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Fighting Poverty With AIFrom identifying the best ways to improve agriculture, finance and education in impoverished areas, to finding those who need help the most through satellite images, fighting poverty with AI is becoming a common practice. Although the idea of using artificial intelligence to address such sensitive issues can be unsettling for some, the technology has delivered some remarkable benefits.

Identifying Poverty

According to a Big Cloud article, identifying poverty is an important first step in addressing it. AI technology can identify the direst of situations, thereby enabling poverty-relief programs to provide aid swiftly, efficiently and effectively. The technology also helps identify the primary causes of poverty in different regions. Factors such as war, a lack of resources and political instability all represent some of the causes of poverty. Each of these situations has different solutions. This means that war-induced poverty should have a different solution from poverty that’s a result of a lack of resources. And AI can facilitate the identification of root causes and appropriate alleviatory measures.

Agriculture

Numerous challenges hinder agricultural work, posing obstacles for farmers worldwide, especially those relying on their yields for sustenance and livelihood. Beyond mere survival, food plays a vital role in employment and personal advancement. The advent of AI not only enhances agricultural practices but also contributes to the fight against poverty.

While farmers in developed nations have access to information on innovative farming techniques and impending natural disasters, their counterparts in developing countries struggle to obtain such resources. Here, AI can offer valuable assistance. One of its key contributions is providing farmers with crucial insights on optimal fertilizers and crops tailored to their specific regions. Additionally, AI enables swift detection of contamination and crop diseases, surpassing the capabilities of traditional farming methods. Consequently, farmers can salvage a greater portion of their yields.

Finance and Education

Employment and education equality are crucial factors that directly impact individuals’ vulnerability to extreme poverty. The use of AI to address these issues holds the potential to aid organizations and governments in the fight against poverty. With the increasing reliance on the internet and AI in finance and education, leveraging these tools becomes more feasible to eliminate inequalities in these domains, as highlighted by Big Cloud. While the idea of AI teachers may evoke apprehension, it also presents exciting possibilities. An AI teacher or a teacher assisted by AI can personalize education based on a student’s needs and abilities.

AI can also create new financial opportunities for impoverished communities worldwide. In developed nations, both employers and job seekers already utilize AI algorithms, and these technologies can effectively strengthen job markets. Additionally, AI has the potential to assist impoverished families in establishing robust credit scores. By prioritizing essential data for families and lenders, AI can facilitate the identification of loans that best suit individuals’ needs, enabling those in dire need to improve their quality of life.

Satellites and AI

AI also works in tandem with other technological solutions to fight poverty. For example, Stanford University scholars used satellite images from throughout sub-Saharan Africa to predict poverty in various regions. Nighttime images of electric lights and daytime images of infrastructure like roads and agriculture were used as indicators of a region’s wealth.

When an algorithm used these images to make these poverty predictions, the level of accuracy was between 81% and 99%, as reported by Big Cloud. Burke and his team suggest that anti-poverty programs and NGOs could use this technology to better understand the most effective ways to fight poverty.

Looking Ahead

AI is proving to be a powerful tool, enabling swift identification of those in need and the root causes of poverty. The technology holds the potential to promote employment and education equality, creating new financial opportunities and personalized learning experiences. Its various applications and capabilities in fighting poverty suggest that it can be a vital tool in the exploration and implementation of initiatives that can improve living conditions for all.

– Christina Albrecht
Photo: Flickr

Fight Poverty in Uganda
When one thinks about ending global poverty, one often thinks about economic possibilities and foreign policy. However, thinking deeper, one may wonder about what specific, pragmatic factors they can focus on as surefire ways to reduce poverty globally. According to researchers at Stanford University, one of those surefire ways is electricity. By looking at Uganda, a developing country in East Africa, these researchers have proved that having access to reliable energy sources is vital in raising the world’s poor out of poverty. Here is how electricity can fight poverty in Uganda.

The Power of Electricity

Though many developed nations take access to reliable sources of electricity for granted, in many regions of the world this basic commodity is still missing. In developing countries, almost a billion people lack access to electricity – with more than half of these people being children under age 18. In a world that is becoming more digital and automated, those living without electricity are at a heavy disadvantage. Many factors that often lead to a better quality of life – such as plumbing, clean cooking and internet access – hinge upon access to electricity.

Even as technology progresses, those in impoverished countries continue to lag in the field of electricity. For example, even though in the past 10 years more of the global population has gained access to electricity, in Sub-Saharan Africa, the number of people without access to electricity has increased. Estimates say that by 2030, 660 million people will still lack access to electricity – most of them in Sub-Saharan Africa.

