↓ Skip to main content

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

Overview of attention for article published in Malaria Journal, January 2014
Altmetric Badge

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
54 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A mathematical model of seropositivity to malaria antigen, allowing seropositivity to be prolonged by exposure
Published in
Malaria Journal, January 2014
DOI 10.1186/1475-2875-13-12
Pubmed ID
Authors

Samuel Bosomprah

Abstract

Malaria 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.

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Burkina Faso 1 2%
Tanzania, United Republic of 1 2%
United Kingdom 1 2%
Belgium 1 2%
United States 1 2%
Unknown 49 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 24%
Student > Ph. D. Student 8 15%
Student > Master 6 11%
Student > Doctoral Student 4 7%
Professor 4 7%
Other 12 22%
Unknown 7 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 33%
Medicine and Dentistry 10 19%
Mathematics 4 7%
Unspecified 3 6%
Immunology and Microbiology 3 6%
Other 9 17%
Unknown 7 13%