Sero-genomic evidence for occult mpox exposure in healthy Nigerian adults

Domain: biology
Published: 2026-01-20
Editorial outcome: major_revision

Abstract
The 2022 multi-country mpox (formerly monkeypox) outbreak, driven by mpox virus (MPXV) Clade IIb poses renewed threat to global public health. The cessation of smallpox vaccination has created large immunologically naïve cohorts, with uncertain implications for contemporary MPXV susceptibility. To assess whether residual vaccination-derived immunity influences exposure risk, we combine serological and phylodynamic analyses. Using a six-plex Luminex assay, we measure immunoglobulin G (IgG) binding to six MPXV antigens in 176 Nigerian adults comprising of 75 healthcare workers sampled in 2021 and 101 community volunteers sampled in 2023. At baseline, 24/176 (13.6%) were MPXV seropositive, predominantly born before 1980. Magnitude-breadth analysis scores were two-fold higher in pre-1980 cohort relative to post-1980 cohort. In 153 participants with follow-up samples (median 9 months), 5/153 (3%) showed evidence of exposure, with ≥2-fold increases in magnitude-breadth scores and antigen-specific responses against ≥4/6 antigens without reported clinical illness. Antigen-specific responses were strongest to B6R (11-fold), followed by M1R and A35R, with marked individual-level heterogeneity. Complementary phylodynamic reconstruction of 105 Nigerian MPXV genomes identified sporadic transmission against frequent dead-end infections. Together, these data show that residual smallpox immunity continues to shape mpox transmission and asymptomatic exposure contributes to under-detected spread, informing surveillance and targeted vaccination strategies.

Introduction
Mpox is a zoonotic disease caused by the mpox virus (MPXV), a member of the Poxviridae family within the Orthopoxvirus genus 1–3. Historically endemic to Central and West Africa, the recent global resurgence of MPXV in endemic and non-endemic regions resulted in its declaration as a Public Health Emergency of International Concern (PHEIC) in both 2022 and 2024 4. Unlike previous outbreaks, the 2022 outbreak, was driven by the MPXV Clade IIb, an evolutionarily distinct, pathogenically attenuated lineage with enhanced human to human transmissibility 5–12. Thus, it has been associated with mild and asymptomatic disease 13–24.

A key determinant of susceptibility to MPXV is residual immunity from historic smallpox vaccination 25–27, discontinued globally in 1980 following eradication 28. MPXV and variola virus (agent of smallpox) share multiple conserved structural and immunogenic viral proteins such as A29L, B6R, and M1R, implicated in viral entry and immune recognition 29–36. Individuals born before 1980 likely received a smallpox vaccine during the WHO-led Smallpox Eradication Programme (1966–1980) 37–40, although coverage was heterogenous ranging from 7% to 60% 25. The vaccines used in these campaigns were based on live-attenuated vaccinia strains such as Lister and EM-63, which induced durable, broad and cross-protective immunity against Orthopoxviruses, including MPXV 41–43.

Although historical smallpox vaccination conferred broad cross-protective immunity against Orthopoxviruses 44–53, its global cessation has created a growing global cohort of immunologically naïve individuals 54, particularly in Africa where vaccinia-based vaccination was never incorporated into routine national programmes. Waning immunity in the post-smallpox era has coincided with the emergence of MPXV transmission patterns, especially across sub-Saharan Africa where surveillance remains limited. Evidence from high-resource setting has demonstrated asymptomatic MPXV infections, particularly among high-risk groups 4,13–22,55. However, whether this smallpox immunity gap has shaped transmission dynamics in West African populations remains unclear due to a paucity of serological and genomic data from these high-burden setting.

To address this gap, we analysed serum samples from two independent Nigerian SARS COV-2 vaccine cohorts (healthcare workers from Lagos, 2021 56, and community participants from Abuja, 2023 57) to evaluate MPXV-specific antibody responses. In parallel, we analysed both time-series case counts and whole-genome MPXV sequences that were generated from the Nigerian epidemic during the same sampling period. This allowed us to contextualise local transmission patterns and infer epidemiological dynamics in real time. Together, these complementary approaches provide insight into the durability of smallpox-derived immunity and reveal evidence of undetected community exposure of MPXV during the SARS-CoV-2 pandemic in the absence of any Orthopoxvirus vaccination efforts.

Results
Study population and cross-sectional MPXV seropositivity
We analysed two independent cohorts originally established to assess SARS-CoV-2 vaccine responses in Nigeria (Fig. 1). The population comprised a total of n=176 participants (timepoint 0; T0) stratified as follows: cohort 1: Healthcare workers (HCW, n=75; enroled during the SARS-CoV-2 vaccination campaign in Lagos in January 2021) and cohort 2: general population (n=101; enroled during community-level vaccination campaign in Abuja in January 2023). The median age was 37 years (IQR: 29-43) with balanced gender representation (Table 1). Follow-up samples were available for 153 participants (cohort 1: n=75; cohort 2: n=78), collected a median of 9 months after T0 (T1). This yielded a total of n=329 samples. This dual-cohort design allows us to capture the potential impact of occupational exposure to MPXV and age-cohort differences on MPXV-specific antibody responses, particularly in the context of historical smallpox vaccination in Nigeria—a country central to the 2022 global outbreak.

