Building a statistical surveillance dashboard for COVID-19 infection worldwide

Barone, Stefano; Chakhunashvili, Alexander; Comelli, Albert

doi:10.1080/08982112.2020.1770791

Cited by 17 publications

(18 citation statements)

References 13 publications

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“…In order to support predictions for thousands of scenarios, we design a lightweight, vectorized implementation of our model, and build a large-scale system to run hundreds of models in parallel. 1 By leveraging this system, we are able to compress 2 years of compute time into the span of a few days.…”

Section: Nginx Servermentioning

confidence: 99%

“…Our resulting interface includes multiple interactive panels, where policymakers can select various proposed changes in mobility, and observe how these changes would aect predicted infections over time and losses in POI visits (Section 3.3). 1 Our code is available at https://github.com/snap-stanford/covid-mobility-tool.…”

Section: Nginx Servermentioning

confidence: 99%

“…During those four weeks, each POI category either maintains its current levels of mobility, or we specify it to have a certain fraction of its "normal" mobility levels, based on SafeGraph data from January 2019. We perform this experiment with 5 POI categories: (1) Restaurants; (2) Essential Retail (grocery stores, convenience stores, drug stores); (3) Gyms; (4) Religious Organizations; and (5) Retail (clothing stores, hardware stores, book stores, pet stores, etc.). For each category, we consider 4 possible mobility settings: maintaining the current mobility level, or keeping 0%, 50%, or 100% of 2019 mobility.…”

Section: Use Casesmentioning

confidence: 99%

“…Recently, there has been an intense interest in developing easy-to-use interfaces to support computational infectious diseases epidemiology. Much of the effort is focused on providing surveillance information [11, 28]; for example, several apps have been built to monitor trends in cases over time [35], tracking their growth rate [1] or aiming to detect clusters [14]. Other modeling tools that are being used for the COVID-19 pandemic include EpiC and Gleamviz [32], for global mobility and epidemic simulations; DiCon, for optimization and control problems related to epidemic dynamics [25]; and, most related to our tool, FRED [26] and GAIA [27], which are open source systems that support research in networked epidemiology.…”

Section: Related Workmentioning

confidence: 99%

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Supporting COVID-19 policy response with large-scale mobility-based modeling

Chang

Wilson

Lewis

et al. 2021

Preprint

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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 model's 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.

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Section: Nginx Servermentioning

confidence: 99%

Section: Nginx Servermentioning

confidence: 99%

Section: Use Casesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Supporting COVID-19 policy response with large-scale mobility-based modeling

Chang

Wilson

Lewis

et al. 2021

Preprint

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“…WHO and Johns Hopkins University were pioneers in representing the pandemic situation in various graphic and cartographic ways, with the Center for Systems Science and Engineering (CSSE) dashboard at Johns Hopkins University [13] being used as an official source of international information. Other examples could be given, and there are even published studies by researchers such as Fernandez-Lozano and Cedron [14] that have developed an interactive and dynamic dashboard for monitoring COVID-19 to support the epidemiological study of the disease in Spain, or Barone et al [15], who propose a set of ways to explore and analyze epidemiological data of COVID-19 from a spatio-temporal perspective with explorative and non-inferential metrics. Without neglecting the importance that these solutions have for epidemiological monitoring and surveillance, it is crucial to address others that add a predictive component to the evolution and that naturally have a greater weight in helping make decisions regarding the control of contagions.…”

Section: Use Of Dashboards In the Context Of The Pandemicmentioning

confidence: 99%

Dashboard COMPRIME_COMPRI_MOv: Multiscalar Spatio-Temporal Monitoring of the COVID-19 Pandemic in Portugal

2021

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Due to its novelty, the recent pandemic of the coronavirus disease (COVID-19), which is associated with the spread of the new severe acute respiratory syndrome coronavirus (SARS-CoV-2), triggered the public’s interest in accessing information, demonstrating the importance of obtaining and analyzing credible and updated information from an epidemiological surveillance context. For this purpose, health authorities, international organizations, and university institutions have published online various graphic and cartographic representations of the evolution of the pandemic with daily updates that allow the almost real-time monitoring of the evolutionary behavior of the spread, lethality, and territorial distribution of the disease. The purpose of this article is to describe the technical solution and the main results associated with the publication of the COMPRIME_COMPRI_MOv dashboard for the dissemination of information and multi-scale knowledge of COVID-19. Under two rapidly implementing research projects for innovative solutions to respond to the COVID-19 pandemic, promoted in Portugal by the FCT (Foundation for Science and Technology), a website was created. That website brings together a diverse set of variables and indicators in a dynamic and interactive way that reflects the evolutionary behavior of the pandemic from a multi-scale perspective, in Portugal, constituting itself as a system for monitoring the evolution of the pandemic. In the current situation, this type of exploratory solutions proves to be crucial to guarantee everyone’s access to information while simultaneously emerging as an epidemiological surveillance tool that is capable of assisting decision-making by public authorities with competence in defining control policies and fight the spread of the new coronavirus.

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