Hope in Uganda

Access to electricity is vital to the fight against poverty in Uganda. While one may consider how crucial it is, according to Stanford researchers, it is incredibly important. Using cutting-edge AI research technology, researchers looked at Uganda, a country that has struggled to access sophisticated technology. Researchers focused on Uganda’s rapidly-expanding power grid, examining how the expansion of electricity services affected the people of Uganda.

The study’s results were clear. Between 2015 and 2020, electricity access in Uganda’s population jumped from 18.5% to 42.1%. When looking at the communities that gained electricity access, the study discovered that they were able to increase their wealth at double the rate of those who still were without access.

One may ask how electricity access in Uganda creates opportunities for economic growth. Considering that almost 75% of all Ugandans work in the agricultural center, having access to electricity means access to new, effective technologies that increase yields and economic prosperity. Electricity access also drastically improves many facets of domestic life, including access to clean cooking fuels and methods. As the access to electricity increases for Ugandans, their wealth increases along with opportunities for improved living standards and long-term economic growth.

Looking Forward

The Stanford researchers hope their new research method, and the findings from their study of electricity access in Uganda, will help inform economic policy globally. As the fight against poverty in places like Uganda continues, considering simple commodities, like electricity, is vital in raising the standards of living of the poor. By understanding how technology can make such a huge economic impact in the fight against poverty in Uganda, better policies can form to help developing countries flourish.

– Elijah Beglyakov
Photo: Pixabay

Poverty and Technology
Stanford University launched its Poverty and Technology Lab in 2016, promoting the collaboration of students and Silicon Valley experts to use their knowledge of the technology world in order to provide practical solutions to poverty.

Reversing the Roles of Technology

Stanford initiated its Poverty and Technology Lab as a project within the university’s Center for Technology and Inequality (CPI). Its goal with this initiative is to redefine the various uses of technology. Experts and students at the university recognize the possible dangers of technology, as it has the potential to decrease employment opportunities and perpetuate global inequalities. This Lab aims to switch this role by applying technology to benefit low-income people, rather than prioritizing the improvement of middle-class lives.

The Technology and Poverty Lab fostered collaboration among students, professors and Silicon Valley technology experts. In addition, the Lab incorporates the voices and opinions of people living in low-income communities into these conversations. This inclusion ensures that the tools being innovated are truly geared toward issues that the impoverished are enduring.

A Unique Approach in the Classroom

Stanford also launched a course series that parallels the goals of this lab. Professor David Grusky, Director of Stanford’s Center on Poverty and Inequality, teaches the first courseEnding Poverty with Technologyas well as the remaining courses.

Grusky explained the unique approach of both his classes and the lab in an interview, stating that “[Stanford’s Poverty and Technology Lab] is an opportunity to not take on problems at their root sources, which is our instinct. …  Sometimes the best way forward is to not take on problems at their causes, at their sources, but rather to approach them less directly and instead opt for approaches that proceed in a different way. It’s kind of a more pragmatic approach.”

Grusky said his classes are largely geared toward teaching students to think in a manner that enables them to create solutions using this unique method: “One of the outputs [of my class] is just training students in how you would think through problems in this way. So it’s not that they actually make headway on the problems themselves, but they learned how to approach problems of this type, and hopefully, in the future, we’ll make headway.”

Forming Projects

Many of the projects that have formed through Stanford’s Poverty and Technology Lab are information-oriented. Examples include services that aid employment, boost access to educational opportunities and enable low-income communities to rate preexisting assistance programs. These projects primarily focus on the process of linking data to evaluate current programs related to these processes.

“We have a lot of work underway in which we negotiate data-sharing agreements and use them to put together linked administrative data sets that then allow us to carry out evaluations,” Grusky reported.

From the United States to Abroad

Stanford created the Poverty and Technology Lab to find poverty solutions in the Bay Area and the United States. However, much of the work by this lab is applicable to impoverished communities across the globe. Acknowledging this global relevance, some students have even begun the process of testing their innovations abroad.

“Although we used the U.S. as a kind of a testbed in trying to understand the problems, some of [the students] actually went on and worked on their projects in other areas,” Grusky revealed.

Experimentation with one such project occurred in Peru, where a female student devised an entrepreneurship app. She proposed this application to include a convenient toolkit for those struggling to secure employment opportunities. This app would help these impoverished individuals to avoid this challenge by learning to start their own business.

