Improving malaria risk assessment in Blantyre district, Malawi by optimizing Anopheles surveillance using open-source and real-time data

Malaria is an important public health problem in Malawi, where in 2017 alone more than 4.3 million cases were reported. It is a disease transmitted between people by a mosquito group called Anopheles. Malaria can be prevented by treating cases, removing Anopheles mosquitoes from an area (e.g. using insecticides) and creating a barrier between the local population and the mosquitoes (e.g. using bed nets). Significant investment in mosquito control and medical care has helped reduce malaria cases in the last decade. Although this has been successful, resources have not been enough to decrease malaria incidence below the annual 4 million cases. As we cannot treat everyone nor use mosquito control methods everywhere, resources need to be allocated in the most efficient and effective way. This requires a better understanding of both the Anopheles mosquitoes and malaria disease dynamics. To understand the malaria dynamic in Malawi, mosquito surveillance is essential. Unfortunately, mosquito surveillance is a costly and time-consuming activity that requires highly experienced and well-trained people.

An important way in which mosquito surveillance can become more efficient and effective is by targeting specific areas: instead of collecting mosquitoes from all area, surveillance is focussed on high risk areas only. These targeted areas can be identified using data from the past (years ago and days ago), which show trends that help predict the future. This has already been done on a larger scale for disease outbreaks, where prevention activities are focussed on high risk provinces or regions. Although valuable, it is unfeasible to implement control activities throughout these large areas. During this fellowship I want to use similar technology at a much finer and more pragmatic scale.

A targeted mosquito surveillance tool will be developed that 1) identifies high-risk areas and 2) selects sites within these high-risk areas where mosquito surveillance should take place for accurate risk assessment. Firstly, I will identify variables (such as temperature, rainfall, land cover and human population density) associated with an increase in Anopheles mosquitoes and subsequent increase in malaria cases. Historical mosquito data, malaria disease data and satellite data that captures environmental variables, will be analysed to identify trends. Secondly, the variables and values associated with an increase in Anopheles mosquitoes will be used to develop a predictive model that identifies high risk areas. Daily satellite data will be included in the model (real-time data) for up-to-date predictions. This model will be linked to a robust mosquito sampling framework that identifies specific sites within these high-risk areas where surveillance should take place. This tool will be validated in the field by comparing it to current mosquito surveillance activities. Finally, the predictive model will be adapted into a user-friendly interface that it requires limited training and provides clear direction. I will work closely with the Malawian Ministry of Health to implement this decision supporting tool in the vector control program.

The aim of this fellowship is to help identify high-risk areas with less resources and bridge the gap between vector control programs and advanced quantitative methods. While statistical models and other advanced statistical approaches can improve efficacy and efficiency of vector control programs, they are rarely used due to their complexity. I will develop an easy-to-use tool that identifies defined areas where mosquito surveillance should take place. It will provide an economically feasible sampling strategy that improves the allocation of the limited resources available. Future development of the surveillance approach to other geographic areas and other mosquito-borne diseases (e.g. dengue and zika) will be investigated.

Malaria is an important public health problem in Malawi. Significant investment in malaria control has reduced malaria burden in the last decade. Unfortunately, it has not been enough to decrease malaria incidence below the annual 4 million cases. To improve malaria control, surveillance of mosquito vectors is essential. Unfortunately, this is costly and time-consuming. Quantitative tools can help maximize the value of data with the least amount of resources required. The aim of this project is to improve mosquito surveillance by developing a targeted surveillance tool. Using transparent and robust statistical methods, this tool will make surveillance more economical, whilst simultaneously improving the quality of data collected. A mosquito database will be established in Malawi by collating data from stakeholders on the species composition, spatial distribution, physiological status and sporozoite rate of malaria vector mosquitoes. This database will be linked to malaria incidence and open access data (e.g. temperature, landcover, human populations), to identify variables associated with Anopheles proliferation and malaria incidence. Next, a real-time spatial predictive model will be developed that identifies areas with a high risk of Anopheles proliferation using real-time data from the daily satellite products (e.g. MODIS, LANDSAT). The model will be implemented, validated and refined in Blantyre district, Malawi by comparing the targeted surveillance to the already established mosquito surveillance. Monthly collections will be done using CDC light traps and proko-pack aspirators. The model will then be adapted into a user-friendly format. I will work with the Ministry of Health to implement this decision supporting tool within the malaria control program. This project will provide an economically feasible sampling strategy which improves the allocation of the limited resources available and provide a new scientific avenue to improve mosquito surveillance.

-Vector control programs: this project will improve mosquito surveillance of vector control programs by developing an entomological surveillance tool. This targeted surveillance tool will make surveillance more economical, whilst improving the quality of data collected using transparent and robust statistical methods. Additionally, the analysis of the historical data will increase knowledge on the disease dynamics in-country, including better understanding of the impact of vector control activities. Moreover, the geo-referenced database itself will be an important source for future analysis. The trainings provided during this project will strengthen the quantitative skills in-country across the different stakeholder groups. I will highlight opportunities in geospatial modelling for day-to-day activities in the control and prevention of malaria. This will lead to more robust study designs and create opportunities for further collaborations with quantitative scientists to improve vector control. To maximize the life-time value of this project, I will pro-actively seek opportunities to expand the tool for other vector-borne diseases and to vector control programs in other countries.
-Governments: The improved understanding of the disease dynamics in-country will benefit the evidence-based decision-making process and succeeding policy development. More effective policies will result in better disease control and subsequently in better economic competitiveness of the country. The implementation of the tool in the vector control program and the capacity building activities will help save resources and highlight new approaches for policy developers at both local and country level.
- Funders of control programs (NGO's and governmental organizations): This project will improve the resource allocation within the vector control programs by limiting mosquito surveillance costs and improving the quality of data. The funding will therefore protect more people from malaria. I will also investigate opportunities to adapt the surveillance tool to useful tools for the different NGO's and governmental organizations that conduct their own mosquito surveillance activities.
-Local population in malaria endemic countries: this project will help increase the health of the population in Malawi by providing operationally relevant data that increases efficacy of the vector control program. Additionally, saving valuable resources in surveillance will allow for allocation of more resources to malaria control efforts. I will increase awareness and understanding of malaria disease and other mosquito-borne diseases of the local population in my field sites during the village meetings.
- Wider public: Money invested by the wider public in charities and through tax payment will protect more people if vector control programs are more efficient. This project is a great example of a multidisciplinary project, where experts from different fields come together to tackle a problem. I hope that the communication of this project to a wider audience will inspire a new generation of researchers.
- Scientific community: The predictive model will advance the scientific knowledge on malaria disease in Malawi. The mosquito surveillance tool will help researchers identify mosquito surveillance sites more transparently. Most importantly, I hope to encourage more integration between the fields of public health entomology and geospatial modelling. I hope to especially stimulate the application of quantitative skills in field-based projects. I will build this bridge during my placements, by publishing high quality publications and presenting at a diversity of conferences.Further strengthening of collaboration between Lancaster University, Malawi-Liverpool-Wellcome Trust Clinical Research and Liverpool School of Tropical Medicine will enhance the competitiveness of UK research.