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SARS-CoV2 a new emerging Corona Virus Disease in humans, which called for containment measures by many countries. The current paper aims to discuss the impact of two different sampling methodologies when executing a drive through COVID-19 survey on the quality of estimated disease burden measures. Secondary data analysis of a pilot cross-sectional survey targeting Qatar's primary health care registered population was done. Two groups with different sampling methods were compared for estimating COVID-19 point prevalence using molecular testing for nasopharyngeal swabs. The first group is a stratified random sample non-proportional to size ( N  = 260). A total of 16 population strata based on age group, gender, and nationality were sampled. The second group is the Open invitation group ( N  = 841). The results showed that the two groups were obviously and significantly different in age and nationality. Besides, reporting of COVID-19 symptoms was more frequent in the open invitation group (28.2%) than the random sample (16.2%). The open invitation group overestimated the symptomatic COVID-19 prevalence rate by more than four times, while it overestimated the asymptomatic COVID-19 cases by a small margin. The overall prevalence rate of active COVID-19 cases in the open invitation sample (13.3%) was almost double that of the random sample (6.9%). Furthermore, using population sampling weights reduced the prevalence rate to 0.8%. The lesson learned here is that it is wise to consider the magnitude of bias introduced in a surveillance system when relying on convenient sampling approaches in response to time constraints.

frontiers in public health

COVID-19 Surveillance in the Primary Health Care Population of Qatar: Experience of Prioritizing Timeliness Over Representativeness When Sampling the Population