Limit the Spread of Epidemics
In the past, there has been some difficulty in tracking and containing epidemics. In 2014, the Ebola virus killed thousands in West Africa. At the time, many national health systems had trouble properly addressing and controlling its spread. With aid agencies not knowing where to dedicate their attention, more people fell to Ebola. Determining where to distribute a vaccine is critical for the future of any region. It is often difficult to make the correct decision when there is not enough information on human mobility, the spread of an epidemic and its lethality in certain areas. People could have better contained Ebola had newer technology been available to help aid agencies track its spread. The Global Epidemic Prevention Platform (GEPP) may be able to limit the spread of epidemics.

A Solution for Limiting the Spread of Epidemics

The Global Epidemic Prevention Platform (GEPP) is a project that Korea Telecom (KT) Corporation and the Ghanaian government created to improve Ghana’s health information system and limit the spread of epidemics. The project employs information and communications technology (ICT) to gather data on epidemics. It works by gathering existing data and by incorporating newer input from its users. It analyzes Call Detail Record (CRD) data to determine the spread of people such as cross-border movement. Its main goal is to prepare its users for possible epidemics, whether its users consist of the general public or the Ghanaian government. Its existence helps detect the early spread of an epidemic, allowing governments more time to respond and giving humanitarian agencies and NGOs the opportunity to identify possible relationships and trends.

GEPP Explained

There are three parts to the GEPP: GEPP Public, GEPP Clinic and GEPP Gov. The GEPP Public’s intention is to inform Ghanaians of epidemic-prone areas. When someone is nearing one such area, they receive a notification and warning of its status. If a user is in an area that may become contaminated soon, the app provides disease information and prevention measures for pre-response during their stay. They also receive a list of nearby hospitals.

The GEPP Clinic is for the public to make real-time reports to nearby health centers in the event of an epidemic outbreak. Users can fill out a report for either themselves or another individual with their symptoms. This report goes into the GEPP Clinic’s database and gives the government a better idea of what is happening in a particular region.

The government uses GEPP Gov, which allows it to access the data gathered from GEPP Public and GEPP Clinic to monitor any possible health crises. As a result of the digitization of airport immigration information, the government can consider immigration levels when monitoring. This also takes away the need to manually compile this information. All of this aims to help developing countries and their governments prepare for and reduce the impact of epidemics.

If a disease has already spread and it is too late to prevent infection, the GEPP can also address the aftermath of disease by conducting communications in the area. Not only can it address health crises, but it can also apply to natural disasters and their control. In the event of a natural disaster, the GEPP can help aid workers provide shelter, food and health care to victims. If an area does not have a working mobile network, as a result of a natural disaster or not, the GEPP can use its collected data to contact them via satellites and Geographical Information Systems (GIS).

GEPP Support

The Ghana Health Service, KT, Mobile Network Operators (MNOs), Resolution 202, Resolution 136, Resolution 36 and WTDC Resolution 34 support the Global Epidemic Prevention Platform. While all of these play a large role in assisting the GEPP in its goal to limit the spread of epidemics, MNOs arguably do the most. MNOs provide the app with its official data. It gathers data from around the world and its software anonymizes it to protect privacy. This data then stays on a server or an International Telecommunication Union (ITU) cloud and can go towards creating a dynamic map for the ITU. Humanitarian actors and NGOs can, with permission, view this data through MNOs.

– Nyssa Jordan
Photo: Flickr

Big Data
Three years into the United Nations’ latest agenda to fight global poverty and promote peace, health and justice, the chief Sustainable Development Goal of 2030 to end extreme poverty has become a contest to procure and deliver the right resources for the world’s most vulnerable people at just the right pace. There is a race against rising inequality and time, but some economic circles have come to regard one performance-enhancing resource as more valuable than oil and with the potential to boost poverty relief — big data.

