As per the records of theWorld Health Organization, the first formally reported incidence of Zika virus occurred in Brazil in May 2015. The disease then rapidly spread to other countries in Americas and East Asia, affecting more than 1,000,000 people. Zika virus is primarily transmitted through bites of infected mosquitoes of the species Aedes (Aedes aegypti and Aedes albopictus). The abundance of mosquitoes and, as a result, the prevalence of Zika virus infections are common in areas which have high precipitation, high temperature, and high population density.Nonlinear spatio-temporal dependency of such data and lack of historical public health records make prediction of the virus spread particularly challenging. In this article, we enhance Zika forecasting by introducing the concepts of topological data analysis and, specifically, persistent homology of atmospheric variables, into the virus spread modeling. The topological summaries allow for capturing higher order dependencies among atmospheric variables that otherwise might be unassessable via conventional spatio-temporal modeling approaches based on geographical proximity assessed via Euclidean distance. We introduce a new concept of cumulative Betti numbers and then integrate the cumulative Betti numbers as topological descriptors into three predictive machine learning models: random forest, generalized boosted regression, and deep neural network. Furthermore, to better quantify for various sources of uncertainties, we combine the resulting individual model forecasts into an ensemble of the Zika spread predictions using Bayesian model averaging. The proposed methodology is illustrated in application to forecasting of the Zika space-time spread in Brazil in the year 2018.

While artificial intelligence has found a lot of use cases in many industries, another area that is witnessing a lot of traction is for Social Good. Many companies and organisations are either collaborating with not-for-profit organisations or are developing solutions that may help communities and society in general by integrating AI. AI is solving some of the pressing social issues. From detecting Zika virus to the Covid-19 pandemic, the use cases for AI for Social good is on the rise. Many global companies such as Google, Microsoft, Facebook and government bodies are deploying AI for positive social initiatives.

Artificial Intelligence technology can today automatically mine through news reports and online content from around the world, helping experts recognize anomalies that could lead to a potential epidemic or, worse, a pandemic. In other words, our new AI overlords might actually help us survive the next plague. These new AI capabilities are on full display with the recent coronavirus outbreak, which was identified early by a Canadian firm called BlueDot, which is one of a number of companies that use data to evaluate public health risks. The company, which says it conducts "automated infectious disease surveillance," notified its customers about the new form of coronavirus at the end of December, days before both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) sent out official notices, as reported by Wired. Now nearing the end of January, the respiratory virus that's been linked to the city of Wuhan in China has already claimed the lives of more than 100 people.

When a mysterious illness first pops up, it can be difficult for governments and public health officials to gather information quickly and coordinate a response. But new artificial intelligence technology can automatically mine through news reports and online content from around the world, helping experts recognize anomalies that could lead to a potential epidemic or, worse, a pandemic. In other words, our new AI overlords might actually help us survive the next plague. These new AI capabilities are on full display with the recent coronavirus outbreak, which was identified early by a Canadian firm called BlueDot, which is one of a number of companies that use data to evaluate public health risks. The company, which says it conducts "automated infectious disease surveillance," notified its customers about the new form of coronavirus at the end of December, days before both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) sent out official notices, as reported by Wired.

Machines learning promises us a future where hefty and complex data can be analyzed in no time and patterns can be logically drawn from an ocean of randomness. Apart from electronic medical records, the human gene and genome has also rendered a sizeable playing field. Machine learning method is either supervised: training involving labeled DNA sequence which marks the start and end location of a gene; unsupervised: training which takes place without training data, or a hybrid of both. The generalizable predictive nature of machine learning had enabled us to predict the impact of drug on a person with DNA mutation (i.e., pharmacogenomics), functional consequences of DNA mutations (i.e., pathogenicity prediction), how a locus mutation impact the expression level of a gene (eQTL mapping) and many more. The peak of this recent hype lies in Google's DeepVariant release last November, when high-throughput sequencing (HTS) is now guaranteed with a greater accuracy via deep learning.

By Karina Toledo Agência FAPESP – A platform that can diagnose several diseases with a high degree of precision using metabolic markers found in patients' blood has been developed by scientists at the University of Campinas (UNICAMP) in Brazil. The method combines mass spectrometry, which can identify tens of thousands of molecules present in blood serum, with an artificial intelligence algorithm capable of finding patterns associated with diseases of viral, bacterial, fungal and even genetic origin. The results have been published in Frontiers in Bioengineering and Biotechnology. "We used infection by Zika virus as a model to develop the platform and showed that in this case, diagnostic accuracy exceeded 95%. One of the main advantages is that the method doesn't lose sensitivity even if the virus mutates," said Melo's supervisor Rodrigo Ramos Catharino, principal investigator for the project.

Washington: Scientists have developed an artificial intelligence system that can accurately diagnose Zika virus and several other viral, bacterial and even genetic diseases from the patient's blood. The platform developed by scientists at the University of Campinas (UNICAMP) in Brazil, can identify tens of thousands of molecules present in blood serum, with an artificial intelligence algorithm. "We used infection by Zika virus as a model to develop the platform and showed that in this case, diagnostic accuracy exceeded 95%. One of the main advantages is that the method doesn't lose sensitivity even if the virus mutates," said Rodrigo Ramos Catharino, principal investigator at UNICAMP. Another strength of the platform, he added, is the capacity to identify positive cases of Zika even in blood serum analysed 30 days after the start of infection, when the acute phase of the disease is over.

Technology has established its place as an integral component of daily life. Industry frontrunners like IBM have been part of the process from the beginning, but the tech mogul is now taking digital intervention to the next level. With the new Science for Social Good program, IBM is using the rise of AI to help solve some of the most significant problems facing the world today. In the early days, artificial intelligence was extremely limited in its capabilities. By the 1980s, more advanced programs came into play.

Of the many great things promised by Crispr gene editing technology, the ability to eliminate disease by modifying organisms might just top the list. But doing that requires perfecting something called a gene drive. Think of gene drives as a means of supercharging evolution to, say, give an entire population of mosquitoes a gene that kills the Zika virus. The trouble is, organisms develop resistance to gene drives, much like they eventually outwit pesticides and antibiotics. Researchers dedicate no small amount of time and thought to creating gene drives that can outsmart evolution because the potential payoffs are so great.

Of the three shopping days this weekend, Cyber Monday is arguably the most chatbot-friendly due to the digital nature of the day. "We're seeing a continued excitement for interacting with AI products, like chatbots, through the heavy holiday promotion of tech products with built-in AI, like Amazon's Echo and Alexa and Google's new Home," said Andrew Seay, director of mobile experience at Vibes. But as for exactly how chatbots played a role in the shopping experience and number of sales, experts say it's only up from here. "Chatbots will be giving Santa some help this holiday season for a few boys and girls on the digital nice list, but they are unlikely to give retailers the needle-moving sales boost they're looking for," said Jake Bennett, CTO at POP. "Chatbots are just too new, and there are too many logistical hurdles for widespread adoption in 2016." Bennett said that, on the one hand, chatbots provide an ideal platform for an interactive holiday gift guide -- quite literally guiding customers to certain products.