At T0 across both cohorts, using binding antibodies above the antigen-specific cut-off for each target, we observed 48/176 (27%) with no reactivity, 46 (26%) with reactivity to one antigen, 37/176 (21%) with reactivity to two antigens and 21/176 (12%) recognising three antigens (Table 2; Supplementary Fig. 1). Applying a tiered algorithm to define seropositivity threshold and to eliminate the likelihood of false positivity from assay artefact or isolated cross-reactivity, we defined MPXV seropositivity as reactivity to at least four of six MPXV antigens. By this definition, 24/176 (13.6%) were seropositive. Seropositivity was strongly associated with birth cohort: 20/59 (34%) of individuals born before 1980 versus only 4/101 (4%) born after 1980 (Fig. 2; Supplementary Table 1). Of note, this birth cohort effect differed by occupational group. Among HCWs, all 13 seropositive cases were confined to those born before 1980, consistent with legacy smallpox vaccination. In contrast, in the general community cohort, while seropositivity was also enriched in those born before 1980 (7/11; 64%), we detected 4 younger individuals (born after 1980) meeting the ≥4-antigen threshold, suggesting possible previous exposure to circulating mpox or other Orthopoxviruses. We note further that only (20/59; 34%) of participants born before 1980 showed MPXV seropositivity suggesting possible low smallpox vaccination coverage of ≤34%.

Broad responses were uncommon; seven participants (six born prior to 1980) were reactive to five antigens and only three participants (all born prior to 1980) were reactive to all six antigens. We note that most individuals (67/116; 58%) born after 1980 had ≤1 reactive antigen (Table 2). Overall, occupational status exerted a lesser effect with 13/75 (17%) of HCWs relative to 11/101 (11%) community participants meeting the ≥4-antigen seropositivity criteria. These baseline patterns highlight both the durability of smallpox-derived immunity in older cohorts and evidence of potential mpox exposure in younger community participants.

Evidence of moderate positive MPXV-specific IgG responses between MPXV-antigens
Following quantification of IgG-specific responses across our study population, we assessed pairwise correlations of log10-transformed binding antibody levels for all six antibodies measured in (n=176) at T0. Correlation coefficients (ρ) were visualised using a matrix heatmap (Supplementary Fig. 2a), alongside pairwise scatter plots illustrating individual-level IgG responses across antigen pairs (Supplementary Fig. 2a). We observed moderate and weak positive correlations between multiple antigens, with the strongest correlation observed between the EEV-associated antigens A35R and B6R (ρ=0.65; p<0.0001). We also observed moderate correlations between A35R and M1R (ρ=0.51; p<0.0001), and between B6R and M1R (ρ=0.53; p<0.0001), suggesting weaker but partially positive pattern between these structurally distinct antigens. All other correlations were relatively weak (ρ range: 0.15–0.43) although statistically significant (p<0.05 for all comparisons). These data indicate a heterogenous but measurable degree of serological correlation, with stronger coupling between certain antigen pairs, particularly those associated with the EEV. Such variation supports the utility of composite metrics to capture the breadth of MPXV-specific antibody reactivity which we apply in subsequent analyses. To exclude the possibility that patterns were diluted by vaccine-naive individuals, we performed sensitivity analysis restricted to individuals born before 1980 (n=59), i.e. likely to retain residual immunity from smallpox vaccination. Results were broadly consistent, although a stronger correlation was observed between A35R and B6R (ρ=0.81; p<0.0001) with some variation in the strength of other pairwise associations. These sensitivity analyses are visualised in Supplementary Fig. 2b, which provides the full correlation matrix for the pre-1980 birth cohort. Notably, no significant correlations were observed for D6L with any other antigen, suggesting it likely elicits a distinct response profile.

Serological profiles suggest persistence of Orthopoxvirus residual immunity by birth cohort
We first compared MPXV-specific IgG binding responses between healthcare workers (HCWs) and community participants at baseline (T0; n=176). To ensure comparability across antigens, we also normalised responses to their antigen-specific cut-off values, presenting both raw mean fluorescence intensity (MFI) and normalised signal-to-cut-off ratios in parallel. This was performed to provide additional perspective by scaling responses relative to assay thresholds. HCWs exhibited modestly higher geometric mean titres (GMTs) across four antigens—A35R (726 vs 503; p=0.53), B6R (396 vs 255; p=0.48), H3L (1904 vs 1522; p=0.21), and M1R (401 vs 313 p=0.0002), this corresponded to ∼1.2–1.6-fold higher responses relative to the general population, though these differences did not reach statistical significance (Fig. 3a). In contrast, responses to A29L (2208 vs 2550; p=0.31) and D6L (1129 vs 1318; p=0.56) were slightly lower in HCWs. These results suggest that occupational exposure may have a limited effect on MPXV seroreactivity in this setting.

Given the absence of responses by occupational status, we next stratified participants by birth cohort, reflecting the cessation of smallpox vaccination in 1980 across (n=176) participants to evaluate the potential impact of residual smallpox immunity. Individuals born before 1980 showed consistently higher antibody levels across all six MPXV antigens - A29L (2948 vs 2161; p=0.001), A35R (1572 vs 358; p<0.0001), B6R (708 vs 202; p<0.0001), D6L (1407 vs 1155; p=0.24), H3L (2406 vs 1394; p=0.0008), and M1R (452 vs 305; p<0.0001), representing ∼1.2–4.4-fold higher titres compared with those born after 1980 (Fig. 3b). Almost all differences were statistically significant. Sensitivity analysis restricted to the general population cohort confirmed these patterns, with older participants showing markedly higher antibody responses but with a subset of younger individuals also meeting seropositivity criteria, confirming that the observed patterns are not driven by occupational biases in the HCW cohort (Supplementary Fig. 3). These findings are consistent with durable cross-reactive antibodies resulting from prior smallpox vaccination. However, the detection of MPXV-reactive IgG antibodies in some individuals born after 1980 suggests possible cryptic exposure to MPXV or cross-reactivity with related Orthopoxviruses.

