Association of close-range contact patterns with SARS-CoV-2: a household transmission study

Article activity feed

1. 

   Version published to 10.7554/elife.84753 on eLife

   Jul 18, 2023
2. 

   eLife

   May 1, 2023

   Author Response

   Reviewer #1 (Public Review):

   This study aimed to estimate contact parameters associated with the transmission of SARS-CoV-2 in unvaccinated South African households over one year. The authors found no correlation between the frequency or duration of contacts and infection risk. Similar parameters (e.g., sharing a room with the index patient) also failed to yield an association. Reassuringly, a robust association was found with the Ct of the index case; female sex and individuals aged 13-17 years were also associated with increased risk. In a more general analysis, obesity, age >5 and <60 y, and non-smoking status were associated with increased risk.

   Strengths of the study are its relatively large size (131 households involving 497 people) with detailed proximity data; frequent testing to enable high ascertainment of …

   Author Response

   Reviewer #1 (Public Review):

   This study aimed to estimate contact parameters associated with the transmission of SARS-CoV-2 in unvaccinated South African households over one year. The authors found no correlation between the frequency or duration of contacts and infection risk. Similar parameters (e.g., sharing a room with the index patient) also failed to yield an association. Reassuringly, a robust association was found with the Ct of the index case; female sex and individuals aged 13-17 years were also associated with increased risk. In a more general analysis, obesity, age >5 and <60 y, and non-smoking status were associated with increased risk.

   Strengths of the study are its relatively large size (131 households involving 497 people) with detailed proximity data; frequent testing to enable high ascertainment of infections; and ability to exclude individuals seropositive at baseline. Additionally, several outcomes were evaluated in the models, partly to accommodate uncertainty in the index case. Different model structures were evaluated to gauge robustness.

   Limitations of the study include the fact that many index cases were likely enrolled after their infectious period, and it is possible that apparent secondary cases in the household arose from a shared exposure with the index case but had a longer latent period. Each of these factors could weaken the perceived effect of close contacts. Statistically, there is the vexing question of what age (gender, smoking, etc.) really represents mechanistically, and whether the models may be conditioning on a collider. Another statistical consideration is that many household contacts were excluded from the study because they were seropositive at baseline. In effect, their households may already have been "challenged" with the virus, and there may be heterogeneities in household susceptibility that are not fully considered by the simple exclusion of individuals with evidence of prior infection. Separating these household types in the analysis might have yielded different results.

   Although conditioning on a collider in the multivariable analysis is theoretically possible, it would be important to consider these co-variates as they had been found to be associated with transmission in both this and previous studies. The fact that there is also no signal for the contact parameters on univariate analysis support these findings.

   We added another sensitivity analysis where any households where individuals were excluded due to seropositivity was excluded from the analysis. The results showed that none of the contact patterns were significantly associated with SARS-CoV-2 transmission in the household.

   All that said, it is telling that in these households, infection is not clearly linked to typically defined close contacts. This is an important result that complements other strong evidence that aerosols are the dominant route of transmission for SARS-CoV-2. This information is critical for the design of effective intervention strategies. Additionally, the authors outline how future studies can be designed to improve on this work.

   Reviewer #3 (Public Review):

   The manuscript by Kleynhans et al analyzes data from household contacts of SARS-CoV-2 cases at two sites in South Africa. Proximity sensors were distributed to household members following diagnosis of the "index case" and measured the frequency and duration of close contacts (defined as being face-to-face within 1.5 meters for at least 20 seconds). The authors then examined the association between the duration, frequency, and average duration of contacts and the risk of a diagnosis of SARS-CoV-2 among household members in the subsequent two weeks, for both contact with the index case and all cases within the household. The risk of infection among household members was high (~60%), but was not significantly associated with the contact metrics examined. The findings may indicate that aerosols may be the predominant mode of SARS-CoV-2 transmission within households; however, there are also a number of limitations associated with the design and analysis of the study, which the authors acknowledge and which may limit the interpretability of the conclusions of this study.

   One important study limitation has to do with the design of the study: Sensors were not distributed to household members until a day or two after the diagnosis of the index case. Since individuals are most infectious with SARS-CoV-2 just prior to symptom onset, contact patterns were measured only after most transmission from the index case likely occurred. Furthermore, household members may have limited their contact with the index case, particularly if the index case attempted to isolate following their diagnosis, so the contact patterns measured are unlikely to be representative of typical mixing within the household.

   Another important limitation has to do with the analytical approach: The logistic regression model assumes that the first person in the household to test positive for SARS-CoV-2 (i.e. the index case) infected all subsequent cases within the household. However, this approach does not account for chains of transmission within the household or transmission from outside the household (possibly from the same source that infected the index case). While this concern is partially addressed by also assessing the association between the risk of infection and contact with all infected household members, more sophisticated methods could be used to infer the most likely infector of each case. The possibility of multiple introductions of the virus from outside the household is also only partially addressed by excluding households in which more than one variant was detected. While these limitations (and others) are appropriately acknowledged by the authors in the Discussion, nevertheless they limit the conclusions that can be drawn from the study results.

