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 Table of Contents  
REVIEW ARTICLE
Year : 2020  |  Volume : 4  |  Issue : 2  |  Page : 83-89

Descriptive review of epidemiological geographic mapping of coronavirus disease 2019 (COVID-19) on the internet


1 Research Unit, School of Health Sciences, The University of Zambia, Lusaka, Zambia
2 Department of Public Health, School of Medicine and Health Sciences, The University of Lusaka, Lusaka, Zambia

Date of Submission30-Mar-2020
Date of Acceptance07-Apr-2020
Date of Web Publication17-Jun-2020

Correspondence Address:
Mr. Brian Chanda Chiluba
Research Unit, School of Health Sciences, The University of Zambia, PO Box 50110, Lusaka
Zambia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/bbrj.bbrj_50_20

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  Abstract 


Geoinformatics significantly enables the identification and tracking of global pandemic outbreaks such as coronavirus disease 2019 (COVID-19), from spatial mapping at various sizes to location-based alerts. This article seeks to review online attempts to geographically map COVID-19, using several established techniques such as choropleth rendering, graduated circles, graduated pie charts, buffering, overlay analysis, and animation. The import of these mapping services is primarily to educate the public (especially travelers to potentially affected areas) and empower public health authorities in mapping spatial and temporal trends and patterns in COVID-19, as well as in assessing/reviewing the efficacy of current control protocols and actions.

Keywords: Coronavirus, coronavirus disease 2019, geographic, mapping, outbreak


How to cite this article:
Chiluba BC, Dube G. Descriptive review of epidemiological geographic mapping of coronavirus disease 2019 (COVID-19) on the internet. Biomed Biotechnol Res J 2020;4:83-9

How to cite this URL:
Chiluba BC, Dube G. Descriptive review of epidemiological geographic mapping of coronavirus disease 2019 (COVID-19) on the internet. Biomed Biotechnol Res J [serial online] 2020 [cited 2020 Jul 13];4:83-9. Available from: http://www.bmbtrj.org/text.asp?2020/4/2/83/286847




  Introduction Top


Coronavirus (CoV) represents a mix of a wide cluster of viruses usually found in animals, thus zoonotic in origin, but that in certain circumstances jump to humans. It is now commonly held that contact with meat from various animals sold in Huanan Wholesale Seafood Market is the likely cause of the first reported human infections.[1] The virus most likely evolved to its current pathogenic state through natural selection in a nonhuman host and then jumped to humans. This is how the previous CoV outbreaks have emerged, with humans contracting the virus after direct exposure to civets (severe acute respiratory syndrome [SARS]) and camels (Middle East respiratory syndrome [MERS]). The virus is usually transmitted through droplets from coughing or sneezing. For the greater part, the majority of the droplet measure <100 μ (0.1 mm) across. Surgical masks are designed to prevent large droplets passing from one person's mouth to another person or surfaces where there may be human interaction. N95 masks protect the wearer from breathing in particles bigger than 0.3 μ in diameter. When fitted correctly, the respirators filter 95% of airborne particles. The World Health Organization (WHO) says that while wearing a mask helps to mitigate the spread of some respiratory diseases, it is not adequate to prevent all infections. The most recommended precautions can be simplified as: washing your hands frequently with soap for about 20 s, using sanitizers with an alcohol content of at least 70%, avoiding touching of the face, observing social distancing protocols of at least a meter from other people, and staying clear of crowded places.

The outbreak of CoV disease 2019 (COVID-19) surfaced in December 2019 in Wuhan, the capital of Hubei Province, and the largest city in central China, with a population of around 11 million people. Epidemiologists have posited that the likely source of the outbreak is the Huanan Seafood Wholesale Market in downtown Wuhan, where it is believed that the virus was transmitted from animals to humans. On March 11, the WHO declared the novel CoV outbreak a global pandemic, underscoring the speed and tenacity with which it has spread.[2] Despite public fears about the outbreak, however, public health experts have called for calm. “It is important to know how to distinguish between the advice and information coming from public health authorities and scientists, versus the misinformation that is instigating unnecessary fear,” says Lauren Gardner, an epidemiologist at Johns Hopkins University (JHU) who is tracking the spread of the outbreak.[3]

