The COVID-19 pandemic has highlighted that new diagnostic technologies are essential for controlling disease transmission. Here, we develop SHINE (Streamlined Highlighting of Infections to Navigate ...Epidemics), a sensitive and specific diagnostic tool that can detect SARS-CoV-2 RNA from unextracted samples. We identify the optimal conditions to allow RPA-based amplification and Cas13-based detection to occur in a single step, simplifying assay preparation and reducing run-time. We improve HUDSON to rapidly inactivate viruses in nasopharyngeal swabs and saliva in 10 min. SHINE's results can be visualized with an in-tube fluorescent readout - reducing contamination risk as amplification reaction tubes remain sealed - and interpreted by a companion smartphone application. We validate SHINE on 50 nasopharyngeal patient samples, demonstrating 90% sensitivity and 100% specificity compared to RT-qPCR with a sample-to-answer time of 50 min. SHINE has the potential to be used outside of hospitals and clinical laboratories, greatly enhancing diagnostic capabilities.
Analysis of 772 complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from early in the Boston-area epidemic revealed numerous introductions of the virus, a small number of ...which led to most cases. The data revealed two superspreading events. One, in a skilled nursing facility, led to rapid transmission and significant mortality in this vulnerable population but little broader spread, whereas other introductions into the facility had little effect. The second, at an international business conference, produced sustained community transmission and was exported, resulting in extensive regional, national, and international spread. The two events also differed substantially in the genetic variation they generated, suggesting varying transmission dynamics in superspreading events. Our results show how genomic epidemiology can help to understand the link between individual clusters and wider community spread.
An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding ...epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals’ second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.
•Outbreak simulation technology can help society mitigate and preempt viral outbreaks•The technology provides social network statistics that power epidemiological models•Those statistics can make interventions more efficient and more effective
Outbreak simulation technology can greatly enhance individual and community pandemic preparedness while helping us understand and mitigate outbreak spread. Building on an existing platform called Operation Outbreak (OO), an app-based program that spreads a virtual pathogen via Bluetooth among participants’ smartphones, we demonstrate the power of this approach. We investigate the first- and second-degree contacts of OO participants, analyzing the differential risk associated with various local contact network structures. We use OO data to construct an epidemiological model with which communities may predict the spread of infectious agents and assess the effectiveness of mitigation measures. Based on our findings, we advocate for wider adoption of outbreak simulation technology to study the implications of social mixing patterns on outbreaks in close-knit communities to aid pandemic preparedness and response.
Specht et al. demonstrate the effectiveness of using outbreak simulation technology to mitigate and preempt viral outbreaks. They first report the distribution, timing, duration, and connectedness of student social contacts based on outbreak simulations at two universities. Using these contact networks, they then construct epidemiological models to predict the transmission pathways of different pathogens. Finally, they show that the granularity of outbreak simulation data enables institutions to improve outbreak mitigation by proactively and strategically testing/vaccinating individuals based on their social interaction levels.
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