Literature DB >> 32296776

Crowdsourcing data to mitigate epidemics.

Gabriel M Leung1, Kathy Leung1.   

Abstract

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Year:  2020        PMID: 32296776      PMCID: PMC7158995          DOI: 10.1016/S2589-7500(20)30055-8

Source DB:  PubMed          Journal:  Lancet Digit Health        ISSN: 2589-7500


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Coronavirus disease 2019 (COVID-19) has spread with unprecedented speed and scale since the first zoonotic event that introduced the causative virus—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—into humans, probably during November, 2019, according to phylogenetic analyses suggesting the most recent common ancestor of the sequenced genomes emerged between Oct 23, and Dec 16, 2019. The reported cumulative number of confirmed patients worldwide already exceeds 70 000 in almost 30 countries and territories as of Feb 19, 2020, although that the actual number of infections is likely to far outnumber this case count.2, 3 During any novel emerging epidemic, let alone one with such magnitude and speed of global spread, a first task is to put together a line list of suspected, probable, and confirmed individuals on the basis of working criteria of the respective case definitions. This line list would allow for quick preliminary assessment of epidemic growth and potential for spread, evidence-based determination of the period of quarantine and isolation, and monitoring of efficiency of detection of potential cases. Frequent refreshing of the line list would further enable real-time updates as more clinical, epidemiological, and virological (including genetic) knowledge become available as the outbreak progresses. Therefore, from a public health viewpoint, a line list is indispensable. Hence, Kaiyuan Sun and colleagues' work, published in The Lancet Digital Health, is very valuable at this key timepoint in the COVID-19 outbreak. Sun and colleagues harnessed Chinese social media, specifically a social network used by health-care professionals, to compile individual-level data on patients with COVID-19 and daily province-level case counts during January, 2020. They distilled this information into a crowdsourced line list, which, when analysed appropriately, aligns closely with that derived from official versions, such as the report published by the Chinese CDC on Jan 29, 2020. For instance, the various delay intervals converge between the first 425 cases in Wuhan in the earlier report and the 507 cases sourced from both China and overseas in the present Article. Although Sun and colleagues' work provides a valuable picture of the outbreak in real time, the geographical coverage is heterogeneous with only a small proportion of cases from the epicenter of Wuhan and Hubei province. We surveyed different and varied sources of possible line lists for COVID-19 (appendix pp 1–4). A bottleneck remains in carefully collating as much relevant data as possible, sifting through and verifying these data, extracting intelligence to forecast and inform outbreak strategies, and thereafter repeating this process in iterative cycles to monitor and evaluate progress. A possible methodological breakthrough would be to develop and validate algorithms for automated bots to search through cyberspaces of all sorts, by text mining and natural language processing (in languages not limited to English) to expedite these processes. In this era of smartphone and their accompanying applications, the authorities are required to combat not only the epidemic per se, but perhaps an even more sinister outbreak of fake news and false rumours, a so-called infodemic. The most obvious consequences of an infodemic are, at best, a noisy cacophony that confuses and can provoke irrational fear, even mass panic, and ultimately imposes a destabilising effect on society when precisely the opposite is required. The images of empty supermarket shelves in the most open free-trade economies of Singapore and Hong Kong, where fewer than 100 cases have been reported to date, provide a salutary reminder of the potential impact of such infodemics. Another example is the worldwide shortage of and some national export bans on face masks. Creating a resource such as Sun and colleagues have compiled in their work would allow scientists and lay observers alike to quickly fill knowledge vacuums that would otherwise fuel infodemics. Related to the infodemic is the so-called geodemic of geopolitical considerations and nationalistic populism apparently being placed ahead of the science of outbreak control. A case in point relates to national border policies that have been suggested to contravene International Health Regulations. Anecdotes of xenophobic treatment of people from different places or fellow natives who look different are doing substantial harm to building the extra solidarity necessary in such times. Finally, the epidemic, infodemic, and geodemic all have economic costs. During the severe acute respiratory syndrome (SARS) outbreak in 2003, China accounted for 4% of global economic output compared with 16% today. Despite the ongoing trade tensions since 2019, China's supply chains and production lines remain closely enmeshed with much of the world's trading markets. These economic uncertainties, of course, have not taken into account how the outbreak might affect the rest of the world, when cases have now been reported on most continents, including Africa. Notwithstanding the above motivations, during the exigency of an outbreak, especially one with a doubling time of 1 week in the world's most populated country, expecting a ready line list for analytical prosecution covering all domestic geographies within a few short weeks would be astounding. Even during the SARS outbreak in 2003, we worked through over 30 versions of the case-contact questionnaire before settling on the final version well over a month after the first case had been confirmed. Notably, China's health protection function is decentralised to provincial and local levels (with over 300 prefecture-level Centers for Disease Control and Prevention branches) and it remains a developing country with differing levels of epidemic preparedness along socioeconomic development gradients across a large geography. Crowdsourced data could be compiled and analysed as timely as, or perhaps even quicker than, officially released data. However, such future developments do not negate the overriding importance of the timely release and updating of official line lists with as much detail as ethics and confidentiality allow. However, such sourcing would go a long way to address and mitigate the epidemics, infodemics, and geodemics that the world will face in the years to come.
  8 in total

