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Operation Outbreak (OO) is a Bluetooth-based simulation platform that teaches students how pathogens spread and the impact of interventions, thereby facilitating the safe reopening of schools. OO also generates data to inform epidemiological models and prevent future outbreaks. Before SARS-CoV-2 was reported, we repeatedly simulated a virus with similar features, correctly predicting many human behaviors later observed during the pandemic.

Operation Outbreak (OO), an app-based, educational outbreak simulator, seeks to engage, educate, and empower citizens to prevent and better respond to infectious disease outbreaks. We examine the utility of OO for further understanding and mitigating the spread of communicable diseases. The OO smartphone app uses Bluetooth to spread a virtual pathogen among nearby participants’ devices to simulate an outbreak, providing an experiential learning opportunity. Deployed at many college campuses and other settings, the app collects these anonymized spatio-temporal data, including the time and duration of the contacts among participants of the simulation. Here, 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 contact networks generated by OO to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we show that the granularity of OO data enables institutions to improve outbreak mitigation by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.BIGGER PICTUREOutbreak simulation technology can greatly enhance individual and community pandemic preparedness while also 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 the wider adoption of outbreak simulation technology to study the implications of social mixing patterns on outbreaks within close-knit communities, to aid pandemic preparedness and response.

Phylogenetics of superspreading One important characteristic of coronavirus epidemiology is the occurrence of superspreading events. These are marked by a disproportionate number of cases originating from often-times asymptomatic individuals. Using a rich sequence dataset from the early stages of the Boston outbreak, Lemieux et al. identified superspreading events in specific settings and analyzed them phylogenetically (see the Perspective by Alizon). Using ancestral trait inference, the authors identified several importation events, further investigated the context and contribution of particular superspreading events to the establishment of local and wider SARS-CoV-2 transmission, and used viral phylogenies to describe sustained transmission. Science , this issue p. eabe3261 ; see also p. 574

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., The COVID-19 pandemic has highlighted the need for user-friendly diagnostic techniques. Here, the authors present SHINE, a streamlined and optimised Cas13-based method with accompanying smartphone app for visual diagnosis.

Developing and deploying new diagnostic tests is difficult, but the need to do so in response to a rapidly emerging pandemic such as COVID-19 is crucially important for an effective response. In the early stages of a pandemic, laboratories play a key role in helping health care providers and public health authorities detect active infection, a task most commonly achieved using nucleic acid-based assays. While the landscape of diagnostics is rapidly evolving, polymerase chain reaction (PCR) remains the gold-standard of nucleic acid-based diagnostic assays, in part due to its reliability, flexibility, and wide deployment. To address a critical local shortage of testing capacity persisting during the COVID-19 outbreak, our hospital set up a molecular based laboratory developed test (LDT) to accurately and safely diagnose SARS-CoV-2. We describe here the process of developing an emergency-use LDT, in the hope that our experience will be useful to other laboratories in future outbreaks and will help to lower barriers to fast and accurate diagnostic testing in crisis conditions.

Recent outbreaks of viral hemorrhagic fevers (VHFs), including Ebola virus disease (EVD) and Lassa fever (LF), highlight the urgent need for sensitive, deployable tests to diagnose these devastating human diseases. Here we develop CRISPR-Cas13a-based (SHERLOCK) diagnostics targeting Ebola virus (EBOV) and Lassa virus (LASV), with both fluorescent and lateral flow readouts. We demonstrate on laboratory and clinical samples the sensitivity of these assays and the capacity of the SHERLOCK platform to handle virus-specific diagnostic challenges. We perform safety testing to demonstrate the efficacy of our HUDSON protocol in heat-inactivating VHF viruses before SHERLOCK testing, eliminating the need for an extraction. We develop a user-friendly protocol and mobile application (HandLens) to report results, facilitating SHERLOCK’s use in endemic regions. Finally, we successfully deploy our tests in Sierra Leone and Nigeria in response to recent outbreaks., Outbreaks of viral hemorrhagic fevers highlight the need for sensitive, field-deployable diagnostics. Here the authors present a CRISPR-based SHERLOCK platform with field protocol and mobile app for Ebola and Lassa fever outbreaks.

Viral hemorrhagic fevers (VHFs) remain some of the most devastating human diseases, and recent outbreaks of Ebola virus disease (EVD) 1,2 and Lassa fever (LF) 3,4 highlight the urgent need for sensitive, field-deployable tests to diagnose them 5,6. Here we develop CRISPR-Cas13a-based (SHERLOCK) diagnostics targeting Ebola virus (EBOV) and Lassa virus (LASV), with both fluorescent and lateral flow readouts. We demonstrate on laboratory and clinical samples the sensitivity of these assays and the capacity of the SHERLOCK platform to handle virus-specific diagnostic challenges. Our EBOV diagnostic detects both the L and NP genes, thereby eliminating the potential for false positive results caused by the rVSVΔG-ZEBOV-GP live attenuated vaccine. Our two LASV diagnostics together capture 90% of known viral diversity and demonstrate that CRISPR-RNAs (crRNAs) can be effectively multiplexed to provide greater coverage of known viral diversity. We performed safety testing to demonstrate the efficacy of our HUDSON protocol in heat-inactivating and chemically treating VHF viruses before SHERLOCK testing, eliminating the need for an extraction. We developed a user-friendly field protocol and mobile application (HandLens) to report results, facilitating SHERLOCK’s use in endemic regions. Finally, we successfully deployed our tests in Sierra Leone and Nigeria in response to recent outbreaks.

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.

While investigating a signal of adaptive evolution in humans at the gene LARGE, we encountered an intriguing finding by Dr. Stefan Kunz that the gene plays a critical role in Lassa virus binding and entry. This led us to pursue field work to test our hypothesis that natural selection acting on LARGE—detected in the Yoruba population of Nigeria—conferred resistance to Lassa Fever in some West African populations. As we delved further, we conjectured that the “emerging” nature of recently discovered diseases like Lassa fever is related to a newfound capacity for detection, rather than a novel viral presence, and that humans have in fact been exposed to the viruses that cause such diseases for much longer than previously suspected. Dr. Stefan Kunz’s critical efforts not only laid the groundwork for this discovery, but also inspired and catalyzed a series of events that birthed Sentinel, an ambitious and large-scale pandemic prevention effort in West Africa. Sentinel aims to detect and characterize deadly pathogens before they spread across the globe, through implementation of its three fundamental pillars: Detect, Connect, and Empower. More specifically, Sentinel is designed to detect known and novel infections rapidly, connect and share information in real time to identify emerging threats, and empower the public health community to improve pandemic preparedness and response anywhere in the world. We are proud to dedicate this work to Stefan Kunz, and eagerly invite new collaborators, experts, and others to join us in our efforts.