To capture overall response patterns, we generated magnitude-breadth plots—analogous to Kaplan-Meier curves, which integrate antibody strength (magnitude) and the number of recognised MPXV antigens (breadth). Consistent with patterns observed in Fig. 3a, b, HCWs displayed a modest rightward shift in magnitude-breadth distribution compared with the community cohort, (GMT: 7070 vs. 8065; p=0.55; Fig. 3c). In contrast, participants born before 1980 demonstrated significantly higher cumulative responses (GMT: 5812 vs. 12328, p<0.0001; Fig. 3d), reinforcing the impact of residual smallpox immunity. To corroborate the serological patterns, we conducted supplementary follow-up in the HCW cohort, where call-response rates were high. We captured smallpox vaccination history and presence of a vaccination scar among the HCW seropositive individuals (n=13). Among 11/13 respondent participants, 9/11 reported prior smallpox vaccination and had visible scars, two participants could not remember receiving a smallpox vaccine although one had a visible smallpox vaccine scar. These findings are consistent with the persistence of residual smallpox-derived immunity.

Immunological signatures reveal evidence of recent MPXV exposure
To investigate evidence of undetected MPXV exposure, we leveraged longitudinal serology, applying composite measures of antibody magnitude and breadth in the absence of any apparent or reported clinical illness. Using our stringent composite definition, we observed (n=5/153; 3%) participants who demonstrated ≥2-fold increase in magnitude-breadth score between baseline (T0) and follow-up (T1; median 9 months), together with ≥2-fold increases in antibody levels against ≥4 of 6 MPXV antigens (Fig. 4a, left panel). These individuals showed marked increase in magnitude-breadth score (GMT: 3876 vs 24898) whereas participants not meeting exposure criteria displayed negligible difference (7570 vs 7066; Fig. 4b, right panel). None of these five participants reported mpox-compatible symptoms during follow-up as part of the parent SARS-CoV-2 vaccine studies, which captured vaccination-related adverse events. However, structured surveillance for mpox-specific symptoms was not undertaken. These data are consistent with evidence of sub-clinical and pauci-symptomatic MPXV exposure during the sampling interval in ostensibly healthy individuals which may be captured only through sensitive multiplex antibody profiling rather than routine clinical surveillance.

To explore potential factors associated with MPXV exposure, we examined antigen-specific boosting profiles among exposed individuals using raw MFI values and normalised MFI values (Fig. 4c). Most participants were born before 1980 (3/5; 60%), mostly males (4/5; 80%) and were all MPXV-seronegative at baseline. Across the six antigens, the largest median fold-increase was observed for B6R (11-fold) and M1R (6.2-fold) followed by A35R (5.2-fold), D6L (4.6-fold), A29L (4-fold) and H3L (2.7-fold). These results indicate that B6R, an EEV-associated antigen, elicited the largest median fold increase among exposed individuals, although the response varied across participants. M1R and A35R also demonstrated notable increases, and together with B6R, appear to represent consistently recognised targets of post-exposure boosting despite individual-level heterogeneity. This highlights the potential value of B6R, A35R, and M1R as antigenic markers for serological surveillance of Orthopoxvirus exposure. Overall, these data provide additional resolution into the heterogeneity of antibody response and boosting patterns following MPXV exposure or re-exposure.

Residual smallpox immunity explains slow mpox growth rate in Nigeria
To contextualise serological evidence of undetected exposure, we combined epidemiological and genomic analyses. We estimated the population dynamics of Clade IIb/sh2017/lineage A58 alone under a non-parametric Skygrid and parametric exponential model. While our analysis overlaps with the dataset reported by Parker and colleagues 8, it differs in scope; whereas Parker et al. focused on phylogenetic reconstruction, we combined the genomic data with contemporaneous incidence data, enabling joint estimation of doubling time, effective population size, and renewal-based Rt dynamics. Our analysis remained consistent previous findings 7,8 which indicated that the epidemic grew exponentially with an estimated doubling time of ≈2.5 years [95% HPD: 1.87–3.43] (Fig. 5a, b). Our coalescent reconstruction of the viral effective population size (Ne) using a Skygrid model indicates a transient expansion since 2015 preceding official case detection in 2017 by the Nigeria CDC. We then used case data to estimate the time varying reproductive number from 2017 onward. Renewal modelling 59–61 of case data from 2017 to 2022 showed R<1 during 2017–2018, consistent with limited human-to-human transmission a subsequent suppression in effective reproductive number (Fig. 5c, d) likely due to control measures rolled out by the Nigeria Centre for Diseases Control and Prevention (NCDC). 10 In contrast, the second phase (2021–2022) shows sustained transmission across all Nigerian regions, with regional R values peaking at ≈1.3 (Southwest/Southeast), signalling exponential growth during this period, consistent with the timing of our cohort study and contemporaneous with observed serological boosting. Overdispersion analysis was performed using the method outlined by Tran-Kiem and Bedford 62 for the 105 sequences sampled by Parker and colleagues 8, which represented the second phase of the epidemic. This analysis revealed a median of seven distinct identical-sequence clusters, each typically comprising 1–3 sequences (Fig. 5e). This pattern indicates many transmission events leading to dead ends. Across ascertainment scenarios of 100%, 50%, and 10% respectively, R was consistently near-to-above unity, for 5–50% ascertainment the epidemic appears mildly supercritical (CIs above 1), while at 100% ascertainment the interval overlaps 1, consistent with near-critical transmission, which is consistent with the case data (Fig. 5f). Dispersion estimates varied with ascertainment (k≥1.85 (5%), k≥1.62 (10%), k≥0.881 (50%), and k≥0.493 (100%) (Fig. 5g). These patterns indicate frequent dead-end infections with modest transmission heterogeneity, and these bottlenecks in transmission likely reflect residual population immunity from historical smallpox vaccination, in line with our serological observations.

