Automated Oracles

Early warning systems buy time and provide a basis for prompt action. They use data to gain insights into potential future scenarios. But they run up against limits, especially in connection with pandemics.


Traffic doesn’t always flow as smoothly as at this intersection in Cologne, Germany. Logistics early warning systems help drivers to avoid construction sites and congestion, and help companies to safeguard their supply chains.Jörg Greuel/Getty Images

Prepa­ra­tion is the buzz­word for effi­cient action. If you know about a traf­fic jam, you can drive around it, when a drought is immi­nent, you can save water. The corona­virus revealed a world unpre­pared. By mid-2020, more than half a mil­lion peo­ple had died from Covid-19 infec­tion. And the ques­tion aris­es: Why is there no early warn­ing sys­tem for pandemics?

Early warn­ing sys­tems are already stan­dard in a num­ber of other con­texts. On Decem­ber 26, 2004, the ocean floor off the coast of north­ern Suma­tra shift­ed. This under­wa­ter earth­quake was one of the strongest in a cen­tu­ry, mea­sur­ing 9.3 on the Richter scale. Its tremors caused waves to surge and slam ever high­er against the island and many other coast­lines of the Indi­an Ocean. Nobody was pre­pared, and more than 25,000 peo­ple died. A con­sor­tium led by the Ger­man Research Cen­tre for Geo­sciences (GFZ) in Pots­dam then set up an early warn­ing sys­tem in the Indi­an Ocean. A net­work of seis­mome­ters detects earth­quake epi­cen­ters, and satel­lites mea­sure move­ments of the earth’s sur­face via GPS. If the sys­tem were to respond to every vibra­tion, there would be many false alarms. GPS buoys and pres­sure sen­sors on the ocean floor help mea­sure each wave fol­low­ing a quake. The data are trans­mit­ted to a pro­cess­ing cen­ter and com­pared with pre­vi­ous records. This wealth of data yields mod­els that can inform oper­a­tors with­in min­utes of the speed, direc­tion, and mag­ni­tude of poten­tial tsunamis—in other words, whether they are harm­less or hazardous.

Analysis via machine learning

Early warn­ing sys­tems can also help com­pa­nies. One exam­ple lets them pre­pare for dis­rup­tions in their sup­ply chains. Resilience360 Sup­ply Watch is the name of a sys­tem used by the Ger­man logis­tics com­pa­ny DHL. The pro­gram defines around 140 risk cat­e­gories, includ­ing finan­cial, envi­ron­men­tal, and social fac­tors. Are media out­lets report­ing crim­i­nal activ­i­ty in a cer­tain region? What is the inci­dence rate of qual­i­ty com­plaints? How do inven­to­ry lev­els look—do ware­hous­es have suf­fi­cient stock? The DHL sys­tem ana­lyzes data from up to 30 mil­lion online and social media posts and makes the results of its risk analy­ses avail­able to clients. The pro­gram itself con­tin­u­ous­ly eval­u­ates the rel­e­vance and poten­tial con­se­quences of its data. It can do this with the help of machine learning.

Machine learn­ing essen­tial­ly means that a sys­tem auto­mat­i­cal­ly com­piles new infor­ma­tion and eval­u­ates it in rela­tion to what it already knows. For exam­ple, a com­muter might nor­mal­ly need twen­ty min­utes to drive from A to B. But the route now has a con­struc­tion site. Because other dri­vers have already had to wait, an app can inform the com­muter before leav­ing home that today’s drive will take an addi­tion­al thir­ty min­utes. The sys­tem ana­lyzes thou­sands of con­struc­tion sites in this man­ner. Based on the over­all pool of data it col­lects, at some point it might deter­mine that when­ev­er an acci­dent blocks one lane of a par­tic­u­lar route, the drive will be delayed by approx­i­mate­ly forty min­utes. Then the app can report this dis­rup­tion imme­di­ate­ly, with­out wait­ing to ana­lyze the results from the first dri­vers to com­plete the route.

If we make perfect use of a road’s capacity, or have the maximum number of cars complete a route within an hour, that is an unstable system.

Dirk HelbingDirk Helbing
Professor of Computational Social Science, ETH Zurich

Effectiveness in numbers

Early warn­ing sys­tems often draw on mil­lions or even bil­lions of data sets. Depend­ing on the field in ques­tion, that might be any­thing: from his­tor­i­cal records to tree rings to Twit­ter posts. Machine learn­ing helps orga­nize these files. It mod­els the inter­play of dif­fer­ent factors.

Dirk Hel­bing, a pro­fes­sor of com­pu­ta­tion­al social sci­ence at ETH Zurich, stud­ies how ana­lyz­ing large vol­umes of data can help us do things like improve the flow of traf­fic. The goal is for sys­tems to be sta­ble. “If we make per­fect use of a road’s capac­i­ty, or have the max­i­mum num­ber of cars com­plete a route with­in an hour, that is an unsta­ble sys­tem,” he says. “The flow will break down. The road’s capac­i­ty will drop, and the result will be con­ges­tion.” It is bet­ter for the road to be used at a con­stant rate, but not at its max­i­mum capac­i­ty. This approach makes the sys­tem less sus­cep­ti­ble to dis­rup­tion. “Every access road or exit, every lane change, every accel­er­a­tion or brake action—all of these are poten­tial dis­rup­tions.” Machine learn­ing can be used to ana­lyze these dis­rup­tive fac­tors and derive a col­lec­tive pic­ture of dri­ving behav­ior. “It won’t do you any good to fur­nish just one car with a pro­gram,” says Hel­bing. Effi­cien­cy comes from a swarm structure—namely, when many cardrivers make their dri­ving pat­terns avail­able for data analy­sis. “Ana­lyz­ing about 40 per­cent of the cars could sig­nif­i­cant­ly improve traf­fic flow.”

