A mathematical model of seropositivity to malaria antigen, allowing seropositivity to be prolonged by exposure

Surveillance of Plasmodium vivax transmission using serological models in the border areas of China-Myanmar

Background

To understand the Plasmodium vivax malaria transmission intensity and to assess the effectiveness of prevention and control measures taken along the China–Myanmar border, a catalytic model was used to calculate the seroconversion rate, an important indicator of malaria transmission intensity with high sensitivity, which is particularly useful in areas of low transmission.

Methods

Five counties in Yunnan Province bordering Myanmar were selected as survey sites, and subjects were obtained in each county by stratified random sampling in 2013–2014. Fingerstick blood was collected from each subject and tested for antibodies to P. vivax Merozoite Surface Protein 1-19 (PvMSP1-19) using indirect ELISA. A catalytic conversion model was used to assess the transmission intensity of P. vivax malaria based on the maximum likelihood of generating a community seroconversion rate.

Results

A total of 3064 valid blood samples were collected. Antibody levels were positively correlated with age. The seroconversion rate (SCR) values for each village were Luoping (0.0054), Jingqiao (0.0061), Longpen (0.0087), Eluo (0.0079), Banwang (0.0042) and Banbie (0.0046), respectively.

Conclusion

Overall, the intensity of P. vivax malaria transmission in the border areas of Yunnan Province is low and not entirely consistent across counties. Consecutive serological surveys are needed to provide a sensitive evaluation of transmission dynamics and can help to confirm areas where infection is no longer present.

A threshold-free approach with age-dependency for estimating malaria seroprevalence

Background

In malaria serology analysis, the standard approach to obtain seroprevalence, i.e the proportion of seropositive individuals in a population, is based on a threshold which is used to classify individuals as seropositive or seronegative. The choice of this threshold is often arbitrary and is based on methods that ignore the age-dependency of the antibody distribution.

Methods

Using cross-sectional antibody data from the Western Kenyan Highlands, this paper introduces a novel approach that has three main advantages over the current threshold-based approach: it avoids the use of thresholds; it accounts for the age dependency of malaria antibodies; and it allows us to propagate the uncertainty from the classification of individuals into seropositive and seronegative when estimating seroprevalence. The reversible catalytic model is used as an example for illustrating how to propagate this uncertainty into the parameter estimates of the model.

Results

This paper finds that accounting for age-dependency leads to a better fit to the data than the standard approach which uses a single threshold across all ages. Additionally, the paper also finds that the proposed threshold-free approach is more robust against the selection of different age-groups when estimating seroprevalence.

Conclusion

The novel threshold-free approach presented in this paper provides a statistically principled and more objective approach to estimating malaria seroprevalence. The introduced statistical framework also provides a means to compare results across studies which may use different age ranges for the estimation of seroprevalence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-04022-4.

A unified and flexible modelling framework for the analysis of malaria serology data

Serology data are an increasingly important tool in malaria surveillance, especially in low transmission settings where the estimation of parasite-based indicators is often problematic. Existing methods rely on the use of thresholds to identify seropositive individuals and estimate transmission intensity, while making assumptions about the temporal dynamics of malaria transmission that are rarely questioned. Here, we present a novel threshold-free approach for the analysis of malaria serology data which avoids dichotomization of continuous antibody measurements and allows us to model changes in the antibody distribution across age in a more flexible way. The proposed unified mechanistic model combines the properties of reversible catalytic and antibody acquisition models, and allows for temporally varying boosting and seroconversion rates. Additionally, as an alternative to the unified mechanistic model, we also propose an empirical approach to analysis where modelling of the age-dependency is informed by the data rather than biological assumptions. Using serology data from Western Kenya, we demonstrate both the usefulness and limitations of the novel modelling framework.

Selection of Antibody Responses Associated With Plasmodium falciparum Infections in the Context of Malaria Elimination