As researchers track and map its trajectories and patterns, governments legislate prevention protocols and measures, and laboratories search for curative solutions, the moot questions on the current outbreak of the novel COVID-19 pandemic are: Where and how many people are infected? What is the geographical spread of the disease? When a disease can travel so rapidly, research information has to be even faster. This underscores the need for map-based dashboards which then become crucial.[4] In the middle of an evident global pandemic where does one get information from? Perhaps more importantly, what maps, web maps, and web data (web map service) portals can one consult for information and updates?

There are many data and information sources, both official and unofficial, that are providing regular updates, however, one must ask themselves whether or not these sources be trusted? The following are a few resources to consider: it is undeniable that the discipline of geography and specifically (geographic information system [GIS]) has been critical in the response to the scourge of the virus SARS-CoV-2, which has evolved to COVID-19. A GIS is a framework for gathering, managing, and analyzing data. Rooted in the science of geography, GIS integrates many types of data. It analyzes spatial location and organizes layers of information into visualizations using maps and three-dimensional scenes. With this unique capability, GIS reveals deeper insights into data, such as patterns, relationships, and situations, helping users make smarter decisions. Waze and Google Maps which fall within the realm of GIS science are useful in the field of mapping and tracking the COVID-19 template. The JHU has also come up with and maintaining an excellent COVID-19 tracking website, which gathers information from multiple data sources.[5]


  Symptoms, Incubation Period, and Severity Top


The most commonly reported clinical symptom in laboratory-confirmed cases is fever (88%), followed by dry cough (68%), fatigue (38%), sputum production (33%), dyspnea (19%), sore throat (14%), headache (14%), and myalgia or arthralgia (15%).[6] Less common symptoms are diarrhea (4%) and vomiting (5%). About 80% of reported cases in China had mild-to-moderate disease (including nonpneumonia and pneumonia cases), 13.8% had severe disease, and 6.1% were critical (respiratory failure, septic shock, and/or multiple organ dysfunction/failure). Current estimates suggest a median incubation period from 5 to 6 days for COVID-19, with a range from 1 to up to 14 days. A recent modeling study confirmed that it remains prudent to consider the incubation period of at least 14 days.[7],[8]


  Case Fatality Top


Accurate and reliable estimates for final case fatality risk (CFR) for COVID-19 are still lacking and biased due to incomplete outcome data and the fact that initial detections were of mostly severe cases in most settings. Based on a large dataset from cases in China, the overall CFR among laboratory-confirmed cases was higher in the early stages of the outbreak (17.3% for cases with symptom onset from January 1 to 10) and has reduced over time to 0.7% for patients with symptom onset after February 1.[9] From data on diagnosed COVID-19 cases in China, Italy, and South Korea, the overall CFR was 2.3%, 2.8%, and 0.5%, respectively, and increased with age in all settings, with the highest CRF among people over 80 years of age (14.8%, 8.2%, and 3.7%, respectively).[10],[11],[12]


  Geographical Mapping in Infectious Diseases: Current Trends Top


In contrast to the 2002/2003 SARS-CoV and the 2012–2014 MERS-CoV, the COVID-19 spreads overwhelmingly fast. It took MERS about 2½ years to infect 1000 people and SARS took almost 4 months, but the novel COVID-19 reached that figure in just 48 days. On January 30, 2020, the WHO declared that the new COVID outbreak constitutes a Public Health Emergency of International Concern.[13],[14],[15],[16],[17],[18],[19],[20],[21],[22],[23] As with the original SARS-CoV epidemic of 2002/2003[24] and with seasonal influenza,[25],[26] GIS and methods, including, among other application possibilities, online real- or near-real time mapping of disease cases and of social media reactions to disease spread. Predictive risk mapping using population travel data, tracing, and mapping super spreader trajectories and contacts across space and time are proving indispensable for our timely understanding of the new disease source, dynamics, and epidemiology, and in shaping our effective response to it.

