Collaborating Authors

Pulmonary/Respiratory Diseases

Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2


A minority of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmit most infections. How does this happen? Sun et al. reconstructed transmission in Hunan, China, up to April 2020. Such detailed data can be used to separate out the relative contribution of transmission control measures aimed at isolating individuals relative to population-level distancing measures. The authors found that most of the secondary transmissions could be traced back to a minority of infected individuals, and well over half of transmission occurred in the presymptomatic phase. Furthermore, the duration of exposure to an infected person combined with closeness and number of household contacts constituted the greatest risks for transmission, particularly when lockdown conditions prevailed. These findings could help in the design of infection control policies that have the potential to minimize both virus transmission and economic strain. Science , this issue p. [eabe2424][1] ### INTRODUCTION The role of transmission heterogeneities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) dynamics remains unclear, particularly those heterogeneities driven by demography, behavior, and interventions. To understand individual heterogeneities and their effect on disease control, we analyze detailed contact-tracing data from Hunan, a province in China adjacent to Hubei and one of the first regions to experience a SARS-CoV-2 outbreak in January to March 2020. The Hunan outbreak was swiftly brought under control by March 2020 through a combination of nonpharmaceutical interventions including population-level mobility restriction (i.e., lockdown), traveler screening, case isolation, contact tracing, and quarantine. In parallel, highly detailed epidemiological information on SARS-CoV-2–infected individuals and their close contacts was collected by the Hunan Provincial Center for Disease Control and Prevention. ### RATIONALE Contact-tracing data provide information to reconstruct transmission chains and understand outbreak dynamics. These data can in turn generate valuable intelligence on key epidemiological parameters and risk factors for transmission, which paves the way for more-targeted and cost-effective interventions. ### RESULTS On the basis of epidemiological information and exposure diaries on 1178 SARS-CoV-2–infected individuals and their 15,648 close contacts, we developed a series of statistical and computational models to stochastically reconstruct transmission chains, identify risk factors for transmission, and infer the infectiousness profile over the course of a typical infection. We observe overdispersion in the distribution of secondary infections, with 80% of secondary cases traced back to 15% of infections, which indicates substantial transmission heterogeneities. We find that SARS-CoV-2 transmission risk scales positively with the duration of exposure and the closeness of social interactions, with the highest per-contact risk estimated in the household. Lockdown interventions increase transmission risk in families and households, whereas the timely isolation of infected individuals reduces risk across all types of contacts. There is a gradient of increasing susceptibility with age but no significant difference in infectivity by age or clinical severity. Early isolation of SARS-CoV-2–infected individuals drastically alters transmission kinetics, leading to shorter generation and serial intervals and a higher fraction of presymptomatic transmission. After adjusting for the censoring effects of isolation, we find that the infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom onset, with 53% of transmission occurring in the presymptomatic phase in an uncontrolled setting. We then use these results to evaluate the effectiveness of individual-based strategies (case isolation and contact quarantine) both alone and in combination with population-level contact reductions. We find that a plausible parameter space for SARS-CoV-2 control is restricted to scenarios where interventions are synergistically combined, owing to the particular transmission kinetics of this virus. ### CONCLUSION There is considerable heterogeneity in SARS-CoV-2 transmission owing to individual differences in biology and contacts that is modulated by the effects of interventions. We estimate that about half of secondary transmission events occur in the presymptomatic phase of a primary case in uncontrolled outbreaks. Achieving epidemic control requires that isolation and contact-tracing interventions are layered with population-level approaches, such as mask wearing, increased teleworking, and restrictions on large gatherings. Our study also demonstrates the value of conducting high-quality contact-tracing investigations to advance our understanding of the transmission dynamics of an emerging pathogen. ![Figure][2] Transmission chains, contact patterns, and transmission kinetics of SARS-CoV-2 in Hunan, China, based on case and contact-tracing data from Hunan, China. (Top left) One realization of the reconstructed transmission chains, with a histogram representing overdispersion in the distribution of secondary infections. (Top right) Contact matrices of community, social, extended family, and household contacts reveal distinct age profiles. (Bottom) Earlier isolation of primary infections shortens the generation and serial intervals while increasing the relative contribution of transmission in the presymptomatic phase. A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, which are driven by demography, behavior, and interventions. On the basis of detailed patient and contact-tracing data in Hunan, China, we find that 80% of secondary infections traced back to 15% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primary infections, which indicates substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, whereas isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates that SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions because of the specific transmission kinetics of this virus. [1]: /lookup/doi/10.1126/science.abe2424 [2]: pending:yes

