Applicants can select from a list of 13 research topics suggested by leading researchers in their field. Each candidate can choose at most two topics and rank them by preference. Alternatively, applicants are welcome to suggest their own research topics. Shortlisted candidates will be put in touch with the supervision teams that proposed their selected topics for discussions on more concrete research ideas in Phase 2 of the application process.

Proposed topics are listed below

1. Geospatial modeling of malaria hotspots in Benin 
2. Enhancing malaria control by leveraging the use of routine surveillance through data science and mathematical modelling in Rwanda 
3. Age-structured malaria intervention models with applications to seasonal malaria chemoprevention in Senegal
4. Mapping sub-national risk to support tailored approaches to malaria control in Mozambique and Rwanda 
5. Understanding the impact of malaria interventions in northern Benin to inform future strategies in the country 
6. Building predictive models of malaria vector larval habitat locations for understanding the spatial determinants of malaria transmission in Mozambique
7. Mathematical modeling and control of malaria transmission dynamics: Using sterile mosquito dissemination by Wolbachia bacteria in Burkina Faso 
8. Molecular surveillance of malaria coupled with mathematical modelling to assess asymptomatic infections in Kenya 
9. Real-time prediction of insecticide resistance in Rwanda 
10. Mechanistic and geospatial models to support genomic surveillance in elimination countries such as Senegal 
11. Impact and risk of deployment of Seasonal Malaria Chemoprevention in Mozambique
12. Biophysical Modelling of Malaria Parasite invasion of Red Blood Cells

Detailed topic descriptions

Topic 1: Geospatial modeling of malaria hotspots in Benin

Malaria infections and morbidity are heterogeneously distributed across both time and space. Malaria elimination, and indeed more cost effective control, requires a more detailed description of this variation and an understanding of its drivers. The student will use mathematical models, spatial statistics and an epidemiological sampling framework to understand what drives hotspots of malaria transmission in endemic settings. 

It is expected at least two publications (one methodological and another one applied). Influence vector interventions measures through the existing mosquitos control programs in Africa.  Apply for funding in order to evaluate the benefit of control measures on the defined hotspot areas. 

This project will suit a student with a Master degree in Epidemiology, Bayesian statistics, biostatistics or ecological modelling. The student will develop a high level of skill in spatial epidemiology and computation. The successful completion of this PhD will provide the opportunity for the student to work in a wide range of academic, public and private health organizations world-wide NMCPs. 

Topic 2: Enhancing malaria control by leveraging the use of routine surveillance through data science and mathematical modelling in Rwanda 

In Rwanda, tremendous efforts have been made over the past years and significant decreases in malaria burden have been achieved. These were due to prompt interventions such as insecticide-treated mosquito nets (ITNs), indoor residual spraying (IRS) and good case management starting from the community level. However, as malaria transmission decreases and becomes more heterogeneous, it is crucial to understand how the different interventions impact transmission and tailor them accordingly in a cost-effective way. The aim of the project is to develop a quantitative, model-based approach to support the National Malaria Control Program (NMCP) of Rwanda for deciding on malaria control strategies, addressing the following objectives: 

• Using the routine data for understanding and characterizing the epidemiological situation in Rwanda, evaluating the routine surveillance system in Rwanda 
• Estimating the impact of deployed interventions over time 
• Estimating the impact of reducing or replacing IRS with other interventions given budget constraints 
• Estimating the effect of case management and identifying its cost-effective coverage as well as supporting human resource and commodities planning 

In this PhD, the candidate will dive deep into understanding the malaria routine surveillance system in Rwanda, specifically the different data collected, consisting of various indicators about malaria burden, intervention deployment, as well as geography-specific data (e.g., seasonality). First, using various statistical methods, the candidate will analyze the historical time series and quantify the effects of deployed interventions over time. The results of this analysis will be subsequently used to parameterize an individual model of malaria transmission reproducing the malaria transmission dynamics at various administrative resolutions across the country. This model will allow analysis of several scenarios investigating the potential impact of alternative control interventions. Specifically, the PhD will entail: 

