Determining seropositivity-A review of approaches to define population seroprevalence when using multiplex bead assays to assess burden of tropical diseases.

BACKGROUND: Serological surveys with multiplex bead assays can be used to assess seroprevalence to multiple pathogens simultaneously. However, multiple methods have been used to generate cut-off values for seropositivity and these may lead to inconsistent interpretation of results. A literature review was conducted to describe the methods used to determine cut-off values for data generated by multiplex bead assays. METHODOLOGY/PRINCIPAL
FINDINGS: A search was conducted in PubMed that included articles published from January 2010 to January 2020, and 308 relevant articles were identified that included the terms "serology", "cut-offs", and "multiplex bead assays". After application of exclusion of articles not relevant to neglected tropical diseases (NTD), vaccine preventable diseases (VPD), or malaria, 55 articles were examined based on their relevance to NTD or VPD. The most frequently applied approaches to determine seropositivity included the use of presumed unexposed populations, mixture models, receiver operating curves (ROC), and international standards. Other methods included the use of quantiles, pre-exposed endemic cohorts, and visual inflection points.
CONCLUSIONS/SIGNIFICANCE: For disease control programmes, seropositivity is a practical and easily interpretable health metric but determining appropriate cut-offs for positivity can be challenging. Considerations for optimal cut-off approaches should include factors such as methods recommended by previous research, transmission dynamics, and the immunological backgrounds of the population. In the absence of international standards for estimating seropositivity in a population, the use of consistent methods that align with individual disease epidemiological data will improve comparability between settings and enable the assessment of changes over time.

Introduction

Neglected tropical diseases (NTDs) and vaccine preventable diseases (VPDs) cause a significant burden on populations in developing countries, and effective surveillance plays an important role in the control and elimination of these diseases. Despite the geographical overlap of co-endemic tropical infections in many regions of the world, surveillance efforts have often focused on separate diseases [1]. Integrated approaches to controlling tropical diseases have been implemented in some programmatic settings. However, asymptomatic infections, poor health seeking behaviour, long latency periods, and inconsistent reporting of cases make effective monitoring difficult when relying on passive case detection alone [2].

Serological surveys can be highly informative when assessing the prevalence of diseases or vaccine coverage within a population [3], since antibodies can be used to detect asymptomatic infection and historical exposure to natural infection or a vaccine [1,4]. As integrated approaches to the management of NTDs are being adopted, multiplex bead assays (MBAs) provide a platform to monitor exposure to multiple pathogens from a single blood sample [5]. MBAs typically measure antibody response in median fluorescence intensity (MFI), which is proportional to the levels of antigen-specific antibodies (most commonly IgG) in the blood [6]. Cross-sectional and longitudinal serological data have been used in various public health settings, including evaluating mass drug administration (MDA) campaigns[7,8], assessing changes in population level exposure [9], monitoring transmission patterns [10,11], assessing the impact of vaccine program coverage [12], and determining prevalence thresholds for confirming disease elimination [7,13].

While serological surveys using MBA provide efficient and cost-effective benefits to integrated pathogen monitoring, a challenge remains in data interpretation. In analysis, the MFI values are often used to estimate the seroprevalence to a particular antigen through calculated or arbitrary cut-off values to define seronegative and seropositive populations. In some cases, higher MFI values are assumed likely to represent more recent or repeated exposure [14,15]. Prior knowledge of specific antibody titres and associated kinetics would be helpful in more accurately interpreting the data. For this review we consider ‘seropositive’ as a general term that could represent either current or previous infection or vaccination, without interpretation pertaining to specific antibody kinetics or longevity.

The use of a binary seropositivity endpoint allows translation of continuous assay-specific MFI values into a common epidemiologic metric: seroprevalence. Different approaches have been used to define a seropositive response, though the rationale or implication of the method choice is rarely made clear. The choice of approaches used are likely the result of a standard laboratory approach, adopting methods applied in previous studies, or simply ease of use. Antibody responses to different pathogens are intrinsically diverse, making it plausible that specific cut-off methods are better suited for specific antigens or situations. Understanding current approaches used for determining seropositivity is a crucial step in developing standardised methods, ensuring appropriate interpretation of the data to support more robust programmatic decision-making. To address this evidence gap, a literature review was performed of existing methods for determining cut-off values for the assessment of seroprevalence for NTDs and VPDs with MBA.

Methods

Review of literature

We conducted a literature review on PubMed for articles published between January 1, 2010 and January 31, 2020. Search terms included “multiplex bead assays” + “serology” + “cut-off”. Studies were excluded if they were not in a published journal (e.g. clinical case reports or conference abstracts), published prior to 2010, did not include serological targets for NTDs, malaria or VPDs, or used serological tools specifically for clinical diagnosis. In total, the initial search identified 308 articles of which 253 articles were not included based on title and abstract. Fifty-five articles met the inclusion criteria for full screening which included serology, NTDs (as defined by the World Health Organization and/or PloS NTD lists)[16], malaria, VPDs in tropical regions, and the use of MBA (Fig 1). Articles were then selected if they described cut-off methods using data based on quantitative antibody levels from MBA platform for the application to seroepidemiology.

Fig 1

Flowchart of article selection for inclusion in literature review.

The PubMed search identified 308 articles with 253 being excluded because they did not meet the inclusion criteria. After review, 55 articles were retained for analysis.

