Predicting Dengue in Africa: How Models Track Mosquitoes

Dengue fever is a growing concern worldwide, and there's no comprehensive vaccine or medication to stop it. This review looked at how scientists in Africa are using math and stats to predict dengue outbreaks and the mosquitoes that spread it. They searched through studies from databases like PubMed and Scopus, focusing on Africa and English-language research. They found 30 studies, most of which (two-thirds) focused on how to guess Aedes mosquito numbers as a way to judge dengue risk. The rest looked at actual dengue cases or how common it is.

Researchers usually get their data from studies on mosquitoes or from surveys. Most studies (56. 7%) included stuff like rainfall, humidity, and temperature in their models, along with other factors like the environment, people's lives, and how many mosquito larvae or pupae there are. The most popular ways to do the math were traditional methods like logistic and linear regression (60. 0%), followed by machine learning (16. 7%) and mixed effects models (13. 3%). Only 36. 7% of the models said how they picked which factors to include, and just 20. 0% checked if their model was working right, which is a big gap in how they report and test their work. This review shows what's being done now in Africa to model dengue and its mosquito vector. It also points out the challenges of not having enough good data to make better predictions for early warning systems.

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