The Internet has commendably become the information hub for, health professionals. It has enabled conventional mapping, and more recently GIS, as critical tools in tracking and combating contagion. The earliest map visualization of the relationship between place and health was in 1694 on plague containment in Italy.[7] The value of the Internet as a repository of maps and data as communication tools blossomed over the last 225 years in the service of understanding and tracking infectious diseases, such as yellow fever, cholera, Ebola, and SARS. A 2014 review of the health GIS literature found that 248 out of 865 included articles (28.7%) focused on infectious disease mapping.[27] Since then, a revolution has been seen in applied health geography through web-based tools.[28],[29] As this resource is exploited and tools are deployed to protect human lives, the Internet has aided the processing of data from the internet sources and displays results in interactive and near-real-time dashboards. These online dashboards have become the central source of information during the COVID-19 outbreak. This article offers to, and describes, a range of available Internet or web-based sources and online/mobile GIS and mapping dashboards and applications for tracking the COVID-19 pandemic and associated events as they unfold around the world. Most of these dashboards and applications are updated in near real time and one can be guaranteed of up-to-date information.


  Geoinformatics' Role in Coronavirus Disease-2019 Pandemic Top


Maybe nothing is more fundamentally “geographic” than researching and monitoring the spread of the disease at a range of acceptable scales.[9] Geoinformatics is the science and technology that gathers, stores, visualizes, analyzes, interprets, models, distributes, and uses spatially referenced information (geographically referenced).[9],[29] Geoinformatics plays a significant role in the research and control of epidemics from spatial mapping to epidemiological modeling and location-based alerting services.[27],[28],[29],[30] This also refers to the major COVID-19 outbreak in 2019.

Maps lead to the birth of epidemiology. In 1854, when cholera broke out in London, everyone considered the cause of this to be particles in the air. Jon Snow, a physician at the time, plotted all the cases of cholera on a map of London and found out that the cause wasn't the foul air, but contaminated water from a street pump!

After the first maps used by John Snow in 1854 to trace the source of a cholera epidemic in the Soho area of London, it has become evident that carefully planned and built maps can be very useful tools for decision support and spatial-temporal analysis. In the case of COVID-19 pandemic, they can help local public health communities to visually track and recognize changes, trends, and patterns hidden in broad datasets that differ over time. Such form of assistance is critical when planning and tracking disease prevention initiatives or when issuing and updating travel advisories for informed decision-making.

Also, worth mentioning in this context is the recent news that remote sensing satellite data (provided by the European Space Agency) and GIS are currently being used to better understand, predict, and help combat Ebola hemorrhagic fever outbreaks in central Africa (http://www.esa.int/export/esaSA/SEMWG5VZJND_earth_2.html).

In addition to the above, various news organizations have come up with interactive maps to give the situational data on the ongoing pandemic. Some of them are:

  • CoV COVID-19 global cases (Johns Hopkins)
  • Novel CoV (COVID-19) outbreak timeline map (HealthMap)
  • Novel CoV infection map (University of Washington)
  • COVID-19 surveillance dashboard (University of Virginia)
  • Novel CoV (COVID-19) situation dashboard (WHO)
  • COVID-19 in the US (Centers for Disease Control and Prevention [CDC])
  • Geographical distribution of COVID-19 cases worldwide (European Centre for Disease Prevention and Control [ECDC])
  • COVID-19 CoV tracker (Kaiser Family Foundation)
  • COVID-19 CoV outbreak (Worldometer)
  • CoV: the new disease COVID-19 explained (South China Morning Post)
  • Mapping the Wuhan CoV outbreak (Esri StoryMaps) by South China Post.



