In epidemiology, serial intervals are measured from when one infected person starts to show symptoms to when the next person infected becomes symptomatic. For any specific infection, the serial interval is assumed to be a fixed characteristic. Using valuable transmission pair data for coronavirus disease (COVID-19) in mainland China, Ali et al. noticed that the average serial interval changed as nonpharmaceutical interventions were introduced. In mid-January 2020, serial intervals were on average 7.8 days, whereas in early February 2020, they decreased to an average of 2.2 days. The more quickly infected persons were identified and isolated, the shorter the serial interval became and the fewer the opportunities for virus transmission. The change in serial interval may not only measure the effectiveness of infection control interventions but may also indicate rising population immunity. Science , this issue p.  Studies of novel coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have reported varying estimates of epidemiological parameters, including serial interval distributions—i.e., the time between illness onset in successive cases in a transmission chain—and reproduction numbers. By compiling a line-list database of transmission pairs in mainland China, we show that mean serial intervals of COVID-19 shortened substantially from 7.8 to 2.6 days within a month (9 January to 13 February 2020). This change was driven by enhanced nonpharmaceutical interventions, particularly case isolation. We also show that using real-time estimation of serial intervals allowing for variation over time provides more accurate estimates of reproduction numbers than using conventionally fixed serial interval distributions. These findings could improve our ability to assess transmission dynamics, forecast future incidence, and estimate the impact of control measures. : /lookup/doi/10.1126/science.abc9004
The power of artificial intelligence has transformed health care by using massive datasets to improve diagnostics, treatment, records management, and patient outcomes. Complex decisions that once took hours -- such as making a breast or lung cancer diagnosis based on imaging studies, or deciding when patients should be discharged -- are now resolved within seconds by machine learning and deep learning applications. Any technology, of course, will have its limitations and flaws. And over the past few years, a steady stream of evidence has demonstrated that some of these AI-powered medical technologies are replicating racial bias and exacerbating historic health care inequities. Now, amid the SARS-CoV-2 pandemic, some researchers are asking whether these new technologies might be contributing to the disproportionately high rates of virus-related illness and death among African Americans. African Americans aged 35 to 44 experience Covid-19 mortality rates that are nine times higher than their White counterparts.
A novel machine learning model developed by researchers at Michigan State University suggests that mutations to the SARS-CoV-2 genome have made the virus more infectious. The model, developed by lead researcher Guowei Wei, professor in the departments of Mathematics and Biochemistry and Molecular Biology, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples. The researchers analyzed mutations to the spike protein--a protein primarily responsible for facilitating infection--and found that five of the six known virus subtypes are now more infectious. As with any virus, many mutations are ultimately benign, posing little to no risk to infected patients. Some mutations even reduce infectiousness.
The development of a vaccine to protect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an urgent biomedical need. Yu et al. designed a series of prototype DNA vaccines against the SARS-CoV-2 spike protein, which is used by the virus to bind and invade human cells. Analysis of the vaccine candidates in rhesus macaques showed that animals developed protective humoral and cellular immune responses when challenged with the virus. Neutralizing antibody titers were also observed at levels similar to those seen in humans who have recovered from SARS-CoV-2 infection. Science , this issue p.  The global coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made the development of a vaccine a top biomedical priority. In this study, we developed a series of DNA vaccine candidates expressing different forms of the SARS-CoV-2 spike (S) protein and evaluated them in 35 rhesus macaques. Vaccinated animals developed humoral and cellular immune responses, including neutralizing antibody titers at levels comparable to those found in convalescent humans and macaques infected with SARS-CoV-2. After vaccination, all animals were challenged with SARS-CoV-2, and the vaccine encoding the full-length S protein resulted in >3.1 and >3.7 log10 reductions in median viral loads in bronchoalveolar lavage and nasal mucosa, respectively, as compared with viral loads in sham controls. Vaccine-elicited neutralizing antibody titers correlated with protective efficacy, suggesting an immune correlate of protection. These data demonstrate vaccine protection against SARS-CoV-2 in nonhuman primates. : /lookup/doi/10.1126/science.abc6284
Everyone knows the terms "machine learning" and "artificial intelligence." Few can define them, much less explain their inestimable value to clinical trials. So, it's not surprising that, despite their ability to minimize risk, improve safety, condense timelines, and save costs, these technology tools are not widely used by the clinical trial industry. There are lots of reasons for resistance: It seems complicated. Those who are not statistically savvy may find the thought of algorithms overwhelming.
Chest radiography is an important diagnostic tool for chest-related diseases. Medical imaging research is currently embracing the automatic detection techniques used in computer vision. Over the past decade, Deep Learning techniques have shown an enormous breakthrough in the field of medical diagnostics. Various automated systems have been proposed for the rapid detection of pneumonia on chest x-rays images Although such detection algorithms are many and varied, they have not been summarized into a review that would assist practitioners in selecting the best methods from a real-time perspective, perceiving the available datasets, and understanding the currently achieved results in this domain. After summarizing the topic, the review analyzes the usability, goodness factors, and computational complexities of the algorithms that implement these techniques.
Over the last several months, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a global pandemic, resulting in nearly 480,000 COVID-19 related deaths as of June 25, 2020 . While the disease can manifest in a variety of ways--ranging from asymptomatic conditions or flu-like symptoms to acute respiratory distress syndrome--the most common presentation associated with morbidity and mortality is the presence of opacities and consolidation in a patient's lungs. Upon inhalation, the virus attacks and inhibits the lungs' alveoli, which are responsible for oxygen exchange. This opacification is visible on computed tomography (CT) scans. Due to their increased densities, these areas appear as partially opaque regions with increased attenuation, which is known as a ground-glass opacity (GGO).
The emergence and outbreak of SARS-CoV-2, the causative agent of COVID-19, has rapidly become a global concern and has highlighted the need for fast, sensitive, and specific tools to surveil circulating viruses. Here we provide assay designs and experimental resources, for use with CRISPR-based nucleic acid detection, that could be valuable for ongoing surveillance. We provide assay designs for detection of 67 viral species and subspecies, including: SARS-CoV-2, phylogenetically-related viruses, and viruses with similar clinical presentation. The designs are outputs of algorithms that we are developing for rapidly designing nucleic acid detection assays that are comprehensive across genomic diversity and predicted to be highly sensitive and specific. Of our design set, we experimentally screened 4 SARS-CoV-2 designs with a CRISPR-Cas13 detection system and then extensively tested the highest-performing SARS-CoV-2 assay.
Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.
A novel machine learning model developed by researchers at Michigan State University suggests that mutations to the SARS-CoV-2 genome have made the virus more infectious. As infections continue to surge across the United States, the concern is that any slight mutation could have drastic consequences. The model, developed by lead research Guo-Wei Wei, professor in the Department of Biochemistry and Molecular Biology, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples. The researchers analyzed mutations to the spike protein -- a protein primarily responsible for facilitating infection -- and found that five of the six known virus subtypes are now more infectious. As with any virus, many mutations are ultimately benign, posing little to no risk to infected patients.