1.  Extracting Medical Information from Paper COVID-19 Assessment Forms.

Authors:  Jacob D Schultz; Colin G White-Dzuro; Cheng Ye; Joseph R Coco; Janet M Myers; Claude Shackelford; S Trent Rosenbloom; Daniel Fabbri
Journal:  Appl Clin Inform       Date:  2021-03-10       Impact factor: 2.342

2.  The shortage of hospital beds for COVID-19 and non-COVID-19 patients during the lockdown of Wuhan, China.

Authors:  Zian Zhuang; Peihua Cao; Shi Zhao; Lefei Han; Daihai He; Lin Yang
Journal:  Ann Transl Med       Date:  2021-02

3.  Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China.

Authors:  Longxiang Su; Na Hong; Xiang Zhou; Jie He; Yingying Ma; Huizhen Jiang; Lin Han; Fengxiang Chang; Guangliang Shan; Weiguo Zhu; Yun Long
Journal:  Front Med (Lausanne)       Date:  2020-05-07

4.  Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study.

Authors:  Cuihua Shen; Anfan Chen; Chen Luo; Jingwen Zhang; Bo Feng; Wang Liao
Journal:  J Med Internet Res       Date:  2020-05-28       Impact factor: 5.428

5.  Fearing fear itself: Crowdsourced longitudinal data on Covid-19-related fear in Sweden.

Authors:  Carol Tishelman; Jonas Hultin-Rosenberg; Anna Hadders; Lars E Eriksson
Journal:  PLoS One       Date:  2021-07-01       Impact factor: 3.240

6.  Impact of National Containment Measures on Decelerating the Increase in Daily New Cases of COVID-19 in 54 Countries and 4 Epicenters of the Pandemic: Comparative Observational Study.

Authors:  Carlos K H Wong; Janet Y H Wong; Eric H M Tang; Chi Ho Au; Kristy T K Lau; Abraham K C Wai
Journal:  J Med Internet Res       Date:  2020-07-22       Impact factor: 5.428

7.  Using mHealth to Increase the Reach of Local Guidance to Health Professionals as Part of an Institutional Response Plan to the COVID-19 Outbreak: Usage Analysis Study.

Authors:  Olivier Windisch; Ido Zamberg; Marie-Céline Zanella; Angèle Gayet-Ageron; Katherine Blondon; Eduardo Schiffer; Thomas Agoritsas
Journal:  JMIR Mhealth Uhealth       Date:  2020-08-19       Impact factor: 4.773

8.  Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data.

Authors:  Timo Mitze; Johannes Rode
Journal:  Sci Rep       Date:  2022-01-18       Impact factor: 4.379

  8 in total

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