A single post on social media is of no use for a projection. But if fever posts cluster in a certain location, that can be an indication.

Avaré Stewart
Data Scientist

Social media as a warning system

Data sci­en­tist Dr. Avaré Stew­art also used swarm intel­li­gence when she led the M‑Eco project at Leib­niz Uni­ver­si­ty Han­nover. Her team want­ed to ana­lyze data from social media to devel­op an early warn­ing sys­tem for pan­demics. “Peo­ple often post about things like fever symp­toms or vis­its to the doc­tor,” says Stew­art. “A sin­gle post is of no use for a pro­jec­tion. But if fever posts clus­ter in a cer­tain loca­tion, that can be an indication.”

The advan­tage of social media lies in the imme­di­ate nature of the data, which can help flag an out­break early on. But it is dif­fi­cult to estab­lish a solid basis for these data. “Peo­ple post about their dog, their niece, or their next vaca­tion. Things that don’t nec­es­sar­i­ly have any­thing to do with ill­ness. To increase the qual­i­ty of your data, you have to fil­ter it.” If the fil­ter is too fine, you will lose impor­tant infor­ma­tion. If it’s too coarse, the sys­tem is unus­able. “Find­ing the right bal­ance here is the biggest chal­lenge,” says Stewart.

Anoth­er prob­lem lies in deter­min­ing how early a warn­ing should be. “We’re com­par­ing the present with knowl­edge about the past,” says Stew­art. “So we can only issue a warn­ing when some­thing has already hap­pened.” In the case of a pan­dem­ic, that means when peo­ple are already infect­ed. “For us, it was too early to pre­dict poten­tial pan­demics sole­ly on the basis of our data.” That was a fac­tor in dis­con­tin­u­ing the project when its EU fund­ing ran out.

Coronavirus alert app: Developed too slowly?

Germany’s corona­virus alert app is an early warn­ing sys­tem as well. It doesn’t actu­al­ly pre­vent infec­tions, but does help to slow their spread. Now that a num­ber of prob­lems in con­nec­tion with this com­plex project have been solved, the app is more reli­able at gen­er­at­ing new numer­i­cal codes every few min­utes, which it sends to all near­by smart­phones via Blue­tooth. Unlike such apps in other coun­tries, no cen­tral­ized com­put­er knows which phones have been in con­tact with other devices. The codes are only saved on the smartphones—which can­not iden­ti­fy any other users. Google and Apple, the two largest providers of smart­phone oper­at­ing sys­tems, have access to all the codes and can link them to the respec­tive devices. If a user reg­is­ters a pos­i­tive test result, this infor­ma­tion is sent to the two IT giants, which then send alerts to all smart­phones whose users have con­tact­ed or been in close spa­tial prox­im­i­ty to the infect­ed individual.

Because Ger­many does not col­lect data in a cen­tral­ized loca­tion or track GPS sig­nals and because it guar­an­tees anonymi­ty, it need­ed more time to devel­op its app than other coun­tries such as China, whose tech giant Aliba­ba took only three days. How­ev­er, it is unclear how well the app worked in China dur­ing the first few weeks. An inves­ti­ga­tion by the Berlin-based Mer­ca­tor Insti­tute for China Stud­ies reports more than 50,000 com­plaints of tech­ni­cal glitch­es in the first week of app usage alone.

Exten­sive debate about data pri­va­cy before the app was intro­duced in Ger­many seems to have had a favor­able effect on its accep­tance in the coun­try. The num­ber of users is a cru­cial fac­tor in how well this sys­tem devel­oped by SAP and Telekom can func­tion. In the week fol­low­ing its release, the corona­virus alert app was down­loaded by around 15 per­cent of the Ger­man pop­u­la­tion. In Aus­tria, a sim­i­lar app called Stopp Coro­na was avail­able a month ear­li­er, but was only being used by around 8 per­cent of the pop­u­la­tion three months after its release. Aus­tria has since improved its warn­ing app and guar­an­teed the same level of data pri­va­cy as in Germany.

Safety versus speed

In Ger­many, every infec­tion report­ed on the app is ver­i­fied. Peo­ple who reg­is­ter a pos­i­tive test result have to pro­vide con­fir­ma­tion. This elim­i­nates the pos­si­bil­i­ty of false alarms. How­ev­er, it also takes more time for alerts to be sent. And with every minute that goes by, the risk of infect­ing more peo­ple increas­es. What is more impor­tant, safe­ty or speed? This dilem­ma keeps appear­ing in con­junc­tion with early warn­ing sys­tems. And even the best sys­tems can­not pro­vide cer­tain­ty about the future, but instead only show options. By lay­ing the foun­da­tions for knowl­edge early on, they buy time. That allows peo­ple to pre­pare, and to take pre­cau­tions suf­fi­cient­ly early to influ­ence the future. The corona­virus app can give warn­ings. But the users them­selves then need to decide to limit their con­tact with others.


Early warning systems in nature, science, and business

  • Elephants have pressure sensors on the soles of their feet that can detect infrasound—around sixteen to twenty hertz below the range of human perception. That enables them to respond early to earthquakes and tsunamis.
  • The German-Indonesian Tsunami Early Warning System for the Indian Ocean operated from Jakarta identifies earthquake epicenters within twenty kilometers and calculates their magnitude within three or four minutes after occurrence.
  • The University of Constance developed its CoronaVis early warning system for intensive-care capacity limits. Its aim is to enable patient transfers before capacities are reached—including to other regions and states within Germany.
  • The Ifo institute compiles a business climate index for Germany by surveying around 9,000 companies monthly about their current situation and expectations for the next six months. Investment advisors use the index as an early warning system.
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