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.  ### INTRODUCTION We used genomic epidemiology to investigate the introduction and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the Boston area across the first wave of the pandemic, from March through May 2020, including high-density sampling early in this period. Our analysis provides a window into the amplification of transmission in an urban setting, including the impact of superspreading events on local, national, and international spread. ### RATIONALE Superspreading is recognized as an important driver of SARS-CoV-2 transmission, but the determinants of superspreading—why apparently similar circumstances can lead to very different outcomes—are poorly understood. The broader impact of such events, both on local transmission and on the overall trajectory of the pandemic, can also be difficult to determine. Our dataset includes hundreds of cases that resulted from superspreading events with different epidemiological features, which allowed us to investigate the nature and effect of superspreading events in the first wave of the pandemic in the Boston area and to track their broader impact. ### RESULTS Our data suggest that there were more than 120 introductions of SARS-CoV-2 into the Boston area, but that only a few of these were responsible for most local transmission: 29% of the introductions accounted for 85% of the cases. At least some of this variation results from superspreading events amplifying some lineages and not others. Analysis of two superspreading events in our dataset illustrate how some introductions can be amplified by superspreading. One occurred in a skilled nursing facility, where multiple introductions of SARS-CoV-2 were detected in a short time period. Only one of these led to rapid and extensive spread within the facility, and significant mortality in this vulnerable population, but there was little onward transmission. A second superspreading event, at an international business conference, led to sustained community transmission, including outbreaks in homeless and other higher-risk communities, and was exported domestically and internationally, ultimately resulting in hundreds of thousands of cases. The two events also differed substantially in the genetic variation they generated, possibly suggesting varying transmission dynamics in superspreading events. Our results also show how genomic data can be used to support cluster investigations in real time—in this case, ruling out connections between contemporaneous cases at Massachusetts General Hospital, where nosocomial transmission was suspected. ### CONCLUSION Our results provide powerful evidence of the importance of superspreading events in shaping the course of this pandemic and illustrate how some introductions, when amplified under unfortunate circumstances, can have an outsized effect with devastating consequences that extend far beyond the initial events themselves. Our findings further highlight the close relationships between seemingly disconnected groups and populations during a pandemic: Viruses introduced at an international business conference seeded major outbreaks among individuals experiencing homelessness; spread throughout the Boston area, including to other higher-risk communities; and were exported extensively to other domestic and international sites. They also illustrate an important reality: Although superspreading among vulnerable populations has a larger immediate impact on mortality, the cost to society is greater for superspreading events that involve younger, healthier, and more mobile populations because of the increased risk of subsequent transmission. This is relevant to ongoing efforts to control the spread of SARS-CoV-2, particularly if vaccines prove to be more effective at preventing disease than blocking transmission. ![Figure] Schematic outline of this genomic epidemiology study. Illustrated are the numerous introductions of SARS-CoV-2 into the Boston area; the minimal spread of most introductions; and the local, national, and international impact of the amplification of one introduction by a large superspreading event. 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. : /lookup/doi/10.1126/science.abe3261 : /lookup/doi/10.1126/science.abg0100 : pending:yes
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the coronavirus disease (COVID-19), continues to spread across the globe. With over 46.9 million people infected so far, it is crucial to determine how the virus spreads to mitigate its effect. Many people infected with the SARS-CoV-2 virus are asymptomatic (have no symptoms) or are pre-symptomatic (yet to present symptoms), which means that they can transmit the virus even if they do not feel any symptom at all. With a large fraction of people having no symptoms, it is hard to pinpoint those infected. Now, a team of researchers at the Massachusetts Institute of Technology (MIT) has found that asymptomatic people may differ from those who are healthy in the way they cough.
Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identification of causal effects from observational data in the cases where the causal graph is identifiable, i.e., the data generating mechanism can be recovered from the joint distribution. However, no such studies have been done to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effect from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and important research question, i.e., the effect of oxygen therapy intervention in the intensive care unit (ICU); the result of this project is useful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC III database, a standard database in the machine learning community that contains 58,976 admissions from an ICU in Boston, MA, for estimating the oxygen therapy effect on morality. We also identified the covariate-specific effect to oxygen therapy from the model for more personalized intervention.
Defense contracts valued at $7 million and above ARMY Moderna TX Inc.,* Cambridge, Massachusetts, was awarded a $1,525,000,000 firm-fixed-price contract for 100 million filled drug production doses of a SARS-CoV-2 mRNA-1273 vaccine. Bids were solicited via the internet with one received. Work will be performed in Cambridge, Massachusetts, with an estimated completion date of March 31, 2022. Fiscal 2020 research, development, test and evaluation (Army) funds in the amount of $1,525,000,000 were obligated at the time of the award. U.S. Army Contracting Command, Aberdeen Proving Ground, Maryland, is the […]
The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
University of Massachusetts Amherst researchers have invented a portable surveillance device powered by machine learning - called FluSense - which can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses and influenza trends. The FluSense creators say the new edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, may expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS. Models like these can be lifesavers by directly informing the public health response during a flu epidemic. These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more. "This may allow us to predict flu trends in a much more accurate manner," says co-author Tauhidur Rahman, assistant professor of computer and information sciences, who advises Ph.D. student and lead author Forsad Al Hossain.
University of Massachusetts Amherst researchers have invented a portable surveillance device powered by machine learning – called FluSense – which can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses and influenza trends. The FluSense creators say the new edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, may expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS. Models like these can be lifesavers by directly informing the public health response during a flu epidemic. These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more. "This may allow us to predict flu trends in a much more accurate manner," says co-author Tauhidur Rahman, assistant professor of computer and information sciences, who advises Ph.D. student and lead author Forsad Al Hossain.