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Digital Health: The new tool in the fight against malaria?

Sustainable Development Goals
The recently launched Sustainable Development Goals aim to eradicate malaria by 2030. There are many who believe this is possible, and with heavyweights such as the Bill and Melinda Gates Foundation giving their full support and a range of new technologies on the horizon, it may well be time we finally resign malaria to the history books.
Using digital health to track malaria

Funding is a critical element to achieving this goal; funding for innovative vaccines, preventative treatment options and for new technologies that will further strengthen the fight. The move towards digital health, something which many African countries are already heavily investing in, includes innovations such as mobile and web-based health communication platform. For example FOLUP is a secure platform on which patients can actively participate in their own health management processes.

Using digital health innovations to track and predict the spread of diseases such as malaria will help us understand how the disease has spread, and who is most vulnerable.

A network of globally distributed handheld molecular diagnostic devices named Q-POC™ will send their results to the cloud creating a real-time map of disease and drug-resistance prevalence called the Internet of Life™. This will put the world’s healthcare systems one step ahead, mobilising resources to areas that are flagged as hot-spots and preventing the spread and impact of the disease.

Evaluating the impact of malaria interventions

The Internet of Life will be a crucial tool for tracking the progress of different interventions in the fight against malaria. “A network of Q-POCs coupled to the Internet of Life have enormous potential as we strive to eradicate malaria.” explains Dr Henry Staines from St George’s Medical and Molecular Parasitology Group

“The ability of Q-POC to digitally upload information to the Internet of Life will provide the ‘bigger picture’. Should a country alter its antimalarial drug policy? Are location specific resources required to stamp out pockets of emerging resistance? Are eradication strategies working? All these questions could be answered at speed, using real-time data. These are exciting times for next-generation malaria diagnostics and we are looking forward to demonstrating their effectiveness.”

Learning lessons from the Zika epidemic

With the emergence of the Zika virus, a larger focus has been placed on modelling the spread of malaria. The Aedes aegypti mosquito is a multiple offender when it comes to spreading disease. Not only is it the prime suspect in the current Zika epidemic, it can also carry dengue. As it likes to spend its time inside human dwellings, it’s even riskier.

Entomologists have started analysing the DNA fingerprint of the blood inside these mosquitoes, giving researchers some insight into how the insects spread the disease.

In a recent study scientists collected mosquitoes from inside 19 households in Iquitos, a Peruvian port city on the Amazon, along with cheek swabs to capture DNA from 275 residents. By comparing DNA signatures, the research team paired the blood meals of 96 mosquitoes to the individuals in the households. They found that people who stayed inside more often, and the larger members of the households, were more likely to get bitten.

The data gathered from this type of research could be used to inform models of possible future spread of diseases by mosquitos, which could in turn inform the health authorities of where resources need to be mobilised. However there aren’t currently enough studies matching mosquito blood meals to individuals to make concrete conclusions about which humans they prefer. Further studies are required.

Improving our disease spread models

Looking at disease spread from a different angle, a study published earlier this year by Logue et al. gathered data on the feeding patterns of disease carriers, such as the malaria carrying Anopheles mosquito. Crucially, they found that the mosquitoes studied fed on more than one host. This information could significantly change how the spread of malaria is modelled.

Speaking to WIRED, the study’s lead scientist Peter Zimmerman explained “If your model required 100 mosquitoes to be infected and every single mosquito fed on just one person, you have that one-to-one relationship and your model has boundaries. Now, if mosquitoes feed on four people at a time, suddenly the mosquito could be spreading its disease to four times the number of people.”

Further research on host preference and how diseases spread is needed to create a clearer picture for diseases such as malaria however, the progress made so far is looking hopeful. And combining digital health systems like the Internet of Life with real-time data and complex mathematical modelling will create a powerful weapon in the fight to end the disease.

 

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