In our aim to eliminate malaria, more sensitive tools to detect residual transmission are quickly becoming essential. Antimalarial antibody responses persist in the blood after a malaria infection and provide a wider window to detect exposure to infection compared to parasite detection metrics. Here, we aimed to select antibody responses associated with recent and cumulative exposure to malaria using cross-sectional survey data from Haiti, an elimination setting. Using a multiplex bead assay, we generated data for antibody responses (immunoglobulin G) to 23 Plasmodium falciparum targets in 29,481 participants across three surveys. This included one community-based survey in which participants were enrolled during household visits and two sentinel group surveys in which participants were enrolled at schools and health facilities. First, we correlated continuous antibody responses with age (Spearman) to determine which showed strong age-related associations indicating accumulation over time with limited loss. AMA-1 and MSP-119 antibody levels showed the strongest correlation with age (0.47 and 0.43, p < 0.001) in the community-based survey, which was most representative of the underlying age structure of the population, thus seropositivity to either of these antibodies was considered representative of cumulative exposure to malaria. Next, in the absence of a gold standard for recent exposure, we included antibody responses to the remaining targets to predict highly sensitive rapid diagnostic test (hsRDT) status using receiver operating characteristic curves. For this, only data from the survey with the highest hsRDT prevalence was used (7.2%; 348/4,849). The performance of the top two antigens in the training dataset (two-thirds of the dataset; n = 3,204)-Etramp 5 ag 1 and GLURP-R0 (area-under-the-curve, AUC, 0.892 and 0.825, respectively)-was confirmed in the test dataset (remaining one-third of the dataset; n = 1,652, AUC 0.903 and 0.848, respectively). As no further improvement was seen by combining seropositivity to GLURP-R0 and Etramp 5 ag 1 (p = 0.266), seropositivity to Etramp 5 ag 1 alone was selected as representative of current or recent exposure to malaria. The validation of antibody responses associated with these exposure histories simplifies analyses and interpretation of antibody data and facilitates the application of results to evaluate programs.

A longitudinal cohort study of malaria exposure and changing serostatus in a malaria endemic area of rural Tanzania

Background

Measurements of anti-malarial antibodies are increasingly used as a proxy of transmission intensity. Most serological surveys are based on the use of cross-sectional data that, when age-stratified, approximates historical patterns of transmission within a population. Comparatively few studies leverage longitudinal data to explicitly relate individual infection events with subsequent antibody responses.

Methods

The occurrence of seroconversion and seroreversion events for two Plasmodium falciparum asexual stage antigens (MSP-1 and AMA-1) was examined using three annual measurements of 691 individuals from a cohort of individuals in a malaria-endemic area of rural east-central Tanzania. Mixed-effect logistic regression models were employed to determine factors associated with changes in serostatus over time.

Results

While the expected population-level relationship between seroprevalence and disease incidence was observed, on an individual level the relationship between individual infections and the antibody response was complex. MSP-1 antibody responses were more dynamic in response to the occurrence and resolution of infection events than AMA-1, while the latter was more correlated with consecutive infections. The MSP-1 antibody response to an observed infection seemed to decay faster over time than the corresponding AMA-1 response. Surprisingly, there was no evidence of an age effect on the occurrence of a conversion or reversion event.

Conclusions

While the population-level results concur with previously published sero-epidemiological surveys, the individual-level results highlight the more complex relationship between detected infections and antibody dynamics than can be analysed using cross-sectional data. The longitudinal analysis of serological data may provide a powerful tool for teasing apart the complex relationship between infection events and the corresponding immune response, thereby improving the ability to rapidly assess the success or failure of malaria control programmes.

Seroprevalence of Antibodies against Plasmodium falciparum Sporozoite Antigens as Predictive Disease Transmission Markers in an Area of Ghana with Seasonal Malaria Transmission

Introduction

As an increasing number of malaria-endemic countries approach the disease elimination phase, sustenance of control efforts and effective monitoring are necessary to ensure success. Mathematical models that estimate anti-parasite antibody seroconversion rates are gaining relevance as more sensitive transmission intensity estimation tools. Models however estimate yearly seroconversion and seroreversion rates and usually predict long term changes in transmission, occurring years before the time of sampling. Another challenge is the identification of appropriate antigen targets since specific antibody levels must directly reflect changes in transmission patterns. We therefore investigated the potential of antibodies to sporozoite and blood stage antigens for detecting short term differences in malaria transmission in two communities in Northern Ghana with marked, seasonal transmission.

Methods

Cross-sectional surveys were conducted during the rainy and dry seasons in two communities, one in close proximity to an irrigation dam and the other at least 20 Km away from the dam. Antibodies against the sporozoite-specific antigens circumsporozoite protein (CSP) and Cell traversal for ookinetes and sporozoites (CelTOS) and the classical blood stage antigen apical membrane antigen 1 (AMA1) were measured by indirect ELISA. Antibody levels and seroprevalence were compared between surveys and between study communities. Antibody seroprevalence data were fitted to a modified reversible catalytic model to estimate the seroconversion and seroreversion rates.

Results

Changes in sporozoite-specific antibody levels and seroprevalence directly reflected differences in parasite prevalence between the rainy and dry seasons and hence the extent of malaria transmission. Seroconversion rate estimates from modelled seroprevalence data did not however support the above observation.

Conclusions

The data confirms the potential utility of sporozoite-specific antigens as useful markers for monitoring short term/seasonal changes in malaria transmission. It may however be essential to update models to allow for assessment of seasonal changes in malaria transmission, which usually occur within four to six months.

Sample size and power calculations for detecting changes in malaria transmission using antibody seroconversion rate

BACKGROUND

Several studies have highlighted the use of serological data in detecting a reduction in malaria transmission intensity. These studies have typically used serology as an adjunct measure and no formal examination of sample size calculations for this approach has been conducted.