Learning the language of viral evolution and escape


Viral mutations that evade neutralizing antibodies, an occurrence known as viral escape, can occur and may impede the development of vaccines. To predict which mutations may lead to viral escape, Hie et al. used a machine learning technique for natural language processing with two components: grammar (or syntax) and meaning (or semantics) (see the Perspective by Kim and Przytycka). Three different unsupervised language models were constructed for influenza A hemagglutinin, HIV-1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system. Science , this issue p. [284][1]; see also p. [233][2] The ability for viruses to mutate and evade the human immune system and cause infection, called viral escape, remains an obstacle to antiviral and vaccine development. Understanding the complex rules that govern escape could inform therapeutic design. We modeled viral escape with machine learning algorithms originally developed for human natural language. We identified escape mutations as those that preserve viral infectivity but cause a virus to look different to the immune system, akin to word changes that preserve a sentence’s grammaticality but change its meaning. With this approach, language models of influenza hemagglutinin, HIV-1 envelope glycoprotein (HIV Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike viral proteins can accurately predict structural escape patterns using sequence data alone. Our study represents a promising conceptual bridge between natural language and viral evolution. [1]: /lookup/doi/10.1126/science.abd7331 [2]: /lookup/doi/10.1126/science.abf6894

Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic


Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence peaked in Manaus, Brazil, in May 2020 with a devastating toll on the city's inhabitants, leaving its health services shattered and cemeteries overwhelmed. Buss et al. collected data from blood donors from Manaus and São Paulo, noted when transmission began to fall, and estimated the final attack rates in October 2020 (see the Perspective by Sridhar and Gurdasani). Heterogeneities in immune protection, population structure, poverty, modes of public transport, and uneven adoption of nonpharmaceutical interventions mean that despite a high attack rate, herd immunity may not have been achieved. This unfortunate city has become a sentinel for how natural population immunity could influence future transmission. Events in Manaus reveal what tragedy and harm to society can unfold if this virus is left to run its course. Science , this issue p. [288][1]; see also p. [230][2] Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly in Manaus, the capital of Amazonas state in northern Brazil. The attack rate there is an estimate of the final size of the largely unmitigated epidemic that occurred in Manaus. We use a convenience sample of blood donors to show that by June 2020, 1 month after the epidemic peak in Manaus, 44% of the population had detectable immunoglobulin G (IgG) antibodies. Correcting for cases without a detectable antibody response and for antibody waning, we estimate a 66% attack rate in June, rising to 76% in October. This is higher than in São Paulo, in southeastern Brazil, where the estimated attack rate in October was 29%. These results confirm that when poorly controlled, COVID-19 can infect a large proportion of the population, causing high mortality. [1]: /lookup/doi/10.1126/science.abe9728 [2]: /lookup/doi/10.1126/science.abf7921