Methods
All research conducted in this study complies with the relevant ethical regulations. Ethical approval was obtained from the Institutional Review Board of the Nigerian Institute of Medical Research (NIMR) (IRB-21-040) for the Lagos cohort and from the Federal Capital Territory Health Research Ethics Committee (FCT HREC) for the Abuja cohort (approval FHREC/2022/01/193/18-10-22). Written informed consent was obtained from all participants in the original Lagos and Abuja cohorts, and this consent covered the use of samples and data for the present secondary analysis.

Study population and sampling
We analysed convenience serum samples obtained from two previously established and published SARS-CoV-2 vaccine cohorts in Nigeria 56,57 (Fig. 1a–b).

Cohort 1. Health care workers (HCWs) at the Nigerian Institute of Medical Research (NIMR) and Federal Medical Centre, Ebute Metta, were enroled in January 2021 as part of the NIMR SARS-CoV-2 vaccine effectiveness 56. Participants received two-dose regimen of ChAdOx1 (AstraZeneca) during this period. All participants (n=75) provided blood samples at baseline (T0, prior to vaccination; January 2021) and at follow-up (T1, October 2021). Paired sera were available for all participants (n=75).

Cohort 2. HIV Negative adults were recruited in January at sites affiliated with Institute of Human Virology Nigeria (IHVN) in Abuja, Nigeria. Eligible participants were men and non-pregnant women >18 years old who had no previous SARS-CoV-2 vaccination and were confirmed HIV negative using the Nigerian national HIV rapid testing algorithm 96,97. Recruitment was conducted through local community outreach and phone recall of registered patients. Participants received two-dose regimen of Ad26.COV.S (Johnson and Johnson) vaccine as part of the national vaccination programme. Blood samples were collected from (n=101) at baseline (T0, prior to vaccination; January 2023) and at follow-up (T1, June 2023). Paired serum samples were available for 78/101 participants.

In total, 329 plasma samples (176 baseline and 153 follow-up) from 176 participants were included in the present analysis. Cohort demographics across both timepoints (T0 and T1) are shown in Supplementary Table 2, which also accounts for attrition between baseline and follow-up.

In addition, and to supplement serological analyses, we conducted follow-up interviews with a subset of healthcare worker (HCW) participants who were seropositive for MPXV antigens (n=13). Telephone interviews were used to collect recall of smallpox vaccination history with consenting participants undergoing a brief clinical assessment by a trained healthcare professional to verify the presence of smallpox vaccination scars. Scar assessment was conducted privately, recorded as present or absent, and anonymised at data entry.

Laboratory methods and sample testing for MPXV-specific binding antibodies
To capture serological signatures of MPXV immunity, total IgG binding antibody to MPXV-specific antigens were measured in serum samples using a custom six-plex Luminex assay. Antigen selection was guided by prior work which identified A29L, H3L, M1R, A35R, B6R and D6L as core immunodominant antigens in response to infection and vaccination by mpox and vaccinia viruses 98–101. This panel explicitly modelled on the multiplex approach 91,102 while ensuring broad coverage and enable the application of a stringent conservative seropositivity threshold. The selected antigens represent distinct viral structural classes: A29L, H3L, and M1R associated with the intracellular mature virion (IMV) form; A35R and B6R associated with the extracellular enveloped virion (EEV); and D6L is a soluble viral protein. For this, six recombinant MPXV proteins were included: A29L (RayBiotech: Cat #230-30237), H3L (RayBiotech; Cat #230-30233), M1R (Sino Biological; Cat #40904-V07H), A35R (RayBiotech; Cat #230-30238), B6R (Sino Biological; Cat #40902-V08H), and D6R (RayBiotech; Cat #230-01194) were covalently coupled to distinctive carboxylated bead sets (Luminex) to form a 6-plex assay 103.

