Figure 1[2]<\/a><\/p>\n HealthMap<\/a> has leveraged this opportunity to develop a free online disease mapping tool that provides a comprehensive global view of the current state of infectious diseases. Every hour, 24 hours a day, HealthMap automatically acquires information from a variety of freely accessible websites across 15 different languages, and then filters and organizes the data based on a machine-learning algorithm. Since its inception in 2006, the algorithm continues to be improved based on feedback from analysts, and sifts through the noise by cross-validating the acquired information and weighting by news source reliability (e.g. higher weight to multilateral agencies) and by the number of unique data sources (e.g. higher weight to multiple sources).[3]<\/a> It is used primarily for early disease detection but also to raise awareness among local health departments, public health agencies, and international travelers to high-risk countries. HealthMap\u2019s most recent claim to fame was its tracking of early press and social media mentions of a hemorrhagic fever in West Africa, a week before the WHO officially identified it as Ebola.<\/p>\n Figure 2[4]<\/a><\/p>\n HealthMap has extraordinary potential to provide real-time and highly local information, and detect emerging outbreaks, much before governments and multilateral agencies do. However, there are a number of challenges it will need to address to further add value to global disease surveillance efforts.<\/p>\n Leveraging information from \u2018dumb\u2019 phones not connected to global information network <\/strong><\/p>\n High mobile penetration, greater access to mobile broadband, significant infrastructure investment in 3G\/4G networks \u2013 these are all examples of Africa\u2019s rapid digitization[5]<\/a> and highlights the increasing sources of information that HealthMap can tap into. While Africa\u2019s digital transformation is attracting much global attention, it is important to remember that for the 80% that do own a phone, 82% only have access to basic (or feature) phones[6]<\/a> and that the majority of households are still isolated from the digital world. Such underdeveloped regions pose the greatest risk of disease outbreaks given their poor infrastructure, and thus investment to connect these communities to global information networks is crucial. Fonetwish<\/a> is one such example, which uses the USSD[7]<\/a> protocol to connect users to social media networks via a text-only functionality.[8]<\/a><\/p>\n Distinguishing signal from noise via more open collaboration <\/strong><\/p>\n Google Flu Trends<\/a> (GFT) provided estimates of influenza activity by analyzing trends in Google search queries, such as headache and chills. Such search terms were then correlated with flu outbreak data from the CDC. GFT closed its doors after failing to predict the peak of the 2013 flu season by 140 percent[9]<\/a>,[10]<\/a><\/sup>, highlighting the pitfalls of big data and letting online algorithms make critical health decisions. While GFT algorithm flaws have since been identified, e.g. not taking into account changes in search behavior over time, the opaque nature of GFT\u2019s data and prediction methodology prevented a meaningful feedback loop with its users. Had GFT been more transparent and allowed for crowdsourcing of ideas, it may have been able to more quickly distinguish \u2018good data\u2019 from \u2018bad data\u2019. HealthMap should take these lessons into account as it acquires and maps its disease data.<\/p>\n Figure 3[11]<\/a><\/p>\n Hedging against risk of state censorship <\/strong><\/p>\n Some governments may censor disease outbreak information to the public due to fear of negative coverage, such as China during the 2003 SARS outbreak or Saudi Arabia during the 2012-2013 MERS outbreak.[12]<\/a> State censorship, particularly in the early crucial weeks, impedes information flow and gives the disease time to spread and potentially mutate, increasing the risk of a widespread outbreak. To hedge against such risks, HealthMap will need to work closely with local partners, multilateral agencies and NGOs in country to obtain information that otherwise may be lost under government censorship.<\/p>\n (795 words)<\/p>\n [1]<\/a> CHUNARA R, FREIFELD CC, BROWNSTEIN JS. New technologies for reporting real-time emergent infections. Parasitology. 2012;139(14):1843-1851. doi:10.1017\/S0031182012000923.<\/a><\/p>\n [2]<\/a> Ibid.<\/p>\n [3]<\/a> Brownstein JS, Freifeld CC, Reis BY, Mandl KD (2008) Surveillance Sans Fronti\u00e8res: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project. PLoS Med 5(7): e151. doi:10.1371\/journal.pmed.0050151<\/a><\/p>\n [4]<\/a> Screenshot of HealthMap.<\/p>\n [5]<\/a> McKinsey Global Institute, “Lions go digital: The Internet\u2019s transformative potential in Africa,” http:\/\/www.mckinsey.com\/industries\/high-tech\/our-insights\/lions-go-digital-the-internets-transformative-potential-in-africa<\/a>, accessed November 2016.<\/p>\n [6]<\/a> Consumer Technology Association, “How Mobile Phones Are Changing the Developing World,” July 27, 2015, https:\/\/www.cta.tech\/News\/Blog\/Articles\/2015\/July\/How-Mobile-Phones-Are-Changing-the-Developing-Worl.aspx<\/a>, accessed November 2016.<\/p>\n [7]<\/a> USSD: Unstructured Supplementary Service Data<\/p>\n [8]<\/a> Quartz India, \u201cTwo-thirds of the world\u2019s mobiles are dumb phones. Meet the company getting them online,\u201d June 8, 2014, http:\/\/qz.com\/217909\/two-thirds-of-the-worlds-mobiles-are-dumb-phones-meet-the-company-getting-them-online\/<\/a>, accessed November 2016.<\/p>\n [9]<\/a> Wired, \u201cWhat We Can Learn From the Epic Failure of Google Flu Trends,\u201d October 1, 2015, https:\/\/www.wired.com\/2015\/10\/can-learn-epic-failure-google-flu-trends\/all\/1<\/a>, accessed November 2016.<\/p>\n [10]<\/a> Nature, \u201cWhen Google got flu wrong,\u201d February 13, 2013, http:\/\/www.nature.com\/news\/when-google-got-flu-wrong-1.12413<\/a>, accessed November 2016.<\/p>\n [11]<\/a> Ibid.<\/p>\n [12]<\/a> Wired, \u201cCensorship Doesn\u2019t Just Stifle Speech \u2014 It Can Spread Disease,\u201d August 21, 2013, https:\/\/www.wired.com\/2013\/08\/ap_mers\/all\/1<\/a>, accessed November 2016.<\/p>\n","protected":false},"excerpt":{"rendered":" How HealthMap uses digital technology to transform health information flows across the world<\/p>\n","protected":false},"author":2599,"featured_media":14817,"comment_status":"open","ping_status":"closed","template":"","categories":[2081,2082,1388],"class_list":["post-14816","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-health-information","category-outbreak","category-public-health"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/digitization-challenge-2016\/","yoast_head":"\n![\"\"](\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3760201\/bin\/nihms504883f2.jpg\")
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