# A Significant Portion of COVID-19 Transmission Is Presymptomatic

*Epistemic status: Not quite settled science, but preprints seem to agree.*

Strong evidence points to presymptomatic sources as a major source of COVID-19 infections, possibly the majority. The exact proportion is environment-dependent; awareness and public health measures reduce symptomatic transmission more than they reduce presymptomatic transmission.

The main reasons for thinking presymptomatic transmission is significant are direct measurements of the serial interval and incubation period, and the outside view of what level of public health measures have and haven't succeeded at containment.

Before delving into papers, a quick aside. If COVID-19 were only transmissible when people were coughing or feverish, containing it would be pretty easy; just tell people to stay home if they have those symptoms. Some people might try to go out anyways, so you might also set up checkpoints where people have their temperature taken and have someone listen to whether they're coughing, but that would pretty much be sufficient. Empirically, however, COVID-19 is successfully spreading in countries which have taken these measures and other more extreme measures, which is what we would expect given presymptomatic transmission, but not what we would expect without it.

(Note: You might think this means that symptomatic people aren’t contagious, but actually it just means that people who show symptoms are doing a good job of isolating themselves. People with COVID-19 symptoms are definitely contagious and need to isolate themselves and notify people they might have spread it to.)

(Note: Presymptomatic transmission is a separate issue from asymptomatic carriers. Presymptomatic transmission is when someone is contagious when they aren't symptomatic *yet*. An asymptomatic carrier is someone who is contagious but who never develops symptoms. Asymptomatic carriers seem to be rare, though not completely nonexistent.)

Serial Interval and Incubation Period
=====================================

The [serial interval](https://en.wikipedia.org/wiki/Serial_interval) is the average length of time between transmissions in a transmission chain; that is, given pairs of people A and B where A was infected on a known date and then infected B on a known date, the serial interval is the average amount of time between those dates. The incubation period is the amount of time between when someone is infected, and when they display symptoms.

If the serial interval is shorter than the incubation period, this implies that a large fraction of transmission must be presymptomatic. So, with that in mind, I went looking for studies which measure COVID-19's incubation period and serial interval. These are in two tables below.

One of these studies, Tapiwa Ganyani et al, estimated the proportion of transmission which was pre-symptomatic: 48% (95% CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin. No other studies estimated this quantitatively, but most stated that their results provided qualitative evidence that presymptomatic transmission is occurring.

Estimates of the Incubation Period
----------------------------------

| **Study** | **Incubation Period (days)** | **Sample Size** | **Data source** |
| --- | --- | --- | --- |
| [Stephen A. Lauer et al](https://annals.org/aim/fullarticle/2762808/incubation-period-coronavirus-disease-2019-covid-19-from-publicly-reported) | 5.1 (Median) | 181 | Travellers "in areas with no known community transmission" |
| [Wei-jie Guan et al](https://www.nejm.org/doi/10.1056/NEJMoa2002032) | 4 (Median) | 1099 | China outside Hubei |
| [Qun Li et al](https://www.nejm.org/doi/full/10.1056/NEJMoa2001316) | 5.2 (Mean) | 425 | Wuhan |
| [Jantien A Backer et al](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.5.2000062) | 6.4 (Mean) | 88  | Travellers from Wuhan |
| [Sijia Tian](https://www.journalofinfection.com/article/S0163-4453(20)30101-8/fulltext) | 6.7 (Median) | 262 | Beijing |
| [Lauren C. Tindale et al](https://www.medrxiv.org/content/medrxiv/early/2020/03/06/2020.03.03.20029983.full.pdf) | 7.1, 9 (Mean) | 228 | Singapore and Tianjin |
| [Kaike Ping et al](https://www.medrxiv.org/content/medrxiv/early/2020/03/06/2020.03.01.20028944.full.pdf) | 8 (Mean) | 162 | Ghuizho, China |