Results

Literature review—Applied methods of determining seropositivity

Eight cut-off approaches were identified based on literature reviewed, with seven methods being applied that provided valid cut-off values (Table 1). A list of all the articles reviewed using each method, antigens within the study, and population origins are listed in S1 Table. Examples of applied methods in different public health settings (Table 1) and the advantages and disadvantages of the different methods (Table 2) are described below.

Table 1

Examples of several applied cut-off approaches using MBAs in various settings for NTDs and VPDs.

MBA used, location, cut-off approach, and the goal of the program are provided in this table to demonstrate the application of cut-off methods in different settings. Where, N refers to the number of studies employing the method and SD refers to standard deviations.

Disease	Location	Additional cut-off details	Goal (study Ref)
Presumed unexposed (N = 23)
Dengue	Haiti	United States/mean +3SD	Disease surveillance [9]
Lymphatic Filariasis	MaliHaiti		Disease recrudescence [17]Disease monitoring after MDA [18]
Trachoma	Haiti		Examining MBA as a monitoring tool [19]
Amoebiasis	Haiti		Disease dissemination [20]
Leishmania	Kenya	Japan/ mean +3SD	Application of multiplex assays [21]
Receiver Operating Curves (N = 15)
Yaws	Ghana	Clinically confirmed negatives and positives.	Evaluate antibody response in MBA [4]
Measles	Kenya	Gold standard lab technique confirmation.	Assess schistosomiasis impact on vaccine preventable diseases [22]
Strongyloidiasis	Cambodia	Presumed unexposed population used for ROC curve	Application of an integrated, multiple disease survey [23]
Mixture Models (N = 14)
Lymphatic Filariasis	Kenya	Mean of negative component + 3SD	Validation of MBA to lymphatic filariasis [21]
Chikungunya	Haiti	Mean of negative component + 2SD	Estimate exposure [24]
International Standards (N = 6)
Tetanus	CambodiaTanzania	>100 MFI units = 0.01 IU/ml = seroprotective	Monitor progress of elimination [12]Assess immunity gaps [25]
Quantile (N = 1)
Influenza	Vietnam	No distinct cut-off, use of antibody titres	Estimating population-level antibodies [26]
Visual Inflection Point (N = 1)
Trachoma	LaosUgandaGambia	Impartial (independent) individuals to determine cut-off	Defining seropositivity thresholds for elimination programs [27]
Pre-exposed Endemic Cohort (N = 1)
GiardiasisCryptosporidiosisAmoebiasisSalmonellosisE. coliNorovirusCholeraCampylobacteriosis	HaitiKenyaTanzania	Longitudinal cohort	Understanding force of transmission among children through seroconversion rates [28]

Table 2

Summary of Advantages and Disadvantages of different seropositivity cut-off methods.

Cut-off Method	Advantages	Disadvantages
Unexposed or presumed unexposed population	- Known seronegative population- Can be used with other classification methods that require a true seronegative population	- Cut-offs may not reflect true immunity of target population, leading to potential misclassification- Requires obtaining a presumed unexposed population- Only appropriate for certain diseases which are absent in the population from where negatives are selected- Potential for cross reactivity
Mixture Model	-Generates cut-off using statistical modelling without external samples needed-Determines an endemic, seronegative population within sample	- May not be appropriate in very high or very low transmission settings- Possibility of an indeterminate range of overlapping seronegative and seropositive individuals
ROC Curve	- Robust cut-off generated from true positives and true negatives	- Often requires “gold standard” confirmation of positive and negatives
International Standards	- Provided by WHO- Universal method of categorizing seropositivity to enable standardization across assays and laboratories	-Fixed cut-off values may not accurately capture differences in natural and vaccinated responses due to its diagnostic purpose.-Not available for many NTDs.
Quantiles	-Visual distribution of MFI intensities and allows for comparison of means	-Choice of which quantiles to use that accurately reflects serostatus must be determined by investigator
Visual Inflection Point	-Simple method	-Arbitrarily decided by investigator-May need a statistical method to confirm- Potential for poor reproducibility across settings
Pre-exposed endemic cohort	-Provides a presumed seronegative population from the population of interest	-Requires longitudinal data and following individuals who were disease free and later developed disease.-Using MFI values of children may not accurately represent MFI values in adults

Presumed unexposed

Cut-off values can be determined by a population that has no expected exposure to the pathogen of interest. Depending on the pathogen, these populations are typically selected based on self-reported claims of no travel or no recent travel history to endemic countries. For NTDs, seronegative populations have been chosen from non-endemic regions, including the United States, Sweden, and Japan [6,21,23,29]. When applying presumed unexposed populations to define cut-offs, a pre-specified number of standard deviations (usually three) above the mean of the MFI values with background subtracted (MFI-bg) in the presumed unexposed population are used. Any result above that MFI-bg value is considered as seropositive, and the number of standard deviations used may depend on stringency of identifying seropositives. Use of presumed unexposed populations to determine cut-off values provides a viable option where a large majority of the study population is exposed due to high transmission. In such settings, an endemic seronegative population, as required by other commonly used approaches described below, may be difficult to identify due to small numbers.

However, there are several potential sources of bias to using a presumed unexposed population to derive seropositivity. Cut-off values from presumed unexposed populations run the risk of bias as the immunological exposure of the populations being compared may not accurately represent the immunological history of the sample population. This could be due to factors such as genetic differences affecting immune responses, age differences between presumed unexposed and study population, nutritional status, and/or co-infections of multiple diseases [30-32]. As a result, cut-off values defined by presumed unexposed populations may be artificially low, leading to inflated prevalence estimates. Moreover, differentiating between active and historical infections may not be captured by an overly sensitive cut-off [21]. Conversely, while presumed unexposed populations by definition have no exposure to the infection being monitored, some individuals may have had unknown contact with the pathogen of interest or cross-reactive pathogens that may generate elevated cut-off values if not excluded.

