Snowball Sampling Study Design for Serosurveys Early in Disease Outbreaks

Area of Science:

• Epidemiology
• Public Health
• Infectious Disease Surveillance

Background:

• Serological surveys are vital for detecting undiagnosed cases, understanding disease severity, and quantifying asymptomatic infections.
• Traditional survey methods require large sample sizes, especially in early outbreak stages with low prevalence.
• Efficient sampling strategies are needed to gather critical epidemiological data during pandemics.

Purpose of the Study:

• To propose and evaluate snowball sampling as an enrichment strategy for serological surveys.
• To assess the efficiency of snowball sampling in estimating key epidemiological parameters.
• To demonstrate the utility of snowball sampling for emerging pandemic preparedness.

Main Methods:

• Introduced snowball sampling to recruit participants by testing contacts of initially identified infected individuals.
• Applied the snowball sampling design to studies within the coronavirus disease 2019 (COVID-19) pandemic.
• Conducted simulation studies to compare snowball sampling efficiency against other schemes.

Main Results:

• Snowball sampling can efficiently estimate the asymptomatic proportion of infections.
• This method aids in determining the probability of clinical presentation in seropositive individuals.
• Snowball sampling facilitates analysis of host/infection characteristics associated with seropositivity among contacts.

Conclusions:

• Snowball sampling offers a more efficient approach for serological surveys, particularly in low-prevalence settings.
• This design is valuable for answering critical questions during the early stages of outbreaks and future pandemics.
• The proposed method can provide timely, actionable data to inform public health interventions.