Estimating Household Transmission of SARS-CoV-2

Abstract

**Introduction and Goals.** SARS-CoV-2 is transmitted both in the community and within households. Social distancing and lockdowns reduce community transmission but do not directly address household transmission. We provide quantitative measures of household transmission based on empirical data, and estimate the contribution of households to overall spread. We highlight policy implications from our analysis of household transmission, and more generally, of changes in contact patterns under social distancing. **Methods. ** We investigate the household secondary attack rate (SAR) for SARS-CoV-2, as well as R_h, which is the average number of within-household infections caused by a single index case. We identify previous works that estimated the SAR. We correct these estimates based on the false-negative rate of PCR testing and the failure to test asymptomatics. Results are pooled by a hierarchical Bayesian random-effects model to provide a meta-analysis estimate of the SAR. We estimate R_h using results from population testing in Vo', Italy and contact tracing data that we curate from Singapore. The code and data behind our analysis are publicly available https://github.com/andrewilyas/covid-household-transmission. **Results.** We identified nine studies of the household secondary attack rate. Our modeling suggests the SAR is heterogeneous across studies. The pooled central estimate of the SAR is 30% but with a posterior 95% credible interval of (0%, 67%) reflecting this heterogeneity. This corresponds to a posterior mean for the SAR of 30% (18%,43%) and a standard deviation of 15% (9%, 27%). If results are not corrected for false negatives and asymptomatics, the pooled central estimate for the SAR is 20% (0%, 43%). From the same nine studies, we estimate R_h to be 0.47 (0.13, 0.77). Using contact tracing data from Singapore, we infer an R_h value of 0.32 (0.22,0.42). Population testing data from Vo' yields an R_h estimate of 0.37 (0.34, 0.40) after correcting for false negatives and asymptomatics. **Interpretation.** Our estimates of R_h suggest that household transmission was a small fraction (5%-35%) of R before social distancing but a large fraction after (30%-55%). This suggests that household transmission may be an effective target for interventions. A remaining uncertainty is whether household infections actually contribute to further community transmission or are contained within households. This can be estimated given high-quality contact tracing data. More broadly, our study points to emerging contact patterns (i.e., increased time at home relative to the community) playing a role in transmission of SARS-CoV-2. We briefly highlight another instance of this phenomenon (differences in contact between essential workers and the rest of the population), provide coarse estimates of its effect on transmission, and discuss how future data could enable a more reliable estimate.

Household transmission of SARS-CoV-2: a systematic review and meta-analysis of secondary attack rate

“… 79 Modeling studies demonstrated that household transmission had a greater impact on R after social distancing (30%–55%) compared to before social distancing (5%–35%). 80 …”

Characteristics associated with household transmission of SARS-CoV-2 in Ontario, Canada

“…The majority of these studies were conducted in Asia, and some in Europe and the United States. [4][5][6][8][9][10] Madewell et al 4 , Lei et al 6 , and Curmei et al 5 conducted meta-analyses of previous studies and found pooled household SARs of 19% (95% CI: 15% -23%), 27% (21% -32%), and 30% (18% -43%), respectively. Some of the included studies compared SARs in household settings verses non-household settings, and pooled estimates found that household SARs were five 4 to ten 6 times as high as non-household SARs, which highlights the role of household transmission in the spread of COVID-19.…”

Mobility network models of COVID-19 explain inequities and inform reopening

“…R base models transmission that is not correlated with activity at POIs in the SafeGraph dataset, including within-household transmission and transmission at POI categories which are not well-captured in the SafeGraph dataset. We chose the lower limit of 0.1 because beyond that point, base transmission would only contribute minimally to overall R, whereas previous work suggests that within-household transmission is a substantial contributor to overall transmission [60][61][62] . Household transmission alone is not estimated to be sufficient to tip overall R 0 above 1; for example, a single infected individual has been estimated to cause an average of 0.32 (0.22, 0.42) secondary within-household infections 60 .…”

“…We chose the lower limit of 0.1 because beyond that point, base transmission would only contribute minimally to overall R, whereas previous work suggests that within-household transmission is a substantial contributor to overall transmission [60][61][62] . Household transmission alone is not estimated to be sufficient to tip overall R 0 above 1; for example, a single infected individual has been estimated to cause an average of 0.32 (0.22, 0.42) secondary within-household infections 60 . However, because R base may also capture transmission at POIs not captured in the SafeGraph dataset, to be conservative, we chose an upper limit of R base = 2; as we describe below, the best-fit models for all 10 metro area s have R base < 2, and 9 out of 10 have R base < 1.…”