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

Abstract

BackgroundMalaria transmission intensity is traditionally estimated from entomological studies as the entomological inoculation rate (EIR), but this is labour intensive and also raises sampling issues due to the large variation from house to house. Incidence of malaria in the control group of a trial or in a cohort study can be used but is difficult to interpret and to compare between different places and between age groups because of differences in levels of acquired immunity. The reversible catalytic model has been developed to estimate malaria transmission intensity using age-stratified serological data. However, the limitation of this model is that it does not allow for persons to have their seropositivity boosted by exposure while they are already seropositive. The aim of this paper is to develop superinfection mathematical models that allow for antibody response to be boosted by exposure.MethodThe superinfection models were fitted to age-stratified serological data using maximum likelihood method.ResultsThe results showed that estimates of seroconversion rate were higher using the superinfection model than catalytic model. This difference was milder when the level of transmission was lower. This suggests that the catalytic model is underestimating the transmission intensity by up to 31%. The duration of seropositivity is shorter with superinfection model, but still seems too long.ConclusionThe model is important because it can produce more realistic estimates of the duration of seropositivity. This is analogous to Dietz model, which allowed for superinfection and produced more realistic estimates of the duration of infection as compared to the original Ross-MacDonald malaria model, which also ignores superinfection.

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

“…The superinfection model, which corrects for the effect of repeated exposures on the rate of antibody decay [21], was used to fit seroprevalence data for the estimation of seroconversion and seroreversion rates. While no changes in λ were observed for cross-sectional data from any of the communities or sampling time points (Figs 3 and 4), it was generally observed that anti-AMA1 and anti-CSP antibodies seroconverted at statistically similar rates during the rainy season but anti-CSP antibody seroconversion was significantly reduced during the dry season (Table 3).…”

“…These antibody seroprevalence models are designed to predict annual changes in seroconversion rates by averaging the patterns of seropositivity changes over an entire year [20,21,32]. This may therefore preclude the possibility of detecting changes in transmission that occur within a 12-month period for areas with seasonal disease transmission.…”

“…[21]. This model which is a modification of the reversible catalytic model, accounts for the fact that antibody responses can be boosted in individuals who are already seropositive.…”

“…Estimates of seroconversion and seroreversion rates from the reversible catalytic model are based on a set of assumptions, one of which is that all individuals in a community, irrespective of age and frequency of exposure to infectious bites, sero-revert to being specific antibody-negative at the same rate [20]. In the current study, the superinfection model [21], which is a modification of the reversible catalytic model and accounts for the effect of repeated infections on the decay rate of antigen-specific antibodies, was used to assess any changes in transmission intensity in the two study communities (Gowrie/Vea and Soe). The ultimate aim of the study was therefore to determine whether sero-epidemiological models based on seroprevalence of antibodies against sporozoite antigens could assess short term differences in transmission between the two seasons with different disease transmission dynamics.…”

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

“…Nevertheless, in high transmission settings, serological point measures could be effective as adjunct tools used in combination with parasite prevalence, as serology provides information on cumulative incidence and is less sensitive to seasonal fluctuations. Exploration of different malaria antigens or alternative models of population seroconversion (e.g., models that take into account age- or exposure-related changes in seroreversion rates [ 48 , 59 – 61 ]) may yield tools that are more informative for retrospective analyses in high endemicity areas. As malaria control receives increasing attention and funding, there will be growing need for tools that can accurately measure the epidemiological impact of interventions over extended periods, particularly in high transmission settings where reductions are most difficult to achieve and sustain.…”