In addition, control analytes included BSA (negative binding control), IL-18BP (mpox-encoded virulence factor), LPS (positive binding control), and WHO MPXV reference standard (NIBSC 22/218). Briefly, beads were activated with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (Thermo Fisher Scientific) in the presence of N-hydroxysuccinimide (Thermo Fisher Scientific), according to the manufacturer’s instructions, to form amine-reactive intermediates. The activated bead sets were incubated with the corresponding proteins at a concentration of 50 μg ml−1 in the reaction mixture for 3 h at room temperature on a rotator. Beads were washed and stored in a blocking buffer (10 mM PBS, 1% BSA, 0.05% NaN). Coupled bead sets were incubated with patient sera at 1/100 dilution for 1 h in 96-well filter plates (MultiScreen HTS; Millipore) at room temperature in the dark on a horizontal shaker. Fluids were aspirated with a vacuum manifold, and beads were washed three times with 10 mM PBS/0.05% Tween 20. Beads were incubated for 30 min with a PE-labelled anti-human IgG-Fc antibody (Leinco/Biotrend), washed as described above, and resuspended in 100 μl PBS/Tween. They were then analysed on a Luminex analyzer (Luminex/R&D Systems) using Exponent Software V31. Specific binding was reported as Mean Fluorescence Intensity. Cut-offs for antigen-specific reactivity were defined using infant samples (n=14) from Nigeria (mothers were born after 1980, presumed MPXV-naïve). Antigen-specific thresholds were: A29L 3461.5, A35R 814.5, B6R 877.9, D6L 1718.6, H3L 1852.0, and M1R 1107.2. Specific binding was reported as Mean Fluorescence Intensity (Supplementary Fig. 4). We also report normalised Mean Fluorescence Intensity values, equal to, per antigen, the Mean Fluorescence Intensity divided by the relevant cut-off (i.e., values >1 indicate reactivity and <1 non-reactivity).

Magnitude breadth analysis
To characterise the profile of MPXV-specific IgG responses across multiple antigens, we applied a magnitude-breadth analytical framework adapted from prior studies of polyclonal antibody responses in HIV 104–106 and SARS-CoV-2 57,107 vaccine studies. This approach estimates the cumulative proportion of participants who retain detectable responses as antigen thresholds increase, effectively capturing both breadth (number of reactive targets) and magnitude (strength of signal) across the tested antigens. To compare distributions of magnitude-breadth scores visually, we used survival functions (also called complementary cumulative distribution functions)—these show the proportion of participants with scores above a given value for the full range of possible values. Curves were stratified by participant groups based on occupational exposure, birth cohort and MPXV exposure status. Groups of participants with higher magnitude responses across more antigens have a right-shifted curve, reflecting greater immunological breadth. Box plots of magnitude-breadth scores are shown next to the curves. Differences in distributions were assessed using Mann-Whitney U tests.

Genomic dataset curation
We obtained 185 high-quality Clade IIb MPXV genomic sequences previously deposited to GenBank. This included all Clade IIb genomes from 2017 to 2022 sampled in Nigeria, sequences from non-endemic countries with a travel history to Nigeria. To select the sequences for the estimation of the cluster size and dispersion parameter we removed sequences sampled before 2021. We used 185 sequences to estimate for the skygrid model and 105 sequences for the cluster size estimation.

Transmission heterogeneity estimation of Nigerian mpox spread
To estimate the effective reproduction number (R) of mpox, we adapted the renewal equation framework 62. Within this framework, the time varying effective reproduction number 60 (defined as the average number of secondary infections generated by one infected individual) was modelled using a fourth-order spline with five evenly spaced knots. We assumed a discretized gamma distribution for the generation interval, (mean of 12.6 days and SD of 5.7 days) 61. The observed case counts from the Nigeria CDC were fit using a Poisson likelihood to derive posterior estimates of R 59.

To analyse the population dynamics of the mpox epidemic in Nigeria, we utilised the 185 genomic data obtained from GenBank 8, to reconstruct epidemic dynamics using the BEAST software package v.10.5 7,8,92–94,108. Our analysis employed a non-parametric skygrid coalescent tree prior, incorporating 96 change points distributed over 10 years, as described 108. We combined two independent Markov chain Monte Carlo (MCMC) runs, each consisting of 100 million states, with sampling occurring every 10,000 states. We used tracer to access that all parameters effective samples size values were above 200, as well as export the summary of the effective population size across the tree posterior. We have deposited the curated sequence list, metadata, and BEAST XMLs at: https://github.com/Ifeanyi-omah/Sero-Genomics_mpox.

To investigate whether a small number of individuals disproportionately contributed to transmission, we estimated the overdispersion parameter using the method outlined in Tran-Kiem and Bedford 55, applying it to a subset of 105 Nigerian sequences generated from 2021 to 2023 (second phase of the epidemic). We used a probability of transmission before mutation of 0.66 59. Given that ≈7% of confirmed cases during this study period were sequenced, we then used sampling proportions (the proportion of total cases represented by the genomic sample) 0.0035 10, 0.0070 21, 0.035 104, 0.070 209 which corresponds to case ascertainment rates of 5%, 10%, 50%, and 100% respectively. Given the APOBEC3-mediated mutations of mpox elevating the mutation rate, we specifically used identical sequences because they indicate very recent transmission events 7,8 However, interpreting identical sequences must be approached cautiously and we acknowledge that identical sequences might represent closely linked transmission chains but may also mask subtle diversification occurring over slightly longer timescales of the genomic data.

