Predicting malaria using ML and satellite data
Who is this presentation for?Data scientists or analysts
Nearly half the world’s population lives in areas at risk of malaria transmission in 91 countries and territories. In 2017, malaria caused an estimated 219 million cases and 435,000 deaths. And most of the factors that increase the chance of malaria can be controlled with the right infrastructure and awareness.
Malaria incidence depends on external factors such as the environment, including temperature, humidity, and rainfall, because survival of female Anopheles, the malaria-transmitting mosquito, is highly dependent on favorable conditions; sanitation infrastructure, including the number of open drains, open wells, water pipelines, households with toilets, and the penetration of social schemes promoting sanitation infrastructure and awareness, which impact the possibility of mosquito breeding ground; and demographic characteristics like population density, slums, access to roads, and variables that facilitate malaria and contact of mosquitos with humans—for example, malaria is most prevalent in tribal regions in India.
Shilpa Arora explains these factors with sources that vary from local and central government open portal to NASA’s satellite data portal. You’ll discover the inference model and the ML prediction model used to understand the factors in these regions and predict expected cases.
Shilpa Arora is a principal data scientist at Atlan, a data product company. She’s also a guest lecture for applied econometrics at IIM-Kashipur College in India. She loves designing and building scalable data products with features that look and feel customized to every user.
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