ZikaHACK 2016 is sponsored by The NHMRC Centre for Research Excellence, Integrated Systems for Epidemic Response (ISER), for multidisciplinary student teams to come together and design and develop a tool for the early detection of disease outbreaks. The tool should be a technology solution that makes use of public domain data and information, with the early detection of the 2015/16 Zika Virus outbreak as a framework. The challenge is to see who can detect an abnormal surveillance signal earliest prior to the official recognition of the Brazilian Zika Virus outbreak. What signal do you look for? How do you identify such a signal? How early could you have picked the outbreak? These questions should be brainstormed within competing teams.
To learn more read on below.
Pandemics, epidemics and outbreaks of infectious diseases are an escalating threat globally. When large scale epidemics or pandemics occur, often our systems cannot deal effectively with them, resulting in preventable deaths and serious illness, as well as catastrophic disruption to society, as seen with Ebola in 2014, and more recently with Zika virus and birth defects.
Epidemic control has many facets, but starts with surveillance and early detection. In public health, the word "surveillance" has a specific meaning. However, intelligence gathering in public health is similar in methodology and principles to intelligence gathering in many other areas. Traditional public health surveillance may use data from doctors, laboratories or sentinel networks. This yields more accurate data, but may not be timely. Earlier detection of outbreaks makes outbreak control more successful, and may even prevent large scale, catastrophic epidemics or pandemics. If an outbreak can be identified very early before it becomes large scale, it can be controlled rapidly.
Essential background on Zika virus:
Zika virus was first identified in monkeys in 1947, and then in humans in 1952 in Uganda (1). Prior to 2007, the virus was not considered a public health problem; it was occasionally reported in equatorial regions of Africa and Asia (2). In 2015, the virus was found to have spread widely throughout Brazil & South America and was associated with an increase in birth defects such as microcephaly (a small head). This prompted the World Health Organization (WHO) to declare the current Zika virus outbreak a public health emergency of international concern on 1st February 2016 (3). WHO/PAHO release an epidemiological alert for possible Zika virus infection in Brazil on 7th May 2015 (4) It is estimated that anywhere between 10-80% of the Brazilian population has been exposed to the virus (5-7).
Most people with Zika infection appear healthy and may have no symptoms at all (8). If signs and symptoms do appear, they are mostly mild and resolve by themselves (8-10). Commonly described symptoms include: fever, rash, joint pain and eye pain but more severe disease is rare. This makes disease detection difficult as these types of symptoms can look a lot like other diseases such as a mild flu, or more severe infections such as dengue and yellow fever. Zika virus infections have also been associated with Guillain-Barre Syndrome (a neurological condition that causes paralysis in people of any age) (11).
People become infected with Zika after being bitten by a mosquito carrying the virus. However not all mosquito species can carry the virus – species which are able to carry the virus include the Aedes Aegypti, commonly known as the Yellow Fever Mosquito, and the Aedes albopictus, commonly known as the Asian Tiger Mosquito. (12, 13) There is also evidence that the common Culex species mosquitoes can carry the Zika virus, however transmission has yet to be confirmed. There is also a small chance of Zika transmission following sexual contact with an infected person (14, 15). Close, non-sexual contact with another infected person has not been reported as a risk factor for infection. Mothers infected with Zika virus during pregnancy transmit the virus to their newborns during pregnancy (16).
Current evidence suggests that Zika virus may cause microcephaly and other severe brain defects in babies born to infected mothers. (17, 18) Many women may not even know they have become infected until their baby is born with a birth defect after nine months gestation. The risk of microcephaly due to Zika is estimated to be low, ranging from 0-5%, however it may be nearer to 30% (5-7).
Babies born with birth defects, such as microcephaly, eye lesions and arthrogryposis (twisted limbs), have been more likely to have a mother with Zika virus infection or symptoms. (17, 18) Many women may not even know they have become infected until their baby is born with a birth defect after nine months. The risk of microcephaly due to Zika is estimated to be low, ranging from 0-5%, but may be nearer to 30%. (5-7) However, the infection has been so widespread in Brazil that between 1st of January 2016 and 2nd of July 2016 approximately 165,907 suspected and confirmed cases of Zika virus have been reported (19).
The first cluster of microcephaly cases in Brazil were reported in August 2015 (20), suggesting a substantial epidemic at least 9 months before. WHO/PAHO issued an epidemiological alert in November 2015 and asked countries to report increases of congenital microcephaly and other central nervous system malformations.
Essential background on Public Health Surveillance:
Effective intervention during epidemics such as Zika virus relies on the rapid and early detection of outbreaks through public health surveillance. Public health surveillance is defined by the World Health Organization (WHO) as “the continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice”. (21-23) Traditional public health disease surveillance, such as collated reports from laboratories and doctors, is often not timely enough for early intervention, because test results have to be validated and checked. Most forms of public health surveillance include elements of case detection, reporting, analysis, validation and dissemination to detect outbreaks and inform control measures.
The sheer increase in public domain data, driven by the internet, smart phones and social media use, mean that a lot of information about our day to day activities is readily accessible. In this deluge lies useful information relevant to various aspects of human life including public health surveillance and epidemiology. Research has implicated the potential use of public domain data in public health surveillance that can complement traditional surveillance. (24, 25) The term “Digital Epidemiology” has come to encompass the use of online data for the purposes of population health surveillance. For example, Twitter has been used to track the incidence of Influenza A H1N1 in the USA, anonymous web logs for screening of pancreatic cancer, Facebook activities for predicting mental illness, and much more.See essential resources below for more detail on these approaches.
One of the most successful health surveillance tools that utilize public domain data is Health Map. This system mines online media sources in 15 different languages for the purposes of monitoring disease outbreaks in real time, and provides a map of these outbreaks online. Other well-known surveillance efforts include Google Flu trends and Google Dengue Trends. Unfortunately, Google’s efforts are no longer maintained amid concerns about the validity and accuracy of their disease outbreak estimates.
Despite these limitations, public domain sources, such as online search engines, social media and blogs, can provide timely alerts for early detection of disease outbreaks (24, 26, 27). The WHO reports that more than 60% of their initial outbreak reports come from unofficial sources (28, 29). Because effective outbreak response relies on the rapid and early detection of disease outbreaks, the use of invalidated yet timely open source information can provide vital early warnings for authorities during epidemics.
A systematic review of using social media to track outbreaks:
Al-garadi MA, Khan MS, Varathan KD, Mujtaba G, Al-Kabsi AM: ‘Using online social networks to track a pandemic: A systematic review. Journal of Biomedical Informatics. 2016; 62:1-11.
A review of the traditional origins, methods, and evaluation of public health surveillance:
An example of using Twitter to track Influenza outbreaks in the USA:
An example of using weblogs to screen for pancreatic cancer:
An example of using Facebook to monitor mental health:
A link to Health Map:
A link to Google Flu Trends: