The probability to remain asymptomatic and its dependence on age, sex and the presence of comorbidities

Funder: Wellcome Trust (OUCRU core funding)

Principal Investigators: Nguyen Thi Minh Nguyet, Le Thanh Hoang Nhat, Ronald Geskus, Marc Choisy (for the OUCRU COVID-19 Modeling Group)

Collaborator(s): Pham Quang Thai, Epidemiology Department, National Institute of Hygiene and Epidemiology (NIHE)

Location of activity: Ho Chi Minh City, Viet Nam


Many individuals that are infected with SARS-CoV-2 don’t show any symptoms. However, estimates of the probability to remain asymptomatic are scarce and likely to be biased. Data are often not collected in a systematic way, and asymptomatic individuals are more likely to be missed, creating a downward bias. One of the few exceptions is the outbreak on the Diamond Princess cruise ship in February 2020.


Study design:

Since the end of March 2020, Viet Nam has performed active contact tracing of all community infected individuals and quarantined these “F1 contacts” in supervised locations. If an F1 contact tests positive, his/her contacts become F1 contacts, etcetera. In this way, individuals within such a network of infections are unlikely to be missed.

Although some (mostly asymptomatic) infection chains may still go unnoticed, this is the closest to a representative sample of infections that can be attained. We use data from the time that contact tracing was implemented in Viet Nam.

The main source of information is the data from the Ministry of Health (MoH), but we will also use data on symptom onset after diagnosis from hospitals. Data are collected and curated by the National Institute of Hygiene and Epidemiology (NIHE).

We perform a logistic regression analysis in order to investigate how the probability of remaining asymptomatic depends on age, sex and the presence of comorbidities.



In many countries, the state of the pandemic is monitored by testing persons with symptoms only. Knowing the proportion of individuals that remains asymptomatic helps to give a better understanding of the state of the pandemic. This holds even stronger if this information is used in a mathematical model for the spread that allows for differences in infectivity by symptomatic status. It may also be an important parameter for a model that estimates the probability of ongoing transmission, given that no symptomatic cases have been found for some period of time.