This case study describes how two climate resilience action projects in Semarang City, Indonesia, were able to provide new mechanisms allowing better engagement between the Semarang city government and its citizens. With the introduction of the Flood Early Warning System (FEWS), flood-prone communities in the Beringin River Basin are now able to evacuate to safe shelters before flood incidents occur. Through the Health Information and Early Warning System (HIEWS), citizens can access real-time information related to dengue fever cases in the city. Although the focus areas are different, both projects aim to help communities become more resilient to the impacts of climate change, specifically floods and vector-borne disease. We find similar patterns in the two cases, in which efforts to enhance community participation are essential to guarantee the success of the projects. Enhanced community engagement is achieved through the thoughtful consideration of local knowledge and social networks, intensive assistance to increase awareness and motivation of the community, and understanding governance structures to ensure that funds are allocated through formal handover processes to continue and expand the results of the interventions. These findings are useful and important to guide any climate change adaptation projects toward better sustainability and ownership, especially in the application of an early warning system or information system that requires technology, sustainable budget allocation from the local government to operate and maintain the system, and buy-in from local communities…(More)”
BBC Four to investigate how flu pandemic spreads by launching BBC Pandemic app
BBC Press Release: “In a first of its kind nationwide citizen science experiment, Dr Hannah Fry is asking volunteers to download the BBC Pandemic App onto their smartphones. The free app will anonymously collect vital data on how far users travel over a 24 hour period. Users will be asked for information about the number of people they have come into contact with during this time. This data will be used to simulate the spread of a highly infectious disease to see what might happen when – not if – a real pandemic hits the UK.
By partnering with researchers at the University of Cambridge and the London School of Hygiene and Tropical Medicine, the BBC Pandemic app will identify the human networks and behaviours that spread infectious disease. The data collated from the app will help improve public health planning and outbreak control.
The results of the experiment will be revealed in a 90 minute landmark documentary, BBC Pandemic which will air in spring 2018 on BBC Four with Dr Hannah Fry and Dr Javid Abdelmoneim. The pair will chart the creation of the first ever life-saving pandemic, provide new insight into the latest pandemic science and use the data collected by the BBC Pandemic app to chart how an outbreak would spread across the UK.
In the last 100 years there have been four major flu pandemics including the Spanish Influenza outbreak of 1918 that killed up to 100 million people world wide. The Government National Risk Register estimates that infectious diseases are an even greater risk since 2015 and pandemic flu is the key concern as 50% of the population could be affected.
“Nobody knows when the next epidemic will hit, how far it will spread, or how many people will be affected. And yet, because of the power of mathematics, we can still be prepared for whatever lies ahead. What’s really important is that every single download will help improve our models so please please do take part – it will make a difference.” explains Dr Fry.
Dr Abdelmoneim says: “We shouldn’t underestimate the flu virus. It could easily be the cause of a major pandemic that could sweep around the world in a matter of weeks. I’m really excited about the BBC Pandemic app. If it can help predict the spread of a disease and be used to work out ways to slow that spread, it will be much easier for society and our healthcare system to manage”.
Cassian Harrison, Editor BBC Four says: “This is a bold and tremendously exciting project; bringing genuine insight and discovery, and taking BBC Four’s Experimental brief absolutely literally!”…(More)”
The ‘Five Safes’: a framework for planning, designing and evaluating data access solutions
Paper by Felix Ritchie: “The ‘Five Safes’ is a popular way to structure thinking about data access solutions. Originally used mainly by statistical agencies and social science academics, in recent years it has been adopted more widely across government, health organisations and private sector bodies. This paper explains the Five Safes, how the concept is used to organise and simplify decision-making, and how it helps to address concerns of different constituencies. We show how it aligns to recent regulation, anticipating the shift towards multi-dimensional data management strategies. We provide a number of practical examples as case studies for further information. We also briefly consider what issues the Five Safes does not address, and how the framework sits within a wider body of work on data access which challenges traditional data access models…(More)”.
Using big data to predict suicide risk among Canadian youth
SAS Insights “Suicide is the second leading cause of death among youth in Canada, according to Statistics Canada, accounting for one-fifth of deaths of people under the age of 25 in 2011. The Canadian Mental Health Association states that among 15 – 24 year olds the number is an even more frightening at 24 percent – the third highest in the industrialized world. Yet despite these disturbing statistics, the signals that an individual plans on self-injury or suicide are hard to isolate….
Team members …collected 2.3 million tweets and used text mining software to identify 1.1 million of them as likely to have been authored by 13 to 17 year olds in Canada by building a machine learning model to predict age, based on the open source PAN author profiling dataset. Their analysis made use of natural language processing, predictive modelling, text mining, and data visualization….