Receiver operating characteristic (ROC) curve

ROC curves can be used to generate cut-offs by plotting the true positive rate (sensitivity) against the false positive rate (specificity) [33]. The optimal cut-off is considered the value that provides the best discrimination between the true seronegative and seropositive populations, or a cut-off that gives equal weight to sensitivity and specificity [34]. Studies have considered presumed unexposed populations, as defined above, as the true negative population, while true positive populations have been considered as those being either a clinically confirmed case or according to established laboratory gold standards for that pathogen [4,8].

A method with perfect discrimination creates an ideal cut-off between the two populations with no overlap [35], however, it can be rare to observe such separation in the general population (Table 2). Accurate ROC curves to define cut-offs rely on the availability of true negative and true positive reference-populations which are seldom available in practice. Additionally, the reference population used to delineate true positive/negative individuals may also bias results, similar to the disadvantages mentioned for presumed unexposed populations [36].

Finite mixture model

A mixture model is a probabilistic model that assumes the presence of at least two normally distributed subpopulations, or components, within the sample population [37]. These components represent underlying populations of varying antibody responses [21,38,39]. The negative population are assumed those within the lowest distribution of MFI values. The cut-off value can be determined using estimated parameters (i.e. mean and standard deviation) of the lowest component specified by the mixture model [40]. Commonly, the cut-off value is then calculated similar to the presumed unexposed approach, using the mean plus a pre-defined number of standard deviations [21,36,41]. Alternatively, mixture models can use joint probabilities of classifying individuals with specific antibody levels as either seropositive or seronegative to specify appropriate cut-off values [39].

Mixture models can, theoretically, provide cut-off values that more closely resemble the target population immunity with a distribution of MFI values representing seronegative individuals within the target population. This is advantageous because baseline seronegative antibody concentrations have been shown to differ between populations due to transmission history of the pathogen of interest, circulating co-infections, and any population-specific genetic factors [21]. Additionally, multiple component mixture models have the potential to identify exposure history. For example, in a mixture model with three components, the lowest component could be considered as seronegative, the middle component as an “indefinite/borderline” or past exposure history group, and the highest component as seropositive or recent exposure group [21,42]. However, the choice of how many components is also a practical challenge for different pathogens and may rely on understanding antigen-specific immunological response. Mixture models can also be fitted to different distributions depending on the pattern of responses of the pathogen of interest, such as in the case of VPDs and distinguishing between stronger antibody responses in naturally infected compared to vaccinated individuals [43,44].

Mixture models may not be appropriate in areas of high transmission or very low transmission [11]. When only one component is observed (e.g. everyone is exposed or unexposed) or when components have significant overlap (e.g. population with large portion of historical exposure or have received treatment), it becomes difficult to identify a reliable cut-off and classify individuals as seronegative or seropositive based on probabilities [21]. Moreover, choice of distribution for fitting mixture model and resulting cut-offs may be rejected if they do not agree with components upon visual inspection and investigator judgment. Co-circulating pathogens that may result in cross-reactivity of antibody response to the antigens being assayed can also be difficult to separate using mixture models [45].

International standards and units

International Standards or International Reference Materials of the World Health Organization (WHO) are used as a simple method for a uniform classification system. This allows comparison of biological targets, such as vaccine induced antibodies, across populations using pre-set cut-off values [46]. This approach requires standard reagents to generate an assay-specific standard cut-off for each of the different antigenic targets which can then be applied consistently across all settings. The main advantage includes the facilitation of between-setting comparisons. However, occasional pre-set international standard cut-off values have been found to overestimate the size of the seronegative population [47] or to classify individuals to incorrect serostatus groups [48]. This could be related to the fact that international standards are decided a priori and without context to the populations of interest. Therefore, any potential biases when applying the standard due to population specific genetics are not accounted for, unless they were developed using populations from all endemic countries. Moreover, the international standards may have been developed for specific applications, such as providing a clinical endpoint, and may be less suitable in a seroepidemiological context [37]. For example, international standards for rubella have been found occasionally to overlook potential immunity, due to high cut-offs set by manufacturer assays to avoid false negatives [37].

Quantiles

Cut-offs can be determined through rank statistics that partition MFI values into quantiles of equal probabilities. Quantiles have been used outside the context of NTDs, such as understanding viral loads in influenza [26]. Theoretically, higher quantiles could be interpreted as seropositive, while lower quantiles would be interpreted as seronegative. The partitions of quantiles may furthermore represent different levels of seropositivity, such as populations of non-exposure, of historical exposure, repeated exposure resulting in ‘boosting’ of antibodies, or populations of active or recent infections. Quantiles require the analyst to subjectively, or based on biological and/or clinical knowledge, choose the number of quantiles for the analysis and then to specify which quantiles are seropositive or seronegative. Additionally, they may assume homogeneity of exposure in quantile groups that could lead to inaccurate estimations [49].

Visual inflection point (VIP)

A single study looked at using crude cut-offs determined by visually examining inflection points within MFI distributions in graphs. Migchelsen et al., in exploring options for determining trachoma cut-offs, did a convenience sample of impartial individuals to visually inspect data curves to determine an inflection point [27]. The final cut-off was considered to be the average of values reported by the participants. The mean reported cut-off values were similar to cut-offs from the mixtures models as applied to the same dataset [27]. Moreover, the process is more straightforward and intuitive compared to the mixture model approach.