At the surface, this project engages students at Stanford University. But it also urges technology experts across the country to examine the impact of their products more broadly. Students and scholars nationwide are collaborating with community members to find practical technological solutions to poverty.

– Hannah Carroll
Photo: Flickr

Poverty Mapping TechniquesHow many people live in poverty? The answer a search engine might give overlooks the complexity of the issue. A great deal of poverty data comes from the World Bank, which still relies on household surveys. These household surveys can be very inaccurate, and statistics like these are critical in the fight against poverty. Thankfully, many organizations are working on creating better poverty mapping techniques to help fight global poverty.

The Need for Poverty Mapping Techniques

Governments, private companies and NGOs must know who needs help, what works and how much they need in order to fight poverty. With more accurate data, aid programs can be rolled out more effectively, directly targeting populations who need it the most. Accurate data also determines the effectiveness of aid or other interventions, which helps agencies discover what works. It is important for the missions of many agencies to have accurate data on poverty, but methods for collecting this data are flawed.

One issue with current data collection is the amount of data available. The World Bank is a leader in the fight against global poverty, and it compiles many official statistics on poverty rates. Historically, the main way the World Bank typically measures poverty is through household surveys. However, these surveys do not reach as many people as they should. For lower-income countries, an annual investment of $1 billion would be required to expand these surveys to generate consistent, accurate data.

Not only are these surveys too narrow, but they are also not frequent enough. Surveys typically happen every few years and even every decade in some countries with lower capacities. Between 2002 and 2012, no poverty data was collected from 29 countries.

The Problems with Current Poverty Mapping Techniques

The most common surveying method employed by the World Bank is the household survey. Unfortunately, household surveys have built-in inaccuracies and miss many people, usually some of the poorest. This method tries to measure poverty by sending surveys to households, but these surveys are ill-suited to measure an atypical home environment. Many people trying to avoid poverty live in open households, whose membership is usually in flux. These households operate to reduce poverty collectively in ways that a typical survey cannot easily measure. When data from these households is not interpreted differently from other household data, overall data on poverty can be skewed.

Satellites Mapping Poverty

This dearth of accurate data was the inspiration for a team of Stanford researchers. Marchall Burke, David Lobell and Stefano Ermon have spent the better half of the last decade creating better poverty mapping techniques. The solution they are working on now is satellite mapping.

The team has used artificial intelligence to map poverty using publicly available satellite imagery. The system examines poverty by analyzing the wealth of assets in a given area as seen from space. By indexing images of wealthy areas and poor areas, the program can identify levels of poverty in other areas. It uses a variety of factors like lighting at night, roofing, infrastructure, roads and other easily recognizable traits to do so. Utilizing deep learning, the program is able to correlate factors and create an idea of poverty in an area with fairly high accuracy. The model explains about 70% of asset wealth variation at the village level. This means the model can predict more accurately than other attempts at mapping poverty using higher resolution imagery and mobile phone mapping. The ability to distinguish poverty at a village level also means that the program can identify levels of poverty in places that surveys never go, with much less cost and time required.

Household surveys have become obsolete compared to more modern and effective methods. Better poverty mapping techniques like the Stanford researchers’ will enable organizations to fight poverty with a greater level of accuracy, which will make this decade of poverty-fighting more efficient than the last.

– Brett Muni
Photo: Flickr

Satellite Technology Can Help Combat Poverty
Over the last two decades, there has been an observed decrease in poverty levels in the eastern and southeastern regions of Asia. Unfortunately, 42% of people in sub-Saharan Africa are still living in absolute poverty conditions. To aid developmental efforts on the continent, scientists and engineers are exploring how satellite technology can combat poverty across the entire continent.

Identifying Poverty 

It is well-known in the academic community that research relating to poverty in comparatively poor regions is hard to come by. Surveys and censuses are not frequent enough to provide an accurate understanding of poverty in Africa. This lack of data makes strategizing and taking action to alleviate poverty in some areas particularly difficult. Luckily, some are conducting new research to explore the possibility of using satellite imagery to identify poverty-stricken areas. In fact, Stanford researchers Marshall Burke, David Lobell and Stefano Ermon spent the last 5 years studying the use of available images to assess poverty conditions in Africa over time.

