Battling disease

The next step in dengue control might be an artificial intelligence prediction tool

A machine-learning system harnesses hundreds of parameters ranging from wind speed to local roof architecture to try and predict the next outbreak.

An artificial intelligence system that claims to predict dengue outbreaks up to three months in advance has been rolled out in a Malaysian state — with several cities across Asia and Latin America doing pilot trials.

The machine-learning system harnesses hundreds of parameters ranging from wind speed to local roof architecture to try and predict where the next outbreak will be. It then advises responders on the intervention likely to be most effective in that particular area, such as fogging or removing water pools.
Dengue is a mosquito-borne viral infection that has grown rapidly in recent decades, with half the world’s population at risk. Cases of infection number in their millions every year, with half a million hospitalised with severe dengue, of whom about 13,000 lose their lives to the disease.
In Asia, vector control costs over $300 million annually, while South America spends US 1 billion to control dengue, according to Dhesi Raja, of the Institute for Medical Research Malaysia, who co-invented the system with Rainier Mallol, selected by the UN as a Young Leader for the Sustainable Development Goals.

Raja, who won a young innovator award from Harvard University’s School of Public Health, told a meeting that the new system grew out of frustration at the current “passive, reactive” way of managing vector-borne disease.

“There is a need for us to do some sort of prediction in real-time,” he told the Geneva Health Forum 2018 last month (April 10 – 12). “A need to log into a system to see what is the amount of reported cases today, where are the cases, where are the outbreaks, where are the predicted outbreaks.
“We have good measures like fumigation, larvicides, GM mosquitoes, we even have Wolbachia [bacteria that reduce the ability of insects to become infected with viruses], but the point is if we do not know when and where this outbreak might occur we spend a lot on unplanned management and nationwide campaigns.”
The system is known as AIME (Artificial Intelligence in Medical Epidemiology). As doctors in the state send in notifications of dengue cases, they feed automatically into the system which then searches through over 90 databases for 276 variables that influence its spread — from local terrain and elevation to roofing types, thunderstorms, water accumulation and population density.
From these, Raja says it deduces where the next outbreaks will be within a 400-metre radius.
The team has tested the system in Manila in the Philippines, the states of Selangor and Penang in Malaysia and Rio de Janeiro in Brazil — comparing what AIME predicted with what actually unfolded. It matched reality with an accuracy of 81 to 84 per cent, Raja told the meeting based on unpublished data.
The state of Penang started using the system at the beginning of 2018, paying $120,000 to run it over the next year.

Oliver Brady, assistant professor at the London School of Hygiene and Tropical Medicine, who is working with the Vietnamese Ministry of Health on predicting dengue outbreaks using satellite data, questions the model’s statistical power to identify meaningful relationships between a “vast array of covariants”, on the one hand, and “the number of dengue cases in an area — which is a very, very small dataset”.

Brady says machine-learning systems can work well for a while, but their value wanes with time. “If your system is really, really good at predicting outbreaks then someone will go out and start fogging, insecticiding in the predicted area — and the transmission dynamics change. So all those important relationships that you’ve been learning over your past years of data might now be completely different”.
But Raja is reporting early signs of success: cases of the disease in one Malaysian state have fallen by three-quarters in the four months since it began operations, he says, careful to note that there is no proof the link is causal.

This article wad first published on SciDev.Net.

Support our journalism by subscribing to Scroll+ here. We welcome your comments at letters@scroll.in.
Sponsored Content BY 

Snippets of wisdom on the health care industry by Dr. Kevin Lofton

His sessions stressed on the importance of patient centric healthcare.

At the Hospital Leadership Summit 2017, Dr Kevin Lofton, CEO Catholic Health Initiatives, spoke on the need to focus on patient experience, the role of the leader and shared some ideas from the practices of his own hospital chain. Here are some snippets from Dr Lofton’s presentation that will provide some food for thought. The Bringing Health to Life content hub contains his and many other insights and best practices for healthcare delivery.

The two kinds of willing patients

During the summit, a consensus emerged that the health care industry needs to learn customer centricity from other industries. However, the health care industry is unique in several ways and one of the fundamental differences is the nature of its customer. Dr Lofton spoke about how the customer i.e. the patient in the health care industry is different by way of motivation. He reminded the gathering that nobody willingly seeks out a doctor.

Play

The paradigm shift needed in health care

Dr Lofton emphasised that patient centricity needs to become a part of the very philosophy of a health care facility, a philosophy that drives every action and motivates every employee in the organisation. He described this revaluation of purpose as a paradigm shift. Dr Lofton spoke about how patient centricity starts much before the patient walks into the hospital, that the patient’s tryst with the health care system starts before a visit to the doctor is warranted. In this clip, Dr Lofton provides an example of one such paradigm shift for health care providers.

Play

At the 2017 Hospital Leadership Summit, Dr Lofton shared several such insights from his experience in the US health care system. He emphasised especially on the need of empathy alongside clinical skill among health care providers.

For more insights and best practices for healthcare delivery, visit Abbott’s Bringing Health to Life portal.

This article was produced on behalf of Abbott by the Scroll.in marketing team and not by the Scroll.in editorial staff.