The combined impact of immunity and SARS-CoV-2 variants on viral kinetics during infections has been unclear.We characterized 1,280 infections from the National Basketball Association occupational health cohort identified between June 2020 and January 2022 using serial RT-qPCR testing. Logistic regression and semi-mechanistic viral RNA kinetics models were used to quantify the effect of age, variant, symptom status, infection history, vaccination status and antibody titer to the founder SARS-CoV-2 strain on the duration of potential infectiousness and overall viral kinetics. The frequency of viral rebounds was quantified under multiple cycle threshold (Ct) value-based definitions.Among individuals detected partway through their infection, 51.0%  remained potentially infectious (Ct <30) 5 days post detection, with small differences across variants and vaccination status. Only seven viral rebounds  were observed, with rebound defined as 3+days with Ct <30 following an initial clearance of 3+days with Ct ≥30. High antibody titers against the founder SARS-CoV-2 strain predicted lower peak viral loads and shorter durations of infection. Among Omicron BA.1 infections, boosted individuals had lower pre-booster antibody titers and longer clearance times than non-boosted individuals.SARS-CoV-2 viral kinetics are partly determined by immunity and variant but dominated by individual-level variation. Since booster vaccination protects against infection, longer clearance times for BA.1-infected, boosted individuals may reflect a less effective immune response, more common in older individuals, that increases infection risk and reduces viral RNA clearance rate. The shifting landscape of viral kinetics underscores the need for continued monitoring to optimize isolation policies and to contextualize the health impacts of therapeutics and vaccines.Supported in part by CDC contract #200-2016-91779, a sponsored research agreement to Yale University from the National Basketball Association contract #21-003529, and the National Basketball Players Association. The viral kinetics of SARS-CoV-2 underlie the epidemiology of COVID-19 and the policies surrounding infection control. The amount and duration of viral shedding influences infectiousness  and the To characterize the viral kinetics of SARS-CoV-2 infection, including rebounds, for the Delta and Omicron (BA.1 lineages BA.1.1529 and BA.1.1) variants in symptomatic and asymptomatic individuals with varied vaccination and infection histories, we measured viral RNA levels using densely-sampled RT-qPCR tests from 1,280 SARS-CoV-2 infections, each taken by combined anterior nares and oral swabs, that occurred between 7th July, 2020, and 26th January, 2022, prior to the detection of BA.2.12.1, BA.4 and BA.5, or the regular detection of BA.2, in this cohort. As a proxy for immune response to SARS-CoV-2, we used antibody titers against the ancestral SARS-CoV-2 (WA1) strain spike protein measured prior to the administration of booster doses, but predominantly after primary vaccination.We interpreted the data in two ways. First, we estimated the probability of an individual having a PCR cycle threshold (Ct) value less than 30, as a proxy for infectiousness, on each day post detection using a logistic regression model. Second, we estimated the peak viral RNA concentrations, viral RNA proliferation rate, and viral RNA clearance rate across variants, immune statuses and age using a semi-mechanistic model. Our findings provide key estimates for the duration and magnitude of viral RNA shedding in the upper respiratory tract and its variation across age, symptom status, variants, immune states, and individuals.The data reported here represent a convenience sample including team staff, players, arena staff, vendors, and others affiliated with the NBA as described previously . The retWe classified all individuals as having Ct value <30 or not on each day post-detection. This threshold was chosen based on a combination of antigen sensitivity and studies of virus culture by Ct, where the presence of culturable virus is often assumed to correlate with infectivity . We straVaccination information was reported and verified by NBA staff and a clinical operational team. 828 individuals had been boosted by the time of their last PCR test, 529 had completed their primary vaccination course (two doses of an mRNA vaccine or one dose of Janssen / Ad.26.COV2.S adenovirus vector-based vaccine), 8 had received one vaccine dose, and 13 confirmed to be unvaccinated. The vaccination statuses of the remaining individuals were unknown. The time course of individual vaccination and exposure times is shown in In accordance with the guidelines of the Yale Human Investigations Committee, this work with de-identified samples was approved for research not involving human subjects by the Yale Institutional Review Board (HIC protocol # 2000028599). This project was designated exempt by the Harvard Institutional Review Board (IRB20-1407).We tagged each series of positive tests buffered by at least 14 days of negative or missing tests on each side as a distinct infection. After an infection was flagged, subsequent positives were not classified as a new infection for 90 days. Isolated positive tests with no other positive within 14 days either side were not considered as detections. We track the cumulative number of exposures (defined as either receiving a vaccination or infection) over time. Individuals who received the Janssen/Ad.26.COV2.S adenovirus vector-based vaccine were counted as having received two vaccine doses. A total of 