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Reconstructing the SARS-CoV-2 epidemic in eastern Uganda through longitudinal serosurveillance in a malaria cohort

Importance: Estimating the true burden of SARS-CoV-2 infection has been difficult in sub-Saharan Africa due to asymptomatic infections and inadequate testing capacity. Antibody responses from serologic surveys can provide an estimate of SARS-CoV-2 exposure at the population level. Objective: To estimate SARS-CoV-2 seroprevalence, attack rates, and re-infection in eastern Uganda using serologic surveillance from 2020 to early 2022. Design: Plasma samples from participants in the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria in Uganda (PRISM) Border Cohort were obtained at four sampling intervals: October-November 2020; March-April 2021; August-September 2021; and February-March 2022. Setting: Tororo and Busia districts, Uganda. Participants: 1,483 samples from 441 participants living in 76 households were tested. Each participant contributed up to 4 time points for SARS-CoV-2 serology, with almost half of all participants contributing at all 4 time points, and almost 90% contributing at 3 or 4 time points. Information on SARS-CoV-2 vaccination status was collected from participants, with the earliest reported vaccinations in the cohort occurring in May 2021. Main Outcome(s) and Measure(s): The main outcomes of this study were antibody responses to the SARS-CoV-2 spike protein as measured with a bead-based serologic assay. Individual-level outcomes were aggregated to population-level SARS-CoV-2 seroprevalence, attack rates, and boosting rates. Estimates were weighted by the local age distribution based on census data. Results: By the end of the Delta wave and before widespread vaccination, nearly 70% of the study population had experienced SARS-CoV-2 infection. During the subsequent Omicron wave, 85% of unvaccinated, previously seronegative individuals were infected for the first time, and ~50% or more of unvaccinated, already seropositive individuals were likely re-infected, leading to an overall 96% seropositivity in this population. Our results suggest a lower probability of re-infection in individuals with higher pre-existing antibody levels. We found evidence of household clustering of SARS-CoV-2 seroconversion. We found no significant associations between SARS-CoV-2 seroconversion and gender, household size, or recent Plasmodium falciparum malaria exposure. Conclusions and Relevance: Findings from this study are consistent with very high infection rates and re-infection rates for SARS-CoV-2 in a rural population from eastern Uganda throughout the pandemic.


INTRODUCTION
Understanding population-level exposure and immunity to SARS-CoV-2 is necessary to measure transmission, determine the susceptible population, and inform public health responses. However, estimating the proportion of the population that has been infected, particularly in resource-limited settings, is complicated by asymptomatic or subclinical infections, inadequate testing capacity, and challenges in collecting routine surveillance data. Seroprevalence surveys can overcome these issues by identifying antibody responses that reflect prior SARS-CoV-2 exposure 1 . A recent review of seroprevalence studies conducted in sub-Saharan Africa when vaccination coverage was low (<5% in September 2021) reported a rise in SARS-CoV-2 seroprevalence from 3% in Q2 2020 to 65% in Q3 2021 2 .
There are little reported SARS-CoV-2 seroprevalence data specific to Uganda to elucidate the true burden of infection and under-ascertainment of cases. Only one population-level serosurvey in Uganda has been published thus far, which identified an overall seroprevalence of 21% in March 2021 3 . To our knowledge, there have also been no published serosurveys in sub-Saharan Africa that estimate population seroprevalence after the Omicron wave or the attack rate of the Omicron variant. Here, we leveraged samples collected as part of a longitudinal malaria cohort study in eastern Uganda to reconstruct the epidemic trajectory of SARS-CoV-2 over the first two years of the pandemic and estimate the attack rate of the main epidemic waves, along with risk factors for seroconversion.

Study design
This study was conducted within the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria in Uganda (PRISM) Border Cohort study in the Tororo and Busia districts of Uganda. The design and population of this ongoing cohort have recently been described elsewhere 4 . At cohort study enrollment, a survey was conducted to collect information on household characteristics, including household wealth tertile 5 , sanitation category, and housing type (traditional versus modern).
Participants from the 80 households enrolled in the cohort were encouraged to come to a dedicated study clinic open 7 days per week for all medical care which they received free of charge. Routine study visits were conducted every 4 weeks and included a standardized evaluation and blood collection. Information on SARS-CoV-2 vaccination status and dates was collected from participants.
For this SARS-CoV-2 study, we leveraged the biospecimen repository of plasma samples collected at monthly routine study visits. We selected four sampling intervals between October 2020 and March 2022, informed by COVID-19 case counts in Uganda ( Figure 1A and Supplementary Figure 1). For sampling, we included all available samples from study participants who had a routine visit during the Round 3 sampling interval.

