Technology Helps Stem Viral Tide

Looking ahead: As long as humankind remains vulnerable to infectious disease, we need to be prepared. Which technologies and approaches could help us stop the next disease outbreak in its tracks?

To contain future disease outbreaks, we have to improve and speed up our methods of identifying the infected and those they have been in contact with. Kevin Frayer/Getty Images

When the Covid-19 pan­dem­ic is behind us, we will have dis­cov­ered a great deal about how to com­bat dis­ease. And we will need that knowl­edge, because we live in a world where the threat of infec­tious dis­ease is ris­ing. New muta­tions of pathogens and zoonot­ic dis­eases that can pass at any time from ani­mal to human pose a con­stant threat that is dif­fi­cult to pre­dict. Increas­ing resis­tance to antimi­cro­bial drugs means med­i­cines are becom­ing less effec­tive. As Covid-19 has shown, our inter­con­nect­ed glob­al­ized world pro­vides an ide­al net­work for the dev­as­tat­ing spread of a life-threat­en­ing pathogen. How can we bol­ster our abil­i­ty to con­tain the next poten­tial pan­dem­ic?


We have learned from SARS, MERS and now Covid-19 that, in the absence of a vac­cine, we are reliant on clas­sic epi­demi­o­log­i­cal con­trols, which include rapid diag­no­sis, con­tact trac­ing, quar­an­tine, phys­i­cal dis­tanc­ing and hygiene mea­sures. How effi­cient these inter­ven­tions are will in turn dic­tate how extreme and, there­fore, how eco­nom­i­cal­ly dam­ag­ing an imposed soci­etal quar­an­tine has to be in order to save lives.

Big data, robotics and AI all have great potential to help us contain future disease outbreaks, provided these solutions are fully developed ahead of time.

Con­tact trac­ing has his­tor­i­cal­ly been a slow process of detec­tive work car­ried out by pub­lic health offi­cials involv­ing inter­view­ing a patient, iden­ti­fy­ing peo­ple who may have been in con­tact with them and alert­ing them as quick­ly as pos­si­ble. The prob­lem with a man­u­al response is that virus­es trav­el too fast to keep up. With Covid-19, the race is on to lever­age the pow­er of big data to speed up this process. Around the world, devel­op­ers are work­ing on trac­ing and track­ing apps, and test­ing their effi­ca­cy. South Korea’s abil­i­ty to cut cas­es of Covid-19 from 909 on Feb­ru­ary 29 to 76 on March 24 showed how effec­tive this approach can be. The Chi­nese app gives cit­i­zens a red, amber or green trav­el code depend­ing on risk of con­ta­gion. In South Korea, apps pub­lish the move­ments of peo­ple with Covid-19 so that you can track them and stay away.


29 apps

to help track and trace Covid-19 are currently available worldwide, according to MIT (as of June 16, 2020)


Two chal­lenges have been raised around con­tact trac­ing apps: data pri­va­cy and take up. Accord­ing to Ste­faan Ver­hulst, co-founder and chief research and devel­op­ment offi­cer for the Gov­er­nance Lab­o­ra­to­ry at New York uni­ver­si­ty, this is a symp­tom of a lack of pre­pared­ness. He argues we need to use big data bet­ter to reduce uncer­tain­ty dur­ing a pan­dem­ic. This is par­tic­u­lar­ly per­ti­nent when we are forced, as with Covid-19, to take an iter­a­tive approach to pol­i­cy, see­ing dai­ly which strate­gies work and which do not. “One of the tragedies of Covid-19 is that despite hav­ing her­ald­ed the arrival of big data for the last 15 years, and the fact we are in a so called data age, we have not man­aged to con­nect the vast amount of data col­lect­ed and archived with the demand side to become smarter about the pan­dem­ic and our options mov­ing for­ward,” says Ver­hulst. He argues that to prop­er­ly uti­lize the ben­e­fits of data col­lec­tion and analy­sis in the future, we must cre­ate a frame­work to use data in an eth­i­cal man­ner. This would involve a gov­er­nance frame­work, estab­lish­ing what data is actu­al­ly need­ed, fund­ing for a data infra­struc­ture and a con­ver­sa­tion with cit­i­zens about the reuse of their data.