351 additional infections were reported to the program outside of the main testing regime, either through an external PCR or rapid antigen test, or from a positive antibody test result (not including the Diasorin Trimeric Assay results described below). We consider these detections as contributing towards an individual’s infection history but are unable to include them in the Ct value trajectory analyses.RNA was extracted and confirmed as SARS-CoV-2 positive by RT-qPCR . Next GeThere were 3 and 482 non-sequenced infections in the window of time when Alpha was replaced by Delta (29th May 2021 to 18th July 2021) and after the first detection of Omicron BA.1 (3rd December 2021 onwards), respectively . We remoIndividuals were tested with the Diasorin Trimeric Assay for IgG antibody titers against the ancestral SARS-CoV-2 (WA1) strain spike protein during the 2021 pre-season period (September-October 2021). The majority (>90%) of blood draws were from mid-September to early October 2021. We classified individuals with a titer of >250 AU/ml as being in the high titer group and in the low titer group otherwise, chosen based on its correlation with authentic virus neutralization results for wildtype and Delta . Specifibrms to fit Bayesian logistic regression models estimating the probability of having Ct value <30 on each day post detection, fitting all models to the frequent testing and delayed detection datasets separately . For the antibody titer analyses, we fitted Bayesian logistic regression models for the probability of Ct value <30 as a function of days since detection, stratified by the interaction of titer group (above or below 250 AU/ml), age group, variant and vaccination status. Further details on the fitting process can be found in the Appendix 1.We ranked models based on the expected log predictive density and evaluated their classification accuracy and area under the receiver operator curve using Appendix 1.We extended a previously reported model for capturing SARS-CoV-2 viral kinetics to estimate the viral proliferation time, viral clearance time, and peak viral load by variant and immune status . The modWe initially identified 2,875 distinct infections from 2,678 individuals in this cohort . By the Of 1086 infections with known symptom status, 766 reported symptoms at some point during the infection (70.5%). Individuals in the delayed detection group were more likely to be symptomatic than in the frequent testing group . Most symptomatic individuals were detected around the time of symptom onset .Based on genome sequencing, 1,561 infections were confirmed to be Omicron , 266 confirmed to be Delta, and 247 confirmed as other lineages. An additional 801 infections were not sequenced; however, due to the rapid replacement of the circulating lineage in this cohort, we classified many of these as suspected Delta or other lineages based on the dominant variant at time of detection . We excl=490) and one Omicron BA.2 infection, those with only binary test results , those for which all Ct-based tests results were beyond 25 days after the time of first detection (n=12), and those for which the vaccination status was missing . Characteristics of these infections are listed in For further analysis, we reduced the dataset to a subset of 1,280 well-documented infections. Beginning with the 2,875 infections, we removed those with an unknown lineage . Similarly, we found that rebounds were more common in boosted individuals, with 32  rebounds in boosted individuals vs. three  and two  rebounds in vaccinated and unvaccinated individuals, respectively .We next characterized the frequency of rebound viral RNA trajectories in this cohort. Viral rebounds may be characterized by the duration of the “quiescent” period of low viral concentration between distinct peaks, the duration of the subsequent rebound, and the timing of rebound onset relative to infection, but no consensus definition of viral rebound based on these quantities exists. We defined rebound as any viral trajectory with a decline in Ct value to <30 for 3+consecutive days of tests (the rebound) after 3+consecutive days of tests with Ct ≥30 or a negative result (the quiescent period) following an initial Ct value <30 (the first detection of infection). Testing often ceased following initial clearance, and thus to minimize the impact of right censoring we only considered those trajectories with at least three days of tests with negative or Ct ≥30 following a Ct value <30 as the denominator (N=999). We detected seven viral rebounds under this definition. Less stringent definitions led to more rebound classifications. For example, 40 (3.00%) of 1,334 infections were identified as rebounds when only 2+consecutive days of Ct ≥30 followed by 2+days of Ct <30 was required to be classified as such . All indk-fold cross-validation. All models were fit to the frequent testing and delayed detection datasets separately.To assess differences in the duration of test positivity and infectiousness by age, variant and immune status, we modeled the probability of an individual having Ct <30 on each day since detection. As a baseline model, we fitted a logistic regression model with a smoothing spline on days since detection as a predictor. We then fitted successively more complex models, adding independent category-specific smoothing splines for the interaction of age group , variant, and exposure history with days since detection (factorized), and compared their predictive accuracy using The best-performing model for predicting the time course of low Ct values included days since