Infection-acquired protection against SARS-CoV-2 infection and clinical severity by number of prior infections

Domain: chemistry
Published: 2026-03-09
Editorial outcome: minor_revision

Abstract
SARS-CoV-2 reinfections have increased since Omicron and are common. Yet, as we approach
endemicity, the frequency, severity, and consequences of reinfections remain poorly understood. Using
data from a household transmission study in Managua, Nicaragua (2020-2024), we evaluated protection
conferred by one, two, and three+ prior infections and compared the severity of first, second, and third+
RT-PCR-confirmed SARS-CoV-2 infections. Compared to those with no prior infections, one, two, and
three+ prior infections were associated with increasing protection from symptomatic infection, with
incidence rate ratios of 0.39, (95% Confidence Interval [CI]: 0.26-0.61), 0.24 (95%CI: 0.13-0.42), and 0.20
(95%CI: 0.09-0.42), respectively. Among RT-PCR-confirmed infections, reinfections were associated with
decreased severity; compared with first infections, second infections had 1.57 (95%CI: 1.15-2.16) times
the proportion of subclinical disease and 0.65 (95%CI: 0.49-0.86) times the proportion of
moderate/severe disease. Our findings suggest that protection generated from two prior infections may
be stronger than one prior infection, although additional benefit beyond the second infection was not
clearly observed. Together, our results suggest we are approaching an endemic phase of SARS-CoV-2
characterized by reduced severity relative to first infections, yet the burden remains high, underscoring
the importance of maintaining immunity to manage ongoing disease.

Introduction
It is still unclear what endemic SARS-CoV-2 will look like and whether future infections and disease will
differ from today. Population immunity continues to build through repeated infection and vaccination,
yet the virus keeps evolving, complicating efforts to achieve protection. Additionally, variant severity has
changed over time, with some pre-Omicron variants associated with increased severity and Omicron
with decreased severity.1 Although substantial literature exists on protection from SARS-CoV-2 infection,
our study addresses a gap by leveraging detailed infection histories in a cohort setting to examine the
associations of between repeated infections, protection and severity. In this work, we use data from a
community-based prospective cohort study in Nicaragua, a setting with high infection and vaccination
rates that is likely close to a stable level of endemicity. We measure the protection associated with prior
infection by comparing SARS-CoV-2 incidence among individuals with one, two, and three+ prior
infections, and we compare the severity of first, second, and third+ SARS-CoV-2 infections.

Results
Between March 2020 and December 2023, 2,388 people aged 0-95 years (median age of 17 years)
participated in the Household Influenza Cohort Study (HICS) contributing a total of 8,947 person-years
(PY) of follow-up (Supplementary Table 1). Sex distribution was balanced among children, but adult male
participation was lower (Supplementary Figure 2). In Nicaragua, the original SARS-CoV-2 strain circulated
in 2020, Gamma and Delta predominated in 2021, and Omicron became dominant starting in 20222,3.
ELISA completeness was high, with blood samples available for an average of 95% of enrolled
participants during sampling times (Supplementary Table 2).

We detected a total of 3,644 serologically- or RT-PCR-confirmed infections among HICS participants.
Only 2 RT-PCR-detected infections were missing any symptom data; these 2 participants were excluded
from analyses, leaving a total of 3,642 infections. Over the course of their enrollment, 1,056 participants
(44.2%) had one detected infection, 782 (32.7%) two infections, 324 (13.6%) three+ infections, and 226
(9.5%) had no detected infections (Supplementary Table 1). The majority of those remaining uninfected
either left the cohort in 2020/2021 (n=164, 71.3%) or were enrolled as infants (n=32, 13.9%). By October
2021, 91.3% of HICS participants had been infected, and by October 2024, 97.7% had been infected
(Figure 1A). This population also experienced a high rate of reinfections, even before Omicron, with
16.6% of the population experiencing a reinfection by the end of 2021 and 58.9% by October 2024
(Supplementary Table 1).

Of the 3,642 total infections with symptom data, 1,145 (31.4%) were detected by RT-PCR. Among the
1,145 RT-PCR-confirmed infections, 346 were first, 615 were second, and 184 were third+ infections
(Figure 2A&B). The majority of RT-PCR-detected infections were subclinical or mild (75.5%), but 19.8%
were moderate and 4.6% were severe. We observed 6 SARS-CoV-2-associated deaths.

Nicaragua did not have access to vaccines as early as many other parts of the world. When vaccines did
become more widely available around September-October 2021, they included a broader selection of
lesser-studied SARS-CoV-2 vaccines 4. A large-scale vaccination effort followed, and by January 2022,
70.0% of HICS participants had been vaccinated (78.5% of adults, Figure 1B).

Protection from reinfection increased with the number of prior infections, and somewhat with the
severity of the outcome (all RT-PCR-confirmed, symptomatic, and moderate/severe infections), although
confidence intervals were wider for more severe outcomes. The highest protection was observed in
those with three+ prior infections against moderate/severe disease (0.19, 95%CI: 0.05-0.79), and the
lowest protection—0.52 (95%CI: 0.36-0.76)—was against all RT-PCR-confirmed infections in those with
one prior infection versus uninfected individuals (Figure 1 & Supplemental Tables 3&5).

The incidence of symptomatic infections was 14.1, 9.2, 5.9, and 4.8 cases per 100 PY for those with 0,
1, 2, and 3+ prior infections (Figure 1C), corresponding to incidence rate ratios (IRRs) of 0.39 (95%CI: 0.26-
0.61), 0.24 (0.13-0.42), and 0.20 (0.09-0.42) for those with 1, 2, and 3+ versus no prior infections (Figure
1D). For moderate/severe disease, incidence was 5.4, 2.8, 1.9, and 1.2 cases per 100 PY in those with 0,
1, 2, and 3+ prior infections, corresponding to IRRs of 0.26 (95%CI: 0.12-0.57), 0.23 (0.08-0.66), and 0.19
(0.05-0.79) for those with 1, 2, and 3+ versus 0 prior infections.

To further assess whether additional prior infections provided incremental protection, we compared
participants with ≥1 and ≥2 prior infections (Supplemental Figure 3). As shown in Supplemental Figure 3
A.1 & A.2, participants with two or more prior infections had significantly lower risk of RT-PCR-
confirmed infection compared to those with only one prior infection (IRRs of 0.61 [0.48-0.77] for 2 vs 1;
and 0.41 [0.26-0.63] for 3+ vs 1); protection also remained significant for 3+ vs 2 prior infections (IRR:
0.60 [0.38-0.93]). Similarly, we observed reduced risk of symptomatic infection for participants with 2 or
3+ infections compared to those with one (IRRs: 0.61 [0.46-0.82] and 0.47 [0.28-0.78], respectively).
However, the number of prior infections was not significantly associated with the risk of moderate or
severe disease (e.g. IRR for 2 vs 1 prior infection: 0.86 [0.52-1.44]) or symptomatic infection when
restricting to participants with ≥2 prior infections (IRR: 0.81 [0.48-1.34]). These findings suggest that
multiple prior infections enhance protection against reinfection, although these associations are weaker
and less precise when examining more severe outcomes.

Similarly, reinfections were less severe than first infections (Figure 2 & Supplemental Tables 4&6),
though with no apparent additional benefit from increasing number of prior infections as seen with
protection from infection. The proportion of moderate/severe disease decreased from 31.5% to 21.5%
to 21.2% in first, second, and third+ infections (Figure 2B), corresponding to relative severities of 0.65
(95%CI: 0.49-0.86) and 0.70 (95%CI: 0.45-1.09) for second and third+ versus first infections (Figure 2C).
Meanwhile, the proportion of subclinical infections increased from 17.3% to 29.9% to 31.0% in first,
second, and third+ infections, corresponding to increased relative severities of 1.57 (95%CI: 1.15-2.16)
and 1.71 (95%CI: 1.11-2.62) for second and third+ infections versus first infections.

Because there are many potentially confounding factors, and for transparency, we have included
Supplemental Figure 4, which shows partially and alternately adjusted models for the main protection
and severity analyses. While all models produced qualitatively similar point estimates indicating
protection, not all models yielded statistically significant associations. In particular, when time since
prior infection or vaccination was included as a covariate, confidence intervals widened and some
estimates were no longer statistically significant (Supplemental Figure S4). For severity analyses,
estimates remained generally similar across model specifications. This stability suggests that variables
that confound infection risk (such as time since infection or vaccination) are not strong confounders of
severity in our community-based cohort once infection has occurred. Additional adjustment for these
variables increased uncertainty (wider confidence intervals) without materially changing the point
estimates, so the simpler model was retained for the main severity analyses.

Stratifying by year (Supplemental Figure 5), we again observed increasing protection with each
subsequent reinfection for 2021 and 2022, however incidence was much lower in 2023 and 2024, and
there were so few first infections in those years that we did not observe protection for 2023 or 2024
(Supplemental Figure 5 & Supplemental Table 7). Reductions in disease severity were apparent in the
pre-Omicron period, but not during Omicron (Supplemental Figure 6 & Supplemental Table 8). During
the pre-Omicron period, second infections were associated with 0.56 (95%CI: 0.40-0.79) times as much
moderate/severe disease and 1.95 (95%CI: 1.33-2.85) times more subclinical infections versus first
infections.

Additionally, the period-of- and time-since- last infection were associated with protection from
reinfection, and, to a lesser extent, severity (Supplemental Figure 7). As expected, protection was
highest when the prior infection matched the circulating variant, and was more recent. Looking at
period of last infection, the highest protection was observed in the pre-Omicron period for those with a
prior pre-Omicron infection (IRR: 0.04, 95%CI: 0.02-0.09 for symptomatic infection) and in the Omicron
era for those with a prior Omicron infection (IRR: 0.06, 95%CI: 0.04-0.09 for any PCR-confirmed
infection). For time since last infection, protection was highest pre-Omicron, for those whose last
infection was 0-180 days prior (IRR of 0.02, 95%CI: 0.00-0.11 for moderate/severe infection). Protection
remained present even for participants with original SARS-CoV-2 infections and infections ≥1 year prior.
For example, during the pre-Omicron era, protection against symptomatic infection for participants with
an original SARS-CoV-2 infection, the IRR was 0.31 (95%CI: 0.25-0.40), and for those with an infection 1-
2 years prior, the IRR was 0.59 (95%CI: 0.45- 0.78). We cannot explain the observed increase in
protection among people with prior infections ≥2 years earlier compared to with those infected 1-2
years earlier, though this may reflect chance variation, differences in variants, or selection effects such
as survivor bias, where those with more durable immunity (e.g., better mucosal immunity) remain
uninfected for longer.

Protection results were largely similar for age groups 18+, 10-17, and 0-9 years (Supplemental Figure 8),
with weaker associations for the youngest children. Likewise, relative severity of reinfection
(Supplemental Figure 9) was similar for adults and older children, but there was no association for the
youngest children.

Methods
Ethics Statement
This study was approved by the institutional review boards at the Nicaraguan Ministry of Health and the
University of Michigan (HUM00119145 and HUM00178355 and CIRE code # NIC-MINSA/CNDR CIRE-
05/04/17-080 Rev 13). Informed consent or parental permission was obtained for all participants.
Assent was obtained from children aged ≥6 years.