Laboratory methods
A multiplex bead assay and protocol previously described for SARS-CoV-2 serology studies 6,7 was modified to assess total IgG responses to the spike protein, the receptor binding domain of the spike protein (RBD), and the nucleocapsid protein. Detailed methods are in the Supplementary Methods. Briefly, plasma samples were assayed at a 1:400 dilution to determine antibody seropositivity. The results were expressed as mean fluorescent intensity (MFI). A standard curve using a pool of positive serum was included on each plate to normalize for plate-to-plate variations (Supplementary Figure 2) and to infer relative antibody concentrations using a 4-parameter logistic model 8 . Samples run in duplicate demonstrated high replicability (Supplementary Figure 3). A subset of samples was tested at a 1:4,000 dilution to assess antibody boosting. A greater dilution for the boosting assay was used, since many seropositive samples were near or outside the assay's linear dynamic range at 1:400.
For negative controls, we used 192 pre-pandemic plasma samples from the PRISM-2 cohort study also in Tororo District, Uganda 9 , collected in 2017 and 2018 (Supplementary Figure 4). These samples were selected to have the same age distribution as the study population and represented an appropriate background for serologic signals (e.g., cross-reactivity from other circulating coronaviruses 10 ). For positive controls, we used 156 plasma samples from 99 volunteers enrolled in the UCSF Long-term Impact of Infection with Novel Coronavirus (LIINC) study 6 . Cutoff for seropositivity was established as the highest value among the negative controls, and therefore specificity was 100% (192/192) by design. Assay sensitivity was calculated as the proportion of the positive controls above the cutoff, and was 92.9% (145/156) (Supplementary Figure 5). As an additional check on assay performance characteristics, we calculated the sensitivity in samples from 11 participants from the PRISM Border Cohort study who tested positive for symptomatic SARS-CoV-2 infection between February and July 2021 (Mwakibete et al, submitted). 10 of 11 (90.9%) seroconverted, and responses remained robust over time (Supplementary Figure 6).

Statistical analyses
We focused on spike antibody responses and used background-subtracted MFIs as the primary outcome. A sensitivity analysis was performed using relative concentrations as the outcome and demonstrated highly concordant results (Supplementary Figure 7). While the assay also measured RBD and nucleocapsid responses, the RBD and spike responses were highly correlated, and the nucleocapsid responses had high background in this assay as previously noted 6 , and thus were not well suited to evaluate primary seroconversion to natural infection (Supplementary Figure 8).
To test for individual and household characteristics (and visit-level patient-reported symptoms and clinician-assigned diagnoses) associated with SARS-CoV-2 seroconversion and boosting, univariate binomial regression was performed. For associations with seroconversion, patients could contribute multiple observations until seroconversion; therefore, generalized estimating equations were used to adjust for repeated measures 15 . For associations with boosting, only children were included because SARS-CoV-2 vaccination had begun in those 18 years and older by the time of sample collection. Malaria incidence between each sampling round was calculated by dividing the number of episodes of clinical malaria by the number of days of observation for each individual. Asymptomatic parasitemia was defined as the presence of P. falciparum parasites via microscopy or varATS qPCR 16 and the absence of fever (measured or by history).
To test for clustering of seroconversions within households, we adapted an approach of estimating pairwise odds ratios to detect correlation structure in binary data [17][18][19] (see Supplementary Methods). All analyses were conducted using the R statistical software (https://www.r-project.org/) and the Stan programming language (https://mc-stan.org/).

Clinical and demographic characteristics of the study participants
A total of 1,483 samples from 441 participants living in 76 households were tested ( Table 1)

