13
views
0
recommends
+1 Recommend
2 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Impact of COVID-19 epidemic curtailment strategies in selected Indian states: An analysis by reproduction number and doubling time with incidence modelling

      research-article
      1 , 2 , 3 , 4 , *
      PLoS ONE
      Public Library of Science

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The Government of India in-network with the state governments has implemented the epidemic curtailment strategies inclusive of case-isolation, quarantine and lockdown in response to ongoing novel coronavirus (COVID-19) outbreak. In this manuscript, we attempt to estimate the impact of these steps across ten selected Indian states using crowd-sourced data. The trajectory of the outbreak was parameterized by the reproduction number ( R 0 ), doubling time, and growth rate. These parameters were estimated at two time-periods after the enforcement of the lockdown on 24 th March 2020, i.e. 15 days into lockdown and 30 days into lockdown. The authors used a crowd sourced database which is available in the public domain. After preparing the data for analysis, R 0 was estimated using maximum likelihood (ML) method which is based on the expectation minimum algorithm where the distribution probability of secondary cases is maximized using the serial interval discretization. The doubling time and growth rate were estimated by the natural log transformation of the exponential growth equation. The overall analysis shows decreasing trends in time-varying reproduction numbers ( R (t) ) and growth rate (with a few exceptions) and increasing trends in doubling time. The curtailment strategies employed by the Indian government seem to be effective in reducing the transmission parameters of the COVID-19 epidemic. The estimated R (t) are still above the threshold of 1, and the resultant absolute case numbers show an increase with time. Future curtailment and mitigation strategies thus may take into account these findings while formulating further course of action.

          Related collections

          Most cited references25

          • Record: found
          • Abstract: found
          • Article: not found

          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China].

            (2020)
            Objective: An outbreak of 2019 novel coronavirus diseases (COVID-19) in Wuhan, China has spread quickly nationwide. Here, we report results of a descriptive, exploratory analysis of all cases diagnosed as of February 11, 2020. Methods: All COVID-19 cases reported through February 11, 2020 were extracted from China's Infectious Disease Information System. Analyses included: 1) summary of patient characteristics; 2) examination of age distributions and sex ratios; 3) calculation of case fatality and mortality rates; 4) geo-temporal analysis of viral spread; 5) epidemiological curve construction; and 6) subgroup analysis. Results: A total of 72 314 patient records-44 672 (61.8%) confirmed cases, 16 186 (22.4%) suspected cases, 10567 (14.6%) clinical diagnosed cases (Hubei only), and 889 asymptomatic cases (1.2%)-contributed data for the analysis. Among confirmed cases, most were aged 30-79 years (86.6%), diagnosed in Hubei (74.7%), and considered mild (80.9%). A total of 1 023 deaths occurred among confirmed cases for an overall case-fatality rate of 2.3%. The COVID-19 spread outward from Hubei sometime after December 2019 and by February 11, 2020, 1 386 counties across all 31 provinces were affected. The epidemic curve of onset of symptoms peaked in January 23-26, then began to decline leading up to February 11. A total of 1 716 health workers have become infected and 5 have died (0.3%). Conclusions: The COVID-19 epidemic has spread very quickly. It only took 30 days to expand from Hubei to the rest of Mainland China. With many people returning from a long holiday, China needs to prepare for the possible rebound of the epidemic.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations

              Summary As coronavirus disease 2019 (COVID-19) spreads across the world, the intensive care unit (ICU) community must prepare for the challenges associated with this pandemic. Streamlining of workflows for rapid diagnosis and isolation, clinical management, and infection prevention will matter not only to patients with COVID-19, but also to health-care workers and other patients who are at risk from nosocomial transmission. Management of acute respiratory failure and haemodynamics is key. ICU practitioners, hospital administrators, governments, and policy makers must prepare for a substantial increase in critical care bed capacity, with a focus not just on infrastructure and supplies, but also on staff management. Critical care triage to allow the rationing of scarce ICU resources might be needed. Researchers must address unanswered questions, including the role of repurposed and experimental therapies. Collaboration at the local, regional, national, and international level offers the best chance of survival for the critically ill.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                16 September 2020
                2020
                16 September 2020
                : 15
                : 9
                : e0239026
                Affiliations
                [1 ] Department of Community and Family Medicine, All India Institute of Medical Sciences, Bhopal, India
                [2 ] Department of Community and Family Medicine, All India Institute of Medical Sciences, Bhopal, India
                [3 ] Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bhubaneshwar, India
                [4 ] Department of Community and Family Medicine, All India Institute of Medical Sciences, Bhopal, India
                National Center for Global Health and Medicine, JAPAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                ‡ Independent Researcher.

                Author information
                http://orcid.org/0000-0001-6742-4033
                http://orcid.org/0000-0003-2897-4141
                http://orcid.org/0000-0002-2351-6259
                http://orcid.org/0000-0002-3766-376X
                Article
                PONE-D-20-14797
                10.1371/journal.pone.0239026
                7494123
                32936811
                6f50b0b5-4469-4e9f-ab68-3c1f508b1cd6
                © 2020 Mitra et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 May 2020
                : 28 August 2020
                Page count
                Figures: 1, Tables: 2, Pages: 11
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Medicine and Health Sciences
                Epidemiology
                People and Places
                Geographical Locations
                Asia
                India
                Medicine and Health Sciences
                Public and Occupational Health
                Medicine and Health Sciences
                Epidemiology
                Epidemiological Methods and Statistics
                Epidemiological Statistics
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Medicine and Health Sciences
                Diagnostic Medicine
                Virus Testing
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Population Density
                Custom metadata
                All data is available through API of crowdsourced database. Code for fetching data is available in Supplementary File. A copy of the cleaned data has been been provided in the Supporting Information for reproducibility of the R Code.
                COVID-19

                Uncategorized
                Uncategorized

                Comments

                Comment on this article