The Household Influenza Cohort Study (HICS)
HICS is an ongoing household cohort study with an embedded household transmission study, located in
district II of Managua, Nicaragua. The neighborhoods in district II range from low to middle class,
including several areas classified as urban slums. It was started in 2017 to study influenza, and in 2020
SARS-CoV-2 was added. Regular blood samples are collected for all cohort participants, twice yearly in
2020 and 2021, then yearly thereafter. The transmission study is activated when a household member
becomes sick and tests positive for SARS-CoV-2 by RT-PCR after seeking care at the study health clinic.
Activated households are visited by study staff regularly, at approximately days 0, 3, 7, 14, 21, and 30,
for collection of respiratory samples and symptom diaries from all household members, regardless of
symptoms.

Laboratory Assays
Blood samples were tested as paired serum samples (current vs baseline) using an enzyme-linked
immunosorbent assays (ELISAs) protocol adapted from the Krammer laboratory 20. The SARS-CoV-2 spike
receptor binding domain (RBD), spike, and nucleoprotein (NP) proteins for ELISAs were produced in
single batches at the Life Sciences Institute at the University of Michigan; these were generated based
on the original SARS-CoV-2 strain. RBD was used for screening (positive/negative) as it is more specific
than spike, and spike was used to titer samples that screened positive by RBD ELISA; NP ELISAs were also
run, to distinguish between vaccine and infection responses. Real-time reverse-transcription polymerase
chain reaction (RT-PCR) was performed according to the protocol from Chu et al. 21.
All RT-PCR and most ELISAs were performed at the Nicaraguan National Virology Laboratory, with a
minority of 2020 annual samples processed at the University of Michigan.

Infection histories
We used positive serology screens, antibody titer values, 4-fold rises in titer, and RT-PCR positivity to
define first and secondary infections, as outlined in Supplemental Figure 1. To be defined as an
infection, serological samples must have been from participants at least 6 months of age, to avoid
incorrectly classifying maternal immunity, and first infections serologically detected by RBD screen or
spike titer must have been before the first COVID vaccination. We estimated the date of serologically-
defined infections to be the median between samples, or for positive screens, we used the median of
the sample date and March 1, 2020 or the participant’s birthday if they were born after 2020 or a
previous negative sample date. RT-PCR-detected infections must have been ≥60 days apart from each
other. Infection sequences based on serology were required to have a longer time span between
infections than those involving a PCR-detected infection given the inherent uncertainty in the exact
timing of serologically-detected infections. Distinct infections detected serologically must have been ≥6
months apart from each other. Paired serology samples that spanned a vaccination event or were
collected soon after vaccination were excluded, as the resulting antibody rise likely reflects vaccine-
induced immunity rather than infection. Sequential infections detected by RT-PCR and 4-fold rise must
have occurred >90 days apart from each other. Any potential infections within 60 days of each other
were flagged for manual inspection.

Severity
Severity was evaluated only for RT-PCR-confirmed infections, as symptom diaries and clinic records were
only available exclusively for these infections. Symptoms reported in daily symptom diaries and clinic
visits were used to define severity, as severe (hospitalized, doctors say they should have been
hospitalized, or death), moderate (lower respiratory symptoms or overall poor condition), mild (loss of
smell/taste, fever, or ≥2 symptoms), or subclinical (≤1 symptom not meeting definitions for higher
severity).

Main Exposures and Outcomes
For protection analyses, all prior infections detected serologically and by RT-PCR were used to define the
exposure, and the outcome was incident RT-PCR-detected infections with defined severity. Severity
analyses were restricted to RT-PCR-detected infections only; the exposure for severity analyses was
infection number (1st, 2nd, 3rd+), and the outcome was severity.

Vaccination
Vaccines (Covishield 19) were available to elderly or high-risk individuals starting in early 2021. In the fall
of 2021, vaccinations became much more widely available, including Sputnik V, Sputnik Light,
AstraZeneca, Abdala, Soberana, Pfizer, Janssen, Sinopharm, and Moderna. Vaccination status was
defined as unvaccinated, partially vaccinated, fully vaccinated (number of doses varied by vaccine;
Sputnik light only required one dose for full vaccination), or boosted; we counted vaccines that occurred
at least 14 days prior. Vaccines targeting the SARS-CoV-2 nucleocapsid were not counted with the
vaccinated groups (only a few people have had a vaccine targeting the nucleocapsid, starting in 2023).

Analysis
While new households were recruited in 2023 to maintain the cohort size, participants were limited to
those enrolled before January 1, 2021 or were born into the cohort (enrolled before age 1y) so that full
infection histories would be known. Person-time, for the protection analysis, included all weeks in the
study period when participants were enrolled, excluding the period immediately following infection. We
excluded up to 52 days post-infection, as this was the shortest interval at which a new infection was
detected in the cohort due to our infection definition. Person-time for an individual changed exposure
categories as new immune events occurred.
Poisson regression and mixed effect Poisson regression models were used to calculate relative risks (RR),
using the glm (base R) and glmer (lme4 package version 1.1-37) functions in R. Percent protection was
calculated as (1- RR)*100. We stratified all analyses by age (0-9y, 10-17y, and 18+y). We also stratified
by year or period, to account for high vaccination rates starting around November 2021 and differences
in variants.

