The contributions of asymptomatic infections to herd immunity and community transmission are key to the resurgence and control of COVID-19, but are difficult to estimate using current models that ignore changes in testing capacity. Using a model that incorporates daily testing information fit to the case and serology data from New York City, we show that the proportion of symptomatic cases is low, ranging from 13 to 18%, and that the reproductive number may be larger than often assumed. Asymptomatic infections contribute substantially to herd immunity, and to community transmission together with presymptomatic ones. If asymptomatic infections transmit at similar rates as symptomatic ones, the overall reproductive number across all classes is larger than often assumed, with estimates ranging from 3.2 to 4.4. If they transmit poorly, then symptomatic cases have a larger reproductive number ranging from 3.9 to 8.1. Even in this regime, presymptomatic and asymptomatic cases together comprise at least 50% of the force of infection at the outbreak peak. We find no regimes in which all infection subpopulations have reproductive numbers lower than three. These findings elucidate the uncertainty that current case and serology data cannot resolve, despite consideration of different model structures. They also emphasize how temporal data on testing can reduce and better define this uncertainty, as we move forward through longer surveillance and second epidemic waves. Complementary information is required to determine the transmissibility of asymptomatic cases, which we discuss. Regardless, current assumptions about the basic reproductive number of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) should be reconsidered.

https://www.pnas.org/content/pnas/118/9/e2019716118.full.pdf

Quantifying asymptomatic infection and transmission of COVID-19 in New York City using observed cases, serology, and testing capacity.

The SARS-CoV-2 pandemic has shown how a rapid rise in demand for patient and community sample testing can quickly overwhelm testing capability globally. With most diagnostic infrastructure dependent on specialized instruments, their exclusive reagent supplies quickly become bottlenecks, creating an urgent need for approaches to boost testing capacity. We address this challenge by refocusing the London Biofoundry onto the development of alternative testing pipelines. Here, we present a reagent-agnostic automated SARS-CoV-2 testing platform that can be quickly deployed and scaled. Using an in-house-generated, open-source, MS2-virus-like particle (VLP) SARS-CoV-2 standard, we validate RNA extraction and RT-qPCR workflows as well as two detection assays based on CRISPR-Cas13a and RT-loop-mediated isothermal amplification (RT-LAMP). In collaboration with an NHS diagnostic testing lab, we report the performance of the overall workflow and detection of SARS-CoV-2 in patient samples using RT-qPCR, CRISPR-Cas13a, and RT-LAMP. The validated RNA extraction and RT-qPCR platform has been installed in NHS diagnostic labs, increasing testing capacity by 1000 samples per day.

/pdf/a-role-for-biofoundries-in-rapid-development-and-validation-4k7v7elre6.pdf

A role for Biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics.

The COVID-19 pandemic has had an enormous impact on economies and health systems globally, therefore a top priority is the development of increasingly better diagnostic and surveillance alternatives to slow down the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In order to establish massive testing and contact tracing policies, it is crucial to have a clear view of the diagnostic options available and their principal advantages and drawbacks. Although classical molecular methods such as RT-qPCR are broadly used, diagnostic alternatives based on technologies such as LAMP, antigen, serological testing, or the application of novel technologies such as CRISPR-Cas for diagnostics, are also discussed. The present review also discusses the most important automation strategies employed to increase testing capability. Several serological-based diagnostic kits are presented, as well as novel nanotechnology-based diagnostic methods. In summary, this review provides a clear diagnostic landscape of the most relevant tools to track COVID-19.