• Using statistical methods to conduct descriptive analysis of the available routine epidemiological data to understand the evolution of the local malaria situation 
• Modelling malaria transmission and the effects of malaria control interventions in Rwanda and conducting scenario analysis to predict the impact of various potential intervention strategies 
• Communicating results of analyses and supporting the NMCP 

We expect from the candidate to have the following skills and qualifications: 

• Master’s Degree in epidemiology, any other public-health-related or quantitative topic with experience in working on infectious diseases 

• Curiosity about the PhD research topic, self-initiative in communicating with the different collaborators, and more specifically with the NMCP to ensure that the conducted analyses are supporting the country needs 
• Strong quantitative background (e.g., mathematics, computer science, physics, bioinformatics) with knowledge of statistics and data analysis (e.g., time series analysis) 
• Strong coding skills, experience with R or Python 
• Being familiar with version control (e.g., git)  
• Being familiar with using Unix systems (i.e., using the Unix console and scripting language) and with running analyses in a high-performance computing environment 

Topic 3: Age-structured malaria intervention models with applications to seasonal malaria chemoprevention in Senegal 

Malaria interventions such as seasonal malaria chemoprevention (SMC) or vaccination showed promising results from randomized control trials or pilot studies. Nevertheless, when implemented by the malaria programs at larger scale, evidence for population-level efficiency is difficult to establish owing to various sources of heterogeneity. 

In this project, the candidate will investigate heterogeneities related to age structures, which are particularly important for chemoprevention and vaccination. Using partial differential equations, we consider the age of infection (infectiousness to vectors), age of host (morbidity, mortality, immunity), age of intervention (time-dependent intervention efficacy). Concurrently with numerical simulations, the candidate will also explore physics-informed neural networks to approximate solutions in analytically intractable situations. 

In collaboration with public health specialists from Senegal, the candidate will apply the modeling framework to evaluate the impact of SMC in the past and how to best combine SMC with vaccination for future planning in terms of age targets and deployment schemes (cohort, catch-up, seasonal) at the subnational level. 

The candidate is required to have a firm quantitative undergraduate background (e.g. physics, mathematics, statistics, computer science) with basic knowledge about machine learning or dynamical systems. We require solid coding skills (e.g. C++, R, python, julia or Matlab) and familiarity with the challenges of high performance computing. Awareness for biological mechanisms and operational challenges, as well as efficient communication in a multidisciplinary environment are a plus. 

Topic 4: Mapping sub-national risk to support tailored approaches to malaria control in Mozambique and Rwanda 

Despite significant declines in malaria burden and mortality, many endemic countries face the challenges of plateaued progress, pressures of external funding and the need to optimize limited resources in strategic and tailored approaches.  

Disease risk maps are an essential tool in the fight against malaria, supporting data-evidenced decision making by enabling better targeting of malaria interventions and can be a standardized resource to track progress and facilitate our understanding of seasonal profiles of malaria in national and sub-national levels. As countries improve their disease surveillance tools, it enables modelers to design and develop more novel statistical and mathematical approaches which incorporate multiple datasets at varied spatial and temporal scales. 

 The main goal of this research is to develop multi-metric geostatistical and mechanistic models tailored to specific operationally relevant questions prioritized by malaria endemic countries. This PhD would take the form of a) scoping a novel problem in collaboration with PNCM Mozambique and Rwanda and then b) solving that problem by developing new statistical methods building on existing work where appropriate. Relevant topics may include modelling spatio-temporal patterns of incidence and prevalence (and their relationship), urban malaria, outbreaks, malaria persistence, vector dynamic and species distribution, risk in special populations (pregnant women and infants) and malaria co-morbidity (with anemia, schistosomiasis/helminths, malnutrition). A successful candidate would be expected to spend time with the MAP team in Perth to learn advanced geostatistical techniques and will have access to MAPs comprehensive library of high-resolution environmental and demographic covariates to supplement their work. Additionally, dissemination of modelled outputs back to PNCM Mozambique and development of tools to embed into the current surveillance system is key. Successful candidate would also contribute to local capacity building to improve uptake of modelled work in decision making processes. 