SARS-CoV-2 spillover events


Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19 all broke out in recent decades and are caused by different strains of coronavirus (CoV). These viruses are considered to originate from bats and to have been transmitted to humans through intermediate hosts. SARS-CoV was identified in palm civets in wildlife markets and MERS-CoV in dromedary camels ([ 1 ][1]), but the direct source of the COVID-19 causative agent, SARS-CoV-2, is still undetermined. On page 172 of this issue, Oude Munnink et al. ([ 2 ][2]) report an in-depth investigation of SARS-CoV-2 infections in animals and humans working or living in 16 mink farms in the Netherlands. SARS-CoV-2 infections were detected in 66 out of 97 (68%) of the owners, workers, and their close contacts. Some people were infected with viral strains with an animal sequence signature, providing evidence of SARS-CoV-2 spillover back and forth between animals and humans within mink farms. Besides mink, multiple species of wild or domestic animals may also carry SARS-CoV-2 or its related viruses. Experimental infections and binding-affinity assays between the SARS-CoV-2 spike (a surface protein that mediates cell entry) and its receptor, angiotensin-converting enzyme II (ACE2), demonstrate that SARS-CoV-2 has a wide host range ([ 3 ][3]). After the SARS-CoV-2 outbreak, several groups reported SARS-related CoVs in horseshoe bats in China and in pangolins smuggled from South Asian countries, but according to genome sequence comparison, none are directly the progenitor virus of SARS-CoV-2 ([ 4 ][4]). Domestic cats and dogs, as well as tigers in zoos, have also been found to be naturally infected by SARS-CoV-2 from humans, but there is no evidence that they can infect humans, and so they are unlikely to be the source hosts of SARS-CoV-2 ([ 4 ][4], [ 5 ][5]). To date, SARS-CoV-2 infections in mink farms have been reported in eight countries (the Netherlands, Denmark, Spain, France, Sweden, Italy, the United States, and Greece), according to the World Organisation for Animal Health ([ 6 ][6]). In addition to animal-to-human transmission in farms, cold food supplier chains are raising substantial concern. In various cities in China, several small-scale COVID-19 outbreaks caused by virus-contaminated uncooked seafood or pork from overseas countries have been documented. It was found that viral genome signatures in these outbreaks were different from the viral strains present in China ([ 7 ][7], [ 8 ][8]). There is evidence that SARS-CoV-2 can survive up to 3 weeks in meat and on the surface of cold food packages without losing infectivity ([ 7 ][7], [ 8 ][8]). Thus, meat from SARS-CoV-2–infected animals or food packaging contaminated by SARS-CoV-2 could be a source of human infection (see the figure). This raises concerns about public health and agriculture in the prevention and control of SARS-CoV-2. Most SARS-CoV-2–infected animals do not display an obvious clinical syndrome, and infections would be unrecognized without routine diagnosis. The massive mink culling of infected farms is an efficient way to prevent further transmission of the virus. However, it cannot be applied to all domestic animals (if other species are found to be SARS-CoV-2 hosts). Thus, out of caution, extensive and strict quarantine measures should be implemented in all domestic farms with high-density animal populations. Because the virus is able to jump between some animals (such as mink) and humans, similar strategies should be applied to people in key occupations involving animal-human interfaces, such as animal farmers, zookeepers, or people who work in slaughterhouses. Notably, there is limited evidence of animal-to-human transmission of SARS-CoV-2 except for mink. Research on whether other domestic animals carry SARS-CoV-2, whether they can transmit it to humans, and factors related to spillover should be conducted. The RNA genome of SARS-CoV-2 seems relatively stable during transmission within human populations, although accumulated mutations have been detected. It is generally accepted that coronaviruses tend to exhibit rapid evolution when jumping to a different species. To keep the replication error rate low, coronaviruses encode several RNA-processing and proofreading enzymes that are thought to increase the fidelity of viral replication. However, viruses tend to have reduced fidelity in favor of adaptation to a new host species ([ 9 ][9]), although the mechanisms underlying this phenomenon are unclear. The coronaviral spike protein is prone to have more mutations because it is the first virus-host interaction protein and thus faces the strongest selection pressure. This molecular evolution can be observed in SARS-CoV genomes, which