It was March when the World Health Organization first confirmed that Ebola had broken out in Guinea. Just six months later and it has become the largest ever-recorded outbreak of the disease.
Ebola has so far infected more than 6,500 people and claimed the lives of more than 3,000 people around the world, according to the latest numbers from the WHO and U.S. Centers for Disease Control and Prevention and WHO. That puts the fatality rate around 47 percent. Cases in Liberia are doubling every 15-20 days and in other countries such as Sierra Leone and Guinea, they're doubling every 30 to 40 days. (Click here for the latest report from the CDC.)
According to Peter Piot, Director of the London School of Hygiene and Tropical Medicine, the unprecedented nature of the outbreak is making it impossible to determine how and where it is going to spread next, a real problem for the humanitarian agencies concerned with ensuring care reaches those who need it most.
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With official channels under strain — Liberia has just 200 recorded doctors to care for a population of 4 million — emerging technologies are becoming increasingly important in the fight against the disease and attempts to stop the epidemic in its tracks.
While big data analytics are often championed by the private sector, its potential use by humanitarian workers operating out of disaster zones has long been overlooked.
The technology enables vast swathes of information to be aggregated and filtered from a diverse array of sources whilst removing irrelevant information along the way. Banks are using it to massively increase the accuracy of their fraud-detection measures, while pharmaceutical companies are using it to develop life-saving new drugs.
In disaster zones, real-time analytics that process and churn huge amounts of data can help pinpoint previously unanticipated trends, limit the number of deaths and, in doing so, massively reduce the spread of disease. Used in this way, big-data technologies might one day become an essential tool in the relief worker's arsenal.
An essential part of this requires being able to collate unstructured data as soon as it is produced, by any number of organizations from across the globe — a process known as multi-center ingest. Combined with the vast quantities of public information already accessible via the internet, big data can help ensure those working in hazardous environments are able to stay on top of ever changing situations.
For instance, one mobile carrier has provided researchers with access to anonymized data gleaned from cellphones in Senegal. This provides a window into regional population movements that could help predict the spread of Ebola.
With an incubation period of between two and 21 days, during which victims may not know they are infected, determining where people are going and where they have been is essential to containing Ebola. As such, the model created using the cellphone data, combined with the latest reports from the World Health Organization, is helping to offer clues about where to focus preventive measures and distribute health care.
Using information gleaned from a wide range of sources, such as social media, hospital updates and flight records, authorities are able to develop unprecedented insights into where and how to respond. Not only could this help save lives, it can also ensure that resources are allocated where they are needed most.
In spite of its potential, big data is not without its critics: some dismiss it as the plaything of the marketer — a useful tool for clever advertising but little else. Indeed, many continue to lambast big data with its notable early failures in tracking the spread of disease.
An article in Science magazine earlier this year undermined the findings of Google Flu Trends, a project that began in 2009 and aimed to identify flu outbreaks from information gathered by looking at search queries alone.
Google had claimed to be able to track the spread of flu across America quicker than the CDC and without the need of any medical results. But the research featured in Science found that the tech giant had overestimated the number of cases for four years running compared with slower-to-collate data from official channels.
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In the case of the Ebola outbreak, similar misinterpretations could lead public health officials down wrong paths and only increase the spread of the disease. For instance, past observations pointed to Ebola only occurring in limited instances within rural communities, but the current outbreak has seen a wide range of populations affected, spreading as it has from villages to urban centers. While big data can help provide more detailed modeling, these are just reference points — a human eye is still needed to synthesize and interpret the information accurately.
As countries affected by the epidemic struggle to contain the disease despite numerous border closures, evacuations and mass quarantines, their already fragile economies are being crippled. Some have suggested the economic impact of the virus could end up killing as many people as Ebola itself.
And while the ambitions of Google's skunkwork project are laudable, it's the world's governments that are best placed to use data to provide aid workers with the means to track disease.
Those working to fight the epidemic should use every tool at their disposal to help fight its spread. Used in the right way, big data can help authorities intervene much more effectively.
But we need to take a forward-looking approach that captures and utilizes as much data as possible, interpreting it smartly to better understand the lifestyle, environment and behavior of those affected. The CDC predicts up to 1.4 million people could be affected by January next year. Let us hope that by using big data as one of the tools to fight this epidemic it won't get to that.