   We will be considering an analytic framework to distinguish the chains of transmission in future analyses.

   It is also worth noting that the contact metrics as defined and analyzed in the model may not be the measures that are most relevant to transmission. The authors examined three different contact measures: the median daily duration of contact, the median daily frequency of contact, and the median daily average duration of contact (i.e. the ratio of the two previous measures). They chose to examine the median daily values because contact duration was heavily skewed and the number of days of follow-up varied after data cleaning, but it may be that longer-duration contacts important to transmission are not appropriately captured by these metrics. Indeed, the median daily duration of the contact is quite short (only ~18 minutes on average). It would be useful to also evaluate a measure such as the total cumulative duration of contact and frequency of contacts divided by the number of days of follow-up, which differs from the measures they calculate and would take into account more prolonged and frequent contacts.

   Additional contact parameters added as described in response to reviewer 2.

   Lastly, the measures of association reported in the manuscript are the odds ratios (ORs) associated with one additional second of contact per day. This is not a very biologically meaningful unit of measure, and when rounded to two significant digits, the ORs are not surprisingly 1.0 with 95% confidence intervals that also round to 1.0. It would be more interpretable to report the ORs associated with a 1-minute (rather than 1-second) increase in the duration of contact, and the biological interpretation of the ORs should be described in the text.

   All time-based contact parameters now expressed in minutes.

   Read the original source
3. 

   eLife

   Mar 2, 2023

   eLife assessment

   This important study examines the association between SARS-CoV-2 infection and close contact among household members, measured using proximity sensors deployed after the first case was identified in the household. The authors provide solid evidence that transmission of SARS-CoV-2 within households is not dependent upon close contact, but the study suffers from a number of limitations that are fully acknowledged.

   Read the original source
4. 

   eLife

   Mar 2, 2023

   Reviewer #1 (Public Review):

   This study aimed to estimate contact parameters associated with the transmission of SARS-CoV-2 in unvaccinated South African households over one year. The authors found no correlation between the frequency or duration of contacts and infection risk. Similar parameters (e.g., sharing a room with the index patient) also failed to yield an association. Reassuringly, a robust association was found with the Ct of the index case; female sex and individuals aged 13-17 years were also associated with increased risk. In a more general analysis, obesity, age >5 and <60 y, and non-smoking status were associated with increased risk.

   Strengths of the study are its relatively large size (131 households involving 497 people) with detailed proximity data; frequent testing to enable high ascertainment of infections; and …

   Reviewer #1 (Public Review):

   This study aimed to estimate contact parameters associated with the transmission of SARS-CoV-2 in unvaccinated South African households over one year. The authors found no correlation between the frequency or duration of contacts and infection risk. Similar parameters (e.g., sharing a room with the index patient) also failed to yield an association. Reassuringly, a robust association was found with the Ct of the index case; female sex and individuals aged 13-17 years were also associated with increased risk. In a more general analysis, obesity, age >5 and <60 y, and non-smoking status were associated with increased risk.

   Strengths of the study are its relatively large size (131 households involving 497 people) with detailed proximity data; frequent testing to enable high ascertainment of infections; and ability to exclude individuals seropositive at baseline. Additionally, several outcomes were evaluated in the models, partly to accommodate uncertainty in the index case. Different model structures were evaluated to gauge robustness.

   Limitations of the study include the fact that many index cases were likely enrolled after their infectious period, and it is possible that apparent secondary cases in the household arose from a shared exposure with the index case but had a longer latent period. Each of these factors could weaken the perceived effect of close contacts. Statistically, there is the vexing question of what age (gender, smoking, etc.) really represents mechanistically, and whether the models may be conditioning on a collider. Another statistical consideration is that many household contacts were excluded from the study because they were seropositive at baseline. In effect, their households may already have been "challenged" with the virus, and there may be heterogeneities in household susceptibility that are not fully considered by the simple exclusion of individuals with evidence of prior infection. Separating these household types in the analysis might have yielded different results.

   All that said, it is telling that in these households, infection is not clearly linked to typically defined close contacts. This is an important result that complements other strong evidence that aerosols are the dominant route of transmission for SARS-CoV-2. This information is critical for the design of effective intervention strategies. Additionally, the authors outline how future studies can be designed to improve on this work.

   Read the original source
5. 

   eLife

   Mar 2, 2023

   Reviewer #2 (Public Review):

   In this manuscript, the authors investigated the association of household close-range contact patterns with SARS-CoV-2 transmission in the household using proximity sensors deployed after the identification of SARS-CoV-2 in the household. They recruited participants in two urban communities in South Africa, Klerksdorp (North West Province) and Soweto (Gauteng Province) from October 2020 through September 2021. Their analysis suggests the lack of an association between close-range proximity events and SARS-CoV-2 household transmission.