were under more adaptive pressure in the early stage of the epidemic (palm civet to human) than in later stages (human to human) ([ 10 ][10]). ![Figure][11] Possible SARS-CoV-2 transmission chains Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spillover likely occurred from bat and/or pangolin (ancestral virus) through unidentified intermediate host animals (direct progenitor virus). Human SARS-CoV-2 strains infect susceptible domestic animals (such as mink) and likely adapt to these species through mutation. The virus can be transmitted from mink back to farm workers and close contacts. SARS-CoV-2 can also be transmitted to humans through contact with contaminated uncooked meat or food packaging. GRAPHIC: MELISSA THOMAS BAUM/ SCIENCE Mutations that occur in SARS-CoV-2 in animals may increase its pathogenesis or transmissibility in humans. Five clusters of SARS-CoV-2 strains were found in mink, each characterized by a specific mink-related variant. In Denmark, the cluster 5 strain of mink SARS-CoV-2 was less immunogenic to COVID-19 patient serum than was human SARS-CoV-2 because of mutations of the spike proteins in the mink strains ([ 11 ][12]). This cluster 5 strain has infected at least 12 people, and the clinical presentation, severity, and transmission among those infected are similar to those of other circulating human SARS-CoV-2 strains ([ 12 ][13]). Currently, there is no evidence that any mutation from mink strains of SARS-CoV-2 escapes neutralization by antibodies designed to target the prevalent human strains. However, considering the possible risk of spillover of SARS-CoV-2 between humans and some animals, it is imperative to closely monitor mutations in the viral genome from infected animals and humans, particularly the genome regions affecting diagnostic tests, antiviral drugs, and vaccine development. It is anticipated that vaccines will allow control of COVID-19. Vaccines have been developed against the current prevalent viral strains and could face challenges if there is continued spillover from animals. The viral genome mutations likely produced during interspecies transmission between animals and humans raise concerns about whether the current vaccines can protect against emerging strains in the future. The extensive sequencing of viral genomes from animals and humans and worldwide data sharing will be central to efforts to monitor the key mutations that could affect vaccine efficacy. Laboratory-based studies should test whether the observed mutations affect key features of the virus, including pathogenesis, immunogenicity, and cross-neutralization. Moreover, preparedness of vaccines based on newly detected variants should be considered in advance. In the long term, vaccination of animals should also be considered to avoid economic losses in agriculture. There has been debate about whether bats or pangolins, which carry coronaviruses with genomes that are ∼90 to 96% similar to human SARS-CoV-2, were the animal source of the first human outbreak ([ 4 ][4]). Evolutionary analyses of viral genomes from bats and pangolins indicate that further adaptions, either in animal hosts or in humans, occurred before the virus caused the COVID-19 pandemic ([ 13 ][14]). Therefore, an animal species that has a high population density to allow natural selection and a competent ACE2 protein for SARS-CoV-2—mink, for example—would be a possible host of the direct progenitor of SARS-CoV-2. Another debate concerns the source of SARS-CoV-2 that caused the COVID-19 outbreak at the end of 2019. The current data question the animal origin of SARS-CoV-2 in the seafood market where the early cases were identified in Wuhan, China. Given the finding of SARS-CoV-2 on the surface of imported food packages, contact with contaminated uncooked food could be an important source of SARS-CoV-2 transmission ([ 8 ][8]). Recently, SARS-CoV-2 antibodies were found in human serum samples taken outside of China before the COVID-19 outbreak was detected ([ 14 ][15], [ 15 ][16]), which suggests that SARS-CoV-2 existed for some time before the first cases were described in Wuhan. Retrospective investigations of preoutbreak samples from mink or other susceptible animals, as well as humans, should be conducted to identify the hosts of the direct progenitor virus and to determine when the virus spilled over into humans. 1. [↵][17]1. J. Cui, 2. F. Li, 3. Z. L. Shi , Nat. Rev. Microbiol. 17, 181 (2019). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. B. B. Oude Munnink et al ., Science 371, 172 (2020). [OpenUrl][22] 3. [↵][23]1. L. Wu et al ., Cell Discov. 6, 68 (2020). [OpenUrl][24] 4. [↵][25]1. B. Hu, 2. H. Guo, 3. P. Zhou, 4. Z. L. Shi , Nat. Rev. Microbiol. 10.1038/s41579-020-00459-7 (2020). 