The Malaria Detection Project in the Low-Burden Regions of Africa: Modeling and Predicting Diseases in the Indian Ocean
There are a lot of questions facing Africa’s Malaria modellers and they show the challenge of reaching zero cases of Malaria. While most of the people in the field are hopeful it can be done, they will look to South Africa and other areas on the edge of Africa’s endemic Malaria belt for inspiration. The test grounds for elimination efforts can include countries such as South Africa. “If you can eliminate malaria in the low-burden countries,” she says, “it’s proof of concept that you can do it in the rest of Africa.”
Even high-resolution predictions can be of limited use in practice. In 2019, scientists using data from the project produced a year-on-year time series for malaria mortality in sub-Saharan Africa from 2000 to 2017, drilling down to a resolution of 5 5 kilometres3. But some question the utility of these data for directing targeted programmes owing to limitations in the model and a lack of good quality district-level data to run it. Malaria prevalence is thought to vary greatly at scales smaller than 5 km.
The new tool is still in its infancy and it is still being worked on. The project will reach out to the potential users as well as the modellers in order to learn more about their needs. Rather than a one-off consultative approach, this will be an ongoing process, says Lisa van Aardenne, who is also at CSAG. In her experience, this kind of collaboration creates better, more lasting, results. She says that it is really useful despite being messy.
Adding climate data will make the models even more complex, says Chris Lennard, a climatologist at CSAG, and Silal’s co-investigator on the five-year project funded by the biomedical charity Wellcome in London. The methodology that can make a data set completely unsuitable is something that has happened to him in the past. This creates a “huge potential” for error and is a potential waste of modelling money.
Patrick Martineau, who studies climate dynamics and its socio-economic impacts at the Japan Agency for Marine-Earth Science and Technology in Yokosuka, has not had such a wait. He employed machine learning to develop a method for predicting malaria in the future. The model is based on sea surface temperatures in the Indian and Pacific oceans. This is a more reliable long-term predictor of the climatic patterns that drive malaria in the South African province than weather forecasts, which can’t project that far ahead. The method provides forecasts at the district level, two of which — Vhembe and Mopani, each covering around 20,000 square kilometres — are of particular interest because of their high risk of malaria.
No More Japan — A Time for Progress and Challenges in Studying Malaria and its Prevention, and Implications for Disease Prevention and Vaccination
We are appreciative of the financial support provided by Ehime University, Medicines for Malaria Venture, Nagasaki University and Malaria. No More Japan with funding from the Bill & Melinda Gates Foundation, Sumitomo Chemical, GHIT Fund and Eiken Chemical, in producing this Outlook. Nature retains the sole responsibility for editorial content.
Despite such obstacles, there are reasons to be hopeful. Researchers are working to find ways to prevent and treat the disease. The major advance of the past few years was the arrival of the first vaccine for malaria. RTS,S is being rolled out to children in a handful of countries in Africa, with more hoping to benefit soon. There is room to improve. Researchers are interested in pursuing alternative vaccines that target the parasites at different points in their life cycle. Vaccines for the protection against other species are also needed. Some researchers hope that monoclonal antibodies could offer another route to protection. Some are trying to find a cure for mosquitoes that transmit parasites, and other are trying to find a solution to the problem of mosquito populations being wiped out.