Brazilian researchers have developed a computer program that locates swimming pools and rooftop water tanks in aerial photographs with the aid of artificial intelligence to help identify areas vulnerable to infestation by Aedes aegypti, the mosquito that transmits dengue, zika, chikungunya, and yellow fever. 

The innovation, which can also be used as a public policy tool for dynamic socio-economic mapping of urban areas, resulted from research and development work by professionals at the University of São Paulo (USP), the Federal University of Minas Gerais (UFMG), and the São Paulo State Department of Health’s Endemic Control Superintendence (SUCEN), as part of a project supported by FAPESP. An article about it is published in the journal PLOS ONE. 

“Our work initially consisted of creating a model based on aerial images and computer science to detect water tanks and pools, and to use them as a socio-economic indicator,” said Francisco Chiaravalloti Neto, last author of the article. He is a professor in the Epidemiology Department at USP’s School of Public Health (FSP), with the first degree in engineering. 

As the article notes, previous research had already shown that dengue tends to be most prevalent in deprived urban areas, so that prevention of dengue, zika, and other diseases transmitted by the mosquito can be made considerably more effective by the use of a relatively dynamic socio-economic mapping model, especially given the long interval between population censuses in Brazil (ten years or more). 

“This is one of the first steps in a broader project,” Chiaravalloti Neto said. Among other aims, he and his team plan to detect other elements of the images and quantify real infestation rates in specific areas to be able to refine and validate the model. 

“We want to create a flow chart that can be used in different cities to pinpoint at-risk areas without the need for inspectors to call on houses, buildings, and other breeding sites, as this is time-consuming and a waste of the taxpayer’s money,” he added.

Machine learning

A previous study used artificial intelligence (AI) to detect water tanks and pools in Belo Horizonte, capital of Minas Gerais state. The researchers first presented satellite images of the city to a computer algorithm with tanks and pools already identified. The deep learning program then found patterns in the images that would make detection possible anywhere, and over time acquired the capability of distinguishing tanks and pools in photographs on its own. 

“It’s genuine machine learning, a sub-area of AI,” said Jefferson Alex dos Santos, a professor in the Computer Science Department at UFMG, and founder of its Pattern Recognition and Earth Observation Laboratory (PATREO).

The more recent study focused on Campinas, the third-largest city in São Paulo state by population. Four areas were chosen, each with different socio-economic conditions according to the census. A drone with a high-resolution camera took aerial photographs of the areas, and two datasets were created, one for water tanks and the other for pools.

The next step entailed training the model and transferring the lessons learned. “We trained the model on Belo Horizonte and applied it to Campinas,” Santos said. With the images obtained in Campinas, the model became more reliable for the region, achieving accuracy rates of 90.23% and 87.53% for pools and tanks respectively. 

Socio-economic indicator

When the algorithm was fully trained, the researchers used other images to detect tanks and pools in the four selected areas of Campinas and cross-referenced them with the census data. The results of the analysis showed larger numbers of roof tanks per square meter in poorer areas and more pools in wealthier areas. 

Even these preliminary findings were useful to predict probable breeding grounds for A. aegypti. “It’s not the final methodology, but it could serve as a basis for a relatively simple practical application such as developing software to map city districts with a high risk of dengue outbreaks,” Santos said. 

According to Chiaravalloti Neto, the model can be used for much more than controlling dengue and other mosquito-borne diseases. “The nation updates its socio-economic database about every ten years, with each population census. Our method could be used for more frequent updates, which in turn could be used to combat other diseases and problems,” he said, adding that more markers can be found in future studies based on aerial images, to refine the algorithms and make them even more accurate.

Drone or satellite imagery?

Although the aerial photographs of Campinas were taken by a drone, the researchers expect the final methodology to use satellite imagery. “We used a drone because it was a pilot project, but large-scale remote sensing and scanning with drones is expensive,” Chiaravalloti Neto said. 

“Also, drones have relatively little range,” Santos added. “For a large-scale project in a major city, we’ll need satellite imagery.” The Belo Horizonte survey used satellite images successfully. These must be high-resolution images so that the software can recognize patterns. Access to this type of image is, fortunately, becoming easier, he said. 

The methodology may seem costly, but actually, it saves time and money by avoiding the need for in-person house calls to map potential breeding grounds. Instead, the city’s public health workers can use the data obtained remotely and processed by AI to select priority areas for physical inspection more assertively.

Next steps

The model currently cannot detect whether water tanks are properly sealed or whether pools are treated to prevent mosquitoes from laying eggs in them. “The methodology could be refined to be capable of distinguishing between properly treated tanks, pools, etc., and others that can or do serve as breeding grounds for the mosquito,” Chiaravalloti Neto said. Detection of such patterns and other signs of potential breeding grounds would make the algorithm even more useful to public health departments.  

