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      Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic

      , PhD , 1 , , PhD 2

      Preventing Chronic Disease

      Centers for Disease Control and Prevention

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          Abstract

          Incorporating geographic information science and technology (GIS&T) into COVID-19 pandemic surveillance, modeling, and response enhances understanding and control of the disease. Applications of GIS&T include 1) developing spatial data infrastructures for surveillance and data sharing, 2) incorporating mobility data in infectious disease forecasting, 3) using geospatial technologies for digital contact tracing, 4) integrating geographic data in COVID-19 modeling, 5) investigating geographic social vulnerabilities and health disparities, and 6) communicating the status of the disease or status of facilities for return-to-normal operations. Locations and availability of personal protective equipment, ventilators, hospital beds, and other items can be optimized with the use of GIS&T. Challenges include protection of individual privacy and civil liberties and closer collaboration among the fields of geography, medicine, public health, and public policy.

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          Most cited references 20

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          Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

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            Dynamic population mapping using mobile phone data.

            During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.
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              Spatial Externalities, Spatial Multipliers, And Spatial Econometrics

               Luc Anselin (2003)
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                Author and article information

                Journal
                Prev Chronic Dis
                Prev Chronic Dis
                PCD
                Preventing Chronic Disease
                Centers for Disease Control and Prevention
                1545-1151
                2020
                09 July 2020
                : 17
                Affiliations
                [1 ]University of California, Berkeley, School of Public Health, Berkeley, California
                [2 ]Temple University, Philadelphia, Pennsylvania
                Author notes
                Corresponding Author: Charlotte D. Smith, School of Public Health, 2121 Berkeley Way #5302, University of California, Berkeley, Berkeley, CA 94720. Telephone: 935-377-1891. Email: charlottesmith@ 123456berkeley.edu .
                Article
                20_0246
                10.5888/pcd17.200246
                7367069
                32644920

                Preventing Chronic Disease is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.

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                Peer Reviewed

                Health & Social care

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