Covariates
For model adjustment, we considered vaccination status (none, partial, full, and boosted), age (in years),
sex, days since last infection and vaccination (both categorized as ‘uninfected/unvaccinated’, [0-14),
[14-49) [vaccination only], then increments of 60 days until [870-2000]), year or period (original SARS-CoV-2,
pre-Omicron variant, and Omicron), and household clustering (as a mixed effect). Not all variables could
be included in stratified analyses. Examples of model specification are shown in Supplementary Tables 3
& 4.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I have reviewed the paper by Maier et al. on SARS-CoV-2 infections and have the following major and minor comments and suggestions to the authors:\n* Major comments\n1. A general limitation of the analysis is that it does not account for sex, and age, which are known to be two key determinants of disease outcome. Additionally, household clustering is probably also important to account for in order for the statistical inference to be correct.\n2. The conclusion that protection from prior infection was stronger pre-Omicron (line 126) must be backed up by a formal statistical interaction analysis.\n3. If conclusions specific to children are to be included (line 126-128), they should be backed up by formal statistical analysis and be based on results presented in the main text.\n4. The number of reinfections during the periods before Omicron seems surprisingly high compared with similar studies, also given the high protection from prior infections that were reported for the present cohort. The authors should comment on this unexpected finding. Is one possible explanation the late roll out of vaccines?\n5. Selection issues should be discussed as a potential, partial explanation for the observed increase in protection from prior infection. How large was the case fatality in the cohort? Even with low case fatality, the reinfected could still represent a group of less susceptible individuals or of less need of shelter against exposure. Is it possible to investigate the association between prior severity of infection and outcome of next infection?\n* Minor comments\n1. The paper lacks a standard Table 1 with descriptive data on the HICS cohort\n2. The abstract lacks clear conclusions of the type formulated in the first paragraph of the Discussion (line 125-128).\n3. Much of the reporting is focused on adults (age 18+), I would suggest that also the supplementary tables is stratified so that results for adults are presented in separate tables.", "reviewer_2": "General\nThis is a research article by Dr. Maier and colleagues comparing the protection of infection-acquired immunity from single vs multiple prior infections against symptomatic infection and clinical severity. The study concludes that infection-acquired immunity against symptomatic infection increases with each subsequent infection and that clinical severity declines with each subsequent infection. The study leverages a household cohort study from Managua, Nicaragua collecting routine serologic testing and symptom-triggered PCR testing over the entire Covid-19 pandemic period (2000-2024). This cohort design and dataset is an impressive strength of the article, given the complete individual histories of vaccination and prior infection based on the longitudinal design; this level of detailed data is rare. However, the current analytic structure has some methodological issues that preclude meaningful interpretation of the study findings in their present form. First, the analysis does not appear to account for timing since the last infection – however we know infection-acquired immunity wanes. This results in downward biasing of the estimate for protection generated by a single infection (which has a longer period of follow up, presumably after substantial waning of protection) compared to protection generated by a second or third infection (with shorter period of follow up, presumably during a period of higher protection before waning). Second, the findings of lower clinical severity from repeated infections could also be explained by both differences in which variants are circulating during 1st vs 2nd vs 3rd infection (and variant-specific clinical severity) and not accounting for waning of protection. Third, there may be differential indirect protection on the household level that is affecting these direct protection estimates. As the number of prior infections increases, so does the immunity of the household members, which may confound measure of direct protection (providing more indirect protection over time in households with members with higher number of prior infections, which could confound the direct protection measures). Finally, the statistical analysis and data processing could be more clearly described to better understand the analysis. I hope my comments can be constructive, as most of my concerns can be addressed and the scientific question and study are both important and interesting.\nMajor\n--Accounting for timing since last infection / waning in protection: It is well established that protection from infection-acquired immunity wanes over time, whether due to immune-mediated mechanisms or due to increasing mismatch of host immune response and evolving viral variants. Therefore, an analysis comparing protection from different numbers of infections needs to account for timing since last infection to account for this waning of protection. The current main analysis does not appear to do so, which will bias the estimates, especially for single infection persons that have a longer follow up period (likely with substantial waning of protection) compared to the two or three infection participants with a relatively shorter follow up period (where protection will be stronger). This is evidenced in the authors’ supplemental analysis. For example, a person with single infection induced protection may have their next infection up to >3 years later, compared to a participant with triple infection induced protection which would have their next infection within 1-2 years. Therefore, is the lower protection from single infection compared to multiple infections due to differences in the immunity or simply timing since last infection? This should be rigorously addressed to determine the validity of the study findings, which currently focus on number of infections rather than timing since last infection. This issue also affects the analysis on clinical severity, which may once again bias downwards protection from single infection due to not accounting for timing since the last infection. The authors could create categories of timing of their prior infection states or perform a more sophisticated rolling matching process (i.e., match 1 vs 0 prior infections over defined time period, match 2 vs 0 prior infections, etc). These proposed approaches each have some challenges (e.g., needing to have overlapping time periods between different immune statuses), but this concern can likely be mostly addressed with additional analysis and should be featured in the main analysis.\n--Clinical severity analysis methodological questions: From the current analysis, it is not entirely clear whether clinical severity is lower due to the protection generated from multiple infections or due to the variants affecting these different prior infection groups (e.g., variants circulating when participants have 1 vs 2 vs 3 infections are different). This is partially addressed by their pre- and post-omicron period analysis, but further work could be done to answer this question. As one potential approach, the authors could perform rolling matching to follow matched participants over the same variant periods (ideally more fine scale than just pre- and post-omicron), while accounting for timing of prior infection and vaccine differences. Alternatively, the authors could also highlight this as a limitation (including in the Abstract) if the sample size does not permit. Furthermore, the abstract mentions only comparing clinical severity of PCR+ cases, however the authors should also account for any differences in the fraction of asymptomatic infection (given their serology data) between 1 vs 2 vs 3 prior infection to answer this question about clinical severity.\n--Confounding from indirect protection: Prior studies have shown that both Covid-19 vaccines and infection-acquired immunity generate indirect protection, reducing risk of infection to nearby social contacts. The current analysis may be affected by this, given that cases with 3+ infections likely co-reside with household members with high immunity, which may falsely appear as higher direct protection to the case, whereas it’s actually due to the indirect protection from the household members. Given the impressive household cohort study done by the authors, this could be directly evaluated and accounted for in the analysis based on data availability.\n--Description of methods: The methods section is a bit sparse, which makes it more challenging to follow. The following would help: formal specification of the statistical model, relevant covariates, definition of person-time, treatment of repeated individuals (same participant in different immune categories), censoring, data processing, relevant reporting checklists. Discussion on why the authors did not use time-to-event modeling and matching. Could the authors clarify when they use PCR+ only vs any infection (PCR or serology) in the analysis (only for clinical severity analysis?), as it’s a bit challenging to follow. Does subclinical infection refer to PCR+ case with only one symptom or serologic asymptomatic cases or both?\n--Vaccination: There is very limited description of the vaccination of this population, which should be expanded. As above, timing of last vaccine should ideally be accounted for in all analyses.\n--Sensitivity analysis: The main analysis focuses on “symptomatic infection” but do the results hold for any infection (asymptomatic + symptomatic)?\nMinor\n--Consider revised title, “Infection-acquired protection against SARS-CoV-2 symptomatic infection and clinical severity by number of prior infections”\n--Abstract: Consider reporting relative risk rather than percentage increases.\n--Writing: Would prepare an Introduction vs Results section, rather than a single section (which basically lacks an Introduction). I would consider reworking the Introduction to focus more on addressing prior literature and the key scientific gap this study aims to fill.\nThank you for the opportunity to review this interesting study.", "reviewer_3": "Overview: This article examines the relationship between prior SARS-CoV-2 infection and subsequent COVID-19 disease severity outcomes. While the protective effect of prior infection is relatively well-established in existing literature, the manuscript's exploration of severity outcomes offers a somewhat more novel contribution. The authors effectively leverage an existing cohort in a high-transmission setting, which is a methodological strength. However, I have substantial concerns about the analytical approach that significantly undermine the validity of the findings. My primary reservations focus on two critical methodological components: (1) the calculation of person-time in the analysis, and (2) the absence of calendar time as a variable in the primary model (though a stratified version is included).\nAbstract:\n• I find the last sentence a bit confusing. Is this analysis among people who were infected and then you compared the risk of severity within those infected or was it a “protection” estimate looking at the overall risk of those severe disease presentations? While this is better explained in the main text, I would suggest re-writing the abstract to make it stand on its own a bit better.\nIntroduction: Generally, I think the introduction was lacking and doesn’t capture the existing knowledge quite as well as I would expect. Specifically:\n• Is there evidence to suggest that there is a change in the severity of COVID-19 over time?\n• How is the change in severity associated with changes in variants or the increase in immunity events?\n• There is already literature about protection from prior infections in regards to likelihood of infection, symptomatic infections and severe outcomes. Within the literature that exists, where is the specific gap you are filling?\n• Please delineate between the introduction and the results (assume this happened at line 42 based on information in the paragraph running from 43-49).\nResults:\n• Based on the methods it seems like you included serological infections as new infections but in the figures it looks like your outcome is PCR detected infections. So is serology-based infections for the exposure? For the rest of the review, I am assuming serology-based infections were specifically used for the exposure and PCR were the outcome. Please clarify this in the results and methods.\n• Please define variant time periods in the legend for the variant specific periods.\n• You observed an increase in protection among people >2 years since last infection relative to the 1-2 year group for all three outcomes, please explain.\nDiscussion/conclusion:\n• I am not sure I fully agree with the statement that you have complete infection histories for each participant. Because of the serological data, you have better data than just clinical PCRs on prior infection data, but it seems possible that secondary infections were missed (ex. if a person didn’t come in for clinical testing for two infections within a 6-month window, my understanding is that they would be classified as one prior infection based on serology).\nMethods: The study relies on an existing cohort of people and gets data from a combination of clinical and household data - which is a strength, but I have a few questions about how infections were captured and what infections were used in the analysis:\n• Infection capture:\no Who are the respiratory samples collected for – mainly are household members tested during the visits? “Activated households are visited by study staff regularly, at approximately days 0, 3, 7, 14, 21, and 30, for collection of respiratory samples and symptom diaries.”\no For the secondary infections identified by serology, what dose “Must not surround vaccination” mean in regards to time?\no Why do you require the serological based first infection detection to be >=90 day prior to vaccination? I understand that after a vaccine, the serological testing may be misclassified but am not sure how it would be misclassified prior to the vaccination event.\no Why are some of the time gaps 90 days and some 180 days (6 months) for serology? Specifically, you state that the first to secondary infections detected through serology need to be 6 months apart (left side of S.figure 1) and that the first PCR infection to secondary serology-based infection need to be 90 days part?