The researchers are now installing traps to catch mosquitoes on some 200 street blocks in Campinas. The state of the properties is being carefully assessed, particularly to predict whether the mosquito is likely to breed there. Socio-economic indicators will also be analyzed. The next step will entail an assessment of aerial images of the areas using the logic described above to classify the risk of the presence of A. aegypti and the diseases it transmits.

“As we observe these urban areas, we’ll build a model that prioritizes dengue control measures for the entire city, and then for the rest of Brazil,” Chiaravalloti Neto said.

In addition to FAPESP, the researchers were funded by the Serrapilheira Institute, the National Council for Scientific and Technological Development (CNPq), USP’s Office of the Pro-Rector for Research, and FAPEMIG, the Minas Gerais Research Agency. SUCEN provided structural support. 

A RIKEN physicist and two colleagues have found that a wormhole, a bridge connecting distant regions of the Universe, helps to shed light on the mystery of what happens to information about matter consumed by black holes. 

Einstein’s theory of general relativity predicts that nothing that falls into a black hole can escape its clutches. But in the 1970s, Stephen Hawking calculated that black holes should emit radiation when quantum mechanics, the theory governing the microscopic realm, is considered. “This is called black hole evaporation because the black hole shrinks, just like an evaporating water droplet,” explains Kanato Goto of the RIKEN Interdisciplinary Theoretical and Mathematical Sciences.

This, however, led to a paradox. Eventually, the black hole will evaporate entirely—and so too will any information about its swallowed contents. But this contradicts a fundamental dictum of quantum physics: that information cannot vanish from the Universe. “This suggests that general relativity and quantum mechanics as they currently stand are inconsistent with each other,” says Goto. “We have to find a unified framework for quantum gravity.”

Many physicists suspect that the information escapes encoded somehow in the radiation. To investigate, they super compute the entropy of the radiation, which measures how much information is lost from the perspective of someone outside the black hole. In 1993, physicist Don Page calculated that if no information is lost, the entropy will initially grow, but will drop to zero as the black hole disappears.

When physicists simply combine quantum mechanics with the standard description of a black hole in general relativity, Page appears to be wrong—the entropy continually grows as the black hole shrinks, indicating information is lost.

But recently, physicists have explored how black holes mimic wormholes—providing an escape route for information. This is not a wormhole in the real world, but a way of mathematically supercomputing the entropy of the radiation, notes Goto. “A wormhole connects the interior of the black hole and the radiation outside, like a bridge.”

When Goto and his two colleagues performed a detailed analysis combining both the standard description and a wormhole picture, their result matched Page’s prediction, suggesting that physicists are right to suspect that information is preserved even after the black hole’s demise.

“We discovered a new spacetime geometry with a wormhole-like structure that had been overlooked in conventional computations,” says Goto. “Entropy computed using this new geometry gives a completely different result.”

But this raises new questions. “We still don’t know the basic mechanism of how information is carried away by the radiation,” Goto says. “We need a theory of quantum gravity.”

A new model to analyze infectious disease outbreak data has been developed by mathematicians that could be used to improve disease tracking and control. 

Researchers from the University of Nottingham developed a new data-driven framework for modeling how infectious diseases spread through a population that could reduce errors in decisions made about disease control measures. 

The COVID-19 pandemic has highlighted that the ability to unravel the dynamics of the spread of infectious diseases is profoundly important for designing effective control strategies and assessing existing ones. Mathematical models of how infectious diseases spread continue to play a vital role in understanding, mitigating, and preventing outbreaks.

Dr. Rowland Seymour led the study and explains: “Most of the infectious disease models contain specific assumptions about how transmission occurs within a population. These assumptions can be arbitrary, particularly when it comes to describing how transmission varies between individuals of different types or in different locations, and can be lacking in appropriate biological or epidemiological justification. this can lead to erroneous scientific conclusions and misleading predictions.”

The researchers developed a data-driven framework for modeling how infectious diseases spread through a population by avoiding strict modeling assumptions which are often difficult to justify. The researchers used the method to enhance understanding of the 2001 UK Foot and Mouth outbreak in which over 6 million animals were culled with a cost to the public and private purse of over £8 billion.

The proposed methodology is very general making it applicable to a wide class of models, including those which take into account the population’s structure (e.g. households, workplaces) and individual’s characteristics (e.g. location and age).

"Infectious diseases both within human and animal populations continue to pose serious health and socioeconomic risks. We have developed a suite of contemporary statistical methods that dispense with the need for the underlying transmission assumptions of existing models. Our approach enables instead the analysis to be driven by evidence in the data and hence allowing policymakers to make data-driven decisions about controlling the spread of disease. Our work is another tool in the fight against the spread of infectious diseases and we are excited to develop this framework further," said Dr. Rowland Seymour.

This work has opened several avenues for further research in this area, including improving its super computational efficiency and being applicable in real-time, i.e. when the outbreak is still ongoing. The latter is of material importance for policymakers and government authorities, so they can be responsive to the data that is emerging from the outbreak.