detection stratified by the cumulative number of previous exposures (infection or vaccination) and its interaction with variant, as well as age group . This inVaccination provides multiple layers of protection against SARS-CoV-2, leading to reduced rates of infection  and fastIt is important to consider the possible confounding effect of age, as boosted individuals in this cohort were typically older than non-boosted individuals at the time of BA.1 infection . The regression model including age group, vaccination status and variant found that older individuals do maintain Ct <30 for longer on average than younger individuals after conditioning on vaccination status . HoweverTo assess the mechanisms behind the unexpected slower clearance in boosted Omicron BA.1 infections, we analyzed viral kinetics stratified by antibody titer. In addition to exposure history information, 979 individuals were tested at least once  with the Diasorin Trimeric Assay for antibody titers against the spike protein from the ancestral SARS-CoV-2 (WA1) strain . Most tiWe hypothesized that these single point-in-time SARS-CoV-2 antibody titer measurements represented a proxy of the strength of the immune response to SARS-CoV-2 and thus would be reflected in the features of viral kinetics over the course of infection. A total of 494 measurements were classified as low antibody titers (≤250 arbitrary units [AU]/ml) and 523 as high titers (>250 AU/ml). This cutoff was chosen as a conservative upper bound for defining risk of Delta infection.We fitted a logistic regression model for the probability of having Ct <30 on each day since detection, stratified by the interaction of an individual’s booster status and their pre-booster antibody titer status, as well as an additional stratification by age group . BoostedThe results were consistent with an age group-level effect also contributing towards differences in the proportion of individuals with Ct <30 over time. Trends were similar within each age group, but we note that at this level of stratification the sample sizes for some subgroups are small and thus there is considerable uncertainty for some combinations of age group, titer group, and vaccination status. We found that younger BA.1-infected individuals had higher antibody titers on average than older BA.1-infected individuals, but that BA.1-infected boosted individuals had consistently lower mean antibody titers than BA.1-infected non-boosted individuals within each age group .To account for potential confounding from waning immunity, in which low titers simply represent a longer time since previous exposure, we restricted the dataset to include only individuals who had their titer measured within 100–200 days of a previous exposure with the aim of comparing antibody titers measured at a similar point in the waning process. We also repeated the analysis after restricting to only infections detected 60–90 days following an antibody titer measurement, here aiming to include only infections for which the measured titer reasonably proxies the titer at the time of infection. These time windows were chosen to improve comparability of immune states while also retaining reasonably large sample sizes. The trend of a higher and longer duration of Ct <30 in Omicron BA.1-infected, boosted, low titer individuals was maintained in both sensitivity analyses .Based on these findings, we hypothesized that boosted individuals who nevertheless were infected with Omicron BA.1 may have had relatively poor BA.1-specific immune responses to prior SARS-CoV-2 exposures, leading to longer infection durations. This is demonstrated by stratifying antibody titers by variant and vaccination status at the time of infection . Antibodvs. 6.2 days (95% CrI: 5.8–6.6), respectively), in line with the results from the logistic regression model. Viral proliferation times and peak viral RNA were similar among boosted and non-boosted individuals with Omicron BA.1 infections  that most people remain with high viral loads 5 days post positive test and that older individuals and those who were boosted  were more likely to remain with high viral loads longer after an Omicron infection. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Neil Ferguson as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Andrew Azman (Reviewer #2).Thank you for submitting your article "Quantifying the impact of immune history and variant on SARS-CoV-2 viral kinetics and infection rebound: a retrospective cohort study" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Please add a description (table?) of the demographics of the participants , stratified by important factors .2) It would be helpful to add the models with age effects (uni and multivariate) to model performance comparison in Tables S3/4.3) While the analyses stratified by antibody levels are interesting, the differences in kinetics that remain, even after stratifying, are also intriguing. It might not be possible to present an analysis stratified by antibody level and age, but it would be useful to discuss to what extent age might explain these differences. Presenting a figure  stratified by age could also be useful. Authors should also discuss alternative hypotheses for differences in kinetics between boosted and unboosted individuals.4) Please clarify whether any of the participants took antivirals.Reviewer #1 (Recommendations for the authors):Although the effect of age as a confounder is accounted for and discussed, I would have found it helpful to see it considered from the beginning as a potentially important factor and tested along with a cumulative number of exposures, lineage, days since previous exposure and vaccination status. It would be interesting to see a model that in addition to the baseline spline also accounts for a single predictor of age in the results presented in Tables S3 and S4. In addition, I would consider showing some of the results for age and Omicron in the main text.I found the methods and results for the logistic regression a bit difficult to follow at some points, with most of the results presented in the supplementary information, implying going back and forth between the two files.