SARS-CoV-2 seroprevalence, attack rates, and boosting
The raw seropositivity to spike was 22.0% during Round 1 (October-November 2020, half a year since the first reported case in Uganda) and increased over time, reaching 91.0% seropositivity . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint We used longitudinal antibody responses to identify seroconversions (Supplementary Figure  10) and to estimate the population-level attack rate of the main epidemic waves (Figure 2B; Supplementary Table 2). The age-weighted attack rate of the Delta wave (Round 2 to Round 3) was estimated to be 46.7% (95% CrI: 39.1%, 54.6%). The age-weighted attack rate of the Omicron wave (Round 3 to Round 4), omitting individuals who were vaccinated at Round 4, was estimated to be 84.8% (95% CrI: 67.9%, 93.7%).
Among the 232 participants who were seropositive at Round 3 and had a Round 4 sample, we looked for evidence of antibody boosting between these rounds. 125/232 participants (53.9%) boosted based on our primary definition of ≥ 4 fold increase in MFI (Supplementary Table 3). This included 70/84 participants vaccinated by Round 4 and 55/148 who had not been vaccinated, consistent with re-infection. Excluding vaccine recipients, and weighting by the age distribution of the population, we estimated that at least 50.8% (95% CrI: 40.6%, 59.7%) of individuals may have been re-infected during the Omicron wave.
Interestingly, we observed more boosting events among individuals who had lower antibody responses at Round 3 than among individuals with higher responses (Figure 3). This was consistent across vaccination status, but was particularly evident among unvaccinated individuals. While boosting was observed among 29/41 (71%) of unvaccinated individuals with antibody levels in the lowest tertile, it was only observed in 7/62 (11%) of unvaccinated individuals with antibody levels in the highest tertile. In addition to tertile, boosting risk increased by age (Supplementary Figure 11). Alternate estimates of boosting using different thresholds and incorporating nucleocapsid responses are shown in Supplementary Figure 12

Individual and household level risk factors for SARS-CoV-2 seroconversion and boosting
In univariate analyses, older age was associated with SARS-CoV-2 seroconversion with OR 1.77 (95% confidence interval [CI]: 1.15, 2.74) for adults 16 years and older, and OR 1.31 (95% CI: 0.89, 1.94) for children 5-15 years compared to children less than 5 years ( Table 2). At each round, seroprevalence was highest among adults, followed by older children, and lowest in younger children (Figure 2A). Attack rates were considerably higher in adults during the first two intervals, but not the final interval ( Figure 2B). We did not find an association between gender and seroconversion.
No household characteristics evaluated were associated with seroconversion ( Table 2). However, we found evidence of clustering of SARS-CoV-2 seroconversions within households (Supplementary Figure 14). Pooled across time intervals and age groups, the odds of seroconversion were 4.33 (95% CrI: 3.27, 5.79) times greater in individuals from households where another individual seroconverted during the same period, as compared to households where other individuals did not seroconvert. Clustering was strongest between child-child pairs (OR 6.42, 95% CrI: 4.31, 9.65), followed by child-adult pairs (OR 2.92, 95% CrI: 1.85, 4.64), and lowest in adult-adult pairs (OR 2.45, 95% CrI: 0.77, 8.06), implying that two children in a . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted September 21, 2022. ; https://doi.org/10.1101/2022.09.20.22280170 doi: medRxiv preprint household were more likely to seroconvert in the same interval than two adults. These findings suggest an elevated within-household secondary attack rate, particularly amongst children 20 .
To assess potential association between SARS-CoV-2 infection and recent P. falciparum infection, we examined the relationship between recent symptomatic malaria and asymptomatic parasitemia with SARS-CoV-2 seroconversion. Malaria incidence and number of episodes of asymptomatic parasitemia were calculated for each SARS-CoV-2 seronegative individual within each time period between sampling rounds. There was no association between an individual's incidence of symptomatic malaria or occurrence of asymptomatic parasitemia between sampling round and SARS-CoV-2 seroconversion ( Table 2).
We explored associations between self-reported symptoms and clinician-assigned diagnoses with SARS-CoV-2 seroconversion during a time interval. Symptoms associated with SARS-CoV-2 seroconversion included cough, headache, and fatigue (Supplementary Table 4). The number of sick visits between sampling rounds, any upper respiratory tract infection (URTI) diagnosis, and total number of URTIs were also associated with seroconversion.
Finally, these individual and household characteristics were investigated for association with boosting of the SARS-CoV-2 antibody response between Rounds 3 and 4 in children (Supplementary Table 5); findings were similar to those in the seroconversion analysis. However, there was an association between antibody boosting and total number of asymptomatic malaria episodes in the time period between Round 3 and Round 4 sampling (OR 1.37, 95% CI: 1.05, 1.81); this association remained but was less strong after adjusting for age (OR 1.29, 95% CI: 0.97, 1.73). There were no specific symptoms or diagnoses associated with increased odds of antibody boosting.