Use of VIP relies on pattern recognition to subjectively generate cut off values, and inflection points may be biased based on groups of individuals asked. In addition, VIP should ideally use impartial participants and mask antigens to reduce bias. Sampling more individuals to determine the inflection point may improve the precision of the estimates of VIP, but recruiting a large number of participants can be time-consuming and challenging in certain situations. With this method there are problems with reproducibility, accuracy is likely associated with the degree of separation between the negative and positive distributions.

Pre-exposed endemic cohort

While serological assays are frequently cross-sectional, longitudinal surveys that have obtained serological data before and after infection can create a cut-off based on the change of MFI values before compared to after exposure. Arnold et al. have explored this cut-off method (termed “presumed unexposed” within their study) for enteric pathogen antibody responses among children from Kenya and Haiti [28]. The resulting cut-offs were comparable to both mixture models and presumed unexposed referent populations, but this method also enabled estimation of cut-offs for particularly high-transmission pathogens where other methods failed. In high transmission settings, fitting mixture models can be challenging in the case where distinct components are not present (see mixture model section above), while cut-offs of presumed unexposed from may not reflect immunological background of study population (see presumed unexposed section). A negative population to use for cut-off determination was generated from MFI values of <1-year-olds who later seroconverted (based on a conservative +2 increase on a log10 scale or a 100-fold increase in MFI). The cut-off was determined by taking the mean of the distribution of measurements before these <1-year-old children seroconverted and then adding three standard deviations.

Identifying a pre-exposed endemic cohort population using measurements from individuals who subsequently seroconvert may be useful for longitudinal studies that have collected data on individuals prior to a point change to seroconversion or infection status. However, using MFI values of unexposed infants may not represent the true seronegative MFI values in the adult population due to inherent differences in the immature and mature immune systems. Maternal antibodies may also be present in infants, leading to potentially higher responses in infants that reflects the exposure history of the mother not of the child. The choice of antibody level increase required to identify “pre-exposed endemic cohort” is a qualitative decision, and so accompanying sensitivity analyses of alternate increases could prove useful [28]. Additionally, longitudinal monitoring may not be logistically feasible for many surveillance programs administering cross-sectional surveys.

Discussion

As programs implement integrated approaches to controlling infectious diseases, effective monitoring is crucial. Serological MBAs provide a convenient method for understanding the population-level burden for multiple diseases simultaneously [50]. This is particularly relevant for those pathogens with long latency periods or with symptoms not sufficiently acute to prompt care-seeking. MBAs can also generate data at a comparatively low cost [1], making it an efficient tool for integrated surveillance of tropical and vaccine preventable diseases. Assessing disease burden through seropositivity is valuable and a more programmatically interpretable metric compared to the continuous MFI values. Additionally, assay and differences in bead coupling concentrations or methods between studies will lead to variability in overall magnitude of antibody levels measured, making the direct comparisons of MFI values almost impossible without appropriate assay standards or a standard metric, such as seropositivity. However, use of seropositivity requires careful consideration of how to define appropriate cut-off values that can meaningfully identify exposed individuals and those with disease burden according to public health programmatic guidelines.

This review highlights several approaches for determining seropositivity cut-offs. The most frequently used approaches were presumed-negative populations, ROC curves, mixture models, and international standards. Other approaches included quantiles, pre-exposed endemic cohort, and visual inflection points. Each method has its respective advantages and disadvantages. For all methods that rely on external samples, such as presumed unexposed population or ROC curve, it is important to acknowledge that antigen-bead coupling efficacy may differ between bead batches and, if not run on the same bead set, potential differences in cut-off values may be observed. In addition, instrumentation differences may impact the stability of the cut-off values. Under these circumstances, additional adjustments to the MFI values maybe required for appropriate comparisons. Additional factors important to consider in identifying the most appropriate method for any given context include: the availability of confirmed seronegative and seropositive populations that are necessary for methods such as ROC and presumed unexposed; use-case scenarios based on program targets or goals; transmission intensity factors that impact the seronegative and seropositive distributions for methods that assume sub-populations; methods previously used in similar settings and diseases; and complexities in certain pathogen-host immunobiology that queries the suitability of strict cut-offs (Box 1).

Box 1: Summary of factors to consider and complexities in choosing cut-off methods

Availability of Seropositive and Seronegative Populations

The availability of expected true seropositive and seronegative populations through screening, clinical confirmation, populations from countries without transmission, or gold standard laboratory techniques justifies the use of presumed unexposed approach, ROC curves, and other supervised classification methods. Additionally, if there are large differences in endemicity within a country, populations from low or non-transmission areas could serve as a seronegative population. Having the presence of seropositive and seronegative populations does not the exclude using other methods, however. Additionally, precision, quality, and interpretation of cut-off values are impacted by a variety factors that should be taken into consideration along with the method of determining cut-offs.

Large sample sizes

As with many statistical methods, larger sample sizes allow for a better estimation of the target population, improving both sensitivity and specificity. Additionally, certain cut-off methods, such as mixture models, can be achieved with larger sample sizes. Smaller sample sizes may require fitting different distributions [56].

Use-case scenarios

Cut off methods can be chosen depending on the goal and design of the study or the program. For example, cut-off methods such as ROC with high sensitivity or specificity may be preferred in the case of assessing program coverage [11, 19]. Cut-off methods such as quantiles or mixture models with several components that can identify multiple levels of seropositivity may be chosen when trying to understand geographic transmission patterns.