– It would be helpful to add a list (or Table) with all possible factors (predictors) considered and the values  they can take .– How is the effect of each factor modelled? It is written in the methods section that "additional logistic regression models, adding additional spline terms capturing the effect of vaccination status, cumulative number of previous exposures or days since previous exposure, and/or lineage with days since detection". Does it mean an additional spline is added for each possible category of each variable? If so, how is "days since previous exposure" treated? I understand that for the models that include more than one predictor, those are always considered to interact – is that true? Please clarify.– The caption of Figure S9 needs to be rewritten and more precise. First, panel (A) only shows the results for a subset of the data . Therefore, I would avoid saying "conditioning on vaccination status and lineage", which suggests that the whole dataset has been used to fit the model. Similar for panel (B). Second, it would be helpful to make clearer which model has been used for the results in panel (A) and which one for the results in panel (B). Now it is only said at the end of the caption. Third, the model for panel (B) is described as including an interaction between days since detection with vaccination status and variant. However, as far as I understand, only Omicron infections are considered here, which means that variant is in practice not used as a covariate, and therefore, the model only has 2 splines .– In most figures, results are presented for the frequent testing group and the delayed detection group. Are the logistic models fitted jointly to the whole dataset or independently to each of the two groups? I understand that the latter. In that case, I think it is not correct to say "stratified". Instead, I would just say that the model has been independently fitted to X and Y.Methods section, Viral kinetic model (lines 589-604). (Again) when the authors say they "stratify" the model by a certain variable, what do they mean? Do they just simply fit the model independently to each subset of data, or something else? Please clarify.Methods section, Incidence of rebounds (lines 145-169). Although it might seem obvious, I think it would be good to make clear they talk about consecutive days in the definition of a rebound.For consistency throughout the manuscript, I would suggest using either: (1) variant or lineage, (2) vaccination status and vaccination history, (3) Omicron or BA.1.I have not seen any statement about the availability of the data. Please clarify.Reviewer #2 (Recommendations for the authors):– It seems like the authors suggest that no one took antivirals after infection in this cohort but I don't see this explicitly stated here. Do you have data on this or it is assumed?– Throughout reading this I found myself wanting to see a better description of the characteristics of the participants , ideally stratified by important factors like vaccination and exposure history and titer class (high/low).– In the models predicting Pr(Ct<30), it is not clear why cross-validation is not being used for AUC and classification accuracy measures. Not an issue with EPLD shown in the supplemental table.– It would be helpful to add the models with age effects to model performance comparison in Tables S3/4.– Figure 2C – 95%CrI shown horizontally seems strange. Are these not meant to be aligned with the y-axis?– Figure S11 is hard to read and I do not believe the legend and caption fully describe what is going on. Would be helpful to update this.– line 131 – can you add the n to this?– Code on github is easy to use and pretty well documented, thank you! Essential revisions:1) Please add a description (table?) of the demographics of the participants , stratified by important factors .We have added an additional table (Table 1). Table 1 gives demographic characteristics attached to each infection  as well as the factor levels used in the modeling analyses. Unfortunately, we are unable to add additional granularity to Table 1 regarding age, sex, co-morbidities or role within the NBA due to the sensitive nature of these data and the data use agreement.2) It would be helpful to add the models with age effects (uni and multivariate) to model performance comparison in Tables S3/4.We have included all of the additional models including age effects in the Tables S3/4 as suggested. We note that this has doubled the number of presented models, as we consider all of the previous 8 models with an additional spline term for age group.3) While the analyses stratified by antibody levels are interesting, the differences in kinetics that remain, even after stratifying, are also intriguing. It might not be possible to present an analysis stratified by antibody level and age, but it would be useful to discuss to what extent age might explain these differences. Presenting a figure  stratified by age could also be useful. Authors should also discuss