A network meta-analysis of secondary attack rates of COVID-19 in different contact environments

Abstract

As the corona virus disease 2019 (COVID-19) pandemic continues around the world, understanding the transmission characteristics of COVID-19 is vital for prevention and control. We conducted the first study aiming to estimate and compare the relative risk of secondary attack rates (SARs) of COVID-19 in different contact environments. Until 26 July 2021, epidemiological studies and cluster epidemic reports of COVID-19 were retrieved from SCI, Embase, PubMed, CNKI, Wanfang and CBM in English and Chinese, respectively. Relative risks (RRs) were estimated in pairwise comparisons of SARs between different contact environments using the frequentist NMA framework, and the ranking of risks in these environments was calculated using the surface under the cumulative ranking curve (SUCRA). Subgroup analysis was performed by regions. Thirty-two studies with 68 260 participants were identified. Compared with meal or gathering, transportation (RR 10.55, 95% confidence interval (CI) 1.43-77.85), medical care (RR 11.68, 95% CI 1.58-86.61) and work or study places (RR 10.15, 95% CI 1.40-73.38) had lower risk ratios for SARs. Overall, the SUCRA rankings from the highest to the lowest were household (95.3%), meal or gathering (81.4%), public places (58.9%), daily conversation (50.1%), transportation (30.8%), medical care (18.2%) and work or study places (15.3%). Household SARs were significantly higher than other environments in the subgroup of mainland China and sensitive analysis without small sample studies (<100). In light of the risks, stratified personal protection and public health measures need to be in place accordingly, so as close contacts categorising and management.

Keywords: COVID-19; Contact environment; network meta-analysis; secondary attack rate.

Fig. 1.

Study selection process.

Fig. 2.

NMA of SAR in seven contact environments. Note: (A) All studies; (B) subgroup in mainland China, (C) studies without small sample sizes and (D) studies without low quality. Width of the line is directly proportional to the number of included studies. Size of the node is proportional to the sample size.