Literature past precedent or international guidelines

Decision to use a certain method could be influenced by or borrowed from other studies that focus on biologically similar diseases. This also includes international guidelines that provide cut-offs for vaccine preventable diseases. This consideration offers a simple and convenient rationale to choosing a certain cut-off method given that the cut-off has already been established. Making comparisons between studies using similar antigens may also determine the use of a certain cut-off method.

Transmission Dynamics

The justification of using certain cut-off methods may depend on the level of transmission of the pathogens. Mixture models and quantiles are more appropriate in transmission areas where the seropositive and seronegative components have some separation evident in the MFI distributions.

Complexity of the immunology of host-pathogens interactions

The use of statistical methods is an attempt to reflect a biological process in terms of exposure or lack of exposure. While statistical methods for cut-offs are important in determining seropositivity, weight should also be placed on understanding the complex immunology of the NTD of interest and the immunological background of the population. Incomplete understanding of serologic response and other immune mechanisms against pathogens of interest may impact interpretation of prevalence estimates generated from cut-offs [47]. For example, population level antibodies due to partial or waning immunity could make it difficult to define a strict cut-off value for seropositive and seronegative groups [11]. It may also be unclear whether responses observed during a chronic infection ever revert to a seronegative state [35]. Therefore, using an indeterminate range or comparing the mean MFIs in these circumstances maybe more appropriate than enforcing a strict cut-off value [59, 60].

Antigen and antibody dynamics

Antibody responses are inherently noisy and imposing a strict cut-off may lead to misclassification [61]. Furthermore, antibody longevity may impact seropositivity classification [60, 62]. Coinfections can also be difficult to detect and separate, as certain pathogens with high titres can dominate detection assays [63].

In addition, antibody dynamics in terms of boosting and decay rates post infection should be taken into consideration. For example, as control programs lead to less disease exposure in populations, lower amounts of infection-specific antibodies circulate in the population and are replaced by residual antibody responses [64]. Roscoe et al. noted S. stercoralis antibodies decreased over time but remained above cut-off values a year and a half after successful treatment [65]. When determining prevalence estimates with cut-off values, some of these responses may actually be the result of cleared infections with residual antibodies.

Moreover, the dynamics of antibody-antigen interactions within age groups such as children and adults should be considered when interpreting cut-off values as they have been shown to differ [66]. For instance, cut-off values determined from a population of children may not be appropriate for the entire population age range when assessing prevalence of certain pathogens as children’s immune systems are predominantly short-lived B-cells, while antigen presentation and helper T-cell function are more developed in the immune systems of adults[11, 67, 68]. Lastly, the inherent nature of antibody classes, such as IgG vs IgM, may be interpreted differently regardless of cut-off method [69].

Laboratory technique and design

Although not a focus of this paper, laboratory techniques impact the quality of MFI values. Thus, the generation of good quality cut-off values and resulting prevalence estimates require appropriate assay validations with sufficient quality control protocols [70]. Additionally, cut-off thresholds are dependent on specific coupling conditions [71], and bead consistency is an absolute requirement for the generation of precise cut-off values, regardless of the cut-off determination method.

As more programs implement serological surveillance strategies for neglected tropical disease monitoring, it is possible that new cut-off methods will be developed and applied. Alternatively, other classification methods without a distinct cut-off, such as K-means clustering, aims to separate high dimensional data (i.e. multiple antigenic targets for the same pathogen) into different clusters of MFI values to represent seronegative and seropositive states could be implemented [51,52]. Use of multiple target antigens will increase the likelihood of detecting previous exposure to infection as well as reducing the likelihood of non-reactivity due to sequence variation in single antigenic targets and differential immunogenicity. However, in multi-disease panels, antigens need to be well-defined in order to avoid potential cross-reactivity that could lead to issues of inaccurate or false results due non-specific binding [53]. Furthermore, heterogeneity of individual responses that influence antibody levels apart from pathogen exposure, e.g. nutrition or health conditions, can cause increased immunoglobulin in sera, such as hypergammaglobulinemia [54]. Refining statistical techniques that allow assessment of multiple and/or combinations to generate seroprevalence will also be of benefit and aid in interpretation of data [55,56].

Within our review, there are several limitations. Our search criteria targeted serological cut-offs according to WHO and PLOS definition of NTDs and VPDs, specifically in PubMed and in English. However, there may have other methods to determine serological cut-offs for diseases were not included in this review from other databases and also outside the specific timeframe we examined. Additionally, the search criteria focused only on the term “cut-off”, which may have overlooked similarly terminology, such as “threshold” or “inflection point” that could have provided additional cut-off approaches. Our study also reviewed cut-offs primarily from MBA and enzyme-linked immunosorbent assay (ELISA) platforms due to our search criteria and did not include other serological or commercial immunoassays that may have used other approaches. However, any additional methods that we could have identified are unlikely to change the conclusions of this work.

International standards based on a large sample of reference standard sera from individuals in known elimination settings will be needed to define universal cut-offs and make program decisions based on specific levels of seropositivity. This would require procuring sera from clinically confirmed individuals with infection and those without infection from a geographically representative number of endemic countries to ensure sufficient diversity of immunological responses, as were recently done for human African trypanosomiasis [57, 58]. Sera from these candidates would then be characterized by different immunological tools to determine consistent measurements of immunological activity (with context to programmatic use) across all platforms in the form of international units. In the absence of these metrics for NTDS, ROC curves with confirmed positives and negatives from the study population are recommended as they would likely generate the most representative cut-offs that consider immunological and genetic backgrounds of the population. Without control sera mixture models are recommended as they may provide statistically robust cut-offs when adjusting for transmission intensity by using appropriate distributions and number of components to identify seropositives. In the context of integrated disease surveillance, the recommendation for an appropriate cut-off method to determine seroprevalence should additionally consider the antigen being assessed, the optimal data that is the closest reflection of true population prevalence, and other important factors and complexities that could impact decision of cut-off method listed in Box 1.

Disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Use of trade names is for identification only and does not imply endorsement by the Public Health Service or by the U.S. Department of Health and Human Services.

List of Articles Reviewed.

(DOCX)

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7 Dec 2020

Dear Mr Chan,

Thank you very much for submitting your manuscript "Determining Seropositivity - A Review of Approaches to Define Seroprevalence when using Multiplex Bead Assays to Assess Burden of Tropical Diseases" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

Two reviewers have suggested that this should be split into two separate papers due to the disparate topics addressed (a systematic review of cut-off determination methods, and a comparison of different methods used in Haiti and Malaysia).

I suggest that, once the issues identified by the reviewers in both sections have been very thoroughly addressed, the authors may re-submit the systematic review of cut-off determination methods under this manuscript number, while a separate submission should be created for the Haiti and Malaysia data.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

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Associate Editor

PLOS Neglected Tropical Diseases

Francesca Tamarozzi

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Two reviewers have suggested that this should be split into two separate papers due to the disparate topics addressed (a systematic review of cut-off determination methods, and a comparison of different methods used in Haiti and Malaysia).

I suggest that, once the issues identified by the reviewers in both sections have been very thoroughly addressed, the authors may re-submit the systematic review of cut-off determination methods under this manuscript number, while a separate submission should be created for the Haiti and Malaysia data.

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: The paper comprises two separate parts -a systematic review of cut-off detemination methods, and a comparison of different methods used in Haiti and Malaysia. The two parts do not depend on one another or hang together particularly well.

Please consider separating this into two papers and then each part could be explored more fully.

The description of the search is confusing - how many more were found from the period between Oct 2018 and Jan 2020 and how many were from ref lists of first included studies? How many studies were in each method/category?

Supp Table 1 is just a list of papers and Table 1 is very minimal - not clear how many did each method. Some locations that are in papers cited in Supp Table 1 do not appear in Table 1 (papers by Won in American Samoa and Gambia, for example). There may be others.

What cutoffs did the authors find in each paper with each antigen and method? DId it vary by transmission setting, technical factors or other?

I am not sure what is gained from the papers to generate Table 2. It's a useful summary of potential biases, but could likely have been written without reading any of them.

Reviewer #2: 1.While the authors reference earlier publications, additional brief summaries to describe the assays used including source of antigens and the performance characteristics would be beneficial to the reader when interpreting the results.

2. Suggest clarifying if the case study results were based on collection of new laboratory data for the sample sets or re-analysis of previously collected serology data for these targets.

Reviewer #3: See attached file

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: There are some serious limitations in presentation. The important new results of the paper are in Table 3 and Fig 2. The title of Table 3 is rather obscure, but seems to be comparing 3 methods used in the case studies.

The antibodies used for LF are Wb123 and Bm14 (not Bm14 and Bm33 as given in Abstract where the results appear to refer to Bm14, but numbers are slightly different and some are from each site). For Strongyloides also, abstract is reporting only one site.

I think in Abstract it would be better to present general relationships between the methods, not just pick a few to report, or mention similarities/differences between the methods/sites. If some are more important and are highlighted, then clearly state which Ag and country being mentioned.

In Fig 2, axis is stated to be MFI-bg, but isn't bg the background that should be subtracted?

Supplemental Table 1 is in the main text and out of order with Table 4 which I find easier to understand.

Language not clear sometimes:

line 382 "Prevalence estimates between non-exposed populations... "

I think you mean "Prevalence estimates derived USING THE DIFFERENT CUT-OFF METHODS OF non-exposed populations,... "

Similar issues elsewhere e.g. line 298 where you refer to the method rather than the cutoff derived from a method.

Suggestion - call them methods A, B, C, or NOP, MM, Qtile and refer to them consistently that way e.g. the NOP method-derived cutoff or perhaps just NOP-cutoff.. Please check whole paper for this issue.

What is meant by lines 386 to 387.. "the choice of cut-off method ... by district were generally consistent". Consistent with what? How are you making the choice?

Table 4 legend: I dont think these are rankings? line 400

If the point is that they rank in same order, maybe colour in the table cells, or a chart would help, as well as putting Supp Table 1 after Table 4. Right now it is hard to see the points made.

Figure 3 is interesting and makes me want to know more about clustering, but the Fig is not very good quality. I cannot see the red mentioned in the legend. What do we infer from this? either method is OK?

In general I did not perceive the results of this work clearly. What method should we be using? Or at least - which method gives consistently high cutoffs and which ones low? The Discussions states some fairly obvious points - e.g. 440-441, lines 458 to 463 that could have been written before this paper and perhaps belong in introduction. We do not need this paper to tell us that cutoffs are important. How does this paper take us further? While the caveats in lines 445 to 447 are true, this is always the case. We should be able to conclude something programmatically (even if provisionally) from the case studies at least, otherwise what is the point of the study? I would have welcomed discussion of the need for site-specific cutoffs which is new information shown in Fig 2 but dismissed as technical issues in the Fig legend. Why not transmssion issues?

For deciding priorities for doing MDA - which cutoff method (or doesn't it matter)?