5. [↵][26]1. D. McAloose et al ., mBio 11, e02220-20 (2020). [OpenUrl][27][Abstract/FREE Full Text][28] 6. [↵][29]World Organisation for Animal Health, COVID-19 Portal: Events in Animals (2020); [][30]. 7. [↵][31]1. P. Liu et al ., Biosaf. Health 10.1016/j.bsheal.2020.11.003 (2020). 8. [↵][32]1. J. Han, 2. X. Zhang, 3. S. He, 4. P. Jia , Environ. Chem. Lett. 10.1007/s10311-020-01101-x (2020). 9. [↵][33]1. R. L. Graham, 2. R. S. Baric , J. Virol. 84, 3134 (2010). [OpenUrl][34][Abstract/FREE Full Text][35] 10. [↵][36]Chinese SARS Molecular Epidemiology Consortium, Science 303, 1666 (2004). [OpenUrl][37][Abstract/FREE Full Text][38] 11. [↵][39]European Centre for Disease Prevention and Control, “Rapid Risk Assessment: Detection of New SARS-CoV-2 Variants Related to Mink” (2020); [][40]. 12. [↵][41]1. R. Lassauniere et al ., “SARS-CoV-2 spike mutations arising in Danish mink and their spread to humans” (2020); . 13. [↵][42]1. K. G. Andersen, 2. A. Rambaut, 3. W. I. Lipkin, 4. E. C. Holmes, 5. R. F. Garry , Nat. Med. 26, 450 (2020). [OpenUrl][43][CrossRef][44][PubMed][45] 14. [↵][46]1. G. Apolone et al ., Tumori J. 10.1177/0300891620974755 (2020). 15. [↵][47]1. S. V. Basavaraju et al ., Clin. Infect. Dis. ciaa1785 (2020). Acknowledgments: Supported by China National Science Foundation for Excellent Scholars award 81822028 (P.Z.) and Strategic Priority Research Program of the Chinese Academy of Sciences awards XDB29010101 (Z.-L.S.) and XDB29010204 (P.Z.). 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Decentralized units for printing, media control, and computing save precious space in your biosafety cabinet and ensure superb heat dissipation. Silent but smart XYZ-drives deliver micrometer precision. In addition, the system comes with a Peltier heater/cooler cartridge for temperature-controlled bioprinting and a built-in UV-source UV-LED pen. Designed to fit and operate in a standard biosafety cabinet, BioScaffolder Prime enables you to undertake your 3D printing applications quickly, safely, and in a sterile environment. Ziath reports strong uptake of its Mohawk semiautomated tube picker in smaller biobanks and biorepositories, which need to select tubes from cold racks straight from the freezer but cannot afford the huge investment in robotics required to automatically pick and place tubes. The small, compact Mohawk can pick up 16 tubes simultaneously from a 96-position tube rack. By elevating sample tubes in racks using solenoids, the Mohawk enables biobank operators to quickly retrieve the correct tubes and put them in the destination racks. Additionally, because the Mohawk can seamlessly connect with Ziath rack scanners, biobank users can read a picking list, select tubes, and verify that the correct tubes are picked—making the process of finding and selecting the right tubes in your biobank more efficient and economical. The Cold Coil II Flow Reactor Module from Uniqsis is a flexible, entry-level solution for low temperature flow chemistry applications. Used in conjunction with an external thermoregulation circulator, the unit can maintain stable temperatures between −78°C and 150°C for extended periods of time. It is compatible with all Uniqsis coil reactors, from 2.0 mL up to 60 mL capacity. A proprietary clamping mechanism holds the coil reactor firmly in place and ensures optimal thermal contact while allowing easy interchange of coil reactors. The Cold Coil II can be easily converted into a photoreactor by coupling it with a Uniqsis PhotoSyn high-power LED light module. It is also compatible with the Uniqsis HotColumn multiple-column reactor adaptor for packed-bed applications. To ensure accurate remote measurement of the Cold Coil II reactor temperature, an optional internal temperature probe can be connected directly via RS232C. The RAPID EPS (Easy Piercing Seal) from BioChromato is designed for scientists looking to prevent contamination issues and autosampler-needle clogging when accessing samples stored in 96-well microplates ready for LC/MS analysis. For LC/MS users, a key criterion for an effective microplate seal is its resistance to solvents such as acetonitrile, methanol, and DMSO, which are commonly used in experiments and analysis. The RAPID EPS uses a synthetic rubber adhesive to create a high-integrity, airtight seal with microplates, and shows no contamination in the eluents. In addition, the unique construction of BioChromato's RAPID EPS does not leave particulate material when pierced, further safeguarding your samples from contamination and eliminating potentially harmful effects to your LC/MS autosampler. The RAPID EPS is proven to offer dependable microplate sealing over a working temperature range of −80°C to 80°C.  [1]: pending:yes