While pri­va­cy issues are putting in ques­tion the effi­ca­cy of con­tact trac­ing apps, some oth­er means of con­tain­ment do not raise such dif­fi­cul­ties. Chi­na, for exam­ple, has explored the use of robots to help con­tain the spread of Covid-19 and keep human work­ers away from risk. Robot tech­nol­o­gy is now advanced enough to be deployed in many set­tings, from mon­i­tor­ing com­pli­ance with quar­an­tine rules to deliv­er­ing med­ica­tion and food, mea­sur­ing vital clin­i­cal signs and han­dling con­t­a­m­i­nat­ed waste. It can be par­tic­u­lar­ly use­ful in clin­i­cal set­tings for diag­no­sis, screen­ing and patient care. Dis­eases are eas­i­ly spread through hos­pi­tal sur­faces. Rather than expose work­ers to this risk, robots can car­ry out dis­in­fec­tion.

Covid-19 could be a catalyst for developing robotic systems that can be deployed to combat infectious diseases in environments unsuitable for human workers.

Mobile robots are also being con­sid­ered for tem­per­a­ture mea­sure­ment in pub­lic areas and for auto­mat­ed dis­ease test­ing, free­ing up front­line med­ical staff for oth­er duties. All these advan­tages had already been rec­og­nized dur­ing the 2014 Ebo­la out­break, but fund­ing for devel­op­ment has remained lim­it­ed. Pro­fes­sor GuangZhong Yang, Dean of the Insti­tute of Med­ical Robot­ics at Shang­hai Jiao Tong Uni­ver­si­ty, and his fel­low researchers have called for a more sus­tain­able approach to research so that, in a future out­break, we have cost-effec­tive robots that can be rapid­ly deployed in a range of sce­nar­ios.

Remote-controlled disinfection robots at work during the Covid-19 outbreak in Wuhan, China.Miguel Medina/Getty Images
A robot at the Circolo di Varese hospital in Italy helps treat patients with Covid-19. FP/Getty Images


It is not just front­line ser­vices that need to be bet­ter pre­pared for dis­ease out­break; gov­ern­ments and busi­ness­es must be pre­pared, too, in order to mit­i­gate the risks and min­i­mize the wider soci­etal impact of an epi­dem­ic. Nita Mad­hav is Chief Exec­u­tive Offi­cer of Cal­i­for­nia-based Metabio­ta and a lead­ing epi­demi­ol­o­gist. Her com­pa­ny curates data from over 400 sources on past and present out­breaks in order to help gov­ern­ments and busi­ness­es iden­ti­fy, quan­ti­fy and mit­i­gate the spe­cif­ic risks they face. “We have devel­oped a his­tor­i­cal data­base that con­tains over 2,500 epi­demics that we’ve painstak­ing­ly struc­tured the data for, while also doing full-scale prob­a­bilis­tic mod­el­ing using hun­dreds of thou­sands of sim­u­la­tions to show on a glob­al scale how dis­eases can spread from coun­try to coun­try and per­son to per­son to help bet­ter under­stand what resources are need­ed to respond to these events,” she says. “We also track the lev­el of pub­lic fear caused by dif­fer­ent pan­demics, as this is tight­ly linked to eco­nom­ic loss.”

Preparedness is about understanding a range of potential events.

Chief Executive Officer, Metabiota, California, USA

As grow­ing com­put­er pow­er enables faster com­pu­ta­tion, and each out­break brings new under­stand­ing, so the mod­el­ing improves, and it is pos­si­ble to cre­ate faster and more accu­rate pre­dic­tions. But a big chal­lenge is that the warn­ings can get lost in a sur­feit of mod­el­ing sim­u­la­tions. “Each group has their own set of mod­el­ers they turn to,”says Mad­hav. “There needs to be some mech­a­nism where mul­ti­ple mod­els can be com­pared with assump­tions doc­u­ment­ed, because some mod­els are com­ing out with wild­ly dif­fer­ent results.” There may still be gaps, and none of these solu­tions will replace tra­di­tion­al epi­demi­ol­o­gy. Nev­er­the­less, advances in big data, robot­ics and AI can bol­ster our defens­es against the next out­break, enabling us to con­tain dis­eases bet­ter before they spread and ensure that our key insti­tu­tions and busi­ness­es do not buck­le under the pres­sure.

Planning and Reality

In 2019, the Global Health Security Index (GHSI) measured how well countries were prepared for a pandemic. These countries were considered the best prepared in each income bracket. How have they fared so far in the Covid-19 outbreak?

As of May 19, 2020. Figures dependent on number of tests carried out and availability and transparency of data.

1 Technology predicted the crisis, and we ignored it. We need to use big data better to reduce uncertainty during a pandemic. 2 Robots can meet many patient needs, offering a solution to shortages in healthcare personnel. However, robot technology requires significant investment. 3 Public health officials must rationalize big data modeling, so we do not ignore the warning for the next major public health crisis.
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