\no Generally confused on when the serologically defined infections were stated to have occurred. The day of the fold change >= 6 months after the last? This is specifically for the time since last event analysis.\n• Person-time:\no Did you include person-time during the periods directly following prior infection, during which people were not “susceptible” to infections based on your definitions of new infections? If so, it seems like this may be driving part of the large amount of protection you observed from prior infections (person time included but outcomes not possible). Am I missing something?\n• Accounting for calendar time in the analysis:\no Can you explain why you didn’t account for calendar time in your primary model – though I recognize you did a stratified version? If calendar time is not accounted for, I fear that some of the severity-based findings may be driven by what variants each exposure group (first, second, third prior infection) preceded. Even within the stratified analysis, I worry that calendar time may be confounding the association of interest.\no Also, because may COVID-19 symptoms are non-specific, I would be concerned that other circulating respiratory pathogens may contribute to the reported symptom burdens differently by time.\n• Was the household correlation structure accounted for in the model? Or if it wasn’t, can you explain why not?"}, "author_response": "Response To Reviewer Comments\nReviewer #1 (Remarks to the Author):\nI have reviewed the paper by Maier et al. on SARS-CoV-2 infections and have the following major and minor\ncomments and suggestions to the authors:\n* Major comments\n1. A general limitation of the analysis is that it does not account for sex, and age, which are known to\nbe two key determinants of disease outcome. Additionally, household clustering is probably also\nimportant to account for in order for the statistical inference to be correct.\nRESPONSE:\nThank you for the suggestion. We considered adjusting for all of the reviewer-suggested variables in both\nthe protection and severity models. In doing so, we compared AICs and likelihood ratio tests. We ended up\nadjusting the protection analysis for vaccination, age, sex, times since last infection and vaccination,\ncalendar time, and household clustering. And we adjusted the severity analysis for vaccination, age, sex,\nand calendar time; the additional variables made the model fit worse for the severity analysis, so they were\nnot included in the main analysis, but we did include a supplemental figure adjusted for time since infection\nand vaccination.\nWe have added the following to the methods analysis section:\n“Poisson regression and mixed effect Poisson regression models were used to calculate relative risks (RR),\nusing the glm (base R) and glmer (lme4 package) functions in R. Percent protection was calculated as (1-\nRR)*100. For model adjustment, we considered vaccination status, age (in years), sex, time since last infection and vaccination (both categorized as ‘uninfected/unvaccinated’, ‘0-180 days’, ‘180-365 days’, ‘1-2\nyears’, and ‘>=2 years’), calendar time (variant period), and household clustering (as a mixed effect). AIC\nand likelihood ratio tests were used to determine whether to include variables in models, and age and sex\nwere included regardless. Not all variables could be included in stratified analyses.”\n(page 16, line 309)\nHere is a comparison of the severity models, for moderate/severe infection:\nRelative Severity for Moderate/Severe Infection\nCrude model Presented in Additionally adjusted**\nManuscript,\ntable S4*\nAIC: 1,345 AIC: 1,294 AIC: 1,303\n2nd infection vs 1st: 0.68 (0.53, 0.88) 0.65 (0.49, 0.86) 0.81 (0.47, 1.41)\n3rd+ infection vs 1st: 0.67 (0.47, 0.97) 0.70 (0.45, 1.09) 0.89 (0.42, 1.92)\n1\n*adjusted for vaccination, age, sex, and calendar time\n**additionally adjusted for time since last infection and vaccination (this model fit is worse)\nAs you can see, the AIC is higher (worse fit) when adjusted for time since last infection and vaccination, but\nthe estimates are in the same direction though attenuated compared to the model presented in the\nmanuscript, and the confidence intervals are much wider.\nHere is the additional Supplemental Figure 9 showing the severity analysis additionally adjusted for times\nsince infection and vaccination:\n2. The conclusion that protection from prior infection was stronger pre-Omicron (line 126) must be\nbacked up by a formal statistical interaction analysis.\nRESPONSE:\nWe appreciate this comment. The interaction terms for period and prior infection were not significantly\ndifferent, so we have removed this conclusion.\n3. If conclusions specific to children are to be included (line 126-128), they should be backed up by\nformal statistical analysis and be based on results presented in the main text.\nRESPONSE:\nAfter adjusting for all of the reviewer-requested covariates, we found that results were now more consistent\nand similar across age groups, so we changed the main analysis to focus on all age groups together and\nhave put age-stratified analyses in the supplement.\n4. The number of reinfections during the periods before Omicron seems surprisingly high compared\nwith similar studies, also given the high protection from prior infections that were reported for the\npresent cohort. The authors should comment on this unexpected finding. Is one possible\nexplanation the late roll out of vaccines?\nRESPONSE:\n“This population also experienced a high rate of reinfections, even before Omicron, with 16.6% of the\npopulation experiencing a reinfection by the end of 2021, 58.9% by October 2024.”\n(page 4, line 58)\nWe also added a sentence in the discussion section:\n“The high rate of reinfections in this population, even pre-Omicron, allowed for some stratified analyses.”\n(page 8, line 159)\n5. Selection issues should be discussed as a potential, partial explanation for the observed increase in\nprotection from prior infection. How large was the case fatality in the cohort? Even with low case\nfatality, the reinfected could still represent a group of less susceptible individuals or of less need of\nshelter against exposure. Is it possible to investigate the association between prior severity of\ninfection and outcome of next infection?\nRESPONSE:\nAs our cohort study is population-based and observes primarily the lower end of the severity spectrum, we\ndid not observe many (n=5) SARS-CoV-2-associated deaths and do not expect this small number to be a\nselection concern. We have added the following to the results and discussion:\n“The majority of RT-PCR-detected infections were subclinical or mild (75.5%), but 19.8% were moderate\nand 4.6% were severe. We observed 5 SARS-CoV-2-associated deaths (as indicated with asterisks in\nFigure 2A).”\n(page 4, line 64)\n“Because this cohort is population-based and observes primarily the lower end of the severity spectrum, we\ndid not have selection concerns associated with deaths.”\n(page 8, line 160)\n* Minor comments\n1. The paper lacks a standard Table 1 with descriptive data on the HICS cohort\nRESPONSE:\nWe agree that although supplemental figure 2 does have data such as the age and sex distribution of the\ncohort, data that is in a standard table 1 would also be useful to readers and have therefore added\nSupplemental table 1. We have also added a sentence to the methods, describing the SES of the cohort:\n“The neighborhoods in district II range from low to middle class, including several areas classified as urban\nslums.”\n(page 13, line 241)\nAny RT-PCR-detected infections\nVariable No N = 1,5061 Yes N = 8801 Overall N = 2,3861\nAge (years) 17 (8, 34) 16 (7, 37) 17 (8, 35)\nAge Group\n18+y 733 (49%) 424 (48%) 1,157 (48%)\n10-17y 337 (22%) 174 (20%) 511 (21%)\n0-9y 436 (29%) 282 (32%) 718 (30%)\nSex\nF 899 (60%) 543 (62%) 1,442 (60%)\nM 607 (40%) 337 (38%) 944 (40%)\n# of infections by end of study\n0 226 (15%) 0 (0%) 226 (9.5%)\n1 877 (58%) 178 (20%) 1,055 (44%)\n2 357 (24%) 424 (48%) 781 (33%)\n3+ 46 (3.1%) 278 (32%) 324 (14%)\n1Median (Q1, Q3); n (%)\nAge is calculated at the start of the study period. Table excludes 2 people with PCR-confirmed infections\nmissing symptom data\n2. The abstract lacks clear conclusions of the type formulated in the first paragraph of the Discussion\n(line 125-128).\nRESPONSE:\nThank you for bringing this to our attention. We have added this sentence:\n“Our findings suggest that reinfections are associated with increased protection against and reduced\nseverity of, subsequent reinfections.”\n(page 2, line 27)\n3. Much of the reporting is focused on adults (age 18+), I would suggest that also the supplementary\ntables is stratified so that results for adults are presented in separate tables.\nRESPONSE:\nWe appreciate this suggestion. As we have changed the main findings to include all ages, we have still\ntaken your suggestion but present tables for all ages first, followed by age-stratified tables.\nReviewer #2 (Remarks to the Author):\nThis is a research article by Dr. Maier and colleagues comparing the protection of infection-acquired\nimmunity from single vs multiple prior infections against symptomatic infection and clinical severity.\nThe study concludes that infection-acquired immunity against symptomatic infection increases with\neach subsequent infection and that clinical severity declines with each subsequent infection. The\nstudy leverages a household cohort study from Managua, Nicaragua collecting routine serologic\ntesting and symptom-triggered PCR testing over the entire Covid-19 pandemic period (2000-2024).\nThis cohort design and dataset is an impressive strength of the article, given the complete individual\nhistories of vaccination and prior infection based on the longitudinal design; this level of detailed\ndata is rare. However, the current analytic structure has some methodological issues that preclude\nmeaningful interpretation of the study findings in their present form. First, the analysis does not\nappear to account for timing since the last infection – however we know infection-acquired immunity\nwanes. This results in downward biasing of the estimate for protection generated by a single\ninfection (which has a longer period of follow up, presumably after substantial waning of protection)\ncompared to protection generated by a second or third infection (with shorter period of follow up,\npresumably during a period of higher protection before waning). Second, the findings of lower\nclinical severity from repeated infections could also be explained by both differences in which\nvariants are circulating during 1st vs 2nd vs 3rd infection (and variant-specific clinical severity) and\nnot accounting for waning of protection. Third, there may be differential indirect protection on the\nhousehold level that is affecting these direct protection estimates. As the number of prior infections\nincreases, so does the immunity of the household members, which may confound measure of direct\nprotection (providing more indirect protection over time in households with members with higher\nnumber of prior infections, which could confound the direct protection measures). Finally, the\nstatistical analysis and data processing could be more clearly described to better understand the\nanalysis. I hope my comments can be constructive, as most of my concerns can be addressed and\nthe scientific question and study are both important and interesting.\nRESPONSE: Thank you very much for your thorough comments. Please see our responses below, as the\nconcerns you raise above are each fleshed out more below.\nMajor\n--Accounting for timing since last infection / waning in protection:\nIt is well established that protection from infection-acquired immunity wanes over time, whether due\nto immune-mediated mechanisms or due to increasing mismatch of host immune response and\nevolving viral variants. Therefore, an analysis comparing protection from different numbers of\ninfections needs to account for timing since last infection to account for this waning of protection.\nThe current main analysis does not appear to do so, which will bias the estimates, especially for\nsingle infection persons that have a longer follow up period (likely with substantial waning of\nprotection) compared to the two or three infection participants with a relatively shorter follow up\nperiod (where protection will be stronger). This is evidenced in the authors’ supplemental analysis.\nFor example, a person with single infection induced protection may have their next infection up to >3\nyears later, compared to a participant with triple infection induced protection which would have their\nnext infection within 1-2 years. Therefore, is the lower protection from single infection compared to\nmultiple infections due to differences in the immunity or simply timing since last infection? This\nshould be rigorously addressed to determine the validity of the study findings, which currently focus\non number of infections rather than timing since last infection. This issue also affects the analysis\non clinical severity, which may once again bias downwards protection from single infection due to\n5\nnot accounting for timing since the last infection. The authors could create categories of timing of\ntheir prior infection states or perform a more sophisticated rolling matching process (i.e., match 1 vs\n0 prior infections over defined time period, match 2 vs 0 prior infections, etc). These proposed\napproaches each have some challenges (e.g., needing to have overlapping time periods between\ndifferent immune statuses), but this concern can likely be mostly addressed with additional analysis\nand should be featured in the main analysis.\nRESPONSE:\nThank you for this suggestion. We agree that examining time since last infection or vaccination is important\nand have addressed this concern. Please see our response to reviewer #1, major comment #1.\n--Clinical severity analysis methodological questions:\nFrom the current analysis, it is not entirely clear whether clinical severity is lower due to the\nprotection generated from multiple infections or due to the variants affecting these different prior\ninfection groups (e.g., variants circulating when participants have 1 vs 2 vs 3 infections are\ndifferent). This is partially addressed by their pre- and post-omicron period analysis, but further\nwork could be done to answer this question. As one potential approach, the authors could perform\nrolling matching to follow matched participants over the same variant periods (ideally more fine\nscale than just pre- and post-omicron), while accounting for timing of prior infection and vaccine\ndifferences. Alternatively, the authors could also highlight this as a limitation (including in the\nAbstract) if the sample size does not permit. Furthermore, the abstract mentions only comparing\nclinical severity of PCR+ cases, however the authors should also account for any differences in the\nfraction of asymptomatic infection (given their serology data) between 1 vs 2 vs 3 prior infection to\nanswer this question about clinical severity.