mteb/CovidQA huggingface dataset:
According to our model, locations above the 95% PI of imported-and-reported cases could have higher case-detection capacity. Locations below the 95% PI might have undetected cases because of expected imported-and-reported case counts under high surveillance. Underdetection of cases could increase the international spread of the outbreak because the transmission chain could be lost, reducing opportunities to deploy case-based control strategies. We recommend rapid strengthening of outbreak surveillance and control efforts in locations below the 95% PI lower bound, particularly Indonesia, to curb potential local transmission. Early detection of cases and implantation of appropriate control measures can reduce the risk for self-sustained transmission in all locations.

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Dr. De Salazar is a research fellow at Harvard T.H. Chan School of Public Health, working on multiscale statistical models of infectious diseases within host, population, and metapopulation models. His research interests include diagnostic laboratory methods and public health response.

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Acknowledgments
We thank Pamela Martinez, Nicholas Jewel, and Stephen Kissler for valuable feedback.

This work was supported by US National Institute of General Medical Sciences (award no. U54GM088558). P.M.D was supported by the Fellowship Foundation Ramon Areces. A.R.T. and C.O.B. were supported by a Maximizing Investigator’s Research Award (no. R35GM124715-02) from the US National Institute of General Medical Sciences.

The authors are solely responsible for this content and it does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.
Declaration of interests: Marc Lipsitch has received consulting fees from Merck. All other authors declare no competing interests.

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References
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Nuclear Threat Initiative and Johns Hopkins Center for Health Security. Global health security index [cited 2020 Feb 14]. https://www.ghsindex.orgExternal Link
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Lamichhane  S, Sen  P, Dickens  AM, Hyötyläinen  T, Orešič  M. An overview of metabolomics data analysis: current tools and future perspectives. In: Jaumot J, Bedia C, Tauler R, editors. Comprehensive analytical chemistry. Vol. 82. Amsterdam: Elsevier; 2018. p. 387–413.
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Wickham  H. ggplot2: elegant graphics for data analysis. New York: Springer; 2016.
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Figures
Figure 1. Regression plot of locations with possible undetected imported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by air travel volume from Wuhan, China. Air travel volume measured in...
Figure 2. Analyses of imported-and-reported cases and daily air travel volume using a model to predict locations with potentially undetected cases of severe acute respiratory virus 2 (SARS-CoV-2). Air travel volume...
Table
Table. Surveillance capacity of locations with and without imported-and-reported cases of severe acute respiratory syndrome coronavirus 2, 2020
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Suggested citation for this article: De Salazar PM, Niehus R, Taylor A, O’Flaherty Buckee C, Lipsitch M. Identifying locations with possible undetected imported severe acute respiratory syndrome coronavirus 2 cases by using importation predictions. Emerg Infect Dis. 2020 Jul [date cited]. https://doi.org/10.3201/eid2607.200250

DOI: 10.3201/eid2607.200250

Original Publication Date: 3/24/2020
1These authors contributed equally to this article.

Table of Contents – Volume 26, Number 7—July 2020
On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management

