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1.
Data & Policy ; 4, 2022.
Article in English | ProQuest Central | ID: covidwho-1699689

ABSTRACT

A number of governmental and nongovernmental organizations have made significant efforts to encourage the development of artificial intelligence in line with a series of aspirational concepts such as transparency, interpretability, explainability, and accountability. The difficulty at present, however, is that these concepts exist at a fairly level, whereas in order for them to have the tangible effects desired they need to become more concrete and specific. This article undertakes precisely this process of concretisation, mapping how the different concepts interrelate and what in particular they each require in order to move from being high-level aspirations to detailed and enforceable requirements. We argue that the key concept in this process is accountability, since unless an entity can be held accountable for compliance with the other concepts, and indeed more generally, those concepts cannot do the work required of them. There is a variety of taxonomies of accountability in the literature. However, at the core of each account appears to be a sense of “answerability”;a need to explain or to give an account. It is this ability to call an entity to account which provides the impetus for each of the other concepts and helps us to understand what they must each require.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324941

ABSTRACT

Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

4.
Preprint in English | medRxiv | ID: ppmedrxiv-21254022

ABSTRACT

Social distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19. However, these mobility restrictions place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures.In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an intuitive dashboard interface that communicates our models predictions for thousands of potential policies, tailored to their jurisdiction. The resulting decision-support environment provides policymakers with much-needed analytical machinery to assess the tradeoffs between future infections and mobility restrictions.

6.
Nature ; 589(7840): 82-87, 2021 01.
Article in English | MEDLINE | ID: covidwho-917538

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Computer Simulation , Locomotion , Physical Distancing , Socioeconomic Factors , COVID-19/transmission , Cell Phone/statistics & numerical data , Data Analysis , Humans , Mobile Applications/statistics & numerical data , Religion , Restaurants/organization & administration , Risk Assessment , Time Factors
7.
Cell Res ; 30(8): 670-677, 2020 08.
Article in English | MEDLINE | ID: covidwho-637104

ABSTRACT

The 2019 novel coronavirus (SARS-CoV-2) outbreak is a major challenge for public health. SARS-CoV-2 infection in human has a broad clinical spectrum ranging from mild to severe cases, with a mortality rate of ~6.4% worldwide (based on World Health Organization daily situation report). However, the dynamics of viral infection, replication and shedding are poorly understood. Here, we show that Rhesus macaques are susceptible to the infection by SARS-CoV-2. After intratracheal inoculation, the first peak of viral RNA was observed in oropharyngeal swabs one day post infection (1 d.p.i.), mainly from the input of the inoculation, while the second peak occurred at 5 d.p.i., which reflected on-site replication in the respiratory tract. Histopathological observation shows that SARS-CoV-2 infection can cause interstitial pneumonia in animals, characterized by hyperemia and edema, and infiltration of monocytes and lymphocytes in alveoli. We also identified SARS-CoV-2 RNA in respiratory tract tissues, including trachea, bronchus and lung; and viruses were also re-isolated from oropharyngeal swabs, bronchus and lung, respectively. Furthermore, we demonstrated that neutralizing antibodies generated from the primary infection could protect the Rhesus macaques from a second-round challenge by SARS-CoV-2. The non-human primate model that we established here provides a valuable platform to study SARS-CoV-2 pathogenesis and to evaluate candidate vaccines and therapeutics.


Subject(s)
Betacoronavirus/genetics , Betacoronavirus/immunology , Coronavirus Infections/pathology , Disease Models, Animal , Macaca mulatta/virology , Pneumonia, Viral/pathology , Animals , Antibodies, Neutralizing/blood , Antibodies, Neutralizing/immunology , Antibodies, Viral/blood , Antibodies, Viral/immunology , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/blood , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/virology , Female , Immunohistochemistry , Male , Pandemics , Pneumonia, Viral/blood , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/virology , RNA, Viral/genetics , Radiography, Thoracic , Real-Time Polymerase Chain Reaction , SARS-CoV-2 , Viral Load , Virus Replication
8.
Preprint in English | medRxiv | ID: ppmedrxiv-20131979

ABSTRACT

Fine-grained epidemiological modeling of the spread of SARS-CoV-2--capturing who is infected at which locations--can aid the development of policy responses that account for heterogeneous risks of different locations as well as the disparities in infections among different demographic groups. Here, we develop a metapopulation SEIR disease model that uses dynamic mobility networks, derived from US cell phone data, to capture the hourly movements of millions of people from local neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants, grocery stores, or religious establishments. We simulate the spread of SARS-CoV-2 from March 1-May 2, 2020 among a population of 98 million people in 10 of the largest US metropolitan statistical areas. We show that by integrating these mobility networks, which connect 57k CBGs to 553k POIs with a total of 5.4 billion hourly edges, even a relatively simple epidemiological model can accurately capture the case trajectory despite dramatic changes in population behavior due to the virus. Furthermore, by modeling detailed information about each POI, like visitor density and visit length, we can estimate the impacts of fine-grained reopening plans: we predict that a small minority of "superspreader" POIs account for a large majority of infections, that reopening some POI categories (like full-service restaurants) poses especially large risks, and that strategies restricting maximum occupancy at each POI are more effective than uniformly reducing mobility. Our models also predict higher infection rates among disadvantaged racial and socio-economic groups solely from differences in mobility: disadvantaged groups have not been able to reduce mobility as sharply, and the POIs they visit (even within the same category) tend to be smaller, more crowded, and therefore more dangerous. By modeling who is infected at which locations, our model supports fine-grained analyses that can inform more effective and equitable policy responses to SARS-CoV-2.

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