Satellite imagery and machine learning can work together to identify poverty and development hot-spots. Images that satellites take during the night could expose the absence of lights in an area that may lack electricity. Images from the day may also show the status of general infrastructures like housing, waterways, agricultural techniques and roads. These features are components of development and identifying their status should be able to help efforts to provide communities with the resources they lack. Neural networks, which are a component of machine learning, use these satellite images to find patterns in communities. The Stanford researchers tested this technology on 20,000 different African villages and created models for the conditions they observed. While machine learning is a new tool for the fight against poverty, it is a promising source of information and understanding that can enhance our response. 

Famine and Natural Disasters

In its response to conditions in Africa, USAID has long known that satellite technology can help combat poverty. In 1986, NASA began working with the U.S. Geological Survey (USGS) to detect and prevent famine through the famine early warning system (FEWS). Using remote sensing and existing satellite data that NASA collected, USAID has been able to predict famine conditions in sub-Saharan Africa. 

In 2000, NASA and USGS also collaborated to establish more effective response-planning mechanisms through an updated FEWS Network. They have used this network to predict floods, landslides, fires and other natural threats to development. In 2017, the network was able to make a credible appeal for food aid in a war-torn Somalia, which reduced deaths from starvation. Researchers have also concluded that the early warning system lowered mortality rates in Kenya, where the number of “severely hungry” Kenyans was 1.75 million in 2017, versus 2.8 million in 2011. Good disaster response can better inform developmental projects in poorer countries, which makes the network a crucial component of a greater effort to alleviate poverty. 

The Long-term

Many are now looking at how satellite technology can combat poverty. The technology can no doubt open more doors to understanding economic development. Experts suggest that satellite intel on land usage could aid non-governmental organizations in crafting policies for better resource allocation in the region and the possibilities do not stop there. Although there is still work to do to alleviate poverty in Africa, viewing this advancing technology as an enabler for further research and action is incredibly exciting.

Arshita Sandhiparthi
Photo: Flickr

Classroom
The Stanford Center on Poverty and Inequality (CPI), a nonpartisan research center, is monitoring trends in poverty and inequality, developing policy and explaining the root causes of poverty. This education begins in the classroom and finishes in the field, such as rural villages in Africa. The Center supports research students and established scholars in the field. All research is published in CPI’s magazine Pathways, which will likely become the new fact-based journal on poverty, inequality, income, discrimination and more.

Since CPI’s beginning in 2006, the Center has received support from the US Department of Health and Human Services, Stanford University, Office of the Assistant Secretary for Planning and Evaluation, Pew Charitable Trusts and others. This type of intellectual approach and curiosity might be the next step needed for a meaningful change in poverty reduction.

Ending Poverty with Technology is just one of many courses within the Center. Stanford students have the opportunity to pick an issue and use the semester to determine how they would better the situation. Sarukkai, a Stanford student majoring in symbolic systems stated, “In the land of opportunity it only makes sense that every human being has access to the same resources and pathways to success—an ideal we are far from achieving.”

As an undergraduate capstone project, one CPI team proposed a web platform and mobile app called “CareSwap.” This app is designed to help low-income families trade childcare within their respected network of friends and family. Although the course has ended, the “CareSwap” team plans to continue to develop and execute its website and app. The ending of a course does not mean the work ends.

The course is simply a place where the inspiration begins—the work ethic and dreams of the Center’s students cannot be diminished by the end of a semester. Poverty reduction begins in the classroom but is carried out during the long hours of the student’s personal time.

“Our idea evolved so much in the last few months after our interviews and conversations with parents and childcare experts,” the students said. “We are excited to develop it further next year. This project has become far more than a class assignment for each of us.” An idea that began in the classroom later developed into an app and website, making thousands of children’s lives easier and safer.

Some of the proposed projects may even be adopted for further development by the Stanford Poverty & Technology Lab, an initiative dedicated to developing technology-based solutions to rising inequality in the United States. Currently, the lab is developing an app, under Bill Behrman, director of the Stanford Data Lab, for “mapping” poverty in California. The app has the potential to help government agencies and nonprofits better target certain demographics by delivering estimates of poverty, unemployment, income and other indicators for very small geographic areas of the state.

Innovative and creative thinking are both necessary to tackle any complex topic, particularly poverty. In the classroom, both attributes are present, as well as the ability to look at the situation from various perspectives. The communal feel and global mindset of Stanford are felt in every classroom of the Center on Poverty and Inequality. “It’s not about a professor teaching and the students learning,” one student said. “We’re all just part of the same team trying to build products that work to reduce poverty.”