Estimates of Serial Interval
----------------------------

| **Study** | **Serial interval (d)** | **Sample Size** | **Data source** |
| --- | --- | --- | --- |
| [Shi Zhao et al](https://www.medrxiv.org/content/medrxiv/early/2020/02/25/2020.02.21.20026559.full.pdf) | 4.4 Mean 3.0 SD | 21 chains, 12 pairs | Hong Kong public data |
| [Nishiura H et al.](https://www.ncbi.nlm.nih.gov/pubmed/32145466) | 4.0 or 4.6 Median | 28 or 18 pairs | Published case reports |
| [Chong You et al](https://www.medrxiv.org/content/medrxiv/early/2020/02/11/2020.02.08.20021253.full.pdf) | 4.41 Mean 3.17 SD | 71 chains | China, outside Hubei |
| [Qun Li et al](https://www.nejm.org/doi/full/10.1056/NEJMoa2001316?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed) | 7.5 Mean | 5 clusters | Hubei case clusters |
| [Zhanwei Du et al](https://www.medrxiv.org/content/medrxiv/early/2020/03/06/2020.02.19.20025452.full.pdf) | 3.96 Mean 4.75 SD | 468 pairs | China, outside Hubei |
| [Tapiwa Ganyani et al](https://www.medrxiv.org/content/10.1101/2020.03.05.20031815v1) | 5.21 Mean, 3.95 SD | 226 cases | Singapore and Tianjin clusters |
| [Lauren C. Tindale et al](https://www.medrxiv.org/content/medrxiv/early/2020/03/06/2020.03.03.20029983.full.pdf) | 4.56 Mean, 4.22 SD | 228 cases | Singapore and Tianjin clusters |
| [Shi Zhao et al](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/D99B2708BAD5983D223E356BA5213D7C/S0899823X20000641a.pdf/timevarying_serial_interval_of_the_coronavirus_disease_covid19_and_its_genderspecific_difference_a_datadriven_analysis_using_public_surveillance_data_in_hong_kong_and_shenzhen_china_from_january_10_to_february_15_2020.pdf) | 5.2 Mean | 48 pairs | Hong Kong and Shenzhen |
| [Kaike Ping et al](https://www.medrxiv.org/content/medrxiv/early/2020/03/06/2020.03.01.20028944.full.pdf) | 6.37 Mean | 57 cases | Guizhou, China |

Tapiwa Ganyani et al and Lauren C Tindale et al appear to have used overlapping public data sources. The sample size column for serial interval studies is unusually painful, as sample-size columns go, because many of the studies needed to account for uncertainty in who infected who; as such, sample sizes are reported varyingly in units of (in order from most to least reliable per sample) pairs, chains, clusters, and cases.

The study with the longest estimated serial interval, Qun Li et al, looks at a small number of clusters and guesses which cases infected which other cases. While it estimates a mean serial interval of 7.5, Its data is also compatible with an interpretation in which the mean serial interval is shorter and some of the transmissions are indirect. This change in interpretation would bring it in line with other studies in this set, which estimate shorter intervals.

One of these studies, Zhanwe Du et al, estimated the serial interval using when people became symptomatic (rather than when they were exposed), and found that in 13% of cases, the infectee showed symptoms before the infector did. This would imply that either in those cases the infector transmitted presymptomatically, the infector had a relatively long incubation period, and the infectee had a relatively short incubation period; or that this data set had major issues identifying who affected who. The distribution of SIs fits a nice Gaussian, which is some evidence that it's the former.

Anecdotal Reports and Case Studies
----------------------------------

To understand what presymptomatic transmission of COVID-19 would look like, I went looking for anecdotes and case studies of known COVID-19 transmission events. You can't use these to infer much about rates, but they're helpful for internalizing what presymptomatic transmission would look like.

> "I believe I caught it when attending a small house party at which no one was coughing, sneezing or otherwise displaying any symptoms of illness. It appears that 40% of the attendees of this party ended up sick."

[https://www.facebook.com/EbethBerkeley/posts/10110434821081713](https://www.facebook.com/EbethBerkeley/posts/10110434821081713)

> (via Google Translate) "On January 24, Li and his grandfather, grandma, and father went to aunt's house for dinner, a total of 9 people. On January 28, Li developed fever. … all 9 people participating in the dinner were confirmed as confirmed cases." 

[http://hlj.people.com.cn/GB/n2/2020/0205/c220024-33767665.html](http://hlj.people.com.cn/GB/n2/2020/0205/c220024-33767665.html)

Practical Implications
----------------------

The main practical implication is that contact tracing is really important.

Contact tracing is where, when you find someone with COVID-19, you identify everyone they might have spread it to and warn them that they've been exposed. People who've been exposed are expected to quarantine themselves for 14 days, which is long enough that if they are in fact infected, there's only a 1% chance they are infected but not yet symptomatic . Back in January, this served two purposes: it ensured that if they had a cough, they wouldn't brush it off as something minor and keep going to work, and it ensured that if they *didn't* have a cough, they wouldn't transmit it while presymptomatic. The first issue is now less of a concern; everyone knows that if someone has a cough, they aren't supposed to go to work, even if it's definitely rhinovirus. The second issue is exactly as much a concern as it was before.