# Phylodynamic Analysis: St.Petersburg,: 2020-04-15

### 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 2020-05-21

### Key points

- This is a preliminary analysis based on a small number of genetic sequences sampled in St.Petersburg uploaded to gisaid before 2020-06-05.
- 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 2.67 which falls to 2.46 [CrI 0.695-3.06] by April 15th. Note the credible interval falls below 1 by this point. 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 1% of the St. Petersburg population are infected by date of last sample, 2020-04-15.

#### This is analysis is based on :

**30 whole genomes**sampled from**within St.Petersburg****35 whole genomes**sampled from outside of**St.Petersburg**- Samples within St.Petersburg were collected between
**2020-03-15**and**2020-04-15**

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 St.Petersburg 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 case numbers from St.Petersburg for comparison are extracted from стопкоронавирус, translation stop coronavirus, which is the site for official information on coronavirus in Russia.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.*

## Estimated infections

In this preliminary analysis, we estimate **52134 [4619-942745]** median [95%CI] cumulative infections at last sample (2020-04-15) by fitting Sars-Cov2 sequence data to our phylogynamic model mentioned prior. The cumulative confirmed infections reported from testing was **929** on the date of last sample. As such, We estimate about **1%** of the population of St. Petersburg to be infected by the date of our last sample (2020-04-15).

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 non-essential activities on 2020-03-30 across most of Russia, including St.Petersburg. However, further anlayses are required to assess the impact of non-pharmaceutical interventions on transmission in this region.

*Figure 2: Estimated cumulative infections through time represented by solid black line (median) and 95% CrI (ribbon). Black points represent reported cases in St.Petersburg. The dashed line indicates the date of last sample in St.Petersburg 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 St.Petersburg. The dashed line indicates the date of last sample in St.Petersburg in this analysis.*

Figure 4 shows estimates of cumulative reporting rate over time. We estimate reporting rate for St. Petersburg 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.

*Figure 4: Estimated percentage of cumulative cases reported in St.Petersburg. Error bars represent the 95% credible interval.*

We estimate the reproduction number to remain relatively stable over time, although we see a slight decrease following the introduction of lockdown in St.Petersburg. The lower bound of the confidence interval drops below 1 by the date of last sample, suggesting the possibility for R to be decreasing below 1 by this point. As we only use 30 internal genome sequences for analyses, we expect the confidence intervals to be quite wide in our analyses. 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 St.Petersburg in this analysis. The red dashed line indicates date of general lockdown in St.Petersburg. *

Reproduction number at last sample (2020-04-15): **2.46 [0.695-3.06]** median [95% CrI]

## How quickly has the epidemic in St.Petersburg grown?

Quantile | Reproduction number | Growth rate (per day) | Doubling time (days) |
---|---|---|---|

50% | 2.67 | 0.160 | 4.33 |

2.5% | 1.97 | 0.102 | 2.97 |

97.5% | 3.69 | 0.234 | 6.78 |

*Table 1. Reproduction number, growth rate and doubling times*

## How has SARS-CoV 2 evolved in St.Petersburg?

We present here a time scaled phylogeny of SARS-CoV-2 in St. Petersburg and the included international reservoir (Figure 6). St. Petersburg sequences tend to cluster closely together suggesting that the majority of sequenced SARS-CoV-2 results from transmission within the region rather than introductions from elsewhere. This is to be expected, as majority of sequences are sampled after lockdown and could be the result of significant local transmission or due to a local sampling strategy targeted at interconnected individuals.

*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 St.Petersburg, blue tips from outside.*

##### Molecular clock rate of evolution: **0.000568 [0.000503-0.000743]** median [95% CrI]

## Methods summary

Details on methods and priors can be found here.

Statistic | mean | ESS |
---|---|---|

posterior | -41671 | 347 |

likelihood | -41598 | 561 |

prior | -72.69 | 72 |

treeLikelihood.algn | -41598 | 561 |

TreeHeight | 0.3847 | 28 |

clockRate | 0.0005825 | 39 |

kappa | 6.446 | 3943 |

PhydynSEIR | -37.08 | 84 |

seir.E | 34.44 | 3566 |

seir.S | 3500402 | 100 |

seir.b | 19.75 | 1555 |

seir.exog | 0.03327 | 326 |

seir.exogGrowthRate | 26.89 | 25 |

seir.importRate | 6.014 | 3086 |

seir.p_h | 0.2193 | 6414 |

seir.tau | 74.01 | 8774 |

freqParameter.1 | 0.2984 | 1601 |

freqParameter.2 | 0.183 | 1647 |

freqParameter.3 | 0.1955 | 1847 |

freqParameter.4 | 0.3231 | 1643 |

gamma0 | 73 | NA |

gamma1 | 121.7 | 4 |

*Table 2. Effective sample size of model parameters*

Model version: seijr0.1.1_coupled

Report version: 20200521-191851-e2ce584f

## 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.