Reducing poverty encompasses so many different aspects of society. However, like anything truly successful it should begin in the classroom. Poverty reduction can better the quality and longevity for millions of people worldwide, as academics and students studying to better the world—it only makes sense to tackle poverty from inside the classroom through innovation and creative thinking.

Danielle Preskitt
Photo: Flickr

Toy Inspires Low-Cost Lab Aid to Detect Malaria
Malaria is a life-threatening disease caused by parasites that are transmitted through the bite of an infected mosquito. In 2015 alone, there were 212 million cases of malaria and 429 thousand deaths. Suffice it to say that malaria is a global health problem.

Even worse is that Sub-Saharan Africa continues to carry a disproportionately high share of the global malaria burden. In 2015, the region was home to 90 percent of malaria cases and 92 percent of malaria deaths.

The good thing is that malaria is preventable and curable, given the proper tools to do so. A device called a centrifuge that spins a blood sample very quickly and separates different cells can detect malaria. Centrifuges, though, are expensive, bulky and require electricity – which makes it inefficient in regions such as Sub-Saharan Africa.

A low-cost lab aid to detect malaria is in dire demand, which is exactly what Manu Prakash, a professor of bioengineering at Stanford University, realized on a trip to Uganda. On his trip, Prakash says he found centrifuges used as doorstops because there was no electricity.

Back in California, Prakash experimented with spinning toys in his search for a model for a low-cost lab aid to detect malaria. Though toys are not the conventional approach to developing a lab aid, Prakesh argues that toys hide profound physical phenomena we take for granted.

After experimenting with several spinning toys, including a yo-yo, they stumbled upon the children’s toy known as the whirligig or buzzer. The toy is made of a disk that spins when the strings that go through it are pulled.

This new low-cost lab aid to detect malaria dubbed the paperfuse, can separate pure plasma from whole blood in less than 1.5 minutes, and isolate malaria parasites in 15 minutes. The paperfuse has an ultra-low-cost of fewer than 20 cents, weighs only two grams and is, therefore, field-portable. The paper fuse could be the tool that helps detect and end malaria in low-income countries in the near future.

Mayan Derhy

Photo: Flickr

solar-powered water purifier
Recently, scientists at Stanford University and the Department of Energy’s SLAC National Accelerator Laboratory have developed a tiny, solar-powered water purifier resembling a rectangular bit of black glass. The new device does not have a name yet, but is being referred to as a “tablet.”

Access to safe drinking water is a problem for 663 million people in the world. The World Health Organization reports that unsafe water supplies, sanitation and hygiene are responsible for 842,000 deaths every year, 361,000 of which are children under the age of 5.

What sets this device apart from other water purifying gadgets on the market is its use of a wider range of light. According to the Global Citizen Organization, the device absorbs 50 percent of incoming sunlight energy, while other purifiers only absorb 4 percent.

According to project leader Chong Liu, “This can greatly enhance the speed of water disinfection. It does not need any additional energy or effort for treating water.” In an experiment, the tablet took only 20 minutes to function. In contrast, other purifying systems that use only UV rays can take up to almost 48 hours.

On the surface of the tablet is a layer of nanoflakes and a small amount of copper. The nanoflakes’ exposure to sunlight and water excites electrons in the device and results in the release of hydrogen peroxide. This chemical kills bacteria in the water, making it safe to drink. As of now, however, the tablet is only capable of killing E. coli and lactic acid bacteria.

In an experiment published in the Nature Nanotechnology journal, researchers placed the solar-powered water purifier in a container with 25 milliliters of water for 20 minutes. It killed 99.99 percent of the bacteria in the water, an impressive amount for such a short amount of time. Even Liu said, “We didn’t expect it to work that well at first.”

Since the device is new and not ready for the market yet, it has no fixed price. But according to Liu, “The material itself is cheap and the synthesis process is facile. So we assume that the device would be of low-cost.”

More experiments and field tests must be done before the tablet can be distributed. Nonetheless, this solar-powered water purifier has the potential to cheaply and quickly help people who struggle to obtain clean drinking water.

Karla Umanzor

Photo: Flickr

Poverty Mapping with the Help of Artificial Intelligence
Poverty mapping has proven to be a difficult task in past years. Poor countries are often reluctant to account for poverty due to corruption or the inability to do so because of ongoing conflicts. The World Bank reports that only 20 African countries conducted two or more population surveys on poverty from 2000 to 2010.

A new study from Stanford University hopes to improve poverty mapping by combining high-resolution satellite imagery with artificial intelligence.