  Johns Hopkins University Centre for Systems Science and Engineering Dashboard Top


Because diseases can now rapidly spread so quickly, the need for information to move even faster cannot be underemphasized. This underscores the need where map-based dashboards become crucial.[31] At the time of this writing in mid-March 2020 (we could edit to end March), seven CoV dashboards were among the top ten used applications from Esri ArcGIS Online service, accumulating over 160 million views. First published on January 22, 2020, in response to the escalating pandemic fears in the late January 2020, the JHU Centre for Systems Science and Engineering (CSSE) dashboard leads the pack, garnering 140 million views. Developed by Lauren Gardner (an epidemiologist) and her team from the JHU CSSE, the dashboard went viral with hundreds of news articles and shares on social media [Figure 1].[32] This overwhelming response to the JHU CSSE and other dashboards demonstrates how eager researchers and policymakers are to track health threats. Anyone with Internet access can learn, in a click, a tremendous amount of information about COVID-19 from these resources.
Figure 1: One of the John Hopkins Maps for coronavirus disease 2019 distribution. Web browser screenshot of one of the world maps of coronavirus distribution global maps as of March 29, 2020. To explain the legend: Red represents the case distribution across the globe, on the left-hand side: Cases per country, on the right-hand side in white, deaths per country and on the right-hand side, in green, recovered cases per country

Click here to view


JHU CSSE is tracking the spread of SARS-CoV-2 in near real time with a map-centric dashboard (using ArcGIS Online) that pulls relevant data from the WHO, US CDC, ECDC, Chinese CDC (CCDC), China's National Health Commission (NHC), and Dingxiangyuan (DXY, China). The intention of the health map is to provide an up-to-date estimate of the geographic spread of this novel CoV and the way that HME in coordination with a lot of organizations around the world is contributing to this is to provide a curated list of cases that are known about and populating it with the appropriate geographic metadata and also adding other information such as age, sex, and the timeline over which an individual becomes infected [Figure 2].
Figure 2: HealthMap for coronavirus disease 2019 distribution. Web browser screenshot of one of the global map distributions as of March 29, 2020. Each with color coded representing the age cases per number shown on the left side of the map. The key shows, number of cases starting from 10 to a 10,000+. One important feature of this map is the provision of the video from when the outbreak started to the March 23, 2020. This is key to know the pattern of the spread from the epic center in Wuhan China to the rest of the world

Click here to view



  Coronavirus Disease 2019 Geographic Information System Hub Top


This “Hub” was set up by the Esri Public Health Team and is also a place where institutions and organizations needing to map, analyze, and share data can get in touch with the Esri team for assistance. It has also become a gateway to other maps and applications that Esri partners and customers are deploying. Numerous story maps, featured data products, and regional applications are available in the Hub. Local government agencies heavily rely on this agency as a key resource [Figure 3].
Figure 3: Novel coronavirus infection map (University of Washington) showing the progression pattern of coronavirus disease 2019-infected countries globally. Web browser screenshot of a map as of March 23, 2020 (https://nssac.bii.virginia.edu/covid-19/dashboard/). One can clearly see at a glance the coronavirus disease 2019 global trend in terms of active confirmed cases, recovered and deaths for coronavirus disease 2019. And, by this time, not so much case fatality rate in Africa

Click here to view



  Surveillance Dashboard Top


This surveillance dashboard is being run by the Network Systems Science and Advanced Computing (NSSAC)'s collaboration page. It is a division of the Biocomplexity Institute and Initiative at the University of Virginia. They use advanced modeling techniques and simulations to study real-world, cross-discipline problems. The focus areas include cognitive and social behaviors, interdependent infrastructures (such as transportation and social media), systems biology, and public health (including epidemics and pandemics such as COVID-19).

The site provides access to materials relevant to the current topics of interest in order to foster communication and collaboration. Key features of this tool include a time slider to view all the historical data, an interactive chart for cumulative and daily number, a visualization of all reported COVID-19 incidence data filtered by date, a heat map of selected attributes on an interactive map, and a query tool that allows users to focus on regions of interest. With the ability to select regions by clicking on the map to select multiple regions at once, by holding the “command” key on the Mac or the “ctrl” key on Windows while clicking, users can export subsets of the data for analysis on external tools. This tool may encourage researchers worldwide to explore the surveillance datasets made available by the WHO, the CDC, the ECDC NHC, DXY, and QQ, and curated by NSSAC [Figure 4].
Figure 4: University of Virginia coronavirus disease 2019 Surveillance Dashboard–Impressive dashboard monitoring active, confirmed, recovered, and deaths from coronavirus. On the left side is the time series slider! Web browser screenshot by this author of a map as of March 29, 2020. The map has a time slider that shows the diseases progression from onset to the current