\nRESPONSE:\nWe appreciate the reviewer bringing up this point. Please see our response to reviewer #1, major comment\n#1.\nWe also stratified the results for clinical severity by period (supplemental Figure 4), finding more pronounced\nreductions in disease severity during the pre-Omicron period, with only 5 3rd+ infections. We found little\ndifference in disease severity during the Omicron period, however, overall disease severity was lower during\nOmicron; as the reviewer points out, this lower severity during Omicron could contribute to the overall\nassociation of severity and infection number.\nWe added this to the discussion:\n“However, sample sizes were small when stratifying by age and period, and we were unable to further\nstratify by Omicron subvariants. Another limitation is that we cannot fully disentangle the effects of variant\ndifferences from those of immune history, as these factors are closely linked, individuals’ immune histories\nare shaped by the variants circulating at the time of their prior infections. While we did stratify by variant era,\npeople in this population who had first and second infections during Omicron may still differ in important\nways.”\n(page 8, line 164)\nWe do not have the same level of symptom data available to match with the serologically confirmed\ninfections over the entire period of this study, so we cannot assess the asymptomatic fraction over time in\nserologically confirmed cases.\n6\n--Confounding from indirect protection:\nPrior studies have shown that both Covid-19 vaccines and infection-acquired immunity generate\nindirect protection, reducing risk of infection to nearby social contacts. The current analysis may be\naffected by this, given that cases with 3+ infections likely co-reside with household members with\nhigh immunity, which may falsely appear as higher direct protection to the case, whereas it’s\nactually due to the indirect protection from the household members. Given the impressive\nhousehold cohort study done by the authors, this could be directly evaluated and accounted for in\nthe analysis based on data availability.\nRESPONSE:\nThank you for bringing this up. We have now adjusted for household clustering. It would be interesting to\nalso adjust for household member immunity status, but that is beyond the scope of this work, as addressing\nit would require a specialized household transmission model.\n--Description of methods:\nThe methods section is a bit sparse, which makes it more challenging to follow. The following\nwould help: formal specification of the statistical model, relevant covariates, definition of person-time,\ntreatment of repeated individuals (same participant in different immune categories), censoring, data\nprocessing, relevant reporting checklists. Discussion on why the authors did not use time-to-event\nmodeling and matching. Could the authors clarify when they use PCR+ only vs any infection (PCR or\nserology) in the analysis (only for clinical severity analysis?), as it’s a bit challenging to follow. Does\nsubclinical infection refer to PCR+ case with only one symptom or serologic asymptomatic cases or\nboth?\nRESPONSE:\n● Thank you for mentioning this--we think the new additions to the methods section are beneficial.\nWe have added the following to the Analysis section of the methods, as well as an additional table,\nsupplementary Table 3 (pasted below):\n“Person-time, for the protection analysis, counted all weeks in the study period when participants\nwere enrolled, but excluded the time directly following infection; we excluded the time up to 52 days\nfollowing infection, as that was the closest we detected infections in the cohort. Person-time for an\nindividual changed exposure categories as new immune events occurred.\nPoisson regression and mixed effect poisson regression models were used to calculate relative\nrisks (RR), using the glm (base R) and glmer (lme4 package) functions in R . Percent protection\nwas calculated as (1- RR)*100. For model adjustment, we considered vaccination status, age (in\nyears), sex, times since last infection and vaccination (both categorized as\n‘uninfected/unvaccinated’, ‘0-180 days’, ‘180-365 days’, ‘1-2 years’, and ‘>=2 years’), calendar time\n(variant period), and household clustering (as a mixed effect). AIC and likelihood ratio tests were\nused to determine whether to include variables in models, and age and sex were included\nregardless. Not all variables could be included in stratified analyses. Examples of model\nspecification are shown in Supplementary Table 3.”\n(page 15, line 304)\n● We could have also investigated time-to-event, but that was not our question in this work.\n● We have clarified when we are using PCR+ vs serological and PCR+ infections:\n■ We have added the following to the Severity section of the methods:\n“Severity was evaluated only for RT-PCR-confirmed infections, as symptom diaries and\nclinic records were only available exclusively for these infections. These RT-PCR-\n7\nconfirmed infections with defined severity were used as the outcome in protection\nanalysis, and the severity analysis compared severity only within this group.”\n(page 15, line 282)\n■ We have also updated the title of Figure 2A from “infection timing” to “RT-PCR-detected\ninfection timing” for clarity.\nIn this study, “subclinical infection” refers to RT-PCR-positive infections with one or fewer\nsymptoms, excluding fever and loss of smell/taste).\nTable S 3 Specification of Models\nProtection Model*\nIncidence Rate\nVariable Value Ratio** p.value\n# prior infections 1 0.20 (0.15, 0.28) 0\n(vs 0) 2 0.09 (0.06, 0.14) 0\n3+ 0.05 (0.03, 0.09) 0\nvaccination partial 1.56 (0.70, 3.48) 0.273\n(vs none) full 1.63 (0.70, 3.77) 0.257\nboosted 1.35 (0.58, 3.14) 0.492\nage (in years) age 1.01 (1.00, 1.01) 0.002\nsex male 0.82 (0.70, 0.96) 0.011\ntime since last infection 0-180 days 1.39 (0.96, 2.01) 0.083\n(vs uninfected) 180-365 days 1.31 (0.95, 1.80) 0.103\n1-2 years 1.96 (1.49, 2.58) 0\ntime since last vaccination 0-180 days 0.70 (0.31, 1.60) 0.398\n(vs unvaccinated) 180-365 days 0.53 (0.23, 1.24) 0.143\n1-2 years 0.30 (0.13, 0.73) 0.007\n>= 2 years 0.27 (0.11, 0.70) 0.007\nperiod pre-omicron variant 5.21 (4.11, 6.60) 0\n(vs original) omicron 7.67 (5.72, 10.28) 0\nSeverity Model***\nVariable Value Relative Severity p.value\ninfection # 2nd 0.65 (0.49, 0.86) 0.003\n(vs 1st) 3rd+ 0.70 (0.45, 1.09) 0.113\nvaccination partial 0.93 (0.61, 1.44) 0.751\n(vs none) full 0.84 (0.54, 1.29) 0.416\nboosted 0.72 (0.46, 1.15) 0.17\nage (in years) age 1.02 (1.02, 1.03) 0\nsex male 0.81 (0.62, 1.05) 0.104\nperiod pre-omicron variant 1.74 (1.14, 2.63) 0.01\n(vs original) omicron 1.48 (0.87, 2.52) 0.151\n* among all ages, outcome = symptomatic infection\n** also adjusted for household\n***among all ages, outcome = moderate/severe disease\n8\n--Vaccination:\nThere is very limited description of the vaccination of this population, which should be expanded. As\nabove, timing of last vaccine should ideally be accounted for in all analyses.\nRESPONSE:\nWe have now added adjustment for time since last vaccination. There is also more information about\nvaccination in a very similar population in the paper cited (ref #3, Zambrana et al, Vaccines 2024). And we\nhave added the following section on Vaccination to the Methods:\n“Vaccination\nVaccines (Covishield 19) were available to elderly or high-risk individuals starting in early 2021. In the fall of\n2021, vaccinations became much more widely available, including Sputnik V, Sputnik Light, AstraZeneca,\nAbdala, Soberana, Pfizer, Janssen, Sinopharm, and Moderna. Vaccination status was defined as\nunvaccinated, partially vaccinated, fully vaccinated (number of doses varied by vaccine; Sputnik light only\nrequired one dose for full vaccination), or boosted; we counted vaccines that occurred at least 14 days\nprior.”\n(page 15, line 291)\nAlso, please see our response to reviewer #1, major comment #1.\n--Sensitivity analysis:\nThe main analysis focuses on “symptomatic infection” but do the results hold for any infection\n(asymptomatic + symptomatic)?\nRESPONSE:\nThank you. We present results for all PCR infections which includes asymptomatic and symptomatic as well\nas moderate/severe infections for the protection analysis, and subclinical and moderate/severe disease for\nthe severity analysis. For the protection analysis, we don’t specifically look at protection from asymptomatic\ninfection alone, because there were too few asymptomatic PCR-confirmed infections to assess\nasymptomatic outcomes on their own.\nMinor\n--Consider revised title, “Infection-acquired protection against SARS-CoV-2 symptomatic infection\nand clinical severity by number of prior infections”\nRESPONSE: Thank you for the suggestion. We have revised the title.\n--Abstract: Consider reporting relative risk rather than percentage increases.\nRESPONSE:\nWe have changed our reporting of percent protection to relative risk (incidence rate ratios).\n--Writing: Would prepare an Introduction vs Results section, rather than a single section (which\nbasically lacks an Introduction). I would consider reworking the Introduction to focus more on\naddressing prior literature and the key scientific gap this study aims to fill.\nRESPONSE:\n“Additionally, variant severity has changed over time, with some pre-Omicron variants associated with\nincreased severity and Omicron with decreased severity.1 While much literature does exist on protection\nfrom SARS-CoV-2 infection, a gap that we fill is the ability to use detailed infection histories in a cohort\nsetting to assess the association of numerous repeated infections with protection and severity.”\n(page 3, line 31)\nThank you for the opportunity to review this interesting study.\nRESPONSE:\nThank you for your helpful suggestions!\nReviewer #3 (Remarks to the Author):\nOverview:\nThis article examines the relationship between prior SARS-CoV-2 infection and subsequent COVID-\n19 disease severity outcomes. While the protective effect of prior infection is relatively well-\nestablished in existing literature, the manuscript's exploration of severity outcomes offers a\nsomewhat more novel contribution. The authors effectively leverage an existing cohort in a high-\ntransmission setting, which is a methodological strength. However, I have substantial concerns\nabout the analytical approach that significantly undermine the validity of the findings. My primary\nreservations focus on two critical methodological components: (1) the calculation of person-time in\nthe analysis, and (2) the absence of calendar time as a variable in the primary model (though a\nstratified version is included).\nRESPONSE: Thank you for your comments. We have considered each point carefully and provide our\nresponses below.\nAbstract:\n● I find the last sentence a bit confusing. Is this analysis among people who were infected and then\nyou compared the risk of severity within those infected or was it a “protection” estimate looking at\nthe overall risk of those severe disease presentations? While this is better explained in the main\ntext, I would suggest re-writing the abstract to make it stand on its own a bit better.\nRESPONSE:\nThanks for pointing this out and we appreciate the suggestion. This sentence has been re-written for clarity,\nto:\n“Among RT-PCR-confirmed infections, reinfections were associated with decreased severity; compared with\nfirst infections, second infections had 1.57 (95%CI: 1.15-2.16) times the proportion of subclinical disease\nand 0.65 (95%CI: 0.49-0.86) times the proportion of moderate/severe disease.”\n(page 2, line 24)\nIntroduction:\n● Generally, I think the introduction was lacking and doesn’t capture the existing knowledge quite as\nwell as I would expect. Specifically:\n10\n○ Is there evidence to suggest that there is a change in the severity of COVID-19 over time?\nRESPONSE:\nWe have added this sentence:\n“Additionally, variant severity has changed over time, with some pre-Omicron variants associated\nwith increased severity and Omicron with decreased severity.1“\n(page 3, line 31)\n○ How is the change in severity associated with changes in variants or the increase in\nimmunity events?\nRESPONSE:\nWe have added this to the discussion:\n“Another limitation is that we cannot fully disentangle the effects of variant differences from those of\nimmune history, as these factors are closely linked, individuals’ immune histories are shaped by the\nvariants circulating at the time of their prior infections. While we did stratify by variant era, people in\nthis population who had first and second infections during Omicron may still differ in important\nways.”\n(page 8, line 165)\n○ There is already literature about protection from prior infections in regards to likelihood of\ninfection, symptomatic infections and severe outcomes. Within the literature that exists,\nwhere is the specific gap you are filling?\nRESPONSE:\nWe have added the following:\n“Although substantial literature exists on protection from SARS-CoV-2 infection, our study\naddresses a gap by leveraging detailed infection histories in a cohort setting to examine the\nassociations of between repeated infections, protection and severity.”\n(page 3, line 33)\n○ Please delineate between the introduction and the results (assume this happened at line 42\nbased on information in the paragraph running from 43-49).\nRESPONSE: We have added a header for the results section to delineate.\nResults:\n● Based on the methods it seems like you included serological infections as new infections but in the\nfigures it looks like your outcome is PCR detected infections. So is serology-based infections for the\nexposure? For the rest of the review, I am assuming serology-based infections were specifically\nused for the exposure and PCR were the outcome. Please clarify this in the results and methods.\nRESPONSE:\nYour assumption was correct: serology (along with PCR) was used for defining the exposure, and PCR-\nconfirmed infections were used as the outcome. Reviewer #2 also requested clarification on this point. To\nmake the distinction clearer, we updated the title of Figure 2A from “infection timing” to “RT-PCR-detected\ninfection timing”. We also added the following clarification to the Severity section of the Methods:\n“Severity was evaluated only for RT-PCR-confirmed infections, as symptom diaries and clinic records were\nonly available exclusively for these infections. These RT-PCR-confirmed infections with defined severity\n11\nwere used as the outcome in protection analysis, and the severity analysis compared severity only within\nthis group.”\n(page 15, line 282)\n● Please define variant time periods in the legend for the variant specific periods.\nRESPONSE:\nWe have indicated in Figure 1 when the variant eras occurred and added the following text to the legends:\n“Variant eras are indicated at the top of the figure: original SARS-CoV-2 in 2020, pre-Omicron variants in\n2021, and Omicron from 2022-2024.”