Secondary attack rate of COVID-19 among contacts and risk factors, Tamil Nadu, March–May 2020: a retrospective cohort study

Karumanagoundar, Kolandaswamy; Raju, Mohankumar; Ponnaiah, Manickam; Kaur, Prabhdeep; Viswanathan, Vidhya; Rubeshkumar, Polani; Sakthivel, M; Shanmugiah, Porchelvan; Ganeshkumar, P.; Muthusamy, Santhosh Kumar; Muthappan, Sendhilkumar; Venkatasamy, Vettrichelvan; Sambath, Irene; Ilangovan, Kumaravel; Jagadeesan, M; R, Govindarajan; Shanmugam, Soundammal; Rajarathinam, Selvakumar; Suresh, Kst; Varadharajan, Meera; Thiagarajan, Manivannan; Jagadeeshkumar, K; Ganesh, Velmurugan; Kumar, Sateesh; Venkatesan, Prakash; Nallathambi, Yogananth; Palani, Sampath; Selvavinayagam, TS; Reddy, M. Venkataswamy; Rajesh, Beela; Murhekar, Manoj

doi:10.1136/bmjopen-2021-051491

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…We identified 2097 records (1791 from PubMed, 306 from medRxiv, and 2 from reference lists of eligible articles) published between June 18, 2021, and March 8, 2022 (eFigure 1 in the Supplement ). Fifty-eight new studies 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 (eTable 2 in the Supplement ) were combined with 77 studies from ou...…”

Section: Resultsmentioning

confidence: 99%

“…We identified 2097 records (1791 from PubMed, 306 from medRxiv, and 2 from reference lists of eligible articles) published between June 18, 2021, and March 8, 2022 (eFigure 1 in the Supplement). Fifty-eight new studies (eTable 2 in the Supplement) were combined with 77 studies from our previous review (see eTable 3 in the Supplement for references included from our previous review), with Figure 1 showing household SAR by study period, resulting in 135 total studies representing 1 375 806 contacts from 36 countries. Four of the new studies were preprints in our previous review that were subsequently published.…”

Section: Resultsmentioning

confidence: 99%

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Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status

et al. 2022

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Section: Resultsmentioning

confidence: 99%

“…We identified 2097 records (1791 from PubMed, 306 from medRxiv, and 2 from reference lists of eligible articles) published between June 18, 2021, and March 8, 2022 (eFigure 1 in the Supplement). Fifty-eight new studies (eTable 2 in the Supplement) were combined with 77 studies from our previous review (see eTable 3 in the Supplement for references included from our previous review), with Figure 1 showing household SAR by study period, resulting in 135 total studies representing 1 375 806 contacts from 36 countries. Four of the new studies were preprints in our previous review that were subsequently published.…”

Section: Resultsmentioning

confidence: 99%

Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status

et al. 2022

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“…The sensitivity analysis method was used to exclude and analyze the indicators of the groups included in the study, and the final results did not change much. So we Bi Q [19] 2020 China 2020-01-01/2020-02-12 1286 7.62% ABF 8 Liu R [20] 2020 China 2020-01-22/2020-02-14 4880 38.42 C 7 Liu T [21] 2020 China 2020-01-15/2020-03-15 11686 3.84% ACD 8 Wu Y [22] 2020 China 2020-01-23/2020-02-08 2994 4.81% ACE 9 Xin H [23] 2020 China 2020-01-20/2020-03-27 17.92% ACD 8 Burke RM [24] 2020 China 2020-01-01/2020-02-26 445 10.50% AB 8 Li W [25] 2020 China 2020-01-01/2020-02-20 392 16.30% A 7 Karumanagoundar [26] 2021 India 2020-03-14/2020-05-30 15702 3.81% AB 9 Luo L [27] 2020 China 2020-01-13/2020-03-06 3410 3.73% AEF 7 P Rajmohan [28] 2021 India 2021-01-01/2021-03-31 387 42.64% A 8 Jing COVID-19 = coronavirus disease-2019, QL [29] 2020 China 2020-01-07/2020-02-18 103 12.4% AC 9…”

Section: Meta-analysis Resultsmentioning

confidence: 99%

Risk factors of infection among close contacts of COVID-19: A systematic review and meta-analysis