Big Data to End Global Poverty

During a lecture at Singularity University Global Summit 2018, a lead economist for the World Bank, Wolfgang Fengler, shared his curiosity about using data to end global poverty by asking, “What would it take to create a data revolution for the SDGs [Sustainable Development Goals]?” Fengler oriented summit-goers to subterranean depths as he compared big data to oil, and emphasized how their values are only realized in the efficiency of its production modes: collection, refinement and delivery in a usable form.

In 1990, 1.9 billion people were considered extremely impoverished; in 2015, the final year of the United Nations Millennium Development Goals, that number was 836 million, a 66 percent decrease. Pointing to the World Poverty Clock, a real-time dashboard for poverty numbers created by Fengler, the current global poverty escape rate is 1.1 people per second. That rate should be 1.6 people per second to put an end to global poverty by 2030.

World Poverty Clock

The World Poverty Clock shows data from specific countries, and these types of snapshots provide reliable stories that can inform effective policy and strategic poverty alleviation practices. According to Singularity Hub, next steps for the World Poverty Clock include presenting data by specific regions within countries with the idea in mind that there are region-specific issues related to poverty.

Forbes Magazine contributor Bernard Marr offers some corrections to the data and oil comparison, one that suggests much richer potential for data. Oil is a finite resource that requires a massive amount of ancillary resources to deliver a final product. Contrarily, data has a low cost of production and can become more useful with every use. He also contends big data is environmentally innocuous and has a wider variety of application beyond its crude state.

New Kind of Renewable Resource

While Marr takes issue with comparing big data to an “old world resource,” he does concede to its versatility and value in shrinking hunger and battling climate change. He likens it more so to a renewable energy source such as wind or the sun. The World Poverty Clock reports that poverty is rising in 13 African nations. Two of those nations are Africa’s largest oil exporters: Angola and Nigeria, which both produced more than 1.5 million barrels of oil in 2017.

Rounding out the rest of the African OPEC nations, Guinea and Libya are labeled as “off track,” or “unable to reach the sustainable development goal target at the current rate;” Gabon and Algeria are also considered to have “no extreme poverty.” In Nigeria, oil production accounts for 10 percent of the GDP of the new world capital of extreme poverty; almost half of the nation’s 180 million people live within poverty’s grips.In Angola, 30 percent of its 25 million live in extreme poverty; oil production is expected to comprise 10 percent of its GDP.

Combatting Poverty

These macro-level findings support The Economist’s and Forbes Magazine’s positions on data’s supremacy to oil as a precious resource for profit and a poverty alleviation tool. Crude oil has less of a guarantee, if any at all, to be wielded as such a resource as it does not necessarily translate to economic stability in nations where the gross national income per capita has been decreasing since 2015.

Just as marketing research uses big data to track discrete consumer insights — such as millennial spending trends or researchers’ use of data to identify the demographic most likely to be excessive sun tanners — big data has the power for direct combat against extreme poverty.

Big Data Around the Globe

In China, the Guizhou province developed a cloud-computing platform that tracks the financial status of 6 million impoverished people in 9,000 villages. China aims to usher 10 million people out of poverty annually from 2016 to 2020. In Tongzi county, the government issued subsidies to needy villagers and a data platform monitors the distribution of these subsidies, minimizing the risk of embezzlement by unscrupulous officials.

Zhou Xing, an expert of the poverty-relief office in Guizhou province, said, “Big data really helps make poverty-relief more precise and efficient.” On the days before big data, Xing added, “poverty relief work was difficult because the information of residents was written by hand and passed to central authorities via a series of local officials, which could be hampered by corruption.”

In Rwanda, American researchers have leveraged cellphone metadata to estimate wealth and poverty distributions and the telephoning habits of the affluent and those with more modest means.

Infinite Possibilities

Putting an end to extreme poverty can potentially be achieved through fiber-optic cables rather than petroleum pipelines. The Sustainable Development Goals for 2030 are fixed; through big data, the potential for precisely architected solutions to end extreme poverty seems infinite.