Topic 5: Understanding the impact of malaria interventions in northern Benin to inform future strategies in the country 

Despite increased funding towards the universal scale-up of malaria control prevention, mainly through insecticide-treated bed nets (ITNs), SMC and treatment with artemisinin-based combination therapy (ACT), progress has stalled in many countries in recent years. There need to be on track to achieving national and global targets for 2020 and 2025 as defined in the World Health Organization Global Technical Strategy. One of the causes is the need for more appropriate allocation of limited resources by the NMCPs from high malaria burden countries.  

To maximize progress in these countries, achieve malaria control and move toward its elimination, it is required for these countries to tailor the malaria interventions based on an adequate selection of intervention mixes for specific risk areas. This approach needs sub-national stratification of multiple malaria risk indicators from vector biology, parasite information, human behavior and routine health surveillance data, which could be combined into an overall malaria risk score per specific risk areas related to the local context in the country. This can be done with the use of mathematical modelling to predict the impact that different strategies might have. 

In Benin, from 2006 to 2010, 2011 to 2018 and from 2017 to 2021, NMCP defined several strategies related to the intensification of malaria control, which was based on the use of Long-lasting Insecticide Treated Nets (LLINs) in all the country, indoor residual spraying (IRS) in some health district, intermittent preventive treatment in pregnant women (IPTp-SP) with sulfadoxine-pyrimethamine, and treatment with artemisinin-based combination therapy (ACT) in all the country. Recently seasonal malaria chemoprevention has been implemented in the northern regions of Benin, where malaria transmission is highly seasonal. New policies are being introduced in the health system through an integrated national strategic plan oriented towards the elimination of HIV/aids, tuberculosis, malaria, viral hepatitis, IST and diseases with epidemic potential (2020-2024).  

In line with World Health Organization (WHO) recommendations, Benin NMCP’s is now poised to define ways to maximize the future malaria control strategies impact, reduce inefficiencies and create a platform to sub-nationally target resources and monitor progress.  

During this PhD, the candidate will use more information than morbidity and mortality data, human, mosquito and parasite data, which reflect diverse transmission dynamics influenced by climate, environment and behavioral factors to accurately and reliably develop appropriate malaria risk stratification; The candidate will then use mathematical modeling to assess technical feasibility and determine which intervention mixes would maximize impact to meet the target and within the constraint of cost-effectiveness. 

Topic 6: Building predictive models of malaria vector larval habitat locations for understanding the spatial determinants of malaria transmission in Mozambique 

Vector control remains the vital component of malaria control and elimination strategies. A potentially important target of vector control for malaria is the larva. It is well recognized that proper management of larval habitats in sub-Saharan countries, particularly during dry seasons, can help suppress vector densities and malaria transmission. However, our understanding of the ecology of malaria vector larvae is still limited. For example, in Mozambique little is known about the causes of spatial heterogeneity in the abundance and distribution of malaria vectors, as well as several larval habitats contributing to malaria vector abundance.

Mechanistic and predictive models that make use of landscape variables and account for seasonal variations in habitat probability based on accumulated precipitation are essential tools for investigating the links between larval habitat distribution and adult malaria vector distribution across a large landscape where manually mapping the larval habitats would be infeasible. Also, such models could be useful for malaria control programs, allowing decision-makers to focus their efforts to areas where larval habitats are most likely to occur.

Therefore, this project proposal intends to develop geospatial and mechanistic models tailored to answer the following objectives: (1) to characterize larval habitats of the malaria vector; (2) to investigate the spatial distribution of the malaria vector by determining the links between the distribution of larval habitats and the distribution of the adult vector of malaria in different geographic landscapes; (3) make use of the information generated to develop and test predictive models of larval habitat sites using landscape variables that predict the likelihood of water bodies, and taking into account the seasonal changes habitat probability based on accumulated rainfall; (4) then investigate how the distribution larval habitats is linked to the current spatial heterogeneity of malaria prevalence in the country.