\n(page 11, figure legend)\n● You observed an increase in protection among people >2 years since last infection relative to the 1-2\nyear group for all three outcomes, please explain.\nRESPONSE:\nWhile we cannot determine the exact cause of this finding with our current data, we considered several\npossible explanations. These include chance variation due to smaller sample sizes in the >= 2-year group,\ndifferences in variants circulating at the time of infection, or selection effects, such as survivor bias, where\nindividuals who remain uninfected for longer may be those with inherently stronger or more durable immune\nresponses (e.g., better mucosal immunity). We have added the following note to the manuscript:\n“We cannot explain the observed increase in protection among people with prior infections ≥2 years earlier\ncompared to with those infected 1-2 years earlier, though this may reflect chance variation, differences in\nvariants, or selection effects such as survivor bias, where those with more durable immunity (e.g., better\nmucosal immunity) remain uninfected for longer.”\n(page 6, line 117)\nDiscussion/conclusion:\n● I am not sure I fully agree with the statement that you have complete infection histories for each\nparticipant. Because of the serological data, you have better data than just clinical PCRs on prior\ninfection data, but it seems possible that secondary infections were missed (ex. if a person didn’t\ncome in for clinical testing for two infections within a 6-month window, my understanding is that\nthey would be classified as one prior infection based on serology).\nRESPONSE:\nYou are correct that some closely spaced infections could be missed in our classification, particularly if two\ninfections occurred within the same serologic sampling interval without clinical testing. To reflect this, we\nhave changed our language from “complete infection histories” to “detailed infection histories”. While not\ncapturing every possible infection, our combination of frequent serologic sampling and ongoing PCR\nsurveillance, with active surveillance when SARS-CoV-2 is present in the household, still provides a\nuniquely comprehensive record of infection history for a community-based cohort.\n(page 7, line 139)\nMethods:\nThe study relies on an existing cohort of people and gets data from a combination of clinical and household\ndata -which is a strength, but I have a few questions about how infections were captured and what infections\nwere used in the analysis:\n● Infection capture:\n12\n○ Who are the respiratory samples collected for – mainly are household members tested\nduring the visits? “Activated households are visited by study staff regularly, at\napproximately days 0, 3, 7, 14, 21, and 30, for collection of respiratory samples and\nsymptom diaries.”\nRESPONSE:\nWe have added the following underlined text for clarity:\n“Activated households are visited by study staff regularly, at approximately days 0, 3, 7, 14, 21, and\n30, for collection of respiratory samples and symptom diaries from all household members,\nregardless of symptoms.”\n(page 13, line 246)\n○ For the secondary infections identified by serology, what dose “Must not surround\nvaccination” mean in regards to time?\nRESPONSE:\nThank you for pointing out the need for clarification. “Must not surround vaccination” means that the\npaired serology samples used to assess a 4-fold rise in Spike antibody titers (criteria for detecting\ninfection) could not include a sample collected soon after vaccination or span vaccination. This\nrestriction is necessary because vaccination itself can induce a substantial antibody rise, which\ncould be misclassified as infection.\nTo make this clearer, we have added the following sentence to the Methods section:\n“Paired serology samples that spanned a vaccination event or were collected soon after\nvaccination were excluded, as the resulting antibody rise likely reflects vaccine-induced immunity\nrather than infection.”\n(page 14, line 275)\nWe have also edited more of the Infection Histories section of the Methods for more clarity:\n“We estimated the date of serologically-defined infections to be the median between samples, or\nfor positive screens, we used the median of the sample date and March 1, 2020 or the participant’s\nbirthday if they were born after 2020 or a previous negative sample date. RT-PCR-detected\ninfections must have been ≥60 days apart from each other. Infection sequences based on serology\nwere required to have a longer time span between infections than those involving a PCR-detected\ninfection given the inherent uncertainty in the exact timing of serologically-detected infections.\nDistinct infections detected serologically must have been ≥6 months apart from each other. Paired\nserology samples that spanned a vaccination event or were collected soon after vaccination were\nexcluded, as the resulting antibody rise likely reflects vaccine-induced immunity rather than\ninfection.\n(page 14, line 268)\n○ Why do you require the serological based first infection detection to be >=90 day prior to\nvaccination? I understand that after a vaccine, the serological testing may be misclassified\nbut am not sure how it would be misclassified prior to the vaccination event.\nRESPONSE:\nWe don’t require all serologically-defined infections to be >=90 days prior to vaccination. Only for\nfirst infections detected serologically only with one screen or titer (not a 4-fold rise between paired\nsera), we do require these infections to be >=90 days before vaccination. This is because the\n13\ntiming of the estimated infection date is less precise. Most of the first infections we detected with\nserology occurred well before vaccinations occurred.\n○ Why are some of the time gaps 90 days and some 180 days (6 months) for serology?\nSpecifically, you state that the first to secondary infections detected through serology need\nto be 6 months apart (left side of S.figure 1) and that the first PCR infection to secondary\nserology-based infection need to be 90 days part?\nRESPONSE:\nSecondary serology-based infections (four-fold rise) were required to be at least 90 days after a\nPCR-based infection. This is similar to the approach taken in another of our studies, IASO. We took\na conservative approach to infection sequences based solely on serology given that there is\ninherent uncertainty in the exact timing of these infections. Thus, we required them to be at least\n180 days (6 months) apart.\nWe have added a note on the left side of Supplementary Figure 1, to clarify:\nAnd we also added text to the infection history methods section:\n“Infection sequences based on serology were required to have a longer time span between\ninfections than those involving a PCR-detected infection given the inherent uncertainty in the exact\ntiming of serologically-detected infections.”\n(page 14, line 272)\n○ Generally confused on when the serologically defined infections were stated to have\noccurred. The day of the fold change >= 6 months after the last? This is specifically for the\ntime since last event analysis.\nRESPONSE:\nThank you for mentioning this lack of clarity. We have added the following to the methods:\n“We estimated the date of serologically-defined infections to be the median between samples, or\nfor positive screens, we used the median of the sample date and March 1, 2020 or the participant’s\nbirthday if they were born after 2020 or a previous negative sample date.”\n(page 14, line 268)\n14\n● Person-time:\n○ Did you include person-time during the periods directly following prior infection, during\nwhich people were not “susceptible” to infections based on your definitions of new\ninfections? If so, it seems like this may be driving part of the large amount of protection you\nobserved from prior infections (person time included but outcomes not possible). Am I\nmissing something?\nRESPONSE:\nWe agree with this point and have revised our approach to exclude person-time immediately\nfollowing infection, when participants would not be considered susceptible under our definition of a\nnew infection. The Methods now read:\n“Person-time, for the protection analysis, included all weeks in the study period when participants\nwere enrolled, excluding the period immediately following infection. We excluded up to 52 days\npost-infection, as this was the shortest interval at which a new infection was detected in the cohort\ndue to our infection definition. Person-time for an individual changed exposure categories as new\nimmune events occurred.”\n(page 15, line 304)\n● Accounting for calendar time in the analysis:\n○ Can you explain why you didn’t account for calendar time in your primary model – though I\nrecognize you did a stratified version? If calendar time is not accounted for, I fear that some\nof the severity-based findings may be driven by what variants each exposure group (first,\nsecond, third prior infection) preceded. Even within the stratified analysis, I worry that\ncalendar time may be confounding the association of interest.\nRESPONSE:\nThank you for this suggestion. We have now adjusted for calendar time in the models. Also see our\nresponse to reviewer #1, comment #1.\n● Also, because may COVID-19 symptoms are non-specific, I would be concerned that other\ncirculating respiratory pathogens may contribute to the reported symptom burdens differently by\ntime.\nRESPONSE:\nWe appreciate this concern. In our analysis, symptoms are recorded only at the clinic when a participant\ntests positive for SARS-CoV-2, or within a household when an index case tests positive. Because symptom\ndata are tied directly to confirmed SARS-CoV-2 infections, we are less concerned about non-specific\nsymptoms from other circulating respiratory pathogens substantially contribute to the reported symptom\nburden.\n● Was the household correlation structure accounted for in the model? Or if it wasn’t, can you explain\nwhy not?\nRESPONSE:\nWe have now adjusted for household clustering. Also see our response to reviewer #1, comment #1.\nReviewer #3 (Remarks on code availability):\ncan be easily run - which is super nice.\nRESPONSE: Thank you.\n16"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have reviewed the revised paper by Maier et al. on SARS-CoV-2 reinfections and have the following remaining major and minor comments and suggestions to the authors:\nMajor comments\n1. Likelihood ratio tests and AIC are not designed to assess confounding control. Therefore, it is not correct to choose adjustment strategy based on these measures. Instead, the choice adjustment factors should be based on the causal structure obtained from subject-matter knowledge and a causal diagram (Directed Acyclic Graph, DAG). Thus, to the extent possible I would recommend that models are adjusted for time since last infection. For transparency, and since there is quite some collinearity between number of infections and time since last infection, it would be feasible to present different adjustment models, at least for the main results.\n2. Even without adjustment for the confounding from time since last infection, it appears that the statistical evidence for an independent effect of the number of prior infections beyond one is fairly week. This should be acknowledged when formulating the conclusion.\nThat the number of infections seems less important after accounting for waning (time since last infections) is consistent with findings for vaccinations where number of doses beyond two does not seem to matter much after accounting for waning (for time since last dose).\nMinor comments\n1. I would suggest that the median age of the participants is stated in the sentence of the Results (line 43).\n2. Number of participants is 2,386 in Table S1, but 2,388 in the text (line 43). Please double-check.\n3. Table S1\n- Clarify that presented participant characteristics are at baseline (March 2020 or at birth)\n- Clarify that “# of infections by end of study” means at end of study or when leaving the cohort (thus at censoring).\n- You may want to add #of infections by end of study among those still in the study to the table. This would align with the percentages you refer to in the text (line 57-58) and what you present in Figure 1A.\n4. P-values are never zero, as in Table S3. I suggest that p-values below 0.001 are reported as “<0.001”, also in the tables.", "reviewer_2": "While the authors’ revision is appreciated, there are some remaining analytic and methodological issues that should be more fully addressed. Since some issues could affect the core study findings, these should be revised to ensure the conclusions are well supported by the data.\nThe study should more closely account for time since last infection/vaccination to ensure their findings are solid. (1) New Figure S9a (compared to Figure 2c) suggests that when accounting for time since last infection/vaccination, the results are no longer statistically significant in the severity analysis. Is that correct? If so, this would change the study conclusion to their observed relationship for severity being based on time since last infection/vaccine rather than the number of infections itself. (2) While the revised statistical adjustment including time since infection is a step in the right direction, the categories are also too broad (0-6 months, 6-12 months, 1-2 year, 2+ years). The observed difference between the protection due to different number of infections could be affected due to poor match for time since last infection within these categories. To determine if this is an issue, I would first perform a descriptive analysis to determine if there is a difference in average time since last infection / vaccine between those with a 1st vs 2nd vs 3rd infection. If there is imbalance, the authors should account for this (ideally with matching), but at a minimum, by a more finely tuned statistical adjustment. If no imbalance within these categories, then only my first comment remains.\nThe study’s conclusion is that higher protection and reduced severity of future SARS-CoV-2 infections are driven by higher infection-acquired protection from each subsequent infection. However, variant traits could equally explain these findings. Based on the statistical design, I do not think this can be disentangled, as simply pre-Omicron or Omicron is far too crude over a 4-year period. While the authors do mention this limitation, the Abstract, Discussion, and much of the manuscript do not sufficiently describe that both explanations are plausible. This should be revised.\nLooking at Figure 1A, there is fairly limited overlap any 1st infection (0 gray) and 2+ (blue) / 3+ (dark blue) reinfections. Therefore, I’d be curious if only reinfections (1 prior infection, 2 prior infections, 3 prior infections) were included, and 1st infections were excluded, would the results hold. It would also lead to a better timing match and temporal match overall (see first comment).