Thomas Benjamin
Photo: Flickr


Humanity is currently producing more data annually than in the rest of human history combined. This data is created all throughout our daily lives, from using mobile phones and social media to just shopping. If analyzed correctly, this information can be used to answer many questions and provide new insights. This massive volume of information is known as Big Data. Big Data is increasingly being used in the humanitarian sector, in a growing movement known as digital humanitarianism.

There are several benefits to using Big Data in humanitarian responses. The most prominent benefit is having access to real-time information, which means that organizations can make more informed decisions by adjusting and adapting plans as the environment changes. Additionally, access to multiple sources increases the reliability of the information.

Big Data can likewise be used to anticipate humanitarian crises. By monitoring sources, patterns and trends, potential crises can be detected and averted. These systems can also be used to improve future preparedness by warning people and seeking their direct feedback.

Several prominent humanitarian organizations like Doctors Without Borders, the Red Cross, the United Nations Children’s Fund, the U.N. Refugee Agency and the Office for the Coordination of Humanitarian Affairs (OCHA) have units working on new technologies in their specific fields.

OCHA, for instance, runs several programs that digitize humanitarian data to make it more readily available. This includes ReliefWeb, a website that provides 24-hour coverage of disasters, conflicts and crises for the international aid community, and the Digital Humanitarian Network, which uses digital networks to support humanitarian response.

This year, OCHA will also open the Centre for Humanitarian Data, the goal of which is to increase the use and impact of data in the humanitarian sector.

However, most humanitarian organizations do not have the staff and resources to cope with the amount of data generated in crisis situations. They thus rely on online activists using crowdsourcing and open source software like Ushahidi and Open Street Maps to map crises. These activists are also part of digital humanitarianism.

Crisis mapping by means of digital humanitarianism is becoming a standard tool in crisis response and has proven useful in several recent events including the 2010 earthquakes in Haiti and Chile, the 2011 uprisings in Libya, the 2014 Ebola outbreak and the 2015 Nepal earthquake.

One of the suggested ways to use Big Data in the humanitarian sector is to improve the sharing of information between communities in need and those who aim to help them. Big Data and increased connectivity allow humanitarian organizations to better understand where to target humanitarian assistance.

Helena Kamper

Photo: Flickr


Big Data Fight Against Poverty
Big Data, as its name would suggest, refers to large sets of unstructured and structured data generated at high speeds from digital and traditional data sources around the globe. The big data movement has gained momentum over the years, particularly in the business sphere, but experts have also realized that insights derived from big data have implications for the fight against poverty.

In the agricultural space for example, The Forum for Agricultural Research in Africa has found that farmers in Africa barely produce what they need to get by.

Food Policy experts have found that helping these farmers in Africa and other parts of the world produce more food is key to lifting millions out of poverty. One of the key ways of attaining this goal, according to the experts, is by providing farmers, scientists and entrepreneurs in the agricultural sector with adequate access to data and information generated at agricultural research centres worldwide.

Ft Magazine highlights how big data is being used to mitigate the harmful effects that accompany natural disasters. It explains that in the aftermath of the devastating Haiti earthquake of 2010, researchers at the Karolinska Institute of Columbia University successfully managed to track the locations of 600,000 displaced people using data mining techniques.

The article in Ft Magazine also illustrates how analyzing data from social media sites like Twitter and Facebook can provide early warning systems for both human and natural disasters. For example, increased references to food or ethnic strife on these sites can serve as indicators of possible famine or civil unrest.

Mark Van Rijmenam, the founder of Datafloq, further empathizes the use of Big data in the disaster response field, saying “Big data offers, for example, the possibility to predict food shortages by combining variables such as drought, weather conditions, migrations, market prices, seasonal variation and previous productions.”

In the area of public health and sanitation, Van Rijmenam talks about harnessing data from call detail records to map variations in the population of low-income dwellers in order to direct efforts at building water pipes and latrine facilities for the slum dwellers. This effort will see improved sanitation in such areas, bringing about better health.