For investigating clustering - which cutoff method (or doesn't it matter)?

Box 1 is very long and diverts into a lot of side topics. It may be useful but doesn't seem to arise from the data in this paper. Again could be valuable in separate review paper, but is strangely ordered after the case studies.

Reviewer #2: (No Response)

Reviewer #3: See attached file

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: The conclusion needs to be rewritten. Currently it is general, states the obvious and could have been written before the study was done. It does not draw any conclusion from the results, such as how many methods were found, or that there were site-specific differences even with the same method. No clear recommendation is made.

Reviewer #2: (No Response)

Reviewer #3: See attached file

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Don't be afraid to draw preliminary conclusions from the findings and make provisional recommendations.

Make sure to label the antibody tests correctly and describe the different methods and cutoffs in a clear, consistent and simple way.

Abstract is cherry picking some results right now. Be clear which ones are being highlighted or synthesize better.

Title - I think it should specify Vaccine Preventable and Neglected Tropical Diseases. 'Tropical Diseases' is too broad. I was expecting malaria to be included.

Minor point: inappropriate use of the word 'confounders' line 195 . It has a specific meaning in epidemiology. I think here it should be 'potential sources of bias' not confounding.

Reviewer #2: 1. A key concern is the cohesion in the paper between the review and the case study. A recommended solution would to consider segregating this into two separate publications or revise the case study with more focus on a couple of diseases as a an example.

2. It is unclear why the authors are emphasizing multiplex bead-based serology assays as a theme of the paper. The initial landscape section regarding analysis approaches could be adapted and would likely be applicable to methods used for defining cut-offs for quantitative serology assays regardless of platform. While there are certain characteristics that are specific to bead-based serology assays (such as read-out using MFI), wouldn't the issue of using different methods for defining cut-offs also be applicable to other quantitative serology platforms some of which have also been used to develop multiplex serology assays. Broadening the focus would likely add value as a more comprehensive review article with broader interest to people conducting seroepidemiology studies.

3. As the article is currently written, I am not really clear on the why they are emphasizing the multiplex component in both the title and the paper. The results and the discussions in the case study focus on the impact of applying these different methods defining cut-offs for individual serologic assays. Perhaps this could be clarified through revision of the case study to focus on 1-2 examples rather focusing 4 diseases.

Reviewer #3: See attached file

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: Overall there is useful information in this paper that deserves to be seen, especially from the case studies. But the two parts do not hang together and more could be made of each.

In the systematic review part, please give more details rather than just a list of who did what method. Make sure list is comprehensive. What cutoffs did they each find? What were the background transmission situations.? What stage was the programme at? Are these antigens/antibodies currently used in decision making? Should they be, now that MBA assays are available? Also, how about comparing with or at least mentioning studies that used mixture models with conventional ELISAs e.g (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6473238/)

In the case study part, it is more a question of interpretation. Results are presented but then seem to be dismissed as not applicable or too biased. Perhaps suggest what size of sample needed to use mixture models or other methods. Investigate site-specific cutoff differences further. Name some situations where one or other methods could or should be used, and what consequences might be for under or overestimating the prevalence.

Reviewer #2: The topic of the publication is an important and relevant one given the increasing interest and potential value of using serology and integrated disease surveillance approaches to provide more cost-effective and operationally feasible solutions to conduct population surveys to inform decisions for NTDs and other disease programs. Suggest minor revision with particularly focus on the presentation of the case study.

Reviewer #3: See attached file

--------------------

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Submitted filename: Chen et al 2020 - MN final.docx

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26 Feb 2021

Submitted filename: ChanYYMBASeroReviewerComments.docx

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6 Apr 2021

Dear Mr Chan,

Thank you very much for submitting your manuscript "Determining Seropositivity - A Review of Approaches to Define Population Seroprevalence when using Multiplex Bead Assays to Assess Burden of Several Vaccine Preventable and Neglected Tropical Diseases" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

The revised version of this manuscript is much improved, but the reviewers have still identified some areas requiring attention, and a minor revision to address these reviewer comment is indicated.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

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Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

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Richard Stewart Bradbury, PhD

Associate Editor

PLOS Neglected Tropical Diseases

Francesca Tamarozzi

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

The revised version of this manuscript is much improved, but the reviewers have still identified some areas requiring attention, and a minor revision to address these reviewer comment is indicated.

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: This is a literature review. The objectives and the search strategy are clearly described now. No statistical analysis is done and no ethical clearance necessary.

Reviewer #3: Line 108-110: the authors said that "Fifty five articles met the inclusion criteria for full

screening which included serology, NTDs (as defined by the World Health Organization)(15), malaria,

VPDs in tropical regions, and the use of MBA (Figure 1). However, they mentioned others pathogens not listed in WHO list. For TABLE 1: The authors have to choose between the NTDs recognized by WHO and NTDs recognized by PLoS NTD (see Hotez et al. PLoS NTD 2020 Jan 30;14(1):e0008001.

E.g Giardia, Entamoeba, ..etc

Reviewer #4: The authors have adequately addressed most of the previous reviewers questions

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: The tables in main document are fine. Table 1 should be placed earlier when it is first mentioned Page 6. The different cutoff determination methods are more clearly described.

Supplementary Table 1 - the cutoff determination methods are not in same order as in the text, and many papers are repeated. I believe Supplementary Table 1 would be much more useful if revised as follows:

List each paper only once and put it in short form e.g. (ref no) Arnold et al 2019. Provide additional columns at right to show which methods the paper uses (Presumed negative, ROC etc) with tick or Y in the column if they used relevant method. It will then be easier to see which papers used which methods, and which tested multiple methods. Most (all?) papers are referenced in main text or can be added so you can use the same number as reference. Or have a list of refs to supp material.