\nThe authors did not address the point of confounding due to indirect protection from household members, which is another plausible explanation for these findings. A random effects design would not address this. At the minimum, this should be highlighted as a key limitation, as it could undermine the study conclusions. Ideally, the authors could at least perform some descriptive analysis to investigate this if data is available.\nWas the adjustment for calendar time just two periods (pre-Omicron vs Omicron) for 4 entire years? If so, “calendar time” is misleading and would suggest being more clear about what this represents, or revising this variable to be more temporally granular.\nThe use of AIC for variable selection is probably not the right approach for this casual inference analysis. The models adjusting for time since infection/vaccine should be the primary model.\nFrom most of results, it’s not clear that 3rd infection vs 2nd infection are different. However, the authors conclusions focus on each infection yielding additional protection. This should be formally tested and interpreted as such.\nIt’s still unclear if the study demonstrates that protection against infection is increased (last sentence of abstract), rather just reduced symptomatic infections (i.e., the same rate of infection when accounting for sub-clinical infection may be present). My understanding is the majority of PCR tests are symptom-triggered, therefore the end point is symptomatic infection (not all infections), as most sub-clinical infections would be missed (since they would never be tested). Would appreciate clarification on this point.\nI’m still not clear which analyses use inferred serologic infections (as outcome or exposure), and it seems that the explanations are conflicting in different parts of manuscript and response letter.\nMost studies define reinfections as 90 days after the prior infection. Would this definition affect the results? Intermittent viral shedding and PCR positivity for weeks is common.\nThe study methods are still hard to follow.\nThe underlying data and study design is very strong, but the analysis and interpretation can hopefully be revised with these changes.", "reviewer_3": "The authors did a very nice job addressing my concerns. I have no further concerns.\n(Remarks on code availability)\nCode appears well commented and easy to read via GitHub. They even provide a fake data generator so it\ncan be easily run - which is super nice."}, "author_response": "REVIEWER COMMENTS\n[formatting and numbering added]\nReviewer #1 (Remarks to the Author):\nI have reviewed the revised paper by Maier et al. on SARS-CoV-2 reinfections and have the\nfollowing remaining major and minor comments and suggestions to the authors:\nMajor comments\n1. Likelihood ratio tests and AIC are not designed to assess confounding control. Therefore,\nit is not correct to choose adjustment strategy based on these measures. Instead, the\nchoice adjustment factors should be based on the causal structure obtained from\nsubject-matter knowledge and a causal diagram (Directed Acyclic Graph, DAG). Thus, to\nthe extent possible I would recommend that models are adjusted for time since last\ninfection. For transparency, and since there is quite some collinearity between number of\ninfections and time since last infection, it would be feasible to present different\nadjustment models, at least for the main results.\nRESPONSE:\nThank you for pointing this out—we agree that model adjustment should be guided by causal\nstructure and subject-matter knowledge rather than statistical fit criteria. We have expanded\nour description of covariate selection and added a new “Covariates” subsection in the\nMethods for clarity:\n“Covariates\nFor model adjustment, we considered vaccination status (none, partial, full, and boosted),\nage (in years), sex, days since last infection and vaccination (both categorized as\n‘uninfected/unvaccinated’, [0-14), [14-49) [vaccination only], then increments of 60 days\nuntil [870-2000]), year or period (original SARS-CoV-2, pre-Omicron variant, and Omicron),\nand household clustering (as a mixed effect). Not all variables could be included in\nstratified analyses. Examples of model specification are shown in Supplementary Tables 3\n& 4.” (methods, page 18, line 340)\nWe revised the Results and added Supplementary Figure 4 to show partially and alternately\nadjusted models for transparency.\nprotection and severity models.\nThree models are shown for each analysis. Main\nmodels are the models used in the primary\nanalyses presented. For the protection analysis\n(A), model partial - 1 (dashed lines) adjusts for\nvaccination, age, sex, and household; model\npartial - 2 (dotted lines) additionally adjusts for\nyear, and the main model (solid lines) further\nadjusts for times since last infection and\nvaccination. For the severity analysis (B), the\npartial model (dashed lines) adjusts for\nvaccination, age, and sex, the main model (solid lines) additionally adjusts for period, and the\nmain + times model (dotted lines) additionally adjusts for times since last infection and\nvaccination.\nWe have also added the following to the results:\n“Because there are many potentially confounding factors, and for transparency, we have\nincluded Supplemental Figure 4, which shows partially and alternately adjusted models for\nthe main protection and severity analyses. While all models showed significant protection,\nthe point estimates varied depending on inclusion of year and time since infection or\nvaccination, covariates that are expected to influence infection and immune history. In\ncontrast, the relative severity estimates remained consistent across models. This stability\nsuggests that variables that confound infection risk (such as time since infection or\nvaccination) are not strong confounders of severity in our community-based cohort once\ninfection has occurred. Additional adjustment for these variables increased uncertainty\n(wider confidence intervals) without materially changing the point estimates, so the simpler\nmodel was retained for the main severity analyses.” (results, page 6, line 110)\n2. Even without adjustment for the confounding from time since last infection, it appears\nthat the statistical evidence for an independent effect of the number of prior infections\nbeyond one is fairly week. This should be acknowledged when formulating the\nconclusion.\nThat the number of infections seems less important after accounting for waning (time\nsince last infections) is consistent with findings for vaccinations where number of doses\nbeyond two does not seem to matter much after accounting for waning (for time since last\ndose).\nRESPONSE:\nWe appreciate the reviewer’s observation, which was also noted by Reviewer #2 (comments # 3\n& 7) and the editor. In the revised analysis, we carefully re-examined this question and added\nSupplemental Figure 3 to more clearly assess whether multiple prior infections provide\nadditional protection once time since last infection is taken into account.\nconfidence intervals widened somewhat as we refined definitions and incorporated additional\nfeedback, and we recognize that we should have been more cautious in interpreting the results\npreviously. However, the updated analyses, now presented in Supplemental Figure 3, show\nstronger and more internally consistent evidence of additional protection from multiple prior\ninfections than in our prior revision. We have revised the text accordingly to better reflect this\nnuance.\nIn addition to the new supplemental Figure 3 (shown below), we have also added the following\ntext about the protection analysis:\n\"To further assess whether additional prior infections provided incremental protection, we\ncompared participants with ≥1 and ≥2 prior infections (Supplemental Figure 3). As show in\nSupplemental Figure 3 A.1 & A.2, participants with two or more prior infections had\nsignificantly lower risk of RT-PCR-confirmed infection compared to those with only one prior\ninfection (IRRs of 0.61 [0.48-0.77] for 2 vs 1; and 0.41 [0.26-0.63] for 3+ vs 1); protection also\nremained significant for 3+ vs 2 prior infections (IRR: 0.60 [0.38-0.93]). Similarly, we\nobserved reduced risk of symptomatic infection for participants with 2 or 3+ infections\ncompared to those with one (IRRs: 0.61 [0.46-0.82] and 0.47 [0.28-0.78], respectively).\nHowever, the number of prior infections was not significantly associated with the risk of\nmoderate or severe disease (e.g. IRR for 2 vs 1 prior infection: 0.86 [0.52-0.1.44]) or\nsymptomatic infection when restricting to participants with ≥2 prior infections (IRR: 0.81\n[0.48-1.34]). These findings suggest that multiple prior infections enhance protection\nagainst reinfection, although these associations are weaker and less precise when\nexamining more severe outcomes.”\n(results, page 5, line 90)\nAnd for the severity analysis, we do agree that the number of infections is less important for\nseverity, and have revised the text to make this more clear:\n“Similarly, reinfections were less severe than first infections (Figure 2 & Supplemental\nTables 4&6), though with no apparent additional benefit from increasing number of prior\ninfections as seen with protection from infection.”\n(results, page 6, line 104)\nProtection\nanalyses\namong\nparticipants\nwith ≥1 and ≥2\nprior\ninfections, to\nassess\npotential\nbenefit of\nadditional prior\ninfections.\nThese figures show the protection from SARS-CoV-2 infection associated with prior\ninfections, assessing new reinfections only, among participants with ≥1 (A.1, B.2, C.2) or ≥2\nprior infections (A.2, B.2, C.2). A) corresponds to Figure 1C, B) to Figure S4B, and C) to Figure\nS7D. Model adjustments are the same as in the corresponding figures.\nMinor comments\n1. I would suggest that the median age of the participants is stated in the sentence of the\nResults (line 43).\nRESPONSE:\nWe have noted the median age.\n2. Number of participants is 2,386 in Table S1, but 2,388 in the text (line 43). Please double-\ncheck.\nRESPONSE:\nThank you for pointing out that this was confusing—the old table S1 had excluded the 2 people\nwith RT-PCR-confirmed infections missing symptom data, but the updated table includes them\n(with a note) for consistency with the text.\n3. Table S1\n- Clarify that presented participant characteristics are at baseline (March 2020 or at birth)\n- Clarify that “# of infections by end of study” means at end of study or when leaving the\ncohort (thus at censoring).\n- You may want to add #of infections by end of study among those still in the study to the\ntable. This would align with the percentages you refer to in the text (line 57-58) and what\nyou present in Figure 1A.\nThank you for these suggestions. We have updated table S1, clarifying when participant\ncharacteristics were measured, clarifying when infections occurred, and adding the # of\ninfections by the end of the study among still enrolled participants, as you suggested, which we\nagree is a nice addition.\n4. P-values are never zero, as in Table S3. I suggest that p-values below 0.001 are reported as\n“<0.001”, also in the tables.\nRESPONSE:\nYes, thank you—this is a fair point and we have updated the p-values below 0.001 to “<0.001”.\nReviewer #2 (Remarks to the Author):\nWhile the authors’ revision is appreciated, there are some remaining analytic and\nmethodological issues that should be more fully addressed. Since some issues could\naffect the core study findings, these should be revised to ensure the conclusions are well\nsupported by the data.\n1. The study should more closely account for time since last infection/vaccination to ensure\ntheir findings are solid.\na. (1) New Figure S9a (compared to Figure 2c) suggests that when accounting for\ntime since last infection/vaccination, the results are no longer statistically\nsignificant in the severity analysis. Is that correct? If so, this would change the\nstudy conclusion to their observed relationship for severity being based on\ntime since last infection/vaccine rather than the number of infections itself.\nRESPONSE:\nThank you for bringing this up. As reviewer #1 also brings up in major comment #1,\nwe are now adjusting for times since infection and vaccination whenever possible,\nso we have removed the old Figure S9. We have added Supplemental Figures 3 & 4\n(please see our responses to reviewer #1 Major comments #1 & 2).\nb. (2) While the revised statistical adjustment including time since infection is a\nstep in the right direction, the categories are also too broad (0-6 months, 6-12\nmonths, 1-2 year, 2+ years). The observed difference between the protection\ndue to different number of infections could be affected due to poor match for\ntime since last infection within these categories. To determine if this is an\nissue, I would first perform a descriptive analysis to determine if there is a\ndifference in average time since last infection / vaccine between those with a\n1st vs 2nd vs 3rd infection. If there is imbalance, the authors should account for\nthis (ideally with matching), but at a minimum, by a more finely tuned statistical\nadjustment. If no imbalance within these categories, then only my first\ncomment remains.\nThank you. You are right, we do see slightly longer times since last infections among\nparticipants with fewer infection and so we have now adjusted for times in much\nsmaller increments (every 60 days through 870 days, then 2000 days), and we have\nalso adjusted for year (smaller time increments than period/variant era previously).\nBecause these updates adjust for more categories, we have split the supplemental\ntable of model specifications into 1 table each for the protection (table S3) and\nseverity (table S4) models.\nWe did also try to include the supplemental analysis of protection and severity\nassociated with period and time since last infection (supplemental figure 7) with the\newer categories of year and finer increments of time, but that was far too messy\n(included below for your reference), so we have kept the original version.\n2. The study’s conclusion is that higher protection and reduced severity of future SARS-CoV-\n2 infections are driven by higher infection-acquired protection from each subsequent\ninfection. However, variant traits could equally explain these findings. Based on the\nstatistical design, I do not think this can be disentangled, as simply pre-Omicron or\nOmicron is far too crude over a 4-year period. While the authors do mention this limitation,\nthe Abstract, Discussion, and much of the manuscript do not sufficiently describe that\nboth explanations are plausible. This should be revised.\nRESPONSE:\nThank you, this is a good point. We have now adjusted for year instead of variant era and believe\nthat this adjustment addresses the concern.