A pilot program by the World Bank in Tanzania called SMS for life has generated major improvements in the distribution of malarial medical stock. By getting clinical workers to send an SMS with their stock count every week, the program has enabled senior coordinating staff to re-stock clinics more accurately.

SMS for life has managed to reduce the number of rural health facilities in Tanzania without medical stock from 78 percent to 26 percent.

World Bank blogger Alla Morrison has likened the transformative potential of big data to the transformative effect that electricity had on industry in the 19th century. She argues that big data is a game changer for business, and notes the unprecedented productivity gains in the second industrial revolution due to businesses in all sectors taking advantage of the new electrical resource.

Likewise, as humanity forges ahead, it is important that organizations, governments and individuals take advantage of big data to address the seemingly intractable challenge of poverty.

June Samo

Sources: Enterra Solutions 1, Enterra Solutions 2, FT Magazine, IEEE, SAS, Smart Data Collective, UN GLOBAL PULSE, World Bank 1, World Bank 2
Photo: Flickr

Big Data matters. It has proven to be accurate in realizing trends, developing strategies, and noticing rising phenomena. It is a tool being used more frequently with each passing year that helps governments, scientists, educators, academics, and businesses operate in the most efficient ways possible.

Statistics and massive data are no longer being used solely by political pollsters and economists. Now, even philanthropy and global aid are reaping the benefits of big data. One example of this relatively new use of big data is the NGO Aid Map, which complies massive amounts of volunteer data into a useful and informative tool.

The NGO Aid Map is an interactive map designed by global aid advocacy group InterAction. The map shows a 2D image of Earth, akin to Google Maps, and features a series of numbered orange circles corresponding to individual countries. The number in the circle represents the number of Non-Governmental Organizations currently active in its respective nation.

Circles vary by size: the larger, the more projects. Users can click on the country that they are interested in. Upon clicking, the map zooms in to frame the specific country and then breaks the initial circle into smaller parts separated by city, town or region.

Clicking on a city or region circle brings users to a list of the ongoing projects in that area, each with an external link, a listing of the NGO conducting the project, and a short description of its mission.

The Aid Map is designed for optimal usability. Users can select from a number of filters to show where aid is needed most based on a series of metrics. These include poverty rate, malnutrition prevalence, agricultural share of GDP, and gross aid income (Official Development Assistance).

Clicking on any one filter will highlight countries based on their score. It is clear based on color contrasts which countries need more assistance in a given area. Some countries rank severely on multiple metrics.

Data for the map is compiled by volunteers on the ground who send their reports back to InterAction. The larger organization then compiles all that data and puts it into the map, where large amounts of numbers tell large amounts of stories. Users can empirically see the missions listed by “sectors.”

For example, InterAction reports that there are 1,679 medical missions, and 1,220 education projects ongoing. It also uses the data to list countries with the most, and alternatively least, amount of projects.

The map is a powerful tool that enables users to gain valuable insight into exactly what is being done around the globe to combat famine, poverty, and disease. InterAction, along with many others, hope that numbers and data will help make aid more efficient and effective in the near future.

Joe Kitaj

Sources: NGO Aid Map, Interaction
Photo: Flickr

cloud computing
As a whole, the African continent has one of the most rapidly growing economies in the world. The area averages a remarkable rate of 5 percent growth per year. And yet a host of problems that hover in the near future threaten to impede such progress. The African continent, as a result, must increasingly rely on the private sector to ensure growth does not stagnate.

One such problem is the worldwide urbanization boom that will experience a 3.5 billion urban population increase to 6.3 billion people. It is expected that Africa will mirror this growth, percentage wise. Additionally, for Africa to stay relevant as a hub for business, it will need to play an integral role in creating jobs for the 500 million who will enter the workforce by 2020.

Technology will always be a key to the future, and experts suggest that by harnessing the power of cloud-based computing, the African continent can grow steadily. Here are some of the reasons and ways in which cloud-based computing is a model for the future.