Click here to view



  the World Health Organization Dashboard Top


The WHO directs and coordinates international health, combating communicable diseases through surveillance, preparedness, and response, and applying GIS technology to this work. On January 26, 2020, the WHO unveiled its ArcGIS Operations Dashboard for COVID-19, which also maps and lists CoV cases and total number of deaths by country and Chinese province, with informational panels about the map and its data resources [Figure 5].[33]
Figure 5: Coronavirus disease 2019 situation analysis globally. The map appearing in this screenshot was taken on March 16, 2020. The map shows lower number of cases in Africa by this time but compared to other regions. The right panel shows country leading in number of cases from the highest to the lowest. By March 29, America had overtaken China, Italy, and Spain in the number of cases within a few weeks (https://who.maps.arcgis.com/apps/opsdashboard/index.html#/c88e37cfc43b4ed3baf977d77e4a0667)

Click here to view



  Situation Dashboard-Coronavirus Disease 2019 Cases in Europe and Worldwide Top


The interface allows users to explore and interact with the latest available data on COVID-19 and switch chart to tables view for details. The situation dashboard now includes more detailed data on cases from the EU/EEA and the UK. The number of cases and deaths can be shown within a specific date range and by country. Enhanced data are available on a subset of cases and include age, gender, hospitalization, and admission to intensive care. The data are uploaded on a daily basis. Certain features only work based on the web browser the user is using.


  European Centre for Disease Prevention and Control Top


Since December 31, 2019, and as of March 16, 2020, 167,414 cases of COVID-19 (in accordance with the applied case definitions in the affected countries) have been reported, including 6507 deaths. The deaths have been reported from China (3217), Italy (1811), Iran (724), Spain (288), France (127), South Korea (75), United States (69), United Kingdom (35), Japan (24), the Netherlands (20), Switzerland (13), Germany (12), the Philippines (12), Iraq (9), an international conveyance in Japan (7), San Marino (7), Australia (5), Indonesia (5), Algeria (4), Belgium (4), Greece (4), Lebanon (3), Poland (3), Sweden (3), Argentina (2), Bulgaria (2), Ecuador (2), Egypt (2), India (2), Ireland (2), Albania (1), Austria (1), Canada (1), Denmark (1), Guatemala (1), Guyana (1), Hungary (1), Luxembourg (1), Morocco (1), Norway (1), Panama (1), Sudan (1), Taiwan (1), and Thailand (1) [Figure 6].
Figure 6: South China Morning Post graphics, Coronavirus: the disease Covid-19 explained. OpenStreetMap, State media, Maps4News, National Health Commission of the PRC; Local Municipal Health Commission; China Health Statistics Yearbook 2018; US' Centres for Disease Control and Prevention (CDC),The Lancet, US Food and Drug Administration (FDA), WHO Summary of probable Sars cases and China's National Health Commission; Baidu Qianxi; BNO News; Flight Master South China Morning Post Published January 21, 2020

Click here to view



  Discussion Top


The first major new infectious disease of the 21st century was SARS which saw the Internet being used in tracking, mapping, and profiling its rapid spread along international air routes. From geographic mapping to location-based alerting services, Geoinformatics has played an important role in the study and control of global outbreaks such as SARS. Carefully planned and designed maps are powerful decision support and spatial-temporal analysis tools. Web-based maps also allow for quick, frequent map updates based on the latest datasets, for interactivity to be incorporated into the maps (desktop GIS-like functionality, e.g. drill-down and zooming), and for wider and more rapid dissemination of information (compared to other publishing media).