https://doi.org/10.3390/healthcare8010046

SHA: 90d04764b497a224a1d969f4e317fc19a5feab35

Authors: Allam, Zaheer; Jones, David S.
Date: 2020
DOI: 10.3390/healthcare8010046
License: cc-by
Abstract: As the Coronavirus (COVID-19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. The placing of entire cities in ‘lockdown’ directly affects urban economies on a multi-lateral level, including from social and economic standpoints. This is being emphasised as the outbreak gains ground in other countries, leading towards a global health emergency, and as global collaboration is sought in numerous quarters. However, while effective protocols in regard to the sharing of health data is emphasised, urban data, on the other hand, specifically relating to urban health and safe city concepts, is still viewed from a nationalist perspective as solely benefiting a nation’s economy and its economic and political influence. This perspective paper, written one month after detection and during the outbreak, surveys the virus outbreak from an urban
standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same.
Text: The novel Coronavirus outbreak, (previously known as the 2019-nCoV and later renamed COVID-19 during the writing of this manuscript) is leading to the closure of entire cities in China, and causing stringent measures to be taken in others. While in distant different continents, far from China where the virus was first reported, places are being placed on high alert. In Wuhan, where the virus broke, schools, roads and markets have been shut down [1] . The same is true in Hong Kong, Beijing and Hubei Province amongst surrounding areas, as precautionary measures are being emphasized to ensure that the spread of the virus is minimized, and complete and accurate information on the virus is being obtained [2] . However, the rate of spread of the virus and the uncertainties surrounding the entire situation has led the World Health Organization (WHO) on 30 January 2019 to declare the Coronavirus outbreak a 'Global Public Health Emergency'. WHO determined, however, not to declare the
outbreak a 'Public Health Emergency of International Concern' (PHEIC) which is a higher level of declaration. A PHEIC is defined as "an extraordinary event which is determined to constitute a public health risk to other States through the international spread of disease and to potentially require a coordinated international response" whose scope may include: serious, sudden, unusual or unexpected; carries implications for public health beyond the affected State's national border; and may require immediate international action [3] .
With the world having experienced some notable influenza pandemics in the past, a Global Initiative on Sharing All Influenza Data (GISAID) platform [4] was established and was instrumental in the rapid sharing of information by the Chinese scientists regarding the emergence of the COVID-19 virus. Through this platform, scientists from other regions were observed to gain access to information and are, subsequently, able to act in a much faster capacity; like in the case of scientists from the Virus Identification Laboratory based at Doherty Institute, Australia, who managed to grow a similar virus in the laboratory after accessing the data shared by the Chinese scientists [5] .
Beyond the aspect of pandemic preparedness and response, the case of COVID-19 virus and its spread provide a fascinating case study for the thematics of urban health. Here, as technological tools and laboratories around the world share data and collectively work to devise tools and cures, similar efforts should be considered between smart city professionals on how collaborative strategies could allow for the maximization of public safety on such and similar scenarios. This is valid as smart cities host a rich array of technological products [6, 7] that can assist in early detection of outbreaks; either through thermal cameras or Internet of Things (IoT) sensors, and early discussions could render efforts towards better management of similar situations in case of future potential outbreaks, and to improve the health fabric of cities generally. While thermal cameras are not sufficient on their own for the detection of pandemics -like the case of the COVID-19, the integration of such
products with artificial intelligence (AI) can provide added benefits. The fact that initial screenings of temperature is being pursued for the case of the COVID-19 at airports and in areas of mass convergence is a testament to its potential in an automated fashion. Kamel Boulos et al. [8] supports that data from various technological products can help enrich health databases, provide more accurate, efficient, comprehensive and real-time information on outbreaks and their dispersal, thus aiding in the provision of better urban fabric risk management decisions.
The above improvements in the healthcare sector can only be achieved if different smart city products are fashioned to support standardized protocols that would allow for seamless communication between themselves. Weber and Podnar Žarko [9] suggest that IoT devices in use should support open protocols, and at the same time, the device provider should ensure that those fashioned uphold data integrity and safety during communication and transmission. Unfortunately, this has not been the case and, as Vermesan and Friess [10] explain, most smart city products use proprietary solutions that are only understood by the service providers. This situation often creates unnecessary fragmentation of information rendering only a partial integrated view on the dynamics of the urban realm. With restricted knowledge on emergent trends, urban managers cannot effectively take decisions to contain outbreaks and adequately act without compromising the social and economic integrity of their city. This
paper, inspired by the case of the COVID-19 virus, explores how urban resilience can be further achieved, and outlines the importance of seeking standardization of communication across and between smart cities.
