Estimating malaria transmission intensity from Plasmodium falciparum serological data using antibody density models

Abstract

BackgroundSerological data are increasingly being used to monitor malaria transmission intensity and have been demonstrated to be particularly useful in areas of low transmission where traditional measures such as EIR and parasite prevalence are limited. The seroconversion rate (SCR) is usually estimated using catalytic models in which the measured antibody levels are used to categorize individuals as seropositive or seronegative. One limitation of this approach is the requirement to impose a fixed cut-off to distinguish seropositive and negative individuals. Furthermore, the continuous variation in antibody levels is ignored thereby potentially reducing the precision of the estimate.MethodsAn age-specific density model which mimics antibody acquisition and loss was developed to make full use of the information provided by serological measures of antibody levels. This was fitted to blood-stage antibody density data from 12 villages at varying transmission intensity in Northern Tanzania to estimate the exposure rate as an alternative measure of transmission intensity.ResultsThe results show a high correlation between the exposure rate estimates obtained and the estimated SCR obtained from a catalytic model (r = 0.95) and with two derived measures of EIR (r = 0.74 and r = 0.81). Estimates of exposure rate obtained with the density model were also more precise than those derived from catalytic models.ConclusionThis approach, if validated across different epidemiological settings, could be a useful alternative framework for quantifying transmission intensity, which makes more complete use of serological data.

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

“…Fitting of antibody data to other models may also help improve on the predictive power of data from cross-sectional surveys. For example, recent sero-epidemiology studies have reported more precise parasite exposure rates based on new models that utilize continuous antibody data compared to seroconversion estimates by the catalytic model based on dichotomized antibody data [32,38]. …”

Inferring infection hazard in wildlife populations by linking data across individual and population scales

“…; Pothin et al . ). As antibody levels decay rapidly over time in many wildlife host‐pathogen systems (especially in non‐mammalian hosts), there is opportunity to learn about temporal disease dynamics in a population by considering antibody levels in individual hosts.…”

“…; Pothin et al . ) that should be treated explicitly (Gay ; Vyse et al . ), and neglecting the potentially valuable information that can be gained from considering the quantitative antibody data (Bollaerts et al .…”

Multiplex serologic testing within a cross-sectional lymphatic filariasis sentinel site survey in coastal Kenya reveals community-level differences in IgG antibody responses to parasitic diseases and vaccines

“…suggesting that measurement of EIR may be a relatively insensitive indicator of malaria transmission in some settings. Although malaria parasite prevalence and/or EIR have traditionally been used for reporting malaria transmission intensity [52], serological markers have increasingly been recognized as useful indicators for estimating malaria transmission intensity, which is key for assessing the impact of control interventions [53][54][55][56]. Because of the longevity of the specific antibody response, seroprevalence reflects cumulative exposure and thus is less affected by seasonality or unstable transmission [57].…”