Spatial Epidemiology and the Integrated Control of Malaria and Lymphatic Filariasis in Africa
Author(s)
Slater, Hannah Claire
Type
Thesis or dissertation
Abstract
Malaria and lymphatic filariasis (LF) cause the largest public health burden of all vector-borne
diseases worldwide. Some 350-500 million clinical episodes and 1 million deaths each year
are caused by malaria, of which approximately 60% and 80% respectively occur in Africa.
More than 50 million people are also thought to be infected with lymphatic filariasis in 39
endemic countries in sub-Saharan Africa, with approximately 14.6 million individuals living in
these endemic countries estimated to suffer from the two major filarial debilitating conditions,
lymphodema or hydrocele. These two infections are co-endemic in large parts of Africa and
are transmitted by the same vector, namely the Anopheles mosquito.
The overall aim of the PhD was to develop a framework which could be used to evaluate the
economic costs and benefits of different control strategies for reducing or eliminating the
transmission of LF and malaria in Africa and, in particular, considering whether integrated
control could offer a more effective approach for disease control. This problem was split into
three key areas:
1) Mapping the geographic distribution of malaria and LF infection in Africa and
identifying areas where the infections are co-endemic. Initially a maximum entropy
modelling approach was used to identify areas at risk of LF based on environmental
factors and population density. Then, a Bayesian spatial modelling approach was
used to map the prevalence of malaria and LF in Africa, to estimate the number of
people infected, and to identify areas of co-endemicity.
2) Developing a combined malaria-LF transmission model for humans and mosquitoes.
This involved integrating a LF transmission model into a malaria transmission model.
Interactions between the infections were captured using the model, specifically the
increase in vector mortality as a result of LF larvae infection, and alterations in the
host immune response as a result of co-infection. The model was used to investigate
how the presence of one infection affected the prevalence of the other.
3) Finally, performing an economic evaluation of the economic costs and benefits of
different control strategies, focusing on the potential for an integrated control
approach. The economic benefits of the two primary control approaches (long lasting
insecticidal nets for malaria and mass drug administration for LF) were modelled for a
range of different scenarios using the co-infection model. The benefit of a control
strategy was defined as the financial cost of the resulting reduction in treatment costs
and work days lost due to infection and disease. The cost-benefit analysis was used
to identify the optimal control strategy for different infection prevalence scenarios and
the results were combined with the maps created in 1) to produce maps showing the
optimal control strategy for different regions of Africa.
diseases worldwide. Some 350-500 million clinical episodes and 1 million deaths each year
are caused by malaria, of which approximately 60% and 80% respectively occur in Africa.
More than 50 million people are also thought to be infected with lymphatic filariasis in 39
endemic countries in sub-Saharan Africa, with approximately 14.6 million individuals living in
these endemic countries estimated to suffer from the two major filarial debilitating conditions,
lymphodema or hydrocele. These two infections are co-endemic in large parts of Africa and
are transmitted by the same vector, namely the Anopheles mosquito.
The overall aim of the PhD was to develop a framework which could be used to evaluate the
economic costs and benefits of different control strategies for reducing or eliminating the
transmission of LF and malaria in Africa and, in particular, considering whether integrated
control could offer a more effective approach for disease control. This problem was split into
three key areas:
1) Mapping the geographic distribution of malaria and LF infection in Africa and
identifying areas where the infections are co-endemic. Initially a maximum entropy
modelling approach was used to identify areas at risk of LF based on environmental
factors and population density. Then, a Bayesian spatial modelling approach was
used to map the prevalence of malaria and LF in Africa, to estimate the number of
people infected, and to identify areas of co-endemicity.
2) Developing a combined malaria-LF transmission model for humans and mosquitoes.
This involved integrating a LF transmission model into a malaria transmission model.
Interactions between the infections were captured using the model, specifically the
increase in vector mortality as a result of LF larvae infection, and alterations in the
host immune response as a result of co-infection. The model was used to investigate
how the presence of one infection affected the prevalence of the other.
3) Finally, performing an economic evaluation of the economic costs and benefits of
different control strategies, focusing on the potential for an integrated control
approach. The economic benefits of the two primary control approaches (long lasting
insecticidal nets for malaria and mass drug administration for LF) were modelled for a
range of different scenarios using the co-infection model. The benefit of a control
strategy was defined as the financial cost of the resulting reduction in treatment costs
and work days lost due to infection and disease. The cost-benefit analysis was used
to identify the optimal control strategy for different infection prevalence scenarios and
the results were combined with the maps created in 1) to produce maps showing the
optimal control strategy for different regions of Africa.
Date Issued
2012
Date Awarded
2012-10
Advisor
Michael, Edwin
Sponsor
Natural Environment Research Council (Great Britain) ; Economic and Social Research Council (Great Britain)
Publisher Department
School of Public Health
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)