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      Integration of multiple geospatial applications and intelligence for responding to COVID-19 in Ghana

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          Summary

          Objective

          We describe the use of integrated geospatial applications for the provision of access to timely and accurate data on samples, visualisation of Spatio-temporal patterns of cases and effective communication between field sample collectors, testing laboratories, Regional Health directors and Government Decision Makers.

          Design

          This study describes how an integrated geospatial platform based on case location and intelligence was developed and used for effective COVID-19 response during the initial stages of COVID-19 in Ghana.

          Data Source

          Collector for ArcGIS, ArcGIS Survey123

          Main outcome measure

          successful development and deployment of integrated geospatial applications and analytics.

          Results

          The Collector for ArcGIS app was customised to collect COVID-19 positive cases location information. Survey 123 was introduced as a COVID-19 contact tracing application to digitise the case-based forms and provide real-time results from the laboratories to GHS and other stakeholders. The laboratory backend allowed the testing laboratories access to specific information about each patient (sample) collected by the fieldworkers. The regional supervisors' backend web application provided accessing test results for confidentiality and timely communication of results.

          Conclusion

          Geospatial platforms were successfully established in Ghana to provide timely results to Regional Health Directors and Government decision-makers. This helped to improve the timeliness of response and contact tracing at the district level.

          Funding

          The development and deployment of the application, COVID-19 pandemic response and writing workshop by the Ghana Field Epidemiology and Laboratory Training Programme (GFELTP) was supported with funding from President Malaria Initiative – CDC, and Korea International Cooperation Agency (on CDC CoAg 6NU2GGH001876) through AFENET and the United States Agency for International Development (USAID) through Results for Development (R4D).

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          Most cited references22

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          The effect of human mobility and control measures on the COVID-19 epidemic in China

          The ongoing COVID-19 outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions have been undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
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            The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study

            Summary Background In December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world. Methods To examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April). Findings Our projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66–97) and 24% (13–90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic. Interpretation Restrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R 0 and the duration of infectiousness. Funding Bill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.
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              Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics

              In December 2019, a new virus (initially called ‘Novel Coronavirus 2019-nCoV’ and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China’s massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics.
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                Author and article information

                Journal
                Ghana Med J
                Ghana Med J
                Ghana Medical Journal
                Ghana Medical Association
                0016-9560
                2616-163X
                June 2021
                : 55
                : 2 Suppl
                : 10-20
                Affiliations
                [1 ] Department of Geography and Resource Development, University of Ghana, Legon, Accra
                [2 ] Department of Biostatistics, School of Public Health, University of Ghana, Legon, Accra, Ghana
                [3 ] GFELTP, Department of Epidemiology and Disease Control, School of Public Health, University of Ghana, Legon, Accra, Ghana
                Author notes
                Corresponding author: Delia Akosua Bandoh deliabandoh@ 123456st.edu.gh

                Conflict of interest: None declared

                Article
                jGMJ.v55.i2s.pg10
                10.4314/gmj.v55i2s.3
                8853695
                35233110
                b60a222c-b813-4745-bd45-3f5fc1e16a01
                Copyright © The Author(s).

                This is an Open Access article under the CC BY license.

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                Categories
                Original Article

                arcgis,geospatial,covid-19,pandemic,distribution
                arcgis, geospatial, covid-19, pandemic, distribution

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