With the advent of the digital age and the plethora of Internet of Things (IoT) devices it brings, there has been a substantial rise in the amount of data gathered by these devices in different sectors like transport, environment, entertainment, sport and health sectors, amongst others [11] . To put this into perspective, it is believed that by the end of 2020, over 2314 exabytes (1 exabyte = 1 billion gigabytes) of data will be generated globally [12] from the health sector. Stanford Medicine [12] acknowledges that this increase, especially in the medical field, is witnessing a proportional increase due to the increase in sources of data that are not limited to hospital records. Rather, the increase is being underpinned by drawing upon a myriad and increasing number of IoT smart devices, that are projected to exponentially increase the global healthcare market to a value of more than USD $543.3 billion by 2025 [13] . However, while the potential for the data market is understood,
such issues like privacy of information, data protection and sharing, and obligatory requirements of healthcare management and monitoring, among others, are critical. Moreover, in the present case of the Coronavirus outbreak, this ought to be handled with care to avoid jeopardizing efforts already in place to combat the pandemic. On the foremost, since these cut across different countries, which are part of the global community and have their unique laws and regulations concerning issues mentioned above, it is paramount to observe them as per the dictate of their source country's laws and regulations; hence, underlining the importance of working towards not only the promoting of data through its usage but also the need for standardized and universally agreed protocols.
While the significance of such data in advancing efficiency, productivity and processes in different sectors is being lauded, there are criticisms arising as to the nature of data collection, storage, management and accessibility by only a small group of users. The latter particularly includes select ICT corporations that are also located in specific geographies [6, [14] [15] [16] [17] . These criticisms are justified, as in recent years, big data is seen as the new 'gold rush' of the 21st century and limiting its access means higher economic returns and increased influence and control at various scales to those who control data. These associated benefits with big data are clearly influencing geopolitical standings, in both corporate and conventional governance realms, and there is increased competition between powerful economies to ensure that they have the maximum control of big data. As case in point is the amount of 'push and pull' that has arisen from Huawei's 5G internet planned
rollout [18] . Though the latter service offers unprecedented opportunities to increase internet speeds, and thereby influence the handling of big data, countries like the U.S. and some European countries that are key proponents and players in global political, economic and health landscapes, are against this rollout, arguing that it is a deceptive way of gathering private data under the guise of espionage. On this, it has been noted that the issue of data control and handling by a few corporations accords with their principles of nationalism, and that these work for their own wellbeing as well as to benefit the territories they are registered in. Therefore, geopolitical issues are expected on the technological front as most large data-rich corporations are located in powerful countries that have influence both economically, health-wise and politically [19] [20] [21] . Such are deemed prized tokens on the international landscape, and it is expected that these economies will continue
to work towards their predominant control as much as possible. On the health sector, the same approach is being upheld where critical information and data are not freely shared between economies as that would be seen to be benefiting other in-competition economies, whereas different economies would cherish the maximization of benefits from such data collections.
In addition to the obvious deep-rooted social issues related to nationalism, other challenges include the increasing movement of people globally that is being enhanced by reduced costs and higher speed. In particular, these challenges are more pronounced when it comes to public health. This is because most of the health-related data collected not only can compromise local nations, but also captures those of travelers. In such cases, in a bid to improve the health status of a nation, it becomes paramount to factor in data from other regions necessitating unhindered sharing of this data.
Such data-sharing truth is emphasized in situations like the recent case of Coronavirus outbreak threatening the global health environment, facilitated by air transportation. The virus was first reported in Wuhan, China, and in a matter of three weeks (by 17th January 2020) over 300 cases were confirmed in that region, and 10 days later (26th January 2020), a total of 2014 cases of Coronavirus have been reported, with 684 of those being confirmed, and with 29 reported outside China. The fatalities from the virus stands at 56 as of 26th January 2020 [22] . The virus had then been confirmed in various countries including Taiwan, South Korea, Japan, Thailand, France, the United States, Singapore and Vietnam [23] .
In the above case, though major cities are known to prepare themselves for potential outbreaks, their health policies and protocols are observed to diverge from one another. Thus, without a global collaborative approach, progress towards working for a cure and universally acceptable policy approach can take longer. Such fears, of a lack of international collaboration, were highlighted by the World Health Organization (WHO) during an emergency meeting in Geneva on 22nd January 2020 to determine whether the virus outbreak had reached a level warranting international emergency concern. However, WHO was satisfied that China was being proactive in this case, unlike in 2002, when China withheld information on the outbreak for far too long, causing delays in addressing the epidemic [3] . As in this instance, it is the opinion in this paper that if there was seamless collaboration and seamless sharing of data between different cities, it would not warrant such a high-level meeting to result
in action, and instead, a decision could have been made much earlier. On this, the saddest part is that some global cities are less prepared to handle the challenges posed by this type of outbreak for lack of information on issues like symptoms of the virus, the protective measures to be taken, and the treatment procedures that an infected person should be processed through, amongst other issues.
