3
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Epidemiology of COVID-19 and effect of public health interventions, Chennai, India, March–October 2020: an analysis of COVID-19 surveillance system

      research-article

      Read this article at

      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

          Objectives

          To describe the public health strategies and their effect in controlling the COVID-19 pandemic from March to October 2020 in Chennai, India.

          Setting

          Chennai, a densely populated metropolitan city in Southern India, was one of the five cities which contributed to more than half of the COVID-19 cases in India from March to May 2020. A comprehensive community-centric public health strategy was implemented for controlling COVID-19, including surveillance, testing, contact tracing, isolation and quarantine. In addition, there were different levels of restrictions between March and October 2020.

          Participants

          We collected the deidentified line list of all the 192 450 COVID-19 cases reported from 17 March to 31 October 2020 in Chennai and their contacts for the analysis. We defined a COVID-19 case based on the real-time reverse transcriptase-PCR (RT-PCR) positive test conducted in one of the government-approved labs.

          Outcome measures

          The primary outcomes of interest were incidence of COVID-19 per million population, case fatality ratio (CFR), deaths per million, and the effective reproduction number (R t). We also analysed the surveillance, testing, contact tracing and isolation indicators.

          Results

          Of the 192 450 RT-PCR confirmed COVID-19 cases reported in Chennai from 17 March to 31 October 2020, 114 889 (60%) were males. The highest incidence was 41 064 per million population among those 61–80 years. The incidence peaked during June 2020 at 5239 per million and declined to 3627 per million in October 2020. The city reported 3543 deaths, with a case fatality ratio of 1.8%. In March, R t was 4.2, dropped below one in July and remained so until October, even with the relaxation of restrictions.

          Conclusion

          The combination of public health strategies might have contributed to controlling the COVID-19 epidemic in a large, densely populated city in India. We recommend continuing the test-trace-isolate strategy and appropriate restrictions to prevent resurgence.

          Related collections

          Most cited references29

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

          Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review

          The coronavirus disease 2019 (COVID-19) pandemic, due to the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide sudden and substantial increase in hospitalizations for pneumonia with multiorgan disease. This review discusses current evidence regarding the pathophysiology, transmission, diagnosis, and management of COVID-19.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

            Abstract The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China

              Abstract Background With its epicenter in Wuhan, China, the COVID-19 outbreak was declared a public health emergency of international concern (PHEIC) by the World Health Organization (WHO). Consequently, many countries have implemented flight restrictions to China. China itself has imposed a lockdown of the population of Wuhan as well as the entire Hubei province. However, whether these two enormous measures have led to significant changes in the spread of COVID-19 cases remains unclear. Methods We analyzed available data on the development of confirmed domestic and international COVID-19 cases before and after lockdown measures. We evaluated the correlation of domestic air traffic to the number of confirmed COVID-19 cases and determined the growth curves of COVID-19 cases within China before and after lockdown as well as after changes in COVID-19 diagnostic criteria. Results Our findings indicate a significant increase in doubling time from 2 days (95% Confidence Interval, CI): 1.9–2.6), to 4 days (95% CI: 3.5–4.3), after imposing lockdown. A further increase is detected after changing diagnostic and testing methodology to 19.3 (95% CI: 15.1–26.3), respectively. Moreover, the correlation between domestic air traffic and COVID-19 spread became weaker following lockdown (before lockdown: r = 0.98, p < 0.05 vs. after lockdown: r = 0.91, p = NS). Conclusions A significantly decreased growth rate and increased doubling time of cases was observed, which is most likely due to Chinese lockdown measures. A more stringent confinement of people in high risk areas seem to have a potential to slow down the spread of COVID-19.
                Bookmark

                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2022
                14 March 2022
                14 March 2022
                : 12
                : 3
                : e052067
                Affiliations
                [1 ]departmentGreater Chennai Corporation , Government of Tamil Nadu , Chennai, Tamil Nadu, India
                [2 ]departmentDivision of Epidemiology , ICMR - National Institute of Epidemiology , Chennai, Tamil Nadu, India
                [3 ]departmentDivision of Non-communicable Diseases , ICMR - National Institute of Epidemiology , Chennai, Tamil Nadu, India
                [4 ]departmentField Epidemiology Training Program , ICMR - National Institute of Epidemiology , Chennai, Tamil Nadu, India
                Author notes
                [Correspondence to ] Dr Prabhdeep Kaur; kprabhdeep@ 123456gmail.com

                JM and PG are joint first authors.

                Author information
                http://orcid.org/0000-0002-2605-3739
                http://orcid.org/0000-0002-0418-7592
                http://orcid.org/0000-0001-9316-8977
                http://orcid.org/0000-0003-0111-511X
                http://orcid.org/0000-0002-1720-7628
                Article
                bmjopen-2021-052067
                10.1136/bmjopen-2021-052067
                8921469
                35288381
                1615029a-85c3-4aa9-87eb-302ed9146542
                © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 07 April 2021
                : 21 February 2022
                Categories
                Public Health
                1506
                2474
                1724
                Original research
                Custom metadata
                unlocked

                Medicine
                covid-19,epidemiology,public health
                Medicine
                covid-19, epidemiology, public health

                Comments

                Comment on this article