The successful candidate would be based at Manhiça Health Research Centre (CISM), with secondments at Eduardo Mondlane University (UEM), University of Johannesburg (UJ) and NMCP.   The candidate should have MSc in Mathematics/Statistics or any related field with strong mathematical/statistical background, with knowledge of programming and statistical software (preferably R), basic knowledge of relational database systems and SQL, knowledge of malaria epidemiology, effective communication and scientific writing skills, and good interpersonal and organization skills.

Topic 7: Mathematical modeling and control of malaria transmission dynamics: Using sterile mosquito dissemination by Wolbachia bacteria in Burkina Faso 

Given that Anopheles mosquitoes are malaria vectors, one of the effective strategies to control malaria transmission relies on the use of insecticides. Accordingly, resistance to insecticides has emerged as a biological threat to malaria control and elimination efforts in endemic areas. Widespread insecticide resistance has increased the malaria burden in many malaria-endemic regions, challenging global malaria eradication. Thus, an effective alternative to insecticides is needed. Notably, the sterile insect technique, in particular the Wolbachia bacteria. The sterile insect technique consists in a massive releasing into the wild of sterilized males to mate with females in the aim to reduce the size of the insect population. It has been first studied by R. Bushland and E. Knipling  and experimented successfully in the early 1950’s by nearly eradicating screw-worm fly in North America. Since then, this technique has been studied on different pests and disease vectors. In particular, it is of interest for control of mosquito populations and has been modeled mathematically and studied in several papers. So, in the research project, we are interested in the development of a mathematical model of the dynamics of mosquito populations subject to human interventions by ODE’s (Ordinary differential equations). Our main goal is the elimination or the reduction of wild mosquitoes under a certain threshold in a targeted area by the release of sterilized males. In this case, it will be a question of establishing a relation between this critical threshold for the release of inseminated mosquitoes and the basic reproduction rate. Thus, our analysis will allow a better understanding of the effectiveness of this technique in the fight against malaria diseases. 

Numerical analysis and computer simulations will be undertaken to put theory and observation together to gain insight into the working biological systems, to estimate relevant parameters from data and validate the proposed models. Those numerical simulations will show the impact of sterile mosquitoes on malaria transmissions global behavior and reveal the effects of time on the persistence and extinction of the disease. 

A successful candidate would be expected to spend time TARGET Malaria team in Bobo Dioulasso to learn advanced biological techniques about virus Wolbachia and will have access to a comprehensive library to start their work.  

Successful candidate must have a good background in mathematical modeling and numerical simulation. 

Successful candidate would also contribute through his numerical simulations results to local capacity building to improve uptake of modelled work in decision making processes. 

Topic 8: Molecular surveillance of malaria coupled with mathematical modelling to assess asymptomatic infections in Kenya 

In this PhD, the candidate will dive deep into understanding the molecular and sero- surveillance system for malaria in Kenya, specifically the different data collected, consisting of various indicators about malaria burden, intervention deployment, as well as geography-specific data (e.g., seasonality). First, using various statistical methods, the candidate will analyse the historical time series and quantify the effects of deployed interventions over time. The results of this analysis will be subsequently used to parameterise an individual-based model of malaria transmission reproducing the malaria transmission dynamics at various administrative levels across the country. This model will allow analysis of several scenarios investigating the potential impact of alternative control interventions. Specifically, the PhD will entail: 

• Using statistical methods to conduct descriptive analysis of the available molecular and sero-epidemiological data in order to understand the evolution of the local malaria situation. 
• Modelling malaria transmission and the effects of malaria control interventions in Kenya and conducting scenario analysis to predict reduction of asymptomatic infections 
• Communicating results of analyses and supporting the NMCP in formulating interventions. 
• Relate  and assess the developed  model with data from other African countries such as Rwanda and Benin.

