Statistical methods for the analysis of multi-antibody data to inform malaria control and elimination strategies
Lead Research Organisation:
Lancaster University
Department Name: Medicine
Abstract
T3 - Evidence into Practice T4 Practice to Policy/Population
Significant progress has been made to reduce the burden of malaria globally. Despite the challenges caused by the disruption of malaria prevention and treatment services, malaria cases and deaths dropped between 2019 and 2021 according to the World Health Organization's World Malaria Report. The decrease in the burden of malaria as created low transmission settings where traditional methods for estimating malaria burden, such as parasite prevalence and entomological inoculation rates, suffer from temporal variation and low precision. To overcome these limitations, there is a growing interest in utilizing immunological data for malaria surveillance. Serology offers valuable insights into transmission intensity by quantifying prior exposure to malaria parasites. However, the lack of standardized approaches for analysing and modelling serological data poses challenges for malaria control programs.
Currently, the commonly used approaches in analysing serological data are mixture models, reversible catalytic models and antibody acquisition models. While they provide valuable insights when compared to traditional malaria metrics like parasite prevalence and entomological inoculation rate, these modelling approaches have different limitations that make them prone to producing biased estimates of malaria transmission. Additionally, the prevailing practice in the analysis of sero-epidemiological data involves the estimation of the transmission of malaria from multiple antibody responses independently, with potential effects on the accuracy and precision.
The limitations of the existing modelling approaches underscore the need for further refinement and development of modelling approaches to enhance their accuracy and robustness. This PhD project aims to address these challenges and contribute to the advancement of modelling strategies for the surveillance of malaria with the potential of extension to other infectious diseases in which serosurveillance is actively
employed. In particular, this project will develop multivariate statistical methods that can overcome the limitations of the common practice of treating multiple antibody responses independently.
Significant progress has been made to reduce the burden of malaria globally. Despite the challenges caused by the disruption of malaria prevention and treatment services, malaria cases and deaths dropped between 2019 and 2021 according to the World Health Organization's World Malaria Report. The decrease in the burden of malaria as created low transmission settings where traditional methods for estimating malaria burden, such as parasite prevalence and entomological inoculation rates, suffer from temporal variation and low precision. To overcome these limitations, there is a growing interest in utilizing immunological data for malaria surveillance. Serology offers valuable insights into transmission intensity by quantifying prior exposure to malaria parasites. However, the lack of standardized approaches for analysing and modelling serological data poses challenges for malaria control programs.
Currently, the commonly used approaches in analysing serological data are mixture models, reversible catalytic models and antibody acquisition models. While they provide valuable insights when compared to traditional malaria metrics like parasite prevalence and entomological inoculation rate, these modelling approaches have different limitations that make them prone to producing biased estimates of malaria transmission. Additionally, the prevailing practice in the analysis of sero-epidemiological data involves the estimation of the transmission of malaria from multiple antibody responses independently, with potential effects on the accuracy and precision.
The limitations of the existing modelling approaches underscore the need for further refinement and development of modelling approaches to enhance their accuracy and robustness. This PhD project aims to address these challenges and contribute to the advancement of modelling strategies for the surveillance of malaria with the potential of extension to other infectious diseases in which serosurveillance is actively
employed. In particular, this project will develop multivariate statistical methods that can overcome the limitations of the common practice of treating multiple antibody responses independently.
Organisations
People |
ORCID iD |
| Emanuele Giorgi (Primary Supervisor) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/W007037/1 | 01/10/2022 | 30/09/2028 | |||
| 2825141 | Studentship | MR/W007037/1 | 03/10/2022 | 02/10/2026 |