alternative hypotheses for differences in kinetics between boosted and unboosted individuals.Figure S15 presents the same analyses as Figure 2 but with an additional stratification by age group. As you allude to, one of the limitations with this additional level of stratification is that the group sizes for some of the age/vaccination status/antibody titer groups are small (or indeed 0), particularly in the 50+ age group. Nonetheless, this stratification shows that (a) the pattern of lower titers among boosted, BA.1-infected individuals remains within each age group and (b) the pattern for longer duration of Ct<30 in boosted, low titer individuals remains for the <30 and 30-50 age groups. We have rearranged the figures and rewritten much of the Results section to better incorporate the effect of age into the narrative:1. Previous Figure S9 has now become main text Figure 2, showing the marginal effect of age group and vaccination status on duration Ct<30 from Omicron BA.1 infections.2. The results from Figure S15 have been combined with Figure 2C to create a new main text Figure 3. This gives the results from the same model as was previously shown in Figure 2B, but now including an additional stratification by age group as suggested by the reviewer. We note that although we only show the Omicron BA.1 results (as they are the only results of interest here), this model did include data from other lineages in the fitting.If the editor and reviewers have suggestions for alternative hypotheses for the differences in the kinetics between boosted and unboosted individuals, we would welcome them.4) Please clarify whether any of the participants took antivirals.They did not. We now state this in the first paragraph of the “Data” section.Reviewer #1 (Recommendations for the authors):Although the effect of age as a confounder is accounted for and discussed, I would have found it helpful to see it considered from the beginning as a potentially important factor and tested along with a cumulative number of exposures, lineage, days since previous exposure and vaccination status. It would be interesting to see a model that in addition to the baseline spline also accounts for a single predictor of age in the results presented in Tables S3 and S4. In addition, I would consider showing some of the results for age and Omicron in the main text.We now refer to the models including age from the start of the logistic regression results. Rather than referring to this as a sensitivity analysis, we now consider all of the models including age as part of the initial model comparison analysis. We have replaced main text Figure 2 with a figure describing the model including vaccination status and age group .I found the methods and results for the logistic regression a bit difficult to follow at some points, with most of the results presented in the supplementary information, implying going back and forth between the two files.Apologies for this. We have tried to reframe the start of the Results section to highlight that we are building successively more complex models from an initial baseline considering only days since detection as a predictor. We hope that the reader will interpret this as an exhaustive comparison of potential classification models rather than walking through the results of each model fit.– It would be helpful to add a list (or Table) with all possible factors (predictors) considered and the values  they can take .We have added new a Table 1 to the main text to summarize the data and show the possible categories for each variable.– How is the effect of each factor modelled? It is written in the methods section that "additional logistic regression models, adding additional spline terms capturing the effect of vaccination status, cumulative number of previous exposures or days since previous exposure, and/or lineage with days since detection". Does it mean an additional spline is added for each possible category of each variable? If so, how is "days since previous exposure" treated? I understand that for the models that include more than one predictor, those are always considered to interact – is that true? Please clarify.We have added text to the methods to clarify how the models consider effects for each predictor. In brief, consider the baseline model as a single smoothing spline on days since detection as a predictor. Each model then adds an intercept and spline term over days since detection (on top of this baseline model) for each possible category. In models including variant, we consider the interaction of variant and exposure history/vaccination status/days since exposure group. We do not add an interaction between age group and any other variable – age group is always an additive effect. These additional spline terms therefore attempt to explain variation not explained by days since detection alone. Regarding the “days since previous exposure” variable, we discretized each exposure into “Naïve”, “<1 month”, “1-3 months” or “>3 months” relative to the most recent prior exposure. This is now described in the methods.