Researchers have developed an automated method that predicts the effectiveness of viral diagnostic tests and designs optimized ones.

The surge of the Omicron variant has highlighted an urgent need for diagnostic tests that accurately detect viruses, even when they mutate. Now, scientists at the Broad Institute of MIT and Harvard have developed the first fully automated system that uses machine learning to design viral diagnostics. 

The method, called ADAPT, helps scientists create highly sensitive diagnostics (can detect low levels of virus) and specific, meaning that they detect only the virus of interest and not others. The researchers used their approach to create diagnostics for each of the nearly 2,000 viruses known to infect vertebrates, including SARS-CoV-2. 

Designing a viral diagnostic involves carefully selecting the best places in a virus’s DNA or RNA for the test to target. Researchers choose those sequences mostly by hand, guided by some rules, but there is also a lot of trial and error. ADAPT, which uses trained algorithms to predict the best sequences for a diagnostic, promises to help scientists rapidly design tests that are more effective for a large number of different viruses and can be quickly modified and scaled as viruses evolve.

“ADAPT is really about developing countermeasures that target the virus that's circulating right now and being prepared to move with the virus as it changes,” said Pardis Sabeti, senior author of the study and an institute member at the Broad. Sabeti is also a Howard Hughes Medical Institute investigator, a professor at the Center for Systems Biology and the Department of Organismic and Evolutionary Biology at Harvard University, and a professor in the Department of Immunology and Infectious Disease at the Harvard T. H. Chan School of Public Health.

“As we’ve watched SARS-CoV-2 adapt in real-time, we’ve learned just how much we need to change with it and other viruses.”

BUILDING A BETTER MODEL

In 2018, a team led by then-graduate student Hayden Metsky in the Sabeti lab began developing a machine learning model to analyze the wealth of viral sequence data being generated by labs around the world.

“Current techniques in machine learning and optimization are really well suited to making sense of all this data,” Metsky said. “Our goal was to better leverage the diverse sequencing data out there to design more effective diagnostics.”

To develop ADAPT, the team first focused their efforts on CRISPR-based tests, which use programmable “guide RNAs” and CRISPR enzymes that find specific viral sequences and generate a fluorescent signal.

The scientists then designed a large number of these tests, each to look for a different target from viral genomes. They used a recently developed Broad technology called CARMEN to measure the effectiveness of thousands of combinations of guide RNAs and viral targets simultaneously. 

Using this large trove of test efficiency data, the researchers then trained a machine learning model to predict which guide RNAs would generate strong signals in a diagnostic test across different viral strains and variants. Metsky says this means that a diagnostic will be likely to detect different lineages — known and even novel ones — as a virus evolves. ADAPT also automatically incorporates new viral genomes from public databases into the design process so that it stays up-to-date as new variants emerge.

“At the core of building good diagnostics is knowing what to target and how to target it,” Sabeti said. “We spend a lot of time building technologies to do that, but we’ve shown that with thoughtful algorithmic work, we can get these methods to work much, much better.”

DETECTING SARS-COV-2 AND BEYOND

Early in 2020, when COVID-19 was beginning its march around the world, Sabeti, and Metsky, by then a postdoctoral fellow, quickly refocused their efforts. 

“When we concentrated on COVID in mid-January 2020, it was remarkable how quickly the global community was generating genomic data on the virus, with 20 genomes at the time and that number growing exponentially,” Metsky said. “We had been building machine learning models and algorithms that accounted for viral variation based on genomic data, and wanted to apply our work to rapidly generate highly sensitive assays for SARS-CoV-2 that maintained that sensitivity as the virus evolves.”

Metsky and the team used ADAPT to create diagnostics for SARS-CoV-2 and 66 other viruses that are genetically related or cause similar symptoms. When they tested four of ADAPT’s designs in the lab, they found that the tests were more sensitive than diagnostics developed according to more traditional rules.

Though the team first used their approach to create CRISPR-based diagnostics, they say ADAPT can be applied to other sequence-based tests as well, and are already adapting it for qPCR, the most widely used viral diagnostic tool. 

Metsky and Broad software engineer Priya Pillai also built a website where researchers can find and visualize diagnostics the team designed for known viruses, or run ADAPT on new data to develop their own. As ADAPT and its user base grow, the team will continue to improve their website to make it easy to use for labs with little in-house supercomputing power or bioinformatics expertise.

Ultimately, the team says other researchers could use ADAPT to create new, highly effective diagnostics for known or emerging viruses. In the meantime, Metsky says tests that distinguish between SARS-CoV-2 and other respiratory viruses that cause similar symptoms will continue to be critical, and ADAPT could be useful in developing those tests. “If COVID becomes endemic, we’ll need to do a better job identifying the wide swath of respiratory viruses that are circulating, including their vast and ever-changing variation,” he said.