Gao

Jiang

Mao³

et al. 2023

Medicine

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Background: Coronavirus disease 2019 (COVID-19) was first detected in December 2019, and declared as a pandemic by the World Health Organization (WHO) on March 11, 2020. The current management of COVID-19 is based generally on supportive therapy and treatment to prevent respiratory failure. Methods: PubMed, Web of Science, Embase, CNKI, and other databases were searched by computer, and relevant literature published from December 2019 to November 2022 on the influencing factors of infection in close contacts with novel coronavirus pneumonia was collected. Meta-analysis was carried out after literature screening, quality assessment, and data extraction. Results: A total of 425 articles were retrieved and 11 were included. Meta-analysis showed that there were 6 risk factors, and the combined OR value and 95% CI of each influencing factor were 5.23 (3.20, 8.57) for family members, 1.63 (0.56, 4.77) for regular contact, 2.14 (0.62, 7.32) for the elderly, 0.58 (0.001569.89) for cohabitation, 1.97 (1.02, 3.82) for women and 0.75 (0.01, 54.07) for others. The Deeks’ funnel diagram indicates that there is no potential publication bias among the included studies. Conclusion: Family members and gender differences are the risk factors of infection among close contacts, and it cannot be proved that there are differences in infection among frequent contact, advanced age, and living together.

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“…The mean time for these transitions ranged from 2 days to the maximum time previously set for each variant. IR was set to range from 10% to 50% [ 1 , 23 , 24 ], and we considered 6 different variants, namely, “alpha,” “beta,” “gamma,” “delta,” “omicron,” and “others” (according to collected data). We assumed that the weekly prevalence of each variant [ 18 ] was equal to the prevalence on each day of that week, and individuals in the φ transition would be distributed to each variant according to that prevalence.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Contact Tracing as an Indicator for Evaluating a Pandemic Situation: Simulation Study

Marques-Cruz¹,

Nogueira-Leite²,

Alves³

et al. 2023

JMIR Public Health Surveill

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Background Contact tracing is a fundamental intervention in public health. When systematically applied, it enables the breaking of chains of transmission, which is important for controlling COVID-19 transmission. In theoretically perfect contact tracing, all new cases should occur among quarantined individuals, and an epidemic should vanish. However, the availability of resources influences the capacity to perform contact tracing. Therefore, it is necessary to estimate its effectiveness threshold. We propose that this effectiveness threshold may be indirectly estimated using the ratio of COVID-19 cases arising from quarantined high-risk contacts, where higher ratios indicate better control and, under a threshold, contact tracing may fail and other restrictions become necessary. Objective This study assessed the ratio of COVID-19 cases in high-risk contacts quarantined through contact tracing and its potential use as an ancillary pandemic control indicator. Methods We built a 6-compartment epidemiological model to emulate COVID-19 infection flow according to publicly available data from Portuguese authorities. Our model extended the usual susceptible-exposed-infected-recovered model by adding a compartment Q with individuals in mandated quarantine who could develop infection or return to the susceptible pool and a compartment P with individuals protected from infection because of vaccination. To model infection dynamics, data on SARS-CoV-2 infection risk (IR), time until infection, and vaccine efficacy were collected. Estimation was needed for vaccine data to reflect the timing of inoculation and booster efficacy. In total, 2 simulations were built: one adjusting for the presence and absence of variants or vaccination and another maximizing IR in quarantined individuals. Both simulations were based on a set of 100 unique parameterizations. The daily ratio of infected cases arising from high-risk contacts (q estimate) was calculated. A theoretical effectiveness threshold of contact tracing was defined for 14-day average q estimates based on the classification of COVID-19 daily cases according to the pandemic phases and was compared with the timing of population lockdowns in Portugal. A sensitivity analysis was performed to understand the relationship between different parameter values and the threshold obtained. Results An inverse relationship was found between the q estimate and daily cases in both simulations (correlations >0.70). The theoretical effectiveness thresholds for both simulations attained an alert phase positive predictive value of >70% and could have anticipated the need for additional measures in at least 4 days for the second and fourth lockdowns. Sensitivity analysis showed that only the IR and booster dose efficacy at inoculation significantly affected the q estimates. Conclusions We demonstrated the impact of applying an effectiveness threshold for contact tracing on decision-making. Although only theoretical thresholds could be provided, their relationship with the number of confirmed cases and the prediction of pandemic phases shows the role as an indirect indicator of the efficacy of contact tracing.

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Secondary attack rate of COVID-19 among contacts and risk factors, Tamil Nadu, March–May 2020: a retrospective cohort study

Cited by 9 publications

References 24 publications

Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status

Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status

Risk factors of infection among close contacts of COVID-19: A systematic review and meta-analysis

COVID-19 Contact Tracing as an Indicator for Evaluating a Pandemic Situation: Simulation Study

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