   Their study design looks reasonable, with useful household contacts data collected in the study. However, their regression analysis only considered a limited set of contact parameters (i.e., median measurements of duration, frequency, and average duration). It's not clear if …

   Reviewer #2 (Public Review):

   In this manuscript, the authors investigated the association of household close-range contact patterns with SARS-CoV-2 transmission in the household using proximity sensors deployed after the identification of SARS-CoV-2 in the household. They recruited participants in two urban communities in South Africa, Klerksdorp (North West Province) and Soweto (Gauteng Province) from October 2020 through September 2021. Their analysis suggests the lack of an association between close-range proximity events and SARS-CoV-2 household transmission.

   Their study design looks reasonable, with useful household contacts data collected in the study. However, their regression analysis only considered a limited set of contact parameters (i.e., median measurements of duration, frequency, and average duration). It's not clear if this limitation will bias the conclusion regarding the lack of an association between close-range proximity events and SARS-CoV-2 household transmission.

   Read the original source
6. 

   eLife

   Mar 2, 2023

   Reviewer #3 (Public Review):

   The manuscript by Kleynhans et al analyzes data from household contacts of SARS-CoV-2 cases at two sites in South Africa. Proximity sensors were distributed to household members following diagnosis of the "index case" and measured the frequency and duration of close contacts (defined as being face-to-face within 1.5 meters for at least 20 seconds). The authors then examined the association between the duration, frequency, and average duration of contacts and the risk of a diagnosis of SARS-CoV-2 among household members in the subsequent two weeks, for both contact with the index case and all cases within the household. The risk of infection among household members was high (~60%), but was not significantly associated with the contact metrics examined. The findings may indicate that aerosols may be the …

   Reviewer #3 (Public Review):

   The manuscript by Kleynhans et al analyzes data from household contacts of SARS-CoV-2 cases at two sites in South Africa. Proximity sensors were distributed to household members following diagnosis of the "index case" and measured the frequency and duration of close contacts (defined as being face-to-face within 1.5 meters for at least 20 seconds). The authors then examined the association between the duration, frequency, and average duration of contacts and the risk of a diagnosis of SARS-CoV-2 among household members in the subsequent two weeks, for both contact with the index case and all cases within the household. The risk of infection among household members was high (~60%), but was not significantly associated with the contact metrics examined. The findings may indicate that aerosols may be the predominant mode of SARS-CoV-2 transmission within households; however, there are also a number of limitations associated with the design and analysis of the study, which the authors acknowledge and which may limit the interpretability of the conclusions of this study.

   One important study limitation has to do with the design of the study: Sensors were not distributed to household members until a day or two after the diagnosis of the index case. Since individuals are most infectious with SARS-CoV-2 just prior to symptom onset, contact patterns were measured only after most transmission from the index case likely occurred. Furthermore, household members may have limited their contact with the index case, particularly if the index case attempted to isolate following their diagnosis, so the contact patterns measured are unlikely to be representative of typical mixing within the household.

   Another important limitation has to do with the analytical approach: The logistic regression model assumes that the first person in the household to test positive for SARS-CoV-2 (i.e. the index case) infected all subsequent cases within the household. However, this approach does not account for chains of transmission within the household or transmission from outside the household (possibly from the same source that infected the index case). While this concern is partially addressed by also assessing the association between the risk of infection and contact with all infected household members, more sophisticated methods could be used to infer the most likely infector of each case. The possibility of multiple introductions of the virus from outside the household is also only partially addressed by excluding households in which more than one variant was detected. While these limitations (and others) are appropriately acknowledged by the authors in the Discussion, nevertheless they limit the conclusions that can be drawn from the study results.

   It is also worth noting that the contact metrics as defined and analyzed in the model may not be the measures that are most relevant to transmission. The authors examined three different contact measures: the median daily duration of contact, the median daily frequency of contact, and the median daily average duration of contact (i.e. the ratio of the two previous measures). They chose to examine the median daily values because contact duration was heavily skewed and the number of days of follow-up varied after data cleaning, but it may be that longer-duration contacts important to transmission are not appropriately captured by these metrics. Indeed, the median daily duration of the contact is quite short (only ~18 minutes on average). It would be useful to also evaluate a measure such as the total cumulative duration of contact and frequency of contacts divided by the number of days of follow-up, which differs from the measures they calculate and would take into account more prolonged and frequent contacts.

   Lastly, the measures of association reported in the manuscript are the odds ratios (ORs) associated with one additional second of contact per day. This is not a very biologically meaningful unit of measure, and when rounded to two significant digits, the ORs are not surprisingly 1.0 with 95% confidence intervals that also round to 1.0. It would be more interpretable to report the ORs associated with a 1-minute (rather than 1-second) increase in the duration of contact, and the biological interpretation of the ORs should be described in the text.

   Read the original source
7. 

   Version published to 10.1101/2022.12.22.22283843v1 on medRxiv

   Dec 22, 2022