DISCUSSION
Findings from this study are consistent with very high attack rates of SARS-CoV-2 infection in this rural population from eastern Uganda throughout the pandemic. By the end of the SARS-CoV-2 Delta wave and before the widespread availability of vaccination, nearly 70% of the study population had experienced SARS-CoV-2 infection. Moreover, during the subsequent Omicron wave in early 2022, 85% of unvaccinated, previously seronegative individuals were infected for the first time, and at least 51% of unvaccinated, already seropositive individuals were likely reinfected. Based on the SARS-CoV-2 cases reported in Uganda during the time period of this study (163,878 through March 29, 2022) and our estimated weighted seroprevalence in unvaccinated individuals (95% at the end of Round 4), we estimate that only about 1 in 270 infections were ascertained by the reporting system.
The availability of longitudinal sampling enabled us to assess seroconversions and re-infections over time. Interestingly, our results based on serologic evidence of boosting suggested a lower probability of re-infection in individuals with higher pre-existing antibody levels; similar patterns have previously been described in a triple-vaccinated population 21 . Our observation could be explained by several potential factors including immunity (i.e., more effective viral neutralization among individuals with higher pre-existing antibody titers), infection histories with different variants which may affect subsequent boosting 22 , antibody homeostasis (i.e., a ceiling for antibody titers), or limited dynamic range of the assay. Of these, we can only rule out the last explanation, as antibody responses in individuals who did not boost were far from the top of the dynamic range of this assay. If immunity is indeed a driver of this observation, it underscores the importance of achieving and maintaining sufficiently high antibody titers in the population, i.e., through vaccination and revaccination at an appropriate frequency.
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The fact that we only tested samples obtained at ~5 month intervals, and therefore do not know the exact timing of SARS-CoV-2 infection, limits our capacity to make precise inferences about associations between SARS-CoV-2 infection and clinical presentation. Similarly, we could not directly test whether P. falciparum infection status affects the risk of SARS-CoV-2 infection or disease. However, we were able to examine whether recent P. falciparum infection history was associated with SARS-CoV-2 seroconversion. We found no definitive evidence of an association between recent P. falciparum infection history and SARS-CoV-2 infection, despite a weak association between asymptomatic parasitemia and boosting during the Omicron wave in individuals under 16 years of age. Understanding potential immunologic interactions between these pathogens remains an important area of inquiry 23 , particularly in this setting where exposures to the malaria parasite are high 4 .
Our results suggest a higher attack rate during the first Omicron wave of SARS-CoV-2 than the Delta wave (85% vs 47%), and the high estimated re-infection rate during Omicron (51%) is consistent with the evidence of increased infectivity and immune evasion associated with this variant 24,25 . Due to the antigenic changes in the spike protein of the Omicron variant, the sensitivity of existing assays measuring spike antibody responses may be reduced against this variant 26 . Gold standard serologic assays are needed to accurately determine re-infections, possibly with variant-specific assays. This will be increasingly important as re-infections become more frequent and new variants that potentially evade pre-existing immunity continue to emerge. We did not have data to adjust for potential variant-specific reduced sensitivity, nor to incorporate seroreversions (though here and in previous work 6 , we found this assay to demonstrate stable responses over the first few months following infection). Either would lead to increased estimates of seroprevalence, attack rates, and boosting.
These results contribute to a growing body of seroprevalence data from low-resource settings that illustrate very high SARS-CoV-2 infection rates despite low case ascertainment. Infection histories have ultimately generated complex landscapes of SARS-CoV-2 hybrid immunity in different populations. It will be increasingly important to consider additional complexities that have since become central for interpreting the results of SARS-CoV-2 serosurveys, including histories of vaccination, breakthrough infection, and re-infection.
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The authors have no competing interests to declare.
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Figure 2: SARS-CoV-2 seroprevalence and attack rates by age group & serosurvey round. (A)
Posterior median and 95% credible intervals for seroprevalence based on spike protein MFIs, accounting for test performance characteristics. (B) Posterior median and 95% credible intervals for attack rate based on spike protein MFIs. Individuals who had received SARS-CoV-2 vaccination by a serosurvey round are removed from the attack rate estimation (e.g., individuals who were vaccinated at Round 4 are removed from the attack rate estimation between Round 3 and Round 4). The colors represent age group-specific estimates. The black values represent the crude estimates in the cohort. The gray values represent estimates weighted by the local age distribution using 2014 census data from the three parishes in Uganda in which study participants reside. All estimates are adjusted for test performance characteristics.
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SUPPLEMENTARY METHODS