We expect from the candidate to have the following skills and qualifications: 

• Master’s Degree in Molecular biology and Bioinformatics, epidemiology, immunology, or any other public-health-related or quantitative topic with experience in working on infectious diseases 
• Curiosity about the PhD research topic, self-initiative in communicating with the different collaborators, and more specifically with the NMCP to ensure that the conducted analyses are supporting the country needs 
• Strong quantitative background (e.g., mathematics, computer science, physics, bioinformatics) with knowledge of statistics and data analysis (e.g., time series analysis) 
• Strong coding skills, experience with R or Python 

• Being familiar with version control (e.g., git)  
• Being familiar with using Unix systems (i.e., using the Unix console and scripting language) and with running analyses in a high-performance computing environment 

Topic 9: Real-time prediction of insecticide resistance in Rwanda 

Successful malaria control depends on the use of insecticide products in long-lasting insecticidal nets and indoor residual spraying. Using these methods, Rwanda has successfully reduced the numbers of cases and deaths due to malaria. However, the effectiveness of these interventions is threatened by the rise and spread of insecticide resistance (IR), which has increased rapidly across malaria-endemic Africa over the past decade and risks undoing the significant gains in controlling malaria cases across the continent. Accurate monitoring and rapid response can enable mosquito control programs to adapt the use of insecticides to mitigate or even prevent the rise of resistance. 
 
The project will develop a predictive modelling tool to pre-empt the development of insecticide resistance and enable the MOPDD, and other malaria control departments in Africa, to respond effectively to the threat of insecticide resistance. The candidate will work closely with the Rwandan National Malaria Control Program (Malaria & Other Parasitic Diseases Division; MOPDD), AIMS Rwanda, and the Malaria Atlas Project in Perth, Australia. 
They will adapt and extend a cutting-edge mathematical and statistical model of phenotypic insecticide resistance, to enable spatio-temporal prediction of insecticide resistance levels in target vector species from genotypic (e.g., KDR marker) and phenotypic resistance, and resistance intensity bioassay data all held by MOPDD, along with environmental data on e.g., agricultural insecticide usage and climate. The model fitting process will elucidate the likely drivers of resistance in Rwanda. The insecticide resistance prediction maps produced will provide an evidence-base to inform MOPDD in switching between different intervention types. The model will also map uncertainty in predictions, enabling prioritization of future IR surveillance activities. 
The candidate will develop computational code enabling this model to be rapidly re-run as new data is collected, and results uploaded to a dashboard to inform the Ministry of Health/MOPDD in real-time. This computer code will be packaged into a user-friendly research software application enabling the tool to be used in other countries, and beyond the end of the project. 
 
Required: 

• MSc in either Mathematics/Statistics (or any related field with strong mathematical/statistical background) or in Biosciences but with experience in data analysis 
• Experience using statistical software, preferably R 
• Knowledge of malaria epidemiology or mosquito biology 
• Good communication and scientific writing skills 
• Good interpersonal and organisation skills 

Topic 10: Mechanistic and geospatial models to support genomic surveillance in elimination countries such as Senegal 

In many elimination settings common surveillance datasets such as cross-sectional surveys lack the statistical power to inform sub nationally tailored intervention strategies, especially when trying to push the final frontier of malaria to zero. The integration of genomic/molecular surveillance into routine surveillance activities has the potential to increase the actionable intelligence for making programmatic decisions on optimal mixes of interventions for elimination by informing on drug and diagnostic resistance; identifying reservoirs of sustained transmission; quantifying importation risk and identifying local transmission foci whilst additionally supporting impact evaluations. 

This project would focus on the integration of novel streams of genomic data into geospatial and mechanistic approaches. A successful candidate would partner with current genomic experts to understand relevant data sources, hierarchical structures and how they inform current geospatial modelling frameworks, with a view to developing/using statistical and mathematical techniques synthesising genomic and other data to answer operationally relevant research questions as identified by a partner national program. This work would be in partnership with genomics researchers and national programs in Senegal. 

Topic 11: Impact and risk of deployment of Seasonal Malaria Chemoprevention in Mozambique 

Seasonal malaria chemoprevention (SMC) is a highly effective community-based intervention for malaria prevention in areas where the malaria burden is high and seasonal transmission occurs. To date, mainly west African countries have been considered for implementation due to their strong seasonality of transmission. SMC had not been previously implemented in east and southern Africa due to concerns over parasite resistance to the antimalarials used in SMC. Mozambique contributes 4% of global malaria cases, and malaria represents one of the four major causes of death in the country.  

