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Register a new userMedical Robots for Infectious Diseases: Lessons and Challenges from the COVID-19 Pandemic
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Added by
Hao Su
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Medical robots can play an important role in mitigating the spread of infectious diseases and delivering quality care to patients during the COVID-19 pandemic. Methods and procedures involving medical robots in the continuum of care, ranging from disease prevention, screening, diagnosis, treatment, and homecare have been extensively deployed and also present incredible opportunities for future development. This paper provides an overview of the current state-of-the-art, highlighting the enabling technologies and unmet needs for prospective technological advances within the next 5-10 years. We also identify key research and knowledge barriers that need to be addressed in developing effective and flexible solutions to ensure preparedness for rapid and scalable deployment to combat infectious diseases.
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This paper reviews 262 reports appearing between March 27 and July 4, 2020, in the press, social media, and scientific literature describing 203 instances of actual use of 104 different models of ground and aerial robots for the COVID19 response. The reports are organized by stakeholders and work domain into a novel taxonomy of six application categories, reflecting major differences in work envelope, adoption strategy, and human-robot interaction constraints. Each application category is further divided into a total of 30 subcategories based on differences in mission. The largest number of reported instances were for public safety (74 out of 203) and clinical care (46), though robots for quality of life (27), continuity of work and education (22), laboratory and supply chain automation (21), and non-clinical care (13) were notable. Ground robots were used more frequently (119) than aerial systems (84), but unlike ground robots, aerial applications appeared to take advantage of existing general purpose platforms that were used for multiple applications and missions. Of the 104 models of robots, 82 were determined to be commercially available or already existed as a prototype, 11 were modifications to existing robots, 11 were built from scratch. Teleoperation dominated the control style (105 instances), with the majority of those applications intentionally providing remote presence and thus not amenable to full autonomy in the future. Automation accounted for 74 instances and taskable agency forms of autonomy, 24. The data suggests areas for further research in autonomy, human-robot interaction, and adaptability.
This article analyses data collected on 338 instances of robots used explicitly in response to COVID-19 from 24 Jan, 2020, to 23 Jan, 2021, in 48 countries. The analysis was guided by four overarching questions: 1) What were robots used for in the COVID-19 response? 2) When were they used? 3) How did different countries innovate? and 4) Did having a national policy on robotics influence a countrys innovation and insertion of robotics for COVID-19? The findings indicate that robots were used for six different sociotechnical work domains and 29 discrete use cases. When robots were used varied greatly on the country; although many countries did report an increase at the beginning of their first surge. To understand the findings of how innovation occurred, the data was examined through the lens of the technologys maturity according to NASAs Technical Readiness Assessment metrics. Through this lens, findings note that existing robots were used for more than 78% of the instances; slightly modified robots made up 10%; and truly novel robots or novel use cases constituted 12% of the instances. The findings clearly indicate that countries with a national robotics initiative were more likely to use robotics more often and for broader purposes. Finally, the dataset and analysis produces a broad set of implications that warrant further study and investigation. The results from this analysis are expected to be of value to the robotics and robotics policy community in preparing robots for rapid insertion into future disasters.
We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al., Comp. Meth. App. Mech. Eng., 356, 44-74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.
We present results of different approaches to model the evolution of the COVID-19 epidemic in Argentina, with a special focus on the megacity conformed by the city of Buenos Aires and its metropolitan area, including a total of 41 districts with over 13 million inhabitants. We first highlight the relevance of interpreting the early stage of the epidemic in light of incoming infectious travelers from abroad. Next, we critically evaluate certain proposed solutions to contain the epidemic based on instantaneous modifications of the reproductive number. Finally, we build increasingly complex and realistic models, ranging from simple homogeneous models used to estimate local reproduction numbers, to fully coupled inhomogeneous (deterministic or stochastic) models incorporating mobility estimates from cell phone location data. The models are capable of producing forecasts highly consistent with the official number of cases with minimal parameter fitting and fine-tuning. We discuss the strengths and limitations of the proposed models, focusing on the validity of different necessary first approximations, and caution future modeling efforts to exercise great care in the interpretation of long-term forecasts, and in the adoption of non-pharmaceutical interventions backed by numerical simulations.
In this paper, we apply statistical methods for functional data to explain the heterogeneity in the evolution of number of deaths of Covid-19 over different regions. We treat the cumulative daily number of deaths in a specific region as a curve (functional data) such that the data comprise of a set of curves over a cross-section of locations. We start by using clustering methods for functional data to identify potential heterogeneity in the curves and their functional derivatives. This first stage is an unconditional descriptive analysis, as we do not use any covariate to estimate the clusters. The estimated clusters are analyzed as levels of alert to identify cities in a possible critical situation. In the second and final stage, we propose a functional quantile regression model of the death curves on a number of scalar socioeconomic and demographic indicators in order to investigate their functional effects at different levels of the cumulative number of deaths over time. The proposed model showed a superior predictive capacity by providing better curve fit at different levels of the cumulative number of deaths compared to the functional regression model based on ordinary least squares.
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