Covalent antigen coupling to magnetic microsphere beads: Antigens were covalently coupled to MagPlex carboxylated magnetic microspheres (Luminex Corp, Austin, USA). 12.5×10 6 beads for each bead region were suspended by vortexing at medium speed for 2 minutes. The beads were transferred from the stock bottle into a 1.5ml micro-centrifuge tube with low protein binding surface (Eppendorf, UK) to minimize bead loss and centrifuged at 16,000g for 5 minutes. The bead pellet was pulled on the tube side by placing into a magnetic rack for 2 minutes before pipetting off the supernatant. The beads were washed twice by suspension in 1000µl distilled water, vortexed for 30 seconds and centrifuged at 16,000g for 5 minutes. Supernatant was removed after settling the beads on a magnetic rack for 2 minutes. Beads were suspended in 800µl monobasic Sodium Phosphate (NaH2PO4, pH 6.2 activation buffer). To activate the carboxyl surface of the beads, 100µl of 50mg/ml Hydroxysulfosuccinimide (Sulfo-NHS) (Thermo Fisher scientific, UK) was added, vortexed briefly and immediately followed by addition of 100µl of 50mg/ml 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) (Thermo Fisher scientific, UK). The beads were incubated for 20 minutes at room temperature in the dark, wrapped in aluminum foil on a plate shaker. Beads were pelleted by centrifuging at 16,000g for 5 minutes, placed tubes in a magnetic holder for 2 minutes, the supernatant carefully removed, and washed 3 times with 1000µl PBS. Previously determined amounts of antigen (38.5µl/ml spike, 42ug/ml RBD, and 29.8ug/ml nucleocapsid proteins) were added to each bead region in PBS to make a final volume of 1000µl. The beads/antigen suspension was incubated for 2 hours on a rotating shaker in the dark, covered in aluminum foil. Coupled beads were pelleted by centrifugation at 16,000g for 5 minutes and washed of excess antigen 3 times with 1000µl of phosphate buffered saline 0.05% tween20 (PBS-TBN). The antigen coupled beads were suspended in 1000µl storage buffer (PBS-TBN-Sodium Azide) and stored at 4°C.
Assay to measure total IgG: Total IgG responses to spike, RBD, and nucleocapsid proteins were assayed in plasma at 1:400 dilution using the multiplex bead array using the Luminex Magpix machine (Luminex Corp, Austin, Texas). 10ul of each of the three coupled bead regions was added to 5ml PBS-TBN. 50µl of the pooled bead suspension was added to each well, washed and incubated with 50µl of 1:400 test plasma or control sample for 1.5 hours at room temperature. The plates were washed and 50ul of 1/250 goat anti-human IgG rPE labeled antibody (Jackson Immuno Research Laboratories) was added and incubated for 1.5 hours on a shaking platform. The plates were washed, 1xPBS added and read on a MagPix machine.The blank well MFI was deducted from each well to determine the net MFI. We required a minimum of 50 beads per analyte for a measurement to be included in this analysis. Cutoff for seropositivity was established as the highest value among the negative controls; this was determined to be 516 for background-subtracted spike median MFI, and 0.024 for relative spike antibody concentration.
Testing for clustering of seroconversions within households using pairwise odds ratios: The pairwise odds ratio is the odds of seroconversion for an individual in a household if another individual from the same household seroconverted, relative to their odds of seroconversion if the individual did not seroconvert. Pairwise odds ratio values greater than 1 indicate clustering within households. We estimated within-age category and between-age category pairwise odds ratios by time interval, and assumed that the pairwise odds ratios were constant across all households. The probabilities and odds were estimated in a multinomial Bayesian framework.
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted September 21, 2022. ; https://doi.org/10.1101/2022.09.20.22280170 doi: medRxiv preprint
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.  and (B) antibody concentrations for 50 samples tested in replicate. The cutoff for seropositivity for each metric, determined based on the highest negative control, is shown in blue. On Panel B, samples that were below the limit of quantitation for antibody concentrations (i.e., below the lowest standard dilution on the plate) are shown in red.
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted September 21, 2022. ; https://doi.org/10.1101/2022.09.20.22280170 doi: medRxiv preprint Supplementary Figure 14: Pairwise odds ratios for clustering of SARS-CoV-2 seroconversions within households, by time period and by age category. The leftmost panel depicts results of pooling seroconversions across all intervals; the subsequent panels depict results stratified by interval. Individuals who had received SARS-CoV-2 vaccination by a serosurvey round are removed from the risk set for seroconversion. For each time period, we estimated within-age category and between-age category pairwise odds ratios for clustering within households ("Adult-Adult", "Adult-Child", "Child-Child"), as well as a single overall pairwise odds ratio for clustering within households ("Overall"). For this analysis, we defined children as individuals 15 years of age or younger, and adults are individuals 16 years of age or older. *A pairwise odds ratio for clustering within adults in households was not estimated for the interval between Round 3 and Round 4, as there were no households that had greater than 1 adult who was unvaccinated and susceptible. The dashed red line is for odds ratio = 1.
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted September 21, 2022. ; https://doi.org/10.1101/2022.09.20.22280170 doi: medRxiv preprint