\n3. Looking at Figure 1A, there is fairly limited overlap any 1st infection (0 gray) and 2+ (blue) /\n3+ (dark blue) reinfections. Therefore, I’d be curious if only reinfections (1 prior infection, 2\nprior infections, 3 prior infections) were included, and 1st infections were excluded, would\n6\nthe results hold. It would also lead to a better timing match and temporal match overall\n(see first comment).\nRESPONSE:\nThank you for pointing this out—it is a limitation that this population was infected so quickly\nand that there is so little overlap in timing of people with 0 prior infections and people with\nmore prior infections. We have now added finer adjustments (see response to your comment\n1b) and also a separate analysis among participants with ≥1 or ≥2 prior infections.\nPlease see our response to reviewer #1 major comment #2.\n4. The authors did not address the point of confounding due to indirect protection from\nhousehold members, which is another plausible explanation for these findings. A random\neffects design would not address this. At the minimum, this should be highlighted as a key\nlimitation, as it could undermine the study conclusions. Ideally, the authors could at least\nperform some descriptive analysis to investigate this if data is available.\nRESPONSE:\nWhile this added analysis is beyond the scope of this work, we have added a note on the\nlimitation:\n“A further limitation is that there may have been confounding from indirect protection from\nhousehold members’ immunity.” (discussion, page 10, line 195)\n5. Was the adjustment for calendar time just two periods (pre-Omicron vs Omicron) for 4\nentire years? If so, “calendar time” is misleading and would suggest being more clear\nabout what this represents, or revising this variable to be more temporally granular.\nRESPONSE:\nThis is a good point, thank you. As also noted in response to your comment 1b, we have\nchanged the adjustment for the protection analysis to be for year, which is more clear and more\ngranular. For the severity analysis, we adjust for period (original, pre-Omicron variant, and\nOmicron periods).\nPlease see our response to reviewer #1 major comment #1.\n6. The use of AIC for variable selection is probably not the right approach for this casual\ninference analysis. The models adjusting for time since infection/vaccine should be the\nprimary model.\nRESPONSE:\nYes, we appreciate that both you and reviewer #1 (in major comment #1) made this comment,\nthank you. We have updated the analyses to adjust for times since infection/vaccination when\npossible, and have not adjusted for times since infection/vaccination for the severity analysis\nbased on the added noise (see our new added supplemental Figure 4, and our response to\nreviewer #1, major comment #1).\n7\n7. From most of results, it’s not clear that 3rd infection vs 2nd infection are different.\nHowever, the authors conclusions focus on each infection yielding additional protection.\nThis should be formally tested and interpreted as such.\nRESPONSE:\nYou’re right. Thank you for pointing out this oversight. We have now added an additional\nsupplemental Figure S 3 (shown above) that we believe shows support of additional protection.\nPlease see our response to reviewer #1 major comment #2.\n8. It’s still unclear if the study demonstrates that protection against infection is increased\n(last sentence of abstract), rather just reduced symptomatic infections (i.e., the same rate\nof infection when accounting for sub-clinical infection may be present). My understanding\nis the majority of PCR tests are symptom-triggered, therefore the end point is\nsymptomatic infection (not all infections), as most sub-clinical infections would be\nmissed (since they would never be tested). Would appreciate clarification on this point.\nRESPONSE:\nThe majority of PCR tests are not symptom-triggered. We test everyone in the households\nregularly, regardless of symptoms.\n9. I’m still not clear which analyses use inferred serologic infections (as outcome or\nexposure), and it seems that the explanations are conflicting in different parts of\nmanuscript and response letter.\nRESPONSE:\nWe appreciate the opportunity to clarify. We have made the following changes:\n• We have added “RT-PCR-detected” to the legend of Figure 2 (and supplemental\nfigures), to clarify that the severity analysis is of RT-PCR-detected infections only.\n• We have also edited the methods, adding a new section, to add clarity on which\ninfections were used when:\n“Main Exposures and Outcomes\nFor protection analyses, all prior infections detected serologically and by RT-PCR\nwere used to define the exposure, and the outcome was incident RT-PCR-detected\ninfections with defined severity. Severity analyses were restricted to RT-PCR-\ndetected infections only; the exposure for severity analyses was infection number\n(1st, 2nd, 3rd+), and the outcome was severity.” (methods, page 13, line 263)\n8\n10. Most studies define reinfections as 90 days after the prior infection. Would this definition\naffect the results? Intermittent viral shedding and PCR positivity for weeks is common.\nRESPONSE:\nChanging the definition of reinfections would not change our results meaningfully. After\ndetailed data cleaning and reviewing all of the available evidence, we are reasonably confident\nthat we did observe several reinfections closer together than 90 days.\n11. The study methods are still hard to follow.\nRESPONSE:\nWe have added new sections (Main Exposures and Outcomes, and Covariates) in response to\nfeedback, and we believe the methods should be clearer now.\n12. The underlying data and study design is very strong, but the analysis and interpretation\ncan hopefully be revised with these changes.\nRESPONSE:\nWe appreciate the feedback from all reviewers and believe that the analysis and interpretation\nshould be stronger now.\nReviewer #3 (Remarks to the Author):\nThe authors did a very nice job addressing my concerns. I have no further concerns.\nRESPONSE:\nThank you very much!\nReviewer #3 (Remarks on code availability):\nCode appears well commented and easy to read via GitHub. They even provide a fake data generator so it\ncan be easily run - which is super nice.\nRESPONSE:\nThank you!\nReviewer #1 (Remarks to the Author):\nThe authors have responded to all of my comments of the previous version and\nincorporated appropriate changes in the manuscript.\nI only have a few minor comments:\n1. Line 105-129 (version with changes marked): Clarify which adjustments that were\nin used in these supplementary analyses. There is also a type on line 106-107, \"As\nshow in...\". Text should not be in italic.\nRESPONSE:\nThank you for pointing out the lack of clarity in the adjustment of models in Figure S3.\nThey are the same as the main models addressed in the methods, therefore, to clarify\nwhat is adjusted for we have updated the legend for that figure to clarify that the models\nin this figure are adjusted for the same covariates as the models in the corresponding\noriginal figures.\nUnderlined is the added text:\n“A) corresponds to Figure 1C, B) to Figure S5B, and C) to Figure S8D. Models in A) are\nadjusted for vaccination, age, sex, time since last infection and vaccination, year, and\nhousehold; models in B) are adjusted for vaccination, age, sex, and household; models in\nC) are adjusted for vaccination, age, sex, time since last infection and vaccination, year,\nand household (as in the corresponding figures).”\nWe have also fixed the italics and typo.\n2. Supplementary Table 5\nAs there are so few first infections in 2023-2024, you could consider to have one\nprior infection as reference in this table to get more meaningful estimates.\nRESPONSE:\nWhile we appreciate the suggestion, we feel that it would be repetitive with Figure 3 in\nthe main text, which uses the comparison of one prior infection as requested and we do\nstill find the question of whether any number of prior infections compared to no prior\ninfections are protective against reinfection to be an important question/comparison.\n3. Table S4\nClarify in the table what you mean by relative severity, is this IRR?\nRESPONSE:\nWe have added a footnote for Table S4 clarifying that the relative severity is the ratio of\ncumulative incidences of severity.\nReviewer #2 (Remarks to the Author):\nThank you to the authors for their revision.\nThe new Figure S3 most directly addresses the concern as to whether the protection seen\nfrom high number of prior infections is instead attributable to timing since last infection.\nThe authors new analysis strengthens their finding that additional protection is derived\nfrom having 2+ versus only 1 prior infection, however there is no consistent additional\nprotection beyond 2 infections. While the new analysis better addresses these concerns,\nsome residual impact of differences in timing since vaccination/infection and novel variants\ncould still contribute to this finding. However, the authors have done substantial work to\naddress this point. Therefore, I would favor a few minor edits to most accurately represent\nthe study findings (described below), after which I am supportive of the study.\nMy suggestion would be to:\n1. Abstract (strong recommendation): Rephrase: “Our findings suggest that protection\ngenerated from two prior infections may be stronger than one prior infection,\nalthough benefit beyond the second infection were not clearly observed.”\nRESPONSE:\nRevised abstract sentence (as suggested by reviewer):\n“Our findings suggest that protection generated from two prior infections may be stronger\nthan one prior infection, although benefit beyond the second infection was not clearly\nobserved.”\nWe are grateful to the reviewer for this clarification, which improves the accuracy and\nreadability of the abstract.\n2. Results (Line 123-132) (strong recommendation): Revise results section that state\nresults “consistent across models” and “all models showed significant protection” in\nthe Results, which isn’t true. Figure S4 (panel B) shows that accounting for time\nsince prior infection/vaccine renders the relationship no longer statistically\nsignificant.\nRESPONSE:\nWe thank the reviewer for this important clarification. We agree that the Results section\nshould more precisely describe the patterns shown in Supplemental Figure S4.\nSpecifically, while protection estimates were directionally similar across models, the\nassociation was not statistically significant in all models when adjusting for time since\nprior infection or vaccination. We have revised the relevant Results text to accurately\nreflect these findings.\nWe appreciate the reviewer’s careful attention to this point and believe the revised\nlanguage more accurately represents the model results.\n“While all models produced qualitatively similar point estimates indicating protection,\nnot all models yielded statistically significant associations. In particular, when time since\nprior infection or vaccination was included as a covariate, confidence intervals widened\nand some estimates were no longer statistically significant (Supplemental Figure S4). For\nseverity analyses, estimates remained generally similar across model specifications.”\n3. The main results should include the models that account for time since last\ninfection/vaccination, which was recommended by two reviewers during the last\nrevision. Indeed, the Methods (‘Covariates’ section) say time since infection\nsuggesting this is the main analysis, which doesn’t seem to be the case.\nRESPONSE:\nWe thank the reviewer for raising this point. As noted in our previous response to\nReviewer #1 (major comment #1), we evaluated models that adjusted for time since prior\ninfection and vaccination for both the protection and severity analyses. In the last\nrevision, we added text to the Results explaining why these variables were included in the\nprotection analyses but not retained for the main severity analyses.\nSpecifically, incorporating time since prior infection/vaccination in the protection models\nchanged the precision of the estimates and, in some cases, the statistical significance, as\nshown in Supplemental Figure S4. For the severity analyses, however, including these\nvariables did not materially alter point estimates and only widened confidence intervals,\nsuggesting they are weaker confounders of severity once infection has occurred. For this\nreason, we retained the simpler severity models while presenting fully adjusted models in\nthe supplement for transparency.\nAs we noted in the previous round of reviews in our response to reviewer #1 major\ncomment #1, we added text to the results (in the last revision), which clarifies why we\ndecided to adjust for time since last infection/vaccination for the protection, but not\nseverity analyses.\nFor ease of reference, we repeat the clarified text here, with minor modifications\nrequested in your comment above:\n“Because there are many potentially confounding factors, and for transparency, we have\nincluded Supplemental Figure 4, which shows partially and alternately adjusted models\nfor the main protection and severity analyses. While all models produced qualitatively\nsimilar point estimates indicating protection, not all models yielded statistically\nsignificant associations. In particular, when time since prior infection or vaccination was\nincluded as a covariate, confidence intervals widened and some estimates were no longer\nstatistically significant (Supplemental Figure S4). For severity analyses, estimates\nremained generally similar across model specifications. This stability suggests that\nvariables that confound infection risk (such as time since infection or vaccination) are\nnot strong confounders of severity in our community-based cohort once infection has\noccurred. Additional adjustment for these variables increased uncertainty (wider\nconfidence intervals) without materially changing the point estimates, so the simpler\nmodel was retained for the main severity analyses.” (results, page 6, line 126)\n4. Abstract (optional, defer to editor): Add sentence to abstract: “A limitation was the\nability to fully attribute protection to number of prior infections or differences in\ntiming since last infection/vaccination or differences in the SARS-CoV-2 variant.”\nRESPONSE:\nWe appreciate the reviewer’s suggestion and agree that the ability to disentangle the\neffects of number of prior infections from timing since last infection/vaccination and\nvariant differences is an important limitation of the study. We have addressed this point\nin the Discussion, where there is more space to fully describe these complexities. Given\nthe length constraints of the abstract and the need to highlight only the most essential\nfindings, we respectfully opt not to add an additional limitation sentence there, but defer\nto the editor should they prefer that we include a brief statement in the abstract.\nThank you to the authors for all their work on this study and the revision.\nRESPONSE:\nWe sincerely appreciate the reviewer’s thoughtful feedback and engagement throughout the\nrevision process. Thank you for your supportive comments!"}]

Decision letter