AI tool ‘98 per cent correct’ to predict mosquitoes’ age

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[DAR-ES-SALAAMI] Using machine learning techniques to predict the age of mosquitoes in different populations can reduce turnaround times in malaria research and improve surveillance programs. study Say.

Knowing the age of mosquitoes helps scientists understand their potential to spread malaria, but existing tools used to predict this are costly, labor-intensive, and often Researchers say it’s prone to human error.

by world health organizationthe African region accounts for about 95% of the 247 million cases malaria Globally in 2021, and scientists innovative Tools to control mosquitoes and prevent the spread of malaria are key to eradicating the disease.

The study included strains of mosquitoes reared at the Ifakara Health Institute, Tanzania and the Glasgow University Institute, Scotland.

Using an analytical tool known as infrared spectroscopy, researchers document the biochemical composition of mosquitoes and use machine learning, a form of artificial intelligence (AI), to develop models that can predict mosquito age. I trained.

Mwanga says machine learning is a more efficient option compared to existing tools for predicting mosquito age, which are laborious and costly.

“There is a challenge that we face with machine learning, and that is the difficulty of accurately determining the age of mosquitoes from different locations,” said Emmanuel Mwanga, lead author of the study. increase. research A scientist at the Ifakara Institute of Health. “This is the main issue that this paper addresses.”

“It’s important to test findings on mosquitoes from different locations and species,” Mwanga explains.

However, the scientists stress that the study only looked at certain types of mosquitoes, so more research is needed. Anopheles mosquitoavailable from only two countries.

Research results published in BMC bioinformatics this month (9 January) show that the machine learning model was able to improve the accuracy of predicting the age of the same mosquito to about 98%.

Mwanga speaks SciDev.Net Better understanding of the exact age, host preferences and species of malaria-carrying pathogens by malaria scientists could lead to improved malaria interventions.

Older mosquitoes are more likely to transmit malaria than younger mosquitoes, researchers say, and mosquitoes that prefer to eat humans are more likely to transmit malaria than mosquitoes that prefer other animals, so malaria It is important to study its properties in order to address

“By accurately predicting these factors, we can identify high-risk populations and make interventions more effective,” Mwanga explains, noting that using machine learning techniques “will save time and Resources can be saved and made available for other aspects of malaria control and eradication efforts,” he added. ”.

“This could ultimately lead to a reduction in the number of malaria cases and deaths in the region, which is an important step towards achieving zero malaria,” he says.

“By accurately predicting these factors, we can identify high-risk populations and make interventions more effective.”

Emmanuel Mwanga, Ifakara Health Institute

According to the researchers, the findings suggest that artificial intelligence could be used to determine the age of mosquitoes in different populations.

“This could help entomologists reduce the time and effort required to dissect large numbers of mosquitoes,” said the study. “Overall, these approaches have the potential to improve model-based surveillance programs, such as assessing the impact of malaria vector control tools, by monitoring the age structure of local vector populations.”

said Frank Mussa, R&D leader at Afia Intelligence, A Tanzania-based company focused on the use of AI. health care,To tell SciDev.Net If the findings are incorporated into malaria interventions, they may help plan malaria interventions.

“[The] I need your findings policy maker It will make it easier to allocate resources, help predict trends, and help develop sound strategic plans to eradicate malaria in Tanzania,” he says.

This article was produced by the Sub-Saharan African English Desk at SciDev.Net.

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