Then you can sum the number of papers using each method in last row.

The Finite Mixture model method is described incorrectly. Lines 168 to 171. It does not use mean and distribution of the 'negative population' since the composition of the negative population is not known. It postulates two or more distinct sub-populations (components) and assesses when probability of a data point/sample being in one or the other is >50% (or other defined value). The text in those lines seems to refer to the Presumed negative population method. Same problem in Table 1 for this method. It is not Mean + 3 SD. The component populations may overlap.

Reviewer #3: Line: 182-184: I could not understand how would this model be able to differentiate between the naturally infected and the vaccinated individuals, is based on the cut-offs as discussed before or comparing to the nature of responses seen during the studies. Because the responses of the population might change due to various factors involved

TABLE 2. should be revised and linked with the references used in the text describing each cut-off method. Otherwise it is very difficult to follow. For example, for the International Standards, the reference # 32 is cited in the disadvantages but not cited in the text.

Reviewer #4: The presnetationof data is appropriate for the goals of analysis

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Conclusions are reasonable. Please explain 'past precedents'. It is not clear to me.

There is a paragraph on limitations but may have missed a few - e.g. only papers in PubMed, only in English? Time boundaries?

Reviewer #3: DISCUSSION:

Line 382: Would you expect any changes that would take place in the cut-offs for the various detection of parasites when they are couped together?

Reviewer #4: The conclusions are supported by the data presented and the authors have acknowledged the limitations of their study design

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: 1. Apparently malaria IS included (and is not in the categories of Vaccine Preventable (not quite yet) or NTDs. So perhaps revernt to using Tropical Diseases or alternatively just just delete the part of title after Mulitplex Bead Assays

2. Explain past precedents line 36 (what was done before?)

3. line 78 - can you give a citation for use in evaluating MDA programs?

4. Table 1 for VIP-Trachoma - what do you mean by Non-impartial? Partial?

5. Line 238. Maybe a word missing like 'and' before accuracy?

6. Lines 245-247. WHy would this method allow estimation of cutoffs for particularly high transmission pathogens where other methods failed? Can you elaborate and/or give an example?

7. Lines 255 to 256. Wouldn't it be maternal immunity (transfer of antibody from mother to child) that is also a reason not to use infants as 'unexposed' individuals?

8. Discussion Para 2 is very long and covers several topics. Please split into separate paragraphs. E.g. line 286 and 292

9. Lines 323 - not clear why monoclonal antibodies are mentioned. To test for true positives and negs?

10. In Box 1. Last para but one. Predominantly not predominately

11. Ref 64 word missing in title after Their.

Reviewer #3: ABSTRACT:

Line 29: neglected tropical diseases or vaccine preventable diseases should be abbreviated, NTD and VPD, respectively.

INTRODUCTION:

Line 71-73: Some individuals might receive treatment for a specific disease and be still antibody positive for a while. How to distinguish dissociate this category and the asymptomatic group. In addition, since most of these NTD are present in developing and Lower-middle-income Countries a non-negligible of people self-medicate themselves.

Line 87-89: So when it is said that all these factors are combined and put under the umbrella of “seropositive”, but they also differ on the basis of the ½ life of antibodies, a little more explanation on how they would be affected would make his more understandable as it seems like a general statement. In addition, it is also good to mention how cross-reactivity would play a significant role when dressing these responses.

RESULTS:

Line 136: "Any result above that MFI-bg value is considered as seropositive" should read ....above that MFI-bg plus 3 times the value of the standard deviation is considered....

Line 148-150: When dealing with cross reactivity, the authors should elaborate and take in account people displaying polyclonal hypergammaglobulinaemia.

REFERENCES:

Line 381 and 384: be consistent.

Line 433: is missing Journal name and volume.

Line 515: Reference of a book Chapter, not complete.

Reviewer #4: No significant modifications recommended

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: Overall this manuscipt is much improved and a useful summary of the possible methods for establishing cutoffs when using multiplex assays.

Reviewer #3: Please see comments above

Reviewer #4: The revised manuscript has largely addressed the points raised by the reviewers, summarises the methodologies applicable to cutoff determination in serological testing and adds insight to the debate on optimal selection of cutoffs for serological bead array-based assays. A missing element in the conclusions are the authors own recommendations for the appropriate methodology in specific settings Box 1 does not add significantly to, or with any more clarity, what is in the results and discussion and appears to be superfluous.

--------------------

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Reviewer #1: No

Reviewer #3: No

Reviewer #4: No

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References

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article's retracted status in the References list and also include a citation and full reference for the retraction notice.

7 May 2021

Submitted filename: ChanY_ReviewerCommentsandResponses.docx

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10 May 2021

Dear Mr Chan,

We are pleased to inform you that your manuscript 'Determining Seropositivity - A Review of Approaches to Define Population Seroprevalence when using Multiplex Bead Assays to Assess Burden of Tropical Diseases' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

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Richard Stewart Bradbury, PhD

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Francesca Tamarozzi

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

23 Jun 2021

Dear Mr Chan,

We are delighted to inform you that your manuscript, "Determining Seropositivity - A Review of Approaches to Define Population Seroprevalence when using Multiplex Bead Assays to Assess Burden of Tropical Diseases," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

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PLOS Neglected Tropical Diseases