METHODS

A sample size calculator is proposed for cross-sectional surveys using data simulation from a reverse catalytic model assuming a reduction in seroconversion rate (SCR) at a given change point before sampling. This calculator is based on logistic approximations for the underlying power curves to detect a reduction in SCR in relation to the hypothesis of a stable SCR for the same data. Sample sizes are illustrated for a hypothetical cross-sectional survey from an African population assuming a known or unknown change point.

RESULTS

Overall, data simulation demonstrates that power is strongly affected by assuming a known or unknown change point. Small sample sizes are sufficient to detect strong reductions in SCR, but invariantly lead to poor precision of estimates for current SCR. In this situation, sample size is better determined by controlling the precision of SCR estimates. Conversely larger sample sizes are required for detecting more subtle reductions in malaria transmission but those invariantly increase precision whilst reducing putative estimation bias.

CONCLUSIONS

The proposed sample size calculator, although based on data simulation, shows promise of being easily applicable to a range of populations and survey types. Since the change point is a major source of uncertainty, obtaining or assuming prior information about this parameter might reduce both the sample size and the chance of generating biased SCR estimates.

Current Mathematical Models for Analyzing Anti-Malarial Antibody Data with an Eye to Malaria Elimination and Eradication

The last decade has witnessed a steady reduction of the malaria burden worldwide. With various countries targeting disease elimination in the near future, the popular parasite infection or entomological inoculation rates are becoming less and less informative of the underlying malaria burden due to a reduced number of infected individuals or mosquitoes at the time of sampling. To overcome such problem, alternative measures based on antibodies against specific malaria antigens have gained recent interest in malaria epidemiology due to the possibility of estimating past disease exposure in absence of infected individuals. This paper aims then to review current mathematical models and corresponding statistical approaches used in antibody data analysis. The application of these models is illustrated with three data sets from Equatorial Guinea, Brazilian Amazonia region, and western Kenyan highlands. A brief discussion is also carried out on the future challenges of using these models in the context of malaria elimination.

Serology describes a profile of declining malaria transmission in Farafenni, The Gambia

Background

Malaria morbidity and mortality has declined in recent years in a number of settings. The ability to describe changes in malaria transmission associated with these declines is important in terms of assessing the potential effects of control interventions, and for monitoring and evaluation purposes.

Methods

Data from five cross-sectional surveys conducted in Farafenni and surrounding villages on the north bank of River Gambia between 1988 and 2011 were compiled. Antibody responses to MSP-1₁₉ were measured in samples from all surveys, data were normalized and expressed as seroprevalence and seroconversion rates (SCR) using different mathematical models.

Results

Results showed declines in serological metrics with seroprevalence in children aged one to 5 years dropping from 19 % (95 % CI 15–23 %) in 1988 to 1 % (0–2 %) in 2011 (p value for trend in proportions < 0.001) and the SCR dropping from 0.069 year⁻¹ (0.059–0.080) to 0.022 year⁻¹ (0.017–0.028; p = 0.004). The serological data were consistent with previously described drops in both parasite prevalence in children aged 1–5 years (62 %, 57–66 %, in 1988 to 2 %, 0–4 %, in 2011; p < 0.001), and all-cause under five mortality rates (37 per 1000 person-years, 34–41, in 1990 to 17, 15–19, in 2006; p = 0.059).

Conclusions

This analysis shows accurate reconstruction of historical malaria transmission patterns in the Farafenni area using anti-malarial antibody responses. Demonstrating congruence between serological measures, and conventional clinical and parasitological measures suggests broader utility for serology in monitoring and evaluation of malaria transmission.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0939-1) contains supplementary material, which is available to authorized users.

Biosignatures of Exposure/Transmission and Immunity

A blood test that captures cumulative exposure over time and assesses levels of naturally acquired immunity (NAI) would provide a critical tool to monitor the impact of interventions to reduce malaria transmission and broaden our understanding of how NAI develops around the world as a function of age and exposure. This article describes a collaborative effort in multiple International Centers of Excellence in Malaria Research (ICEMRs) to develop such tests using malaria-specific antibody responses as biosignatures of transmission and immunity. The focus is on the use of Plasmodium falciparum and Plasmodium vivax protein microarrays to identify a panel of the most informative antibody responses in diverse malaria-endemic settings representing an unparalleled spectrum of malaria transmission and malaria species mixes before and after interventions to reduce malaria transmission.

Sample size determination for estimating antibody seroconversion rate under stable malaria transmission intensity

Background

In the last decade, several epidemiological studies have demonstrated the potential of using seroprevalence (SP) and seroconversion rate (SCR) as informative indicators of malaria burden in low transmission settings or in populations on the cusp of elimination. However, most of studies are designed to control ensuing statistical inference over parasite rates and not on these alternative malaria burden measures. SP is in essence a proportion and, thus, many methods exist for the respective sample size determination. In contrast, designing a study where SCR is the primary endpoint, is not an easy task because precision and statistical power are affected by the age distribution of a given population.

Methods

Two sample size calculators for SCR estimation are proposed. The first one consists of transforming the confidence interval for SP into the corresponding one for SCR given a known seroreversion rate (SRR). The second calculator extends the previous one to the most common situation where SRR is unknown. In this situation, data simulation was used together with linear regression in order to study the expected relationship between sample size and precision.

Results

The performance of the first sample size calculator was studied in terms of the coverage of the confidence intervals for SCR. The results pointed out to eventual problems of under or over coverage for sample sizes ≤250 in very low and high malaria transmission settings (SCR ≤ 0.0036 and SCR ≥ 0.29, respectively). The correct coverage was obtained for the remaining transmission intensities with sample sizes ≥ 50. Sample size determination was then carried out for cross-sectional surveys using realistic SCRs from past sero-epidemiological studies and typical age distributions from African and non-African populations. For SCR < 0.058, African studies require a larger sample size than their non-African counterparts in order to obtain the same precision. The opposite happens for the remaining transmission intensities. With respect to the second sample size calculator, simulation unravelled the likelihood of not having enough information to estimate SRR in low transmission settings (SCR ≤ 0.0108). In that case, the respective estimates tend to underestimate the true SCR. This problem is minimized by sample sizes of no less than 500 individuals. The sample sizes determined by this second method highlighted the prior expectation that, when SRR is not known, sample sizes are increased in relation to the situation of a known SRR. In contrast to the first sample size calculation, African studies would now require lesser individuals than their counterparts conducted elsewhere, irrespective of the transmission intensity.

Conclusions

Although the proposed sample size calculators can be instrumental to design future cross-sectional surveys, the choice of a particular sample size must be seen as a much broader exercise that involves weighting statistical precision with ethical issues, available human and economic resources, and possible time constraints. Moreover, if the sample size determination is carried out on varying transmission intensities, as done here, the respective sample sizes can also be used in studies comparing sites with different malaria transmission intensities. In conclusion, the proposed sample size calculators are a step towards the design of better sero-epidemiological studies. Their basic ideas show promise to be applied to the planning of alternative sampling schemes that may target or oversample specific age groups.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0661-z) contains supplementary material, which is available to authorized users.

Serological markers for monitoring historical changes in malaria transmission intensity in a highly endemic region of Western Kenya, 1994-2009

Background

Monitoring local malaria transmission intensity is essential for planning evidence-based control strategies and evaluating their impact over time. Anti-malarial antibodies provide information on cumulative exposure and have proven useful, in areas where transmission has dropped to low sustained levels, for retrospectively reconstructing the timing and magnitude of transmission reduction. It is unclear whether serological markers are also informative in high transmission settings, where interventions may reduce transmission, but to a level where considerable exposure continues.

Methods

This study was conducted through ongoing KEMRI and CDC collaboration. Asembo, in Western Kenya, is an area where intense malaria transmission was drastically reduced during a 1997–1999 community-randomized, controlled insecticide-treated net (ITN) trial. Two approaches were taken to reconstruct malaria transmission history during the period from 1994 to 2009. First, point measurements were calculated for seroprevalence, mean antibody titre, and seroconversion rate (SCR) against three Plasmodium falciparum antigens (AMA-1, MSP-1₁₉, and CSP) at five time points for comparison against traditional malaria indices (parasite prevalence and entomological inoculation rate). Second, within individual post-ITN years, age-stratified seroprevalence data were analysed retrospectively for an abrupt drop in SCR by fitting alternative reversible catalytic conversion models that allowed for change in SCR.

Results

Generally, point measurements of seroprevalence, antibody titres and SCR produced consistent patterns indicating that a gradual but substantial drop in malaria transmission (46-70%) occurred from 1994 to 2007, followed by a marginal increase beginning in 2008 or 2009. In particular, proportionate changes in seroprevalence and SCR point estimates (relative to 1994 baseline values) for AMA-1 and CSP, but not MSP-1₁₉, correlated closely with trends in parasite prevalence throughout the entire 15-year study period. However, retrospective analyses using datasets from 2007, 2008 and 2009 failed to detect any abrupt drop in transmission coinciding with the timing of the 1997–1999 ITN trial.

Conclusions

In this highly endemic area, serological markers were useful for generating accurate point estimates of malaria transmission intensity, but not for retrospective analysis of historical changes. Further investigation, including exploration of different malaria antigens and/or alternative models of population seroconversion, may yield serological tools that are more informative in high transmission settings.

Electronic supplementary material

The online version of this article (doi:10.1186/1475-2875-13-451) contains supplementary material, which is available to authorized users.