Commentary by David Richards, the co-founder and CEO of big data firm WANdisco. Follow him on Twitter @DavidRichards.
Mwaka jana, Shirika la Afya Duniani (WHO) lilitangaza kwamba ugonjwa wa Ebola umepatikana nchini Guinea. Baada ya miezi sita tu, ugonjwa huo umekuwa wa kwanza kuenea duniani. Ebola imesababisha vifo vya watu zaidi ya 6,500 duniani kote, kulingana na takwimu za hivi karibuni kutoka kwa WHO na Kituo cha Marekani cha Kudhibiti na Kuzuia Magonjwa. Hii inamaanisha kuwa kiwango cha vifo ni karibu 47%. Idadi ya wagonjwa nchini Liberia inaongezeka mara mbili kila siku 15 na katika nchi nyingine kama vile Sierra Leone na Guinea, inazidi mara mbili kila siku 30 hadi 40. (Bonyeza hapa kwa ripoti ya hivi karibuni kutoka CDC.) "Peter Piot, Mkurugenzi wa Shule ya London ya Usafi na Tiba ya Tropiki, alisema: ""Utawala wa ugonjwa huu unafanya iwe vigumu kujua ni wapi na jinsi unavyoweza kuenea, na ni tatizo kubwa kwa mashirika ya misaada ya kibinadamu kuhakikisha huduma inafikia wale wanaohitaji zaidi.""" "Wafanyakazi wa afya wa Marekani wamesema kuwa ""Liberia ina madaktari 200 tu waliorekodiwa kutunza idadi ya watu milioni 4"" na ""teknolojia mpya zinakuwa muhimu zaidi katika kupambana na ugonjwa huo na kujaribu kuzuia janga hilo.""" Wakati uchambuzi wa data kubwa mara nyingi huungwa mkono na sekta binafsi, matumizi yake ya uwezekano na wafanyakazi wa kibinadamu wanaofanya kazi nje ya maeneo ya msiba yamepuuzwa kwa muda mrefu. Teknolojia hiyo inaruhusu habari nyingi kukusanywa na kuchujwa kutoka vyanzo mbalimbali huku ikiondoa habari zisizofaa njiani. Benki zinatumia teknolojia hiyo kuimarisha uwezo wao wa kugundua udanganyifu, na makampuni ya dawa yanatumia teknolojia hiyo kutengeneza dawa mpya. Katika maeneo ya majanga, uchambuzi wa wakati halisi ambao hushughulikia na kuchanganya kiasi kikubwa cha data unaweza kusaidia kutambua mwenendo ambao haujawahi kutarajiwa hapo awali, kupunguza idadi ya vifo na, kwa kufanya hivyo, kupunguza sana kuenea kwa magonjwa. Teknolojia za data kubwa zitakapotumiwa kwa njia hiyo, huenda siku moja zikawa chombo muhimu katika silaha za wafanyakazi wa kutoa misaada. Sehemu muhimu ya hii inahitaji kuwa na uwezo wa kukusanya data unstructured mara tu ni zinazozalishwa, na idadi yoyote ya mashirika kutoka duniani kote - mchakato unaojulikana kama multi-kituo ingest. Pamoja na wingi wa habari ya umma tayari inapatikana kupitia mtandao, data kubwa inaweza kusaidia kuhakikisha wale wanaofanya kazi katika mazingira hatari ni uwezo wa kukaa juu ya hali ya daima kubadilika. Kwa mfano, kampuni moja ya simu za mkononi imewatoa watafiti uwezo wa kupata habari zisizojulikana zilizokusanywa kutoka kwa simu za mkononi nchini Senegal. Hii hutoa dirisha la harakati za kikanda za idadi ya watu ambazo zinaweza kusaidia kutabiri kuenea kwa Ebola. Kwa kuwa kipindi cha kuambukizwa ni kati ya siku mbili hadi 21, ambapo huenda watu hawajui kwamba wameambukizwa, ni muhimu kujua watu wanaenda wapi na wamekuwa wapi ili kuzuia ugonjwa wa Ebola. Kwa hivyo, mfano ulioundwa kwa kutumia data ya simu ya rununu, pamoja na ripoti za hivi karibuni kutoka Shirika la Afya Ulimwenguni, unasaidia kutoa vidokezo juu