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Students

Duah Dwomoh,
PHD Candidate

Project Title
Statistical Modeling of Age Speci c Risk of Malaria

Executive Summary


Malaria is a life-threatening disease caused by Plasmodium parasites and is transmitted to people through the bites of infected anopheles mosquitoes. It remains one of the most prevalent and lethal human infectious diseases worldwide.
Most deaths occur among children living in Africa where a child dies every minute from malaria according to the WHO report 2012 and Plasmodium falciparum (Pf ) malaria is the leading cause of morbidity in Ghana. The aim of the study is to develop an age-specific risk model for clinical malaria that predicts the probability of a child developing clinical malaria within one complete malaria season (one year).
The model will elucidate the variability of Pf infection outcome based on the relative contribution of host immunological, genetic, socio-demographic, and environmental and parasite factors and quantify how much variance the individual components explain.
The study will further evaluate the validity and efficiency of surrogate endpoint of clinical malaria. In developing the model, Binary Logistic Regression Analysis technique will be employed but parameter estimation for the predictive model will be based on penalized maximum likelihood (ridge regression).
The predictive performance of the resulting models will be evaluated using the Brier score. Internal and external model validation will be based on repeated cross-validation and data from next period of longitudinal malaria cohort study (May 2016 to January 2017) respectively. The statistical uncertainty of the personalized predicted risk of malaria will be ascertained based on the model using a Bootstrap technique. It is expected that the age-specific malaria risk internet based model calculator and a smart phone application will provide immediate access personalized risks of a child at a specified time and contribute tremendously to prognosis and care.

     Objective
  • To develop an age-speci c risk model that predicts the probability of a child developing clinical malaria within a
    period of one year.
  • To measure the e ect of the interaction between quantity and quality of malaria speci c antibodies (IgG, IgG1, IgG2,
    IgG3 and IgG4) and polymorphism in host genes (ITGB2, FCGR2A, FCGR2B, FCGR3A, FCGR3B, and IGHG3)
    in relation to the risk of Plasmodium falciparum infection Outcome.
  • To assess the expression levels of genes that encodes proteins crucial for parasite invasion of erythrocytes (MSP1,
    AMA1, Rh5 and EBA175), transmission (Pf s25, Pfs48/45 and Pfs230), cytoadherence (Pf EMP1 DBL2 and CIDR1
    domains) and the antigens MSP3 and GLURP on children who develops severe malaria.
  • To assess the e ect of temperature, relative humidity, atmospheric pressure and precipitation on risk of Plasmodium
    falciparum infection outcome.
  • To quantify the e ect of socio-demographic factors on risk of Plasmodium falciparum infection outcome.
  • To determine factors associated with time to developing clinical malaria.
  • To determine factors associated with multiple episodes of malaria within one complete malaria season.





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