– The caption of Figure S9 needs to be rewritten and more precise. First, panel (A) only shows the results for a subset of the data . Therefore, I would avoid saying "conditioning on vaccination status and lineage", which suggests that the whole dataset has been used to fit the model. Similar for panel (B). Second, it would be helpful to make clearer which model has been used for the results in panel (A) and which one for the results in panel (B). Now it is only said at the end of the caption. Third, the model for panel (B) is described as including an interaction between days since detection with vaccination status and variant. However, as far as I understand, only Omicron infections are considered here, which means that variant is in practice not used as a covariate, and therefore, the model only has 2 splines .Apologies for the confusion here – all results in this figure are from the same model. This figure shows the conditional effect of age or vaccination status from the same model which includes spline terms for age group and the interaction of variant with vaccination status. The splines shown are the conditional effects of (A) age group after conditioning on being a BA.1 infection and boosted, or (B) vaccination status after conditioning on being a BA.1 infection and age group <30 years. Thus, it is correct that the whole dataset was used to fit this model, as the figure shows conditional effects. We have updated the figure caption to clarify these points.– In most figures, results are presented for the frequent testing group and the delayed detection group. Are the logistic models fitted jointly to the whole dataset or independently to each of the two groups? I understand that the latter. In that case, I think it is not correct to say "stratified". Instead, I would just say that the model has been independently fitted to X and Y.We have clarified that in all instances, the logistic regression models are fit independently to the two groups.Methods section, Viral kinetic model (lines 589-604). (Again) when the authors say they "stratify" the model by a certain variable, what do they mean? Do they just simply fit the model independently to each subset of data, or something else? Please clarify.To stratify the viral kinetic model, we chose a reference category and fitted independent additive random effects for the other categories. We clarify this in the new Methods section.Methods section, Incidence of rebounds (lines 145-169). Although it might seem obvious, I think it would be good to make clear they talk about consecutive days in the definition of a rebound.Thank you. We have clarified this point.For consistency throughout the manuscript, I would suggest using either: (1) variant or lineage, (2) vaccination status and vaccination history, (3) Omicron or BA.1.We have changed all instances of “vaccination history” to “vaccination status”. Regarding variant vs. lineage and Omicron vs. BA.1, we have checked for consistency but note that in some instances the distinction between the terms is important. For example, although we refer to Omicron BA.1 infections throughout much of the text, there are some instances where variant or lineage are appropriate, such as “pre-Omicron variants”, “BA.1 lineages BA.1.1529 and BA.1.1” or “non-Omicron variant”, which must remain as such to retain their intended meaning.I have not seen any statement about the availability of the data. Please clarify.The Data Availability statement contains a link to the data and code used for this study.Reviewer #2 (Recommendations for the authors):– It seems like the authors suggest that no one took antivirals after infection in this cohort but I don't see this explicitly stated here. Do you have data on this or it is assumed?We confirm that none of the study participants took antivirals. We now state this in the first paragraph of the revised Data section.– Throughout reading this I found myself wanting to see a better description of the characteristics of the participants , ideally stratified by important factors like vaccination and exposure history and titer class (high/low).We have added this information in Table 1 of the revised manuscript. We note that due to the sensitive nature of the data we are unable to provide more granularity on characteristics such as age, role within the NBA, co-morbidities etc.– In the models predicting Pr(Ct<30), it is not clear why cross-validation is not being used for AUC and classification accuracy measures. Not an issue with EPLD shown in the supplemental table.As suggested, we have replaced the AUC and classification accuracy measures with the results from the k-folds cross-validation analyses rather than using the within-sample measures. We note that although cross-validation results are generally regarded to be more reliable, this change has not affected any of the results.– It would be helpful to add the models with age effects to model performance comparison in Tables S3/4.We have added the age effects models into the comparison table as suggested. Models including age perform better than models without in terms of predictive accuracy, but they do not have substantially better classification accuracies. We refer to this in the Results section.– Figure 2C – 95%CrI shown horizontally seems strange. Are these not meant to be aligned with the y-axis?These CIs were correctly aligned with the y-axis, but were confusing given how narrow the intervals are. We elected to remove the CIs from the plot and simply show the mean as a horizontal line. We left the CIs as labels. We describe this in the figure caption.– Figure S11 is hard to read and I do not believe the legend and caption fully describe what is going on. Would be helpful to update this.We updated the figure caption to better describe this figure. Although there is a lot going on, we intended for this figure to be an exhaustive representation of all of the events of interest explored in the dataset over time.– line 131 – can you add the n to this?Added as suggested.– Code on github is easy to use and pretty well documented, thank you!You are very welcome.