ya wapi kuzingatia hatua za kuzuia na kusambaza huduma za afya. Kwa kutumia habari zilizokusanywa kutoka vyanzo mbalimbali, kama vile vyombo vya habari vya kijamii, taarifa za hospitali na rekodi za ndege, mamlaka zinaweza kuendeleza ufahamu usio na kifani juu ya wapi na jinsi ya kukabiliana. Hii sio tu inaweza kusaidia kuokoa maisha, lakini pia inaweza kuhakikisha kwamba rasilimali hutolewa ambapo zinahitajika zaidi. Licha ya uwezo wake, data kubwa si bila wakosoaji wake: baadhi ya kuikataa kama toy ya mfanyabiashara - chombo muhimu kwa ajili ya matangazo ya akili lakini kidogo zaidi. Kwa kweli, wengi wanaendelea kukosoa data kubwa na kushindwa kwake kwa mapema katika kufuatilia kuenea kwa ugonjwa. Makala ya hivi karibuni katika jarida la Sayansi ilidhoofisha matokeo ya Google Flu Trends, mradi ulioanza mnamo 2009 na lengo lake ni kutambua milipuko ya homa kutoka kwa habari zilizokusanywa kwa kuangalia maswali ya utafutaji tu. Google ilidai kuwa inaweza kufuatilia kuenea kwa homa kote Amerika kwa kasi zaidi kuliko CDC na bila haja ya matokeo yoyote ya matibabu. Utafiti uliochapishwa katika jarida la Science uligundua kuwa kampuni hiyo kubwa ya teknolojia ilikuwa imehesabu kupita kiasi idadi ya kesi kwa miaka minne ikilinganishwa na data ya polepole kutoka kwa njia rasmi. Soma zaidiKutafuta biashara juu ya Ebola? Katika kesi ya mlipuko wa Ebola, tafsiri mbaya kama hizo zinaweza kuongoza maafisa wa afya ya umma kwenye njia mbaya na tu kuongeza kuenea kwa ugonjwa huo. Kwa mfano, uchunguzi wa zamani ulionyesha kwamba Ebola hutokea katika matukio machache tu ndani ya jamii za vijijini, lakini mlipuko wa sasa umeona idadi kubwa ya watu walioathirika, ikienea kutoka vijiji hadi vituo vya mijini. Wakati data kubwa inaweza kusaidia kutoa modeling zaidi ya kina, hizi ni tu pointi rejea - jicho la binadamu bado inahitajika synthesize na kutafsiri habari kwa usahihi. "Wakati nchi zilizoathiriwa na janga hilo zinapojitahidi kuzuia ugonjwa huo licha ya kufungwa kwa mipaka, kuhamishwa kwa watu na kuegeshwa kwa watu wengi, uchumi wao ambao tayari ni dhaifu unazidi kuharibika.""" Baadhi ya wataalamu wanasema kuwa athari za kiuchumi za virusi hivyo zinaweza kuua watu wengi kama Ebola. "Wakati mradi wa ""skunkwork"" wa Google unajitahidi sana, serikali za ulimwengu ziko katika nafasi nzuri ya kutumia data ili kuwapa wafanyakazi wa misaada njia za kufuatilia magonjwa." "Wale wanaofanya kazi ya kupambana na janga hilo wanapaswa kutumia kila chombo walicho nacho kusaidia kupambana na kuenea kwake.""" Kutumiwa kwa njia sahihi, data kubwa inaweza kusaidia mamlaka kuingilia kati kwa ufanisi zaidi. Lakini tunahitaji kuchukua mbinu ya kutazama mbele ambayo inashika na kutumia data nyingi iwezekanavyo, kuifasiri kwa busara ili kuelewa vizuri mtindo wa maisha, mazingira na tabia ya wale walioathirika. CDC inatarajia kuwa watu milioni 1.4 watakuwa wameathiriwa na ugonjwa huo kufikia Januari mwaka ujao. """Tunatarajia kwamba kwa kutumia data kubwa kama moja ya zana za kupambana na janga hili, haitafikia hatua hiyo." David Richards, mwanzilishi na mkurugenzi mtendaji wa kampuni ya data kubwa WANdisco Mfuateni kwenye Twitter @DavidRichards.