However, there were challenges. Ages are not revealed on Twitter, so the team had to figure out how to tease out the data for 13 – 17 year olds in Canada. “We had a text data set, and we created a model to identify if people were in that age group based on how they talked in their tweets,” Soehl said. “From there, we picked some specific buzzwords and created topics around them, and our software mined those tweets to collect the people.”
Another issue was the restrictions Twitter places on pulling data, though Soehl believes that once this analysis becomes an established solution, Twitter may work with researchers to expedite the process. “Now that we’ve shown it’s possible, there are a lot of places we can go with it,” said Soehl. “Once you know your path and figure out what’s going to be valuable, things come together quickly.”
The team looked at the percentage of people in the group who were talking about depression or suicide, and what they were talking about. Horne said that when SAS’ work went in front of a Canadian audience working in health care, they said that it definitely filled a gap in their data — and that was the validation he’d been looking for. The team also won $10,000 for creating the best answer to this question (the team donated the award money to two mental health charities: Mind Your Mind and Rise Asset Development)
What’s next?
That doesn’t mean the work is done, said Jos Polfliet. “We’re just scraping the surface of what can be done with the information.” Another way to use the results is to look at patterns and trends….(More)”
Advancing Urban Health and Wellbeing Through Collective and Artificial Intelligence: A Systems Approach 3.0
Policy brief by Franz Gatzweiler: “Many problems of urban health and wellbeing, such as pollution, obesity, ageing, mental health, cardiovascular diseases, infectious diseases, inequality and poverty (WHO 2016), are highly complex and beyond the reach of individual problem solving capabilities. Biodiversity loss, climate change, and urban health problems emerge at aggregate scales and are unpredictable. They are the consequence of complex interactions between many individual agents and their environments across urban sectors and scales. Another challenge of complex urban health problems is the knowledge approach we apply to understand and solve them. We are challenged to create a new, innovative knowledge approach to understand and solve the problems of urban health. The positivist approach of separating cause from effect, or observer from observed, is insufficient when human agents are both part of the problemand the solution.
Problems emerging from complexity can only be solved collectively by applying rules which govern complexity. For example, the law of requisite variety (Ashby 1960) tells us that we need as much variety in our problemsolving toolbox as there are different types of problemsto be solved, and we need to address these problems at the respective scale. No individual, hasthe intelligence to solve emergent problems of urban health alone….
- Complex problems of urban health and wellbeing cause millions of premature deaths annually and are beyond the reach of individual problem-solving capabilities.
- Collective and artificial intelligence (CI+AI) working together can address the complex challenges of urban health
- The systems approach (SA) is an adaptive, intelligent and intelligence-creating, “data-metabolic” mechanism for solving such complex challenges
- Design principles have been identified to successfully create CI and AI. Data metabolic costs are the limiting factor.
- A call for collaborative action to build an “urban brain” by means of next generation systems approaches is required to save lives in the face of failure to tackle complex urban health challenges….(More)”.
Building the Learning City
Daniel Castro at GovTech: “…Like other technologies, smart cities will evolve and mature over time. The earliest will provide basic insights from data and enable local leaders to engage in evidence-based governance. These efforts will be important, but they will represent only incremental change from what cities have already been doing. For example, Baltimore created its CitiStat program in 1999 to measure all municipal functions and improve oversight and accountability of city agencies. Early smart cities will have substantially more data at their disposal, but they will not necessarily use this data in fundamentally new ways.
The second stage of smart cities will use predictive analytics to identify patterns and forecast trends. These types of insights will be especially valuable to city planners and local officials responsible for improving municipal services and responding to changing demands. These cities will reduce downtime on critical municipal infrastructure by performing preventive maintenance on vehicles, bridges and buildings, and more quickly intervene when public health and safety issues arise. This stage will rely on powerful data-driven technologies, such as the systems that enable Netflix to offer movie recommendations and Amazon to suggest additional products for customers.
The third stage of smart cities will focus on using “prescriptive analytics” to use data to optimize processes automatically. Whereas the second stage of smart cities will be primarily about using data to supply insights about the future that will allow city leaders to evaluate different choices, this third stage will be about relying on algorithms to make many of these decisions independently. Much like a system of smart traffic signals uses real-time data to optimize traffic flow, these algorithms will help to automate more government functions and increase the productivity of municipal employees.
At all three stages of smart city development, there is an opportunity for city leaders to look beyond local needs and consider how they can design a smart city that will be part of a larger network of cities that share and learn from one another. On its own, a smart city can use data to track local trends, but as part of a network, a smart city can benchmark itself against a set of similar peers. For example, water and waste management departments can compare metrics to assess their relative performance and identify opportunities for change.