Tracking SARS-CoV-2: Novel Trends and Diagnostic Strategies

The SARS-CoV-2 pandemic has shown how the rapid rise in demand for patient and community sample testing, required for tracing and containing a highly infectious disease, has quickly overwhelmed testing capability globally. With most diagnostic infrastructure dependent on specialised instruments, their exclusive reagent supplies quickly become bottlenecks in times of peak demand, creating an urgent need for novel approaches to boost testing capacity. We address this challenge by refocusing the full synthetic biology stack available at the London Biofoundry onto the development of alternative patient sample testing pipelines. We present a reagent-agnostic automated SARS-CoV-2 testing platform that can be quickly deployed and scaled, and that accepts a diverse range of reagents. Using an in-house-generated, open-source, MS2-virus-like-particle-SARS-CoV-2 standard, we validate RNA extraction and RT-qPCR workflows as well as two novel detection assays based on CRISPR-Cas and Loop-mediated isothermal Amplification (LAMP) approaches. In collaboration with an NHS diagnostic testing lab, we report the performance of the overall workflow and benchmark SARS-CoV-2 detection in patient samples via RT-qPCR, CRISPR-Cas, and LAMP against clinical test sets. The validated RNA extraction and RT-qPCR platform has been installed in NHS diagnostic labs with a testing capacity of 1000 samples per day and now contributes to increased patient sample processing in the UK while we continue to refine and develop novel high-throughput diagnostic methods. Finally, our workflows and protocols can be quickly implemented and adapted by members of the Global Biofoundry Alliance and the wider scientific and medical diagnostics community.

/pdf/a-new-role-for-biofoundries-in-rapid-prototyping-development-3x9ndpi09r.pdf

A new role for Biofoundries in rapid prototyping, development, and validation of automated clinical diagnostic tests for SARS-CoV-2

The COVID-19 pandemic has become a global challenge for the healthcare systems of many countries with 6 million people having lost their lives and 530 million more having tested positive for the virus. Robust testing and a comprehensive track and trace process for positive patients are essential for effective pandemic control, leading to high demand for diagnostic testing. In order to comply with demand and increase testing capacity worldwide, automated workflows have come into prominence as they enable high-throughput screening, faster processing, exclusion of human error, repeatability, reproducibility and diagnostic precision. The gold standard for COVID-19 testing so far has been RT-qPCR, however, different SARS-CoV-2 testing methods have been developed to be combined with high throughput testing to improve diagnosis. Case studies in China, Spain and the United Kingdom have been reviewed and automation has been proven to be promising for mass testing. Free and Open Source scientific and medical Hardware (FOSH) plays a vital role in this matter but there are some challenges to be overcome before automation can be fully implemented. This review discusses the importance of automated high-throughput testing, the different equipment available, the bottlenecks of its implementation and key selected case studies that due to their high effectiveness are already in use in hospitals and research centres.

/pdf/high-throughput-and-automated-screening-for-covid-19-mcnyefxj.pdf

High—throughput and automated screening for COVID-19

Background China adopted an unprecedented province-scale quarantine since January 23rd 2020, after the novel coronavirus (COVID-19) broke out in Wuhan in December 2019. Responding to the challenge of limited testing capacity, large-scale standardized and fully-automated laboratory (Huo-Yan) was built as an ad-hoc measure. There was so far no empirical data or mathematical model to reveal the impact of the testing capacity improvement since the quarantine. Methods We integrated public data released by the Health Commission of Hubei Province and Huo-Yan Laboratory testing data into a novel differential model with non-linear transfer coefficients and competitive compartments, to evaluate the trends of suspected cases under different nucleic acid testing capacities. Results Without the establishment of Huo-Yan, the suspected cases would increased by 47% to 33,700, the corresponding cost of the quarantine would be doubled, and the turning point of the increment of suspected cases and the achievement of “daily settlement” (all daily new discovered suspected cases were diagnosed according to the nucleic acid testing results) would be delayed for a whole week and 11 days. If the Huo-Yan Laboratory ran at its full capacity, the number of suspected cases would decrease at least a week earlier, the peak of suspected cases would be reduced by at least 44% and the quarantine cost could be reduced by more than 72%. Ideally, if a daily testing capacity of 10,500 could achieved immediately after the Hubei lockdown, “daily settlement” for all suspected cases would be achieved immediately. Conclusions Large-scale and standardized clinical testing platform with nucleic acid testing, high-throughput sequencing and immunoprotein assessment capabilities need to be implemented simultaneously in order to maximize the effect of quarantine and minimize the duration and cost. Such infrastructure like Huo-Yan, is of great significance for the early prevention and control of infectious diseases for both common times and emergencies.

/pdf/effect-of-large-scale-testing-platform-in-prevention-and-ofc66q23nk.pdf

Effect of large-scale testing platform in prevention and control of the COVID-19 pandemic: an empirical study with a novel numerical model

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Effect of large-scale testing platform in prevention and control of the COVID-19 pandemic: an empirical study with a novel numerical model