Definition of terms and statistical analysis
We compositely defined MPXV exposure as a ≥2-fold increase in total binding antibody magnitude between baseline and follow-up, in combination with a ≥2-fold increase in antibody levels to at least 4/6 MPXV-specific antigens tested. This definition was designed to capture both the magnitude and breadth of the humoral immune response, providing a stringent immunological marker of recent exposure to MPXV or closely related Orthopoxviruses and was informed by prior work in West African cohorts 109 demonstrating that reliance on single antibody markers substantially underestimates true exposure and that multi-marker or functional antibody approaches offer superior discriminatory accuracy. Baseline characteristics of participants were summarised as counts and percentages for categorical variables, and medians with interquartile ranges (IQR) for continuous variables. Antibody responses were described using both the antigen-specific antibody levels (raw and normalised) and overall magnitude-breadth scores. Central tendencies were described by geometric mean titres (GMTs). Distributions of continuous measures between two groups were compared using the Mann-Whitney U test. Distributions were visualised using box plots (showing quartiles), with individual data points also shown; and survival functions (also called complementary cumulative distribution functions) which describe proportions of participants (y-axis) with measurements above each value (x-axis) in the range of possible values. All statistical analyses and data processing were performed by Python or R (version 4.2.2) or GraphPad Prism (v10.4.1).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The article “Sero-genomic evidence for silent mpox transmission in healthy Nigerian adults” by Abdullahi et al represents an important contribution to the mpox literature by examining MPXV seropositivity in a West African population, which is a current knowledge gap. The study combines two cohorts to understand the impact of occupational exposure as well as age on mpox seropositivity and then supplements their study with genomic analyses. That being said, I believe that the publication is unclear at times and can be strengthened by the inclusion of more detail in the methods and results section and more in-depth editing of the manuscript for clarity. I hope that the comments below are useful to allow the authors to improve this work!\nMajor comments:\n- It would be great to have a table comparing the demographic characteristics of the first and second samples each cohort, especially considering the loss of >20% of participants in the general community cohort. Additionally how were the two cohorts constructed? What was the inclusion and exclusion criteria? If this is part of a prior publication, please state so clearly. If not, maybe a flow diagram or better description of the exclusions and exclusions would be useful.\n- I admit that I am a bit confused regarding the seropositivity results. My understanding from reference 54 (Yates et al.) is that that using a combination of mpxv specific antigens could be useful for differentiating between mpxv specific exposure and prior smallpox vaccination. But the discussion (such as in line 318), makes it seem like the assay used in the current study is not specific and could just be capturing cross reactivity from prior smallpox vaccination. Was a goal of this study to discover novel antigenic targets for mpox assays? If so, that should be made clearer and the rationale for the specific targets should be better explained. If not, why did the authors use similar but different targets than those validated in Yates et al (Ref 54)? More clarity and detail around this is needed.\n- There’s mention of what percentage of the individuals showing seropositivity to MPXV were HCW and separately were born before 1980 in line 151. While supp table 1 includes the birth cohort stratification in HCW vs General Population, It’d be useful to discuss these differences in the results section, given that birth cohort seems to play a different effect in the two cohorts (i.e all the seropositive in the HCW cohort come from individuals born before 1980, which isn’t true in the general population cohort).\n- I don’t see the results of the sensitivity analysis where the pairwise correlations of antibody levels were estimated for individuals born before 1980 other than the correlation between A35R and B6R in the text. Please consider adding in that correlation matrix as a supplementary figure.\n- While I don’t doubt the results in Fig 4b, I think your conclusions would be strengthened by repeating the analysis on just the General Population cohort stratified between pre and post 1980. HCWs could represent a unique population of individuals that are not representative of the general population and combining the cohorts could be introducing unknown sampling biases or confounders., especially given the limited cohort characteristics analyzed in table 1 (which do show some differences between the cohorts).\n- For the 5 individuals who showed a >2 fold increase in antibody levels, did you have access to their health records to confirm that they never had mpox symptoms (i.e that it was truly subclinical)?\n- I think that using both titers and seropositivity to investigate the role of occupational exposure with the two different cohorts was really clever!\n- Line 263, slow epidemic doubling time over what time course? How many sequences were used? How were they sampled? The methods are unclear about the genomic sampling. How is it similar or different from Parker et al? More information is needed here.\n- I admit that I’m a bit confused between lines 263 and 282. Line 263 made it seem like the authors of this publication estimated the doubling time via phylo methods, but this is highly similar to the conclusion made in Parker et al. Meanwhile line 283 talks about analyzing 103 sequences from a specific phase. Is line 263 just providing background information or highlighting results done for this specific study? If it is the latter (this specific study) then please consider showing the posterior distribution of the doubling time from BEAST in the supplement. If not, then please edit to make clear that line 263 is referring to previously published work.\n- Similarly, there’s not much information about the 103 sequences that make up the sample. Please provide more information other than the fact that they are identical sequences. Where are they taken from? What time period?\n- Figure 6A seems to be estimating a Rt using cases from different regions via the renewal equation framework outlined in Figgins et al. But these results are never discussed in the text. In addition to writing about these results in the results section, can you also provide information about the actual cases in these regions and maybe a map to contextualize the information?