Phylodynamic Analysis: San Francisco, CA, USA: 2020-04-30
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 2020-06-03
This report uses full genome sequence data for San Francisco shared publicly by UCSF Clinical Microbiology Laboratory and Chan-Zuckerberg Biohub and a set of international background sequences from GISAID (laboratory acknowledgements)
Key points
- We estimate a number of key epidemiological parameters for San Francisco 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 preliminary analysis we estimate a basic reproduction number (R0) of 2.04 in San Francisco at the start of the epidemic with R(t) falling below 1 at the beginning of April.
- We estimate a low reporting rate in San Francisco (below 5%) with a median estimate from the phylodynamic model of 50,030 cases at the end of April compared to 1,647 reported cases at the same time point.
This analysis is based on :
- 50 whole genomes sampled from within San Francisco
- 47 whole genomes sampled from outside of San Francisco
- Samples within San Francisco were collected between 2020-03-05 and 2020-04-30
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 DataSF. 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 Francisco?
In this analysis we estimate 50030 [10273-229885] ** median [95%CI] cumulative infections at the time of the the last sample (2020-04-30). At the same time point there were **1647 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 Francisco. The dashed line indicates the date of last sample in San Francisco in this analysis.
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Estimated cumulative infections at last sample (2020-04-30): 50030 [10273-229885] median [95%CI]
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Cumulative confirmed infections reported at 2020-04-30: 1647
Figure 3: Estimated daily infections through time represented by solid black line (median) and 95% CrI (ribbon). Black points represent reported cases in San Francisco. The dashed line indicates the date of last sample in San Francisco in this analysis.
Figure 4: Estimated percentage of cases reported in San Francisco. 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 Francisco in this analysis. The red dashed line indicates the date of the general lockdown in San Francisco and the rest of California. *
Reproduction number at last sample (2020-04-30): 0.472 [0.291-1.09] median [95% CrI]
How quickly has the epidemic in San Francisco grown?
Quantile | Reproduction number | Growth rate (per day) | Doubling time (days) |
---|---|---|---|
50% | 2.04 | 0.108 | 6.43 |
2.5% | 1.75 | 0.0809 | 4.87 |
97.5% | 2.44 | 0.142 | 8.56 |
Table 1: Reproduction number, growth rate and doubling times
How has SARS-CoV 2 evolved in San Francisco?
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 Francisco, blue tips from outside.
Molecular clock rate of evolution: 0.000634 [0.000504-0.000934] median [95% CrI]
Predicted cumulative infections over next 14 days (assuming exponential growth):
Methods summary
Details on methods and priors can be found here.
Statistic | mean | ESS |
---|---|---|
posterior | -43036 | 130 |
likelihood | -42922 | 562 |
prior | -114.6 | 117 |
treeLikelihood.algn | -42922 | 562 |
TreeHeight | 0.5041 | 171 |
clockRate | 0.0006528 | 122 |
kappa | 4.119 | 22544 |
PhydynSEIR | -83.4 | 116 |
seir.E | 17.27 | 154 |
seir.S | 98542 | 1137 |
seir.b | 15.66 | 375 |
seir.exog | 0.2734 | 372 |
seir.exogGrowthRate | 22.93 | 149 |
seir.importRate | 7.22 | 412 |
seir.p_h | 0.2091 | 341 |
seir.tau | 73.75 | 946 |
freqParameter.1 | 0.298 | 7741 |
freqParameter.2 | 0.1823 | 8741 |
freqParameter.3 | 0.1954 | 7953 |
freqParameter.4 | 0.3243 | 7593 |
Table 2: Effective sample size of model parameters
Model version: seijr0.1.1
Report version: 20200603-222403-6500a88b
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.