Group Testing for SARS-CoV-2: Forward to the Past?

Application of pooled testing in estimating the prevalence of COVID-19

Testing at a mass scale has been widely accepted as an effective way to contain the spread of the SARS-CoV-2 Virus. In the initial stages, the shortage of test kits severely restricted mass-scale testing. Pooled testing was offered as a partial solution to this problem. However, it is a relatively lesser-known fact that pooled testing can also result in significant gains, both in terms of cost savings as well as measurement accuracy, in prevalence estimation surveys. We review here the statistical theory of pooled testing for screening as well as for prevalence estimation. We study the impact of the diagnostic errors, and misspecification of the sensitivity and the specificity on the performances of the pooled as well as individual testing procedures. Our investigation clarifies some of the issues hotly debated in the context of COVID-19 and shows the potential gains for the Indian Council for Medical Research (ICMR) in using a pooled sampling for their upcoming COVID-19 prevalence surveys.

Group testing as a strategy for COVID-19 epidemiological monitoring and community surveillance

We propose an analysis and applications of sample pooling to the epidemiologic monitoring of COVID-19. We first introduce a model of the RT-qPCR process used to test for the presence of virus in a sample and construct a statistical model for the viral load in a typical infected individual inspired by large-scale clinical datasets. We present an application of group testing for the prevention of epidemic outbreak in closed connected communities. We then propose a method for the measure of the prevalence in a population taking into account the increased number of false negatives associated with the group testing method.

A numerical approach to maximize the number of testing of COVID-19 using conditional cluster sampling method

The COVID-19 pandemic is the defining health crisis of the world in 2020 and the world economy is affected as well. Bangladesh is also one of the impacted countries, which needs to conduct sufficient tests to identify patients and accordingly adopt measures to limit the massive outbreak of this viral infection. But due to economic drawbacks and also unavailability of testing equipment, Bangladesh is lagging critically behind in test numbers. This study shows a pool testing method named Conditional Cluster Sampling (CCS) that utilizes soft computing and data analysis techniques to reduce the expense of total testing equipment. The proposed method also demonstrates its effectiveness compared to the traditional individual testing method. Firstly, according to patients’ symptoms and severity of their conditions, they are classified into four classes- Minor, Moderate, Major, Critical. After that Random Forest Classifier (RFC) is used to predict the class. Then random sampling is done from each class according to CCS. Finally, using Monte Carlo Simulation (MCS) for 100 cycles, the effectiveness of CCS is demonstrated for different probability levels of infection. It is shown that the CCS method can save up to 22% of the test kits that can save a huge amount of money as well as testing time.

Gichuki B, Njuguna S, Riako D, Mutua S, Gitonga JN, Sein Y, Bartilol B, Mwangi SJ, O. Omuoyo D, M. Morobe J, de Laurent ZR, Bejon P, Ochola-Oyier LI, Tsofa B. Pooled testing conserves SARS-CoV-2 laboratory resources and improves test turn-around time: experience on the Kenyan Coast

Background. International recommendations for the control of the coronavirus disease 2019 (COVID-19) pandemic emphasize the central role of laboratory testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent, at scale. The availability of testing reagents, laboratory equipment and qualified staff are important bottlenecks to achieving this. Elsewhere, pooled testing (i.e. combining multiple samples in the same reaction) has been suggested to increase testing capacities in the pandemic period. Methods. We discuss our experience with SARS-CoV-2 pooled testing using real-time reverse transcription polymerase chain reaction (RT-PCR) on the Kenyan Coast. Results. In mid-May, 2020, our RT-PCR testing capacity for SARS-CoV-2 was improved by ~100% as a result of adoption of a six-sample pooled testing strategy. This was accompanied with a concomitant saving of ~50% of SARS-CoV-2 laboratory test kits at both the RNA extraction and RT-PCR stages. However, pooled testing came with a slight decline of test sensitivity. The RT-PCR cycle threshold value (ΔCt) was ~1.59 higher for samples tested in pools compared to samples tested singly. Conclusions. Pooled testing is a useful strategy to increase SARS-CoV-2 laboratory testing capacity especially in low-income settings.

Considerations for Pooled Testing of Employees for SARS-CoV-2

OBJECTIVES

To identify important background information on pooled tested of employees that employers workers, and health authorities should consider.

METHODS

This paper is a commentary based on the review by the authors of pertinent literature generally from preprints in medrixiv.org prior to August 2020.

RESULTS/CONCLUSIONS

Pooled testing may be particularly useful to employers in communities with low prevalence of COVID-19. It can be used to reduce the number of tests and associated financial costs. For effective and efficient pooled testing employers should consider it as part of a broader, more comprehensive workplace COVID-19 prevention and control program. Pooled testing of asymptomatic employees can prevent transmission of SARS-CoV-2 and help assure employers and customers that employees are not infectious.