Easy for Startups

To put it simply, cloud computing systems offer a much cheaper way to get businesses off the ground. Old, stack-oriented servers required entrepreneurs to hire workers, rent an office space and market the company. Cloud-based systems require just a few dollars and mediocre broadband access.

Mobile Access

“Africa is a Disneyland for entrepreneurs!” said Derek Kudsee. The 600 million mobile users in Africa are great consumers. And what these consumers need is new apps, content and mobile services. Cloud technology provides this speed that old stack-based technology simply cannot.

Business Agility

Consider the influx of individuals coming into the cities. Studies have shown that cloud computing is excellent for business agility (which is the ability of the business to adapt rapidly and efficiently in response to changes in the business environment).

Helping Big Data

Managers across Africa are beginning to notice some of the fallout of the urban population influx—clogged roads, for example. Big data that is powered by cloud computing provides quick and cost-efficient analysis of this problem. By pairing these two together, individuals will be able to quickly improve African infrastructure.

While technology should not be the only solution we look to, it can certainly be helpful in guiding the way to the future.

– Andrew Rywak

Sources: IT News Africa 1, IT News Africa 2, ITWeb Africa
Photo: Humanipo

Downsides of Big Data
It is easy to get excited about all the new information we now have about the world’s development projects. Maps and tables, charts and graphs flood our inboxes with ‘big data.’ Most recently, AidData published a huge dataset on Tracking Chinese Aid to Africa. All the hype has caused some backlash, and rightfully so. Big data is still data and requires the same careful handling as any other dataset. This is not meant to dull enthusiasm or lessen the use of data. This is a precaution against the misuse and overgeneralization of big data. One size does not fit all, and overgeneralizations from large or small datasets can be dangerous. Here are Big Data’s 4 downsides found by practitioners and academics.

1) Big data is not a panacea. One size does not fit all. The dynamic nature of development projects means that many are time-place specific. While sweeping data collection projects can lead to better practices at high-level institutions, implementing policies based on improperly generalized data is bad policy and poor use of data.

2) Difficulties in filtering relevant information. Data from developing countries regarding health systems, political upheaval, natural disasters, etc. are most often reported by vulnerable people experiencing the event first hand. The sourcing of the data is often social media. Aside from possible problems with the validity of the data, the sheer amount of potential data is enormous. Key word searches across selected media yield thousands of data points which have to be carefully reviewed to filter for relevancy. The computer programs are simply not nuanced enough to pull out the differences between hate speech, for example, and slang (as shown in a study on mapping hate speech in twitter recently). Additionally, a parallel problem is availability of reliable and secure statistical processing. Unlike data processing for pharmaceutical companies, aid data processing is not backed by billions of dollars in profit.

3) Data exhaustion on the ground. By the time social scientists are through cleaning, manipulating, and making sense of the data, the situation on the ground has often changed. This is called “data exhaustion.” The big data collectors (UN, World Bank, USAID, AidData) are constantly playing catch up. This means that the people on the ground are not able to use the most up-to-date information. The use of social media has mitigated the delay; however, data extraction and implementation of policies based on data is a top-down approach that may not accord with the culture of the project or practical feasibility. For example, the best way to empower women according to big data analyses might be to get women into the work place allowing them independent incomes. The on-the-ground reality might be that they are already responsible for non-paid work, such as childcare or maintaining subsistence crops, which already takes up their whole day.

4) Validity of data is questionable. As indicated by the debates over the validity of AidData’s Tracking Chinese Aid to Africa, socially sourced data cannot be the only source of data to influence policy. Self-reporting has inherent “barriers, blindspots and biases.” For example, the information collected from the Arab Spring was based on self-reporting of goings-on. The outside world used information from texts, Tweets, Facebook and blog posts to analyze the situation.

These four potential downsides of big data all suggest the need for caution in using data to inform development policy.

– Katherine Zobre

Source: Relief Web