily Geidelberg, David Jorgensen, Manon Ragonnet, Igor Siveroni, Erik Volz and the Imperial College COVID-19 Response Team
Report prepared on 2020-06-08
This report uses full genome sequence data for San Diego shared publicly by The Scripps Research Institute and Rady Children’s Hospital and a set of international background sequences from GISAID (laboratory acknowledgements)
Key points
- We estimate a number of key epidemiological parameters for San Diego based on the genetic diversity of these samples alongside a set of closely related sequences from elsewhere which act as a global reservoir.
- In this analysis we estimate a basic reproduction number (R0) of 1.91 in San Diego with R falling below 1 by the time of the last available sequence data at the beginning of April.
- We estimate a reporting rate of around 10% in San Diego with a median estimate from the phylodynamic model of 28,960 cases at the end of April compared to 3,432 reported cases at the same time point.
This analysis is based on :
- 35 whole genomes sampled from within San Diego
- 51 whole genomes sampled from outside of San Diego
- Samples within San Diego were collected between 2020-03-11 and 2020-04-29
Duplicate sequences were removed because they may represent infections associated with the same contact-traced transmission chain. Figure 1 shows the distribution of the sequences analysed over time, including external sequences.
Reported cases for comparison to our model predictions are taken from San Diego County. These data are used for comparison purposes and to estimate the reporting rate and do not influence the phylodynamic analysis.
Figure 1: Sampling distributions over time of number of sequences included within the region versus sequences included from the international reservoir.
How many are infected in San Diego?
In this analysis we estimate 28960 [8103-162788] median [95%CI] cumulative infections at the time of the the last sample (2020-04-29). At the same time point there were 3432 reported cases. The estimates follow a similar trajectory to the reported cases at a different magnitude.
Figure 2: Estimated cumulative infections through time represented by solid black line (median) and 95% CrI (ribbon). Black points represent reported cases in San Diego. The dashed line indicates the date of last sample in San Diego in this analysis.
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Estimated cumulative infections at last sample (2020-04-29): 28960 [8103-162788] median [95%CI]
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Cumulative confirmed infections reported at 2020-04-29: 3432
Figure 3: Estimated daily infections through time represented by solid black line (median) and 95% CrI (ribbon). Black points represent reported cases in San Diego. The dashed line indicates the date of last sample in San Diego in this analysis.
Figure 4: Estimated percentage of cases reported in San Diego. Error bars represent the 95% credible interval.
Figure 5: Reproduction number through time. The black vertical dashed line indicates the date of last sample in San Diego in this analysis. The red dashed line indicates the date of the general lockdown in San Diego and the rest of California.
Reproduction number at last sample (2020-04-29): 0.575 [0.307-1.35] median [95% CrI]
How quickly has the epidemic in San Diego grown?
Quantile | Reproduction number | Growth rate (per day) | Doubling time (days) |
---|---|---|---|
50% | 1.91 | 0.0963 | 7.2 |
2.5% | 1.58 | 0.0647 | 5 |
97.5% | 2.4 | 0.139 | 10.7 |
Table 1: Reproduction number, growth rate and doubling times
How has SARS-CoV 2 evolved in San Diego?
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 San Diego, blue tips from outside.
Molecular clock rate of evolution: 0.000634 [0.000512-0.000836] median [95% CrI]
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Methods summary
Details on methods and priors can be found here.
Statistic | mean | ESS |
---|---|---|
posterior | -42560 | 639 |
likelihood | -42461 | 5768 |
prior | -99.58 | 610 |
treeLikelihood.algn | -42461 | 5768 |
TreeHeight | 0.4495 | 391 |
clockRate | 0.0006439 | 474 |
kappa | 5.96 | 42416 |
PhydynSEIR | -68.96 | 630 |
seir.E | 16.49 | 307 |
seir.S | 81953 | 1142 |
seir.b | 14.95 | 900 |
seir.exog | 0.09486 | 1031 |
seir.exogGrowthRate | 24.2 | 232 |
seir.importRate | 5.133 | 771 |
seir.p_h | 0.2068 | 946 |
seir.tau | 73.54 | 1457 |
freqParameter.1 | 0.2983 | 14956 |
freqParameter.2 | 0.1827 | 16421 |
freqParameter.3 | 0.1949 | 15535 |
freqParameter.4 | 0.3242 | 15396 |
Table 2: Effective sample size of model parameters
Model version: seijr0.1.1
Report version: 20200608-124449-b5e991fd
Acknowledgements
This work was supported by the MRC Centre for Global Infectious Disease Analysis at Imperial College London.
Sequence data were provided by GISAID and these laboratories.