Modelling pool testing for SARS-CoV-2: addressing heterogeneity in populations

Amplifying the testing capacity and making better use of testing resources is a crucial measure when fighting any pandemic. A pooled testing strategy for SARS-CoV-2 has theoretically been shown to increase the testing capacity of a country, especially when applied in low prevalence settings. Experimental studies have shown that the sensitivity of reverse transcription-polymerase chain reaction is not affected when implemented in small groups. Previous models estimated the optimum group size as a function of the historical prevalence; however, this implies a homogeneous distribution of the disease within the population. This study aimed to explore whether separating individuals by age groups when pooling samples results in any further savings on test kits or affects the optimum group size estimation compared to Dorfman's pooling, based on historical prevalence. For this evaluation, age groups of interest were defined as 0-19 years, 20-59 years and over 60 years old. Generalisation of Dorfman's pooling was performed by adding statistical weight to the age groups based on the number of confirmed cases and tests performed in the segment. The findings showed that when the pooling samples are based on age groups, there is a decrease in the number of tests per subject needed to diagnose one subject. Although this decrease is minuscule, it might account for considerable savings when applied on a large scale. In addition, the savings are considerably higher in settings where there is a high standard deviation among the positivity rate of the age segments of the general population.

Sample pooling strategies for SARS-CoV-2 detection

The worldwide COVID-19 pandemic outburst has caused a serious public health issue with increasing needs of accurate and rapid diagnostic and screening testing. This situation requires an optimized management of the chemical reagents, the consumables, and the human resources, in order to respond accurately and effectively, controlling the spread of the disease. Testing on pooled samples maximizes the number of tested samples, by minimizing the time and the lab supplies needed. The general conceptualization of the pooling method is based on mixing samples together in a batch. Individual testing is needed only if a specific pool exhibits a positive result. The development of alternative hybrid methods, based on "in house" protocols, utilizing commercially available consumables, in combination with a reliable pooling method would provide a solution, focusing on the better exploitation of the personnel and the lab supplies, allowing for rapid screening of a population in a reasonably short time.

Optimizing COVID-19 surveillance in long-term care facilities: a modelling study

BACKGROUND

Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources.

METHODS

We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing.

RESULTS

In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6-224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34-66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19-36% probability of detecting outbreaks prior to any nosocomial transmission, and 26-46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16-27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6-9 additional tests and 11-28 additional swabs to detect outbreaks 1-6 days earlier, prior to an additional 11-22 infections.

CONCLUSIONS

COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.

Assessment of sample pooling for SARS-CoV-2 molecular testing for screening of asymptomatic persons in Tunisia

The aim of this study is to test a pooling approach for the RT-PCR test to detect low viral loads of SARS-CoV-2. We found that a single positive specimen can still be detected in pools of up to 10. Each laboratory should conduct its own evaluation and validation of pooling protocols according to its specific context.

Gichuki B, Njuguna S, Riako D, Mutua S, Gitonga JN, Sein Y, Bartilol B, Mwangi SJ, O. Omuoyo D, M. Morobe J, de Laurent ZR, Bejon P, Ochola-Oyier LI, Tsofa B. Pooled testing conserves SARS-CoV-2 laboratory resources and improves test turn-around time: experience on the Kenyan Coast

Background. International recommendations for the control of the coronavirus disease 2019 (COVID-19) pandemic emphasize the central role of laboratory testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent, at scale. The availability of testing reagents, laboratory equipment and qualified staff are important bottlenecks to achieving this. Elsewhere, pooled testing (i.e. combining multiple samples in the same reaction) has been suggested to increase testing capacities in the pandemic period. Methods. We discuss our experience with SARS-CoV-2 pooled testing using real-time reverse transcription polymerase chain reaction (RT-PCR) on the Kenyan Coast. Results. In mid-May, 2020, our RT-PCR testing capacity for SARS-CoV-2 was improved by ~100% as a result of adoption of a six-sample pooled testing strategy. This was accompanied with a concomitant saving of ~50% of SARS-CoV-2 laboratory test kits at both the RNA extraction and RT-PCR stages. However, pooled testing came with a slight decline of test sensitivity. The RT-PCR cycle threshold value (ΔCt) was ~1.59 higher for samples tested in pools compared to samples tested singly. Conclusions. Pooled testing is a useful strategy to increase SARS-CoV-2 laboratory testing capacity especially in low-income settings.

An Epidemiological Study on the Prevalence of the Clinical Features of SARS-CoV-2 Infection in Romanian People

The aim of this study was to investigate the prevalence of the clinical features of the SARS-CoV-2 infection in Romanian population through a novel online survey. The survey included categorical socio-demographic and health-related variables. A total of 1830 participants were selected for statistical data processing (a response rate of 90.9%). We determined reasonable reliability of the survey section for clinical features of SARS-CoV-2 infection (Cronbach's Alpha 0.671). Two meaningful dimensions were identified through CATPCA (Categorical Principal Component Analysis) for the survey's items. We separated two significant clusters of items, each measuring a distinct factor: the sociodemographic characteristics linked to social distancing and the relevant clinical features of SARS-CoV-2 infection. Next, a two-step cluster analysis helped to classify the sample group taking into consideration the similarity of subjects. The clustering revealed a three-cluster solution, with significant differences between clusters and allowed the cluster detection of a group of individuals, possibly more affected by the infection with the SARS-CoV-2 virus. Through binomial logistic regression analysis, we identified a statistically significant prediction model for the presumptive diagnostic of some relevant clinical features of SARS-CoV-2 infection. Our study validated a cost-effective model for rapid assessment of the health status of subjects, adapted to the context of SARS-CoV-2 pandemic.