Opportunities for machine learning use in cystic fibrosis care


Accurately predicting how an individual's chronic illness is going to progress is critical to delivering better-personalised, precision medicine. Yet there is an enormous challenge in accurately predicting the clinical trajectories of people for chronic health conditions such as cystic fibrosis (CF), cancer, cardiovascular disease and Alzheimer's disease. AI technology developed by the Cambridge Centre for AI in Medicine and their colleagues offers a glimpse of the future of precision medicine, and the predictive power which may be available to clinicians caring for individuals with the life-limiting condition cystic fibrosis. "Prediction problems in healthcare are fiendishly complex," said Professor Mihaela van der Schaar, Director of the Cambridge Centre for AI in Medicine (CCAIM). "Even machine learning approaches, which deal in complexity, struggle to deliver meaningful benefits to patients and clinicians, and to medical science more broadly. Off-the-shelf machine learning solutions, so useful in many areas, simply do not cut it in predictive medicine."

Chest X-Rays with Artificial Intelligence Catches More Lung Cancer


Lung cancer detection and radiologist performance can get a boost from an artificial intelligence (AI) algorithm that pinpoints previously un-detected cancers on chest X-rays. In a study published in the Dec. 10 Radiology: Cardiothoracic Imaging, investigators from Seoul National University Hospital outlined how a commercially available deep-learning algorithm outperformed four thoracic radiologists on both first and second reads. Overall, said the team led by Ju Gang Nam, M.D., the algorithm offered both higher sensitivity and higher specificity, and it improved providers' performance as a seconder reader, leading to significantly improved detection rates. But, to date, the team said, adoption of computer-aided detection with chest X-ray has been slow because many providers still have lingering questions about whether it can perform well enough in clinical practice. To answer that question, Nam's team used an enriched dataset of 50 normal chest X-rays, as well as 168 posteroanterior chest X-rays with lung cancers.

What Big Tech and Big Tobacco research funding have in common


Amid declining sales and evidence that smoking causes lung cancer, in the 1950s tobacco companies undertook PR campaigns to reinvent themselves as socially responsible and to shape public opinions. They also started funding research into the relationship between health and tobacco. Now, Big Tech companies like Amazon, Facebook, and Google are following the same playbook to fund AI ethics research in academia, according to a recently published paper by University of Toronto Center for Ethics PhD student Mohamed Abdalla and Harvard Medical School student Moustafa Abdalla. The coauthors conclude that effective solutions to the problem will need to come from institutional or governmental policy changes. The Abdalla brothers argue Big Tech companies aren't just involved with, but are leading, ethics discussions in academic settings.

AI-Assisted Cough Tracking Could Help Detect the Next Pandemic


When Joe Brew worked for the Florida Department of Health as an epidemiologist for two years starting in 2013, he helped with syndromic surveillance, meaning he had the arduous job of reviewing the symptoms of patients coming into the emergency departments from all across the state. The goal of such work: to detect an abnormal spike of symptoms in an area that may indicate there's a public health concern. Public health authorities worldwide continue to use this type of surveillance. The outbreak of a novel pathogen in Wuhan, China in late 2019, for instance, was in part detected by a large uptick of patients coming to the hospital with symptoms of a respiratory infection, with unknown etiology. But Brew says this system fails to prevent the transmission of a virus like SARS-CoV-2 because by the time patients arrive at the hospital, they have likely already been infectious for a matter of days.

An Approach to Intelligent Pneumonia Detection and Integration Artificial Intelligence

Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, the usage of AI in pneumonia detection is limited, in particular, due to challenges in generalizing a locally achieved result. In this report, we propose a roadmap for creating and integrating a system that attempts to solve this challenge. We also address various technical, legal, ethical, and logistical issues, with a blueprint of possible solutions.