If they hope to successfully develop into learning cities, cities can begin the process of setting up to work jointly with their peers by participating in forums such as the Global City Teams Challenge, an initiative to bring together government and industry stakeholders working on common smart city problems. But longer-term change will require city leaders to reorient their planning to consider not only the needs of their city, but also how they fit into the larger network….(More)”
Data Sharing Vital in Fight Against Childhood Obesity
According to UNICEF, solving some of the most complex problems affecting children around the world will require access to different data sets and expertise from diverse sectors. The rapid rise in the availability of quality data offers a wealth of information to address complex problems affecting children. The charity has identified an opportunity to tap into this potential through collaborative working, prompting the development of DataCollaboratives.org in partnership with The Governance Lab at the NYU Tandon School of Engineering, and the Omidyar Network. The aim for DataCollaboratives is to encourage organisations from different sectors, including private companies, research institutions, government agencies and others, to exchange and share data to help solve public problems.
The initiative is now being promoted in Scotland through UNICEF’s partnership with The Data Lab, who will work together to deliver a Data Collaboratives hub in Scotland where data scientists and strategists will work on some of the most important issues facing children around the world. Finding solutions to these problems has the potential to transform the lives of some of the most disadvantaged children in Scotland, the UK, and around the globe….(More)”.
Blockchain: Blueprint for a New Economy
Book by Melanie Swan: “Bitcoin is starting to come into its own as a digital currency, but the blockchain technology behind it could prove to be much more significant. This book takes you beyond the currency (“Blockchain 1.0”) and smart contracts (“Blockchain 2.0”) to demonstrate how the blockchain is in position to become the fifth disruptive computing paradigm after mainframes, PCs, the Internet, and mobile/social networking.
Author Melanie Swan, Founder of the Institute for Blockchain Studies, explains that the blockchain is essentially a public ledger with potential as a worldwide, decentralized record for the registration, inventory, and transfer of all assets—not just finances, but property and intangible assets such as votes, software, health data, and ideas.
Topics include:
- Concepts, features, and functionality of Bitcoin and the blockchain
- Using the blockchain for automated tracking of all digital endeavors
- Enabling censorship?resistant organizational models
- Creating a decentralized digital repository to verify identity
- Possibility of cheaper, more efficient services traditionally provided by nations
- Blockchain for science: making better use of the data-mining network
- Personal health record storage, including access to one’s own genomic data
- Open access academic publishing on the blockchain…(More)”.
Crowdsourcing Accountability: ICT for Service Delivery
Paper by Guy Grossman, Melina Platas and Jonathan Rodden: “We examine the effect on service delivery outcomes of a new information communication technology (ICT) platform that allows citizens to send free and anonymous messages to local government officials, thus reducing the cost and increasing the efficiency of communication about public services. In particular, we use a field experiment to assess the extent to which the introduction of this ICT platform improved monitoring by the district, effort by service providers, and inputs at service points in health, education and water in Arua District, Uganda. Despite relatively high levels of system uptake, enthusiasm of district officials, and anecdotal success stories, we find evidence of only marginal and uneven short-term improvements in health and water services, and no discernible long-term effects. Relatively few messages from citizens provided specific, actionable information about service provision within the purview and resource constraints of district officials, and users were often discouraged by officials’ responses. Our findings suggest that for crowd-sourced ICT programs to move from isolated success stories to long-term accountability enhancement, the quality and specific content of reports and responses provided by users and officials is centrally important….(More)”.
Patient Power: Crowdsourcing in Cancer
Bonnie J. Addario at the HuffPost: “…Understanding how to manage and manipulate vast sums of medical data to improve research and treatments has become a top priority in the cancer enterprise. Researchers at the University of North Carolina Chapel Hill are using IBM’s Watson and its artificial intelligence computing power to great effect. Dr. Norman Sharpless told Charlie Rose from CBS’ 60 Minutes that Watson is reading tens of millions of medical papers weekly (8,000 new cancer research papers are published every day) and regularly scanning the web for new clinical trials most people, including researchers, are unaware of. The task is “essentially undoable” he said, for even the best, well-informed experts.
UNC’s effort is truly wonderful albeit a macro approach, less tailored and accessible only to certain medical centers. My experience tells me what the real problem is: How does a patient newly diagnosed with lung cancer, fragile and scared find the most relevant information without being overwhelmed and giving up? If the experts can’t easily find key data without Watson’s help, and Google’s first try turns up millions upon millions of semi-useful results, how do we build hope that there are good online answers for our patients?
We’ve thought about this a lot at the Addario Lung Cancer Foundation and figured out that the answer lies with the patients themselves. Why not crowdsource it with people who have lung cancer, their caregivers and family members?
So, we created the first-ever global Lung Cancer Patient Registry that simplifies the collection, management and distribution of critical health-related information – all in one place so that researchers and patients can easily access and find data specific to lung cancer patients.
This is a data-rich environment for those focusing solely on finding a cure for lung cancer. And it gives patients access to other patients to compare notes and generally feel safe sharing intimate details with their peers….(More)”