The timely response by stakeholders in regard to this new outbreak are commendable compared to previous cases. The latter includes the Severe Acute Respiratory Syndrome (SARS) outbreak in 2002 that took substantial time (from November 2002 to April 2003) to identify and be dealt with [24] ; the Ebola outbreak in West Africa in 2013 that took months to determine; and the Zika Virus that was first reported in 2014 before being successfully identified in 2015.
With the Coronavirus (COVID-19) , it took only 17 days (31st December 2019 to 17th January 2020) to be identified. The sharing of data has also been quicker, as immediately after the virus' genetic sequence was discovered, Chinese scientists were able to share the information with the WHO, thus helping in its identification and enabling the auctioning of precautionary measures in other countries. Latest technological tools have also allowed for the receipt of information in realtime, in contrast to traditional epidemiological approaches that would have required months to identify the outbreak type [25] . Similarly, though substantial data and information on the disease has been shared, Wetsman [26] acknowledges that there is a lack of some vital information, like the ease of spread of the virus from person-to-person, and this is a key to containing the disease as interactions between people from different parts of the globe are still active. This hindrance can be made further possible
as many cities advance in their smart and safe city model implementation towards constructing sufficient soft and hard urban infrastructures equipped with, for example, thermal imagery sensors to allow for early detections. However, while that is the case, data access to many is a challenge because the information is often seen as being sensitive for national security reasons, whilst at the same time, acknowledging that a virus outbreak is an equal threat to both national security and the economy.
The outbreak of any disease has significant impacts on local economies across the globe. For instance, when SARS (Severe Acute Respiratory Syndrome) (SARS-CoV) broke in China in 2002, it was estimated, that the Asian region incurred tremendous negative impacts socially, health-wise and economically, potentially amounting to Asian regional economy losses of between USD $12-18 billion from tourism, travel and retail sales industries alone [27] . The Zika virus outbreak, spread by daytime-active Aedes mosquitoes, is estimated to have cost equator-belt local economies in affected areas between USD $7 and USD $18 billion [28] . The Ebola virus (or Ebola hemorrhagic fever (EHF)) caused an estimated loss of USD $2.2 billion in GDP in three West African economies (Guinea, Liberia and Sierra Leone) in 2015 alone [29] . In regard to the current epidemic of Coronavirus, though it is too early to quantify or project its impacts on the global economy, there are fears that it may take the precedent
of other outbreaks where billions of dollars will be lost. The foundations for this escalating loss can be witnessed in the rapid growth of travel bans being enacted by some countries and their international airports, especially specifically restricting people from visiting the affected regions in China and their growth into general non-Chinese travel movements. On this, noting that the outbreak came almost on the eve of the Lunar New Year celebrations, and that it had been estimated that over 400 million people were expected to travel in different parts of the world and China to observe this festivity, the majority have had to reconsider their options as to flights, hotels and entertainment events due to service provider cancellations [30] . Those who had already booked their flights are expected to receive their refunds following the directive by the Civil Aviation Administration of China, however, this move has already affected the share value of Chinese airline companies [30] .
The above impacts demonstrate that the issues of virus outbreaks transcend urban safety and impacts upon all other facets of our urban fabric. Therefore, it becomes paramount to ensure that the measures taken to contain a virus transcend nationalist agendas where data and information sharing is normally restricted, to a more global agenda where humanity and global order are encouraged. With such an approach, it would be easier to share urban health data across geographies to better monitor emerging health threats in order to provide more economic stability, thereby ensuring no disruptions on such sectors like tourism and travel industries, amongst others. This is possible by ensuring collaborative, proactive measures to control outbreak spread and thus, human movements. This would remove fears on travelers, and would have positive impacts upon the tourism industry, that has been seen to bear the economic brunt whenever such outbreaks occur. This can be achieved by ensuring that
protocols on data sharing are calibrated to remove all hurdles pertaining to sharing of information. On this, Lawpoolsri et al. [31] posits that such issues, like transparency, timelessness of sharing and access and quality of data, should be upheld so that continuous monitoring and assessment can be pursued.
Virus outbreaks in recent years have shown that, in the urban realm, data, including health data, can be sourced from diverse places. Presently, in the case of Coronavirus (COVID-19) outbreak, data is being collected from airports through screening and monitoring, through the use of smart sensors installed in airport infrastructures and from personnel working in those air/seaports. For instance, it has been reported that in the U.S.A., screening is being carried out at 20 different airports to ensure that possible affected people are intercepted for quarantine at the point of entry. Beside airports, as reported by Buckley and May [2] , data is also being collected at bus terminals, market places (in Wuhan), subways, and also in health facilities where patients are taken for further medical attention. Such is prevalent especially in China, and other Asian regions where cases of the virus have been recorded and confirmed.