However, Mozambique has a very high number of malaria cases in some parts and it is believed that SMC would have a strong impact, due to its rainfall patterns concentrating malaria cases during well-defined periods. The High Burden to High Impact initiative launched in 2018 promotes the use of evidence to support national malaria strategies. In this light, dynamical modelling can serve as a useful tool to provide insight and simulate what would be the expected impact of SMC in untested areas. Based on recommendations in the midterm review of the Malaria Strategic Plan, Malaria Consortium, in partnership with the Mozambican National Malaria Control Program (NMCP), initiated a two-year SMC implementation evaluation in the northern province of Nampula. These studies showed high levels of effectiveness, while resistance was also high, but not negatively impacted by SMC. 

Often, the counter-arguments for using drug-based interventions include the risk of adding drug pressure and increasing the risk of emergence and spread of drug-resistant Plasmodium falciparum. Indeed, it has been assumed until now that drug resistance would render the intervention ineffective but no real world evidence has been collected to date. So here again, modelling can help quantify what this risk could be expected to be given specific deployment and geographic characteristics. 

In order to explore all these aspects, the project would be articulated around the following objectives. 

• Simulate current epidemiology and burden of malaria in Mozambique  
• Explore the impact of SMC in different parts of the country and for different implementation regimens 
• Explore the effectiveness of SMC as a result of potential emerging resistance in Mozambique. 

Prior experience or study in at least one of the following will be required: 

• mathematical or statistical modelling (in any quantitative discipline) 
• quantitative analysis in infectious disease epidemiology 
• quantitative population ecology 

The following specific skills and experience are desirable: 

• programming skills in R 
• malaria chemoprevention 
• epidemiology of mosquito-borne diseases 

Idealism, humility, and desire to see quantitative approaches make a difference in the world 

Topic12: Biophysical Modelling of Malaria Parasite invasion of Red Blood Cells

The asexual proliferation of merozoite (malaria parasite) inside human red blood cells (RBCs) has devastating effects on human health. When merozoites enter the bloodstream from the liver, they must invade RBCs within a few minutes to survive. Thus understanding the invasion mechanism is critical to fighting the disease.

The merozoite generates force using actin and myosin, but in a different way to other cells, using a ring structure unique to apicomplexa. Detailed positions of protein complexes involved are still not well established but from a physics perspective it is necessary that actin filaments and myosin motors are attached to a rigid structure, one in the RBC and one in the merozoite. This enables the myosin motors to push the merozoite, sliding it with respect to the RBC. To invade, the merozoite needs to get through the RBC spectrin network on the inside of its membrane.

In this project the candidate will be based at AIMS Ghana and will calculate the active propulsion force generated by the merozoite that is necessary for successful invasion. This will involve developing a simple model of the molecular components and calculating the energy required to make a hole in the RBC spectrin network by stretching and breaking bonds. We expect only a few filaments are required, meaning that the stochastic fluctuations inherent in the system will be important in determining whether or not a merozoite successfully invades. We will model the stochasticity of motor binding using master equations and disorder in the spectrin network using disordered polymer network theory. We will validate our model and test predictions with experimental images taken by our WACCBIP (University of Ghana) collaborators. This will enable us to determine the spatial arrangement, identities and numbers of cytoskeleton components and inform target choice for future antimalarial drugs or vaccines. Through regular meetings with the Ghanaian National Malaria Control Program (NMCP) team, we will tailor the development of the modelling work and target choice to best address the NMCP goals.

Requirements:

• Masters Level Degree (e.g. MSc) in Physics or Mathematics (or any related field with a strong mathematical background)
• Good communication and scientific writing skills 
• Good interpersonal and organization skills 
• Experience in programming &/or using mathematical software 
• Some knowledge of cell or molecular biology is an advantage