\n- Similarly to above, can you describe the Ne results from Fig 6B in the results section? Why is there so much more uncertainty in 2021-2022? It says in the legend that there was exponential growth in 2021-2022 but the inset with 6B is showing a pretty flat skyline plot? Can you discuss further?\n- I think the claim in line 342 regarding the 3% asymptomatic rate at a population level is a bit strong. This 3% comes from a pooled group of both the general population and the HCW cohort. Given that the HCW cohort is not necessarily (and probably isn’t) representative of the population as a whole (as seen in table 1), it is difficult to extrapolate the 3% to a “general population”. I don’t think that the conclusion regarding subclinical cryptic transmission is invalid, I just don’t think you can put a lot of generalizability into the 3% estimate. Happy to be convinced otherwise!\n- The claim in line 253 that B6R is the most immunodominant feels overly strong given that the large increases in B6R was really only seen in samples LPHC 111 and 112 but not for the other three individuals. I appreciated the more nuanced claims in the discussion surrounding the combination of B6R alongside A35R and M1R as being potential targets, but the claim of “immunodominance” seems like a weak one given that there is always potential for individual variability.\n- Line 512, talks about using a zero-inflated negative binomial for the renewal equation framework. While hard to judge without seeing the raw case data for each region (which further shows the need for a bit more descriptive epidemiology), I wonder why the authors chose a zero-inflated distribution rather than something like a Poisson. Are there a lot of zeros in the data that the authors believe are due to an independent process such as surveillance interruptions?\n- No code or data were provided, preventing me from evaluating the reproducibility of the results. I’d strongly encourage the authors to upload their code and data in a public repository for reproducibility (if not possible due to privacy concerns, then at least the code, including information about the sequences and BEAST XML files).\nMinor comments:\n- Line 88, I think it’s technically still the monkeypox virus while the disease is termed mpox?\n- I think the 27% in line 151 should be 17%\n- Line 280, what defines the “second phase”?\n- The figure legend of fig 5 talks about panels A-C but there are only A and B letterings in the figure, please edit accordingly. Additionally is the dashed red line in Fig 5A supposed to say T1 instead of T0?\n- Line 288, “half of all reported cases […] went unreported” This is confusing wording. If you’re following the examples in Tran-Kiem & Bedford, I think the assumption is regarding to the fraction of total infections detected, similar to as mentioned in the text, ascertainment bias. Please edit that sentence for clarity. It might be useful to also consider a very low reporting fraction like 5 or 10% given that argument of cryptic transmission presented in the current publication.\n- In figure 6C, what does the black cross represent?\n- Line 310, consider changing “clinical infection” to “clinical illness” or “clinical symptoms” for clarity.\n- Can you cite references for the three transmission cycles referenced in line 343?", "reviewer_2": "General Comments:\nAbdullahi et al. submitted the manuscript “Sero-genomic evidence for silent mpox transmission in healthy Nigerian adults”. The authors asked whether residual smallpox vaccination-derived immunity shaped the risk to, or outcome of Mpox infection during the 2022 Mpox outbreak. To answer this question, the authors combine serological analysis of 2 SARS-CoV-2 cohorts and genomic analysis of Mpox transmission. Serological analysis revealed that 3% (5/153) of the test cohort showed a significant boost in antibody reactivity to Mpox antigens during the study period. Considering the serological increase in 3% of the cohort, combined with the transmission dynamics modeled in Figure 6, Abdullahi et al conclude that asymptomatic, silent transmission of mpox occurred in this otherwise healthy cohort. In my opinion, the authors have not provided sufficient evidence to support this claim. First, the antigens used in the serological assay (A29L, A35R, B6R, D6L, H3L, and M1R) are known to be serologically cross-reactive with other orthopoxviruses, including vaccinia virus, the virus used for smallpox vaccination. In fact, the protection afforded by vaccinia vaccination relies on this cross-reactivity to provide protection against both variola virus and mpxv, the causative agent of smallpox and Mpox, respectively. To provide specificity, the authors used the breadth (number of antigens) of the antibody response the sole measure of an Mpox-specificity. This approach is flawed as an increase in the number of IgG-reactive antigens can also occur in cases of recent vaccinia vaccination or boost. Second, an increase in antibody reactivity is clearly seen in the 5 individuals highlighted in Figure 5, but the cause of that increase is not. The authors did not mention vaccinia vaccination as a potential cause or query the sample population for potential symptomatic infection with Mpox. It is also unclear how the data presented in Figure 6 corresponds to the population studied in Figures 1-5. Therefore, the data presented in the manuscript does not support the conclusion that Mpox transmission has occurred silently in the study population.\nAccording to https://www.cdc.gov/mpox/outbreaks/2022/index-1.html, Mpox as found in West Africa prior to the 2022 global outbreak.\nSpecific Comments:\nMethods – The methods are not sufficiently detailed to fully understand the work submitted.\nFigure 1 – The timepoints specified for Cohort 1 (March 2021 – October 2021) and Cohort 2 (January 2023 – June 2023) do not represent the height of the 2022 Mpox Clade II outbreak. In addition, the timepoints shown in Figure 1 do not match the timepoints reported in the Methods section.\nFigure 3 – The lack of significant correlation for the levels of IgG reactivity between the 6 MPXV antigens tested is surprising. Antibody responses generally rise and fall together, even if the magnitude of the response to some analytes is different. The results shown in Figure 3 are likely due to imbalanced numbers of positive and negative specimens used for the study. If the cohorts had been parsed into “positive” and “negative” groups overall, the correlation data would likely be much different.\nFigures 4 and 5: Data in Figures 4 and 5 are represented as the total IgG mean fluorescence intensity (MFI) for each MPXV tested. However, each antigen also has a distinct cutoff, as described in the Methods section. The authors should consider normalizing each antigen to its respective cutoff when comparing multiple antigens on the same graph.