Cases from the ongoing outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV) exported from mainland China can lead to self-sustained outbreaks in other populations.

Risk of COVID-19 infection in Wuhan has been estimated using imported case counts of international travelers, often under the assumption that all cases in travelers are ascertained. Recent work indicates variation among countries in detection capacity for imported cases. Singapore has historically had very strong epidemiological surveillance and contact-tracing capacity and has shown in the COVID-19 epidemic evidence of a high sensitivity of case detection. We therefore used a Bayesian modeling approach to estimate the relative imported case detection capacity for other countries compared to that of Singapore.We estimate that the global ability to detect imported cases is 38% (95% HPDI 22% -64%) of Singapore's capacity. Equivalently, an estimate of 2.8 (95% HPDI 1.5 -4.4) times the current number of imported cases, could have been detected, if all countries had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, we found that the ability to detect imported cases relative to Singapore among high surveillance locations is 40% (95% HPDI 22% -67%), among intermediate surveillance locations it is 37% (95% HPDI 18% -68%), and among low surveillance locations it is 11% (95% HPDI 0% -42%). Using a simple mathematical model, we further find that treating all travelers as if they were residents (rather than accounting for the brief stay of some of these travelers in Wuhan) can modestly contribute to underestimation of prevalence as well. We conclude that estimates of case counts in Wuhan based on assumptions of perfect detection in travelers . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. : medRxiv preprintterm can be well approximated as We plot this approximation of V given doubling times . V ≈ 1 − e −(r+γ)d aligned with 4 , a range of durations of detectable infection and a range of lengths of stay 2 ( Figure 2 ). Results:We estimate that the global ability to detect imported cases is 38% (95% HPDI 22% -64%) of Singapore's capacity. Equivalently, an estimate of 2.8 (95% HPDI 1.5 -4.4) times the current number of imported cases, could have been detected, if all countries had had the same detection capacity as Singapore, which leads to 1.8 (95% CI 0.5 -3.4) undetected cases per detected case. The ability to detect imported cases among high surveillance countries is 40% (95% HPDI 22% -67%), among intermediate surveillance countries it is 37% (95% HPDI 18% -68%), and among low surveillance countries it is 11% (95% HPDI 0% -42%). : medRxiv preprint 7In this paper, we have aimed to test two assumptions underlying the estimation of incidence at the epicentre of the SARS-Cov2 outbreak. The first of these is that the capacity for detection of international imported cases is 100% sensitive and specific across locations. While we know of no reason to doubt specificity of detection, we tested the assumption of perfec...

ObjectivesConvenience sampling is an imperfect but important tool for seroprevalence studies. For COVID-19, local geographic variation in cases or vaccination can confound studies that rely on the geographically skewed recruitment inherent to convenience sampling. The objectives of this study were: (1) quantifying how geographically skewed recruitment influences SARS-CoV-2 seroprevalence estimates obtained via convenience sampling and (2) developing new methods that employ Global Positioning System (GPS)-derived foot traffic data to measure and minimise bias and uncertainty due to geographically skewed recruitment.DesignWe used data from a local convenience-sampled seroprevalence study to map the geographic distribution of study participants’ reported home locations and compared this to the geographic distribution of reported COVID-19 cases across the study catchment area. Using a numerical simulation, we quantified bias and uncertainty in SARS-CoV-2 seroprevalence estimates obtained using different geographically skewed recruitment scenarios. We employed GPS-derived foot traffic data to estimate the geographic distribution of participants for different recruitment locations and used this data to identify recruitment locations that minimise bias and uncertainty in resulting seroprevalence estimates.ResultsThe geographic distribution of participants in convenience-sampled seroprevalence surveys can be strongly skewed towards individuals living near the study recruitment location. Uncertainty in seroprevalence estimates increased when neighbourhoods with higher disease burden or larger populations were undersampled. Failure to account for undersampling or oversampling across neighbourhoods also resulted in biased seroprevalence estimates. GPS-derived foot traffic data correlated with the geographic distribution of serosurveillance study participants.ConclusionsLocal geographic variation in seropositivity is an important concern in SARS-CoV-2 serosurveillance studies that rely on geographically skewed recruitment strategies. Using GPS-derived foot traffic data to select recruitment sites and recording participants’ home locations can improve study design and interpretation.

Tracking the dynamics and spread of COVID-19 is critical to mounting an effective response to the pandemic. In the absence of randomized representative serological surveys, many SARS-CoV-2 serosurveillance studies have relied on convenience sampling to estimate cumulative incidence. One common approach is to recruit at frequently visited community locations ("venue-based" sampling), but the sources of bias and uncertainty associated with this strategy are still poorly understood. Here, we used data from a venue-based community serosurveillance study, GPS-estimated foot traffic data, and data on confirmed COVID-19 cases to report an estimate of cumulative incidence in Somerville, Massachusetts, and a methodological strategy to quantify and reduce uncertainty in serology-based cumulative incidence estimates obtained via convenience sampling. The mismatch between the geographic distribution of participants' home locations (the "participant catchment distribution") and the geographic distribution of infections is an important determinant of uncertainty in venue-based and other convenience sampling strategies. We found that uncertainty in cumulative incidence estimates can vary by a factor of two depending how well the participant catchment distribution matches the known or expected geographic distribution of prior infections. GPS-estimated business foot traffic data provides an important proxy measure for the participant catchment area and can be used to select venue locations that minimize uncertainty in cumulative incidence.