According to a feature article published by online tech magazine Motherboard, Neal Jean, a Ph.D. engineering student at Stanford, has designed a machine learning algorithm that can predict poverty in Malawi, Nigeria, Rwanda, Tanzania, and Uganda.

Using satellite imagery to determine “nightlights” and levels of economic activity as a method of poverty mapping is nothing new. What’s different about the algorithm designed by Jean and his team is that it looks at daylight images of infrastructure, such as roads and metropolitan areas, which it then uses to identify nighttime patterns.

“Our basic approach involved a machine learning technique called ‘transfer learning,’ which is the idea that you can solve a hard problem – in our case, predicting poverty from satellite images – by trying to solve an easier one,” Jean said.

According to Motherboard, the algorithm may prove to be a very effective method of poverty mapping, especially given the cost of traditional household surveys and the lack of viable alternatives. Another advantage of the machine learning model is its transparency, as it doesn’t rely on private or protected information.

Jean told Motherboard that he hopes to make the technology open-source and cooperate with NGOs to put the algorithm to use. “If we could provide them with high-resolution poverty maps, they could overlay them on regions where operations already exist, and ultimately inform where they distribute funding,” he argued.

Jean’s machine learning algorithm is not the only artificial intelligence tool that is providing better data for poverty alleviation efforts. South African computer scientist Muthoni Masinde developed a solution that can forecast droughts with 98 percent accuracy, combining traditional knowledge with new technologies. In recognition of her achievements, she received a Distinguished Young Women Researcher award at the 2016 South African Women in Science Awards.

Technological advance has been the greatest impetus for poverty reduction throughout history, and artificial intelligence is the future of poverty mapping. It provides economists and scientists with better data in order to pinpoint and resolve problems that are holding developing countries back.

Philip Katz

Photo: Flickr

Rosenkranz Prize
The Rosenkranz Prize aims to fund the work of Stanford University’s rising research stars who have the desire to improve healthcare in developing countries but who lack the necessary resources.

Most grants in the scientific field are awarded to established researchers. But because the Rosenkranz Prize is awarded to rising researchers, it is able to split funds between two young researchers.

Marcella Alsan, MD, PhD, is investigating how the division of labor among men and women begins at a young age in the developing world. Alsan theorizes that this is because young girls are responsible for taking care of younger siblings, missing endless days of school.

Alsan states, “Anecdotally, girls must sacrifice their education to help out with domestic tasks, including taking care of children, a job that becomes more onerous if their youngest siblings are ill.”

More than 100 million girls worldwide do not complete secondary school. Alsan will be analyzing whether medical interventions in children under the age of 5 show an increasing trend in schooling for their older sisters.

By analyzing this data, Alsan will be able to prove or disprove if sick siblings affect their older sister’s school participation. If this thesis proves true, implementing medical interventions in younger children will increase the number of girls in school. By completing school, girls will be able to not only take care of family and their own children but also have a strong background in education.

The second Rosenkranz Prize winner, Jason Andrews, an infectious disease specialist, is focusing his funds on the development of cheap, effective diagnostic tools for infectious diseases.

Andrews recalls working in rural Nepal as an undergraduate student and “founded a nonprofit organization that provides free medical services in one of the most remote and impoverished parts of the country . . . one of the consistent and critical challenges I encountered in this setting was routine diagnosis of infectious disease.”

Andrews realizes that the diagnoses are hindered by lack of electricity, limited laboratory resources and lack of trained personnel. To eliminate these obstacles, Andrews is developing “an electricity-free, culture-based incubation and identification for typhoid; low-cost portable microscopes to detect parasitic worm infections; and most recently an easy-to-use molecular diagnostic tool that does not require electricity.”

Andrews does not want to develop new diagnostic approaches. Rather, Andrews believes he can develop the diagnostic approaches already in place to function in an affordable and accessible manner.

With the Rosenkranz Prize, Andrews is also able to develop a simple, rapid, molecular diagnostic or cholera that is 10 times more sensitive than the tests currently available. Andrews plans to test this new technology in Nepal.

The Rosenkranz Prize has allowed two individuals dedicated to helping healthcare in developing countries by providing the necessary funding. With the help of Alsan, girls may be able to attend school without worrying about ill siblings, and Andrews has shed light on the problems facing many developing countries when providing medical help. But by further developing the diagnostic approaches available, healthcare will change for the better.

– Kerri Szulak

Sources: Scope, Stanford
Photo: PickPik