\nFigure 6: Neither the legend nor the methods section describes the origin of the data presented in Figure 6. On what sample type was the genomic analysis done? What cohort/s was the analysis done? What primers were used to ensure MPXV specificity of the genomic data?", "reviewer_3": "Dear Editor,\nI have carefully reviewed the manuscript entitled “Sero-genomic evidence for silent mpox transmission in healthy Nigerian adults” by Abdulahi and colleagues. I would like to commend the authors for this well-executed and highly relevant study, which I recommend for acceptance.\nThe manuscript makes an important contribution to the evolving understanding of Mpox epidemiology. By combining robust serological analyses with genomic data, the study provides compelling evidence for sustained asymptomatic transmission of Mpox in Nigeria. This challenges the traditional surveillance paradigm that has historically focused on symptomatic case detection, and it underscores the urgent need to strengthen laboratory-based surveillance systems capable of detecting covert transmission.\nThe authors’ identification of Mpox antigens (particularly B6R, M1R, and A35R) as strong markers of silent exposure is especially valuable. These findings have direct implications for the design of sero-surveillance tools, and they highlight novel opportunities for integrating serology with genomic monitoring to track both virus circulation and population immunity. Importantly, the work demonstrates how sero-genomic approaches can bridge gaps left by case-based surveillance, thereby providing a more accurate picture of transmission dynamics.\nThe complementary phylogenetic analysis adds depth to the findings, revealing patterns of superspreading alongside dead-end infections, which reflect the complex ecology of Mpox transmission. This dual perspective—serological and genomic—provides a strong methodological model for future research and for shaping preparedness and response strategies.\nFrom a policy and practice standpoint, this study is critical for the Mpox transition into national health systems. It highlights how silent infections can influence virus diversity, clinical relevance, and transmission dynamics across different clades. The findings reinforce the need for context-specific responses that consider population immunity, behavioral risk factors, and viral genetic variation. Moreover, the evidence presented strengthens the case for vaccination strategies that extend beyond symptomatic populations, while also guiding laboratory investments in antigen-based assays.\nOverall, this is a timely, rigorous, and impactful study. It advances science, informs policy, and provides practical insights for the global Mpox response, particularly in endemic African settings. I strongly recommend its acceptance, with only minor editorial adjustments as needed for clarity and readability.\nSincerely,\nProf Yap Boum II"}, "author_response": "We thank you and the reviewers for the thoughtful and constructive feedback on our manuscript.\nWe are grateful for the opportunity to revise and clarify key aspects of our study. Below,\nwe address each of the reviewers’ comments in detail to provide further nuanced understanding into evidence of\nsilent mpox exposure in this population.\nTo ensure clarity, and to avoid interpretational overreach, we have revised our manuscript to emphasise that our\nresults provide evidence “consistent with” asymptomatic transmission, and we frame our results as indicative of\ncryptic and occult exposure within the study population. We have edited the title to reflect exposure: Sero-\ngenomic evidence for occult mpox exposure in healthy Nigerian adults. We have also significantly refined\nthe figures including moving the correlation plots to the supplementary to ensure compact scientific messaging.\nAs also advised by the editors, we performed multiple sensitivity analyses for different comparator groups (pre-\nvs. post-1980 birth cohorts, healthcare workers vs. community participants, baseline seropositive vs.\nseronegative, and longitudinally boosted vs. non-boosted individuals).\nReviewer #1\n[...]\nResponse:\nWe would like to thank the reviewer for their thoughtful and constructive comments and for recognising the\nsignificance of our study in addressing the current knowledge gap on Mpox in West Africa. We acknowledge the\nneed for clearer presentation and additional detail in specific sections. We have taken time to significantly refine\nthe entire manuscript to ensure readability and succinctness. We provide point by point response below:\nMajor comments:\nIt would be great to have a table comparing the demographic characteristics of the first and second samples each\ncohort, especially considering the loss of >20% of participants in the general community cohort. Additionally,\nhow were the two cohorts constructed? What was the inclusion and exclusion criteria? If this is part of a prior\npublication, please state so clearly. If not, maybe a flow diagram or better description of the exclusions and\nexclusions would be useful.\nResponse:\nWe would like to thank the reviewer for this comment. In response and detailed in (Lines: 446-476) we have:\ni) Added a new comparative demographic table (Supplementary table 2) summarising the characteristics of\nparticipants across both cohorts and sampling timeframe (i.e. T0 and T1), including age, gender and\noccupational category. This table highlights the representativeness of the study populations across both\ntimepoints and accounts for the attrition observed in the general community cohort based on available samples.\n(Lines: 465-468)\nii) Clarified cohort construction and the inclusion criteria (Figure 1). We specifically indicate that we utilised\nsamples from two previously published studies and that the HCW cohort were recruited from Lagos, Nigeria in\nMarch 2021 as part of the SARS-CoV-2 vaccine effectiveness where they received two-dose regimen of ChAdOx1\n(AstraZeneca; Abdullahi et al, 2022; Nature Communications).1 We further highlighted that the general\ncommunity was recruited local community outreach across IHVN’s partner sites in Abuja, Nigeria where\nparticipants also received two-dose regimen of Ad26.COV2.S, which was approved as a two-dose regimen by\nthe Nigerian government (Johnson and Johnson; Abdullahi et al, 2025 under review) 2 (Lines: 450-468)\niii) Inserted a flow diagram and clarified prior publication (Figure 1; below): We have included a flow diagram\noutlining participants and sample utilisation approach and final number of samples analysed across both\ntimepoints. To aid clarity, we also added a map of Nigeria with study timelines and a schematic illustration of\nmpox antigenic targets, alongside a justification for our antigen selection based on published work (Lines: 450-\n451; 480-485)\n[...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors for their extensive revision of the manuscript. It is much improved with regards to clarity and support for their conclusions! They have adequately responded to my concerns.", "reviewer_2": "The authors have satisfactorily responded to the comments of the previous review. Publication of this important study is recommended."}, "author_response": ""}]

Decision letter