Epidemiology of Coronavirus Disease 2019 during the Second and Third Wave in Chennai, India: An Analysis of the Coronavirus Disease 2019 Surveillance System, February 2021-February 2022.

INTRODUCTION: Analysis of the coronavirus disease 2019 (COVID-19) surveillance system in the first wave indicated that the data-driven approach helped in resource allocation and public health interventions. OBJECTIVES: We described the epidemiology of COVID-19 cases in Chennai, Tamil Nadu, India, from February 2021 to February 2022. MATERIALS AND METHODS: We analyzed the COVID-19 surveillance data from Chennai City, Tamil Nadu, India's Greater Chennai Corporation. We described the deidentified line list of COVID-19 cases and deaths by months, zones, age, and gender. We estimated the incidence of COVID-19 cases per million population, test positivity rate (TPR), and case fatality ratio (CFR). RESULTS: Of the 434,040 cases reported in Chennai from February 1, 2021, to February 28, 2022, 53% were male. The incidence per million peaked in May 2021 (19,210) and January 2022 (15,881). Age groups more than 60 years reported maximum incidence. Southern region zones reported higher incidence. Overall TPR was 5.8%, peaked in May 2021 (17.5%) and January 2022 (15.1%). Over half of the 4929 reported deaths were in May 2021 (56%). Almost half of the deaths were 61-80 years (52%), followed by 41-60 years (26%). Overall CFR was 1%, which peaked in June 2021 (4%). CONCLUSION: We conclude that Chennai city experienced a surge in COVID-19 due to delta and omicron variants. Understanding descriptive epidemiology is vital for planning the public health response, resource allocation, vaccination policies, and risk communication to the community.

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

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 (Rt). 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, Rt 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.

Epidemiology of hospital-based COVID- 19 cluster in a tertiary care cancer hospital, Chennai, India 2020.

OBJECTIVES: To identify risk factors associated with Coronavirus disease 2019 (COVID-19) in a Tertiary care cancer hospital-based cluster and recommend control measures. METHODS: We conducted tracing and confirmation among hospital and community contacts. We telephonically interviewed and abstracted information from hospital records and registers. We described the cluster by time, place and person. We conducted unmatched case-control study to compare risk factors and computed Odds Ratio (OR) and 95% confidence interval. RESULTS: We confirmed COVID-19 in 21 of 1478 tested (1.4%). Secondary attack (%) of COVID-19 among 824 contacts was higher among in-patients of block A (18), household contacts (3.4), housekeeping staff (3.3) and nurses (1.7). The cluster started on April 22 with two successive peaks five days apart and lasted until May 8. Being male, patients aged >33 years [OR = 30·7; 95% CI = 3·6 to 264], having hypertension [OR = 4·3; 95% CI = 1·1 to 16·7] or diabetes [OR = 3·8; 95% CI = 1·0 to 14·1] were associated with COVID-19. Mask compliance was poor (20%) among hospital workers. DISCUSSION: We recommended screening of all patients for diabetes and hypertension and isolation/testing of anyone with influenza-like illness for preventing COVID-19 clusters in hospital settings.