There is a myriad of explanations for mapping the geographical distribution of an infectious disease. Mapping is a primary objective in spatial epidemiology.[30],[31],[32],[34],[35],[36],[37],[38] Maps of disease distribution and intensity create a pictorial visualization and conceptualization of the extent and magnitude of the public health problem. When interpreted from an empirical evidence perspective, maps can support carefully weighted assessments by decision makers on the pros and cons of alternative courses of action.[5],[6],[7],[8] These may vary from helping plan national-scale interventional strategies[10],[11] to empower individuals with information on whether to vaccinate and/or provide prophylaxis before travel.[6],[12] These maps can also document a baseline from which intervention success or failure can be monitored.

In addition, modes of data gathering evolve and improve (e.g. through enhanced electronic surveillance[5] and Internet-based health reporting,[13] including HealthMap/ProMED,[3],[4] BioCaster,[5],[6] and Argus)[18],[19] and techniques develop to exploit these data (e.g. semi-automated rapid mapping). These geographical distributions (often referred to in this literature as baseline disease risk assessments) can also provide a “normal” against which real-time outbreak alerts can be assessed for international bio surveillance.[20],[21]

Quantitatively, mapping the spatial distributions of infectious diseases such as CoV is key to both understanding and investigating their epidemiology as well as identifying populations at risk of infection. Important advances in data quality and methodologies have allowed for better investigation of disease risk and its association with environmental factors. Basically, the applications that are used in public health sciences can be grouped into three distinct programs: ArcView, HealthMapper, and EpiMap, although the entire applications used shape file format, which is the most common format of data availability.[22],[23],[24],[25],[27],[28],[29],[39],[40]

Almost all COVID-19 mapping is globally are using disaggregated case data at individual building level in near real time. This, together with some well-established public health confidentiality laws is an opportunity for leverage of mapping research and hence the very detailed COVID-19 internet mapping services listed in this article. With its unprecedented capacity for the near-instant, wide-scale exchange and distribution of information, the Internet has proven to be an invaluable and very efficient resource for understanding, responding rapidly to, and successfully managing global outbreaks such as the CoV pandemic.


  Conclusion Top


Communication through map dashboards and modern geographic technology provides valuable information for people around the globe who are focused on protecting themselves and their communities. This type of tool enhances data quality and assists the authorities in disseminating the data. The dashboards definitely took center stage when hearing of COVID-19 outbreaks. Yet, we hope readers can see how a robust spatial mapping system which facilitates the entire process of infections. New GIS technologies rely on web-based infrastructure, enhanced data sharing, and real-time knowledge to promote strategic decision-making. The dashboards exemplify those concepts and were incredibly popular to communicate and understand the spread of COVID-19.

Current GIS systems focus on web-based software, improved data sharing, and knowledge in real time to support essential decision-taking. Dashboards exemplify these values and were extremely common in sharing and understanding COVID-19's spread.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Chinese Center for Disease Control and Prevention (CCDC). The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19)–China; 2020. Available from: http://weekly.china cdc.cn/en/artic le/id/e5394 6e2-c6c4-41e9-9a9b-fea8d b1a8f 51. [Last accessed on 2020 Feb 17].  Back to cited text no. 1
    
2.
World Health Organization. Statement on the Second Meeting of the International Health Regulations Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-nCoV); 2005. Geneva, Switzerland: World Health Organization; 30 January; 2020.  Back to cited text no. 2
    
3.
Statement on the second meeting of the international health regulations (2005) emergency committee regarding the outbreak of novel coronavirus (2019-ncov). Available from: https://www.who.int/news-room/detail/. [Last accessed on 2020 Jan 30].  Back to cited text no. 3
    
4.
Public Health – Seattle and King County. Press Release: Update: King County COVID-19 Case Numbers for; 9 March, 2020.  Back to cited text no. 4
    
5.
Johns Hopkins CSSE. Corona Virus 2019-nCoV Cases (The Living Atlas). Available from: https://livin gatlas.arcgis.com/en/brows e/#d=2 &q=%22Cor ona%20Virus%20201 9%20nCo V%20Cas es%22. [Last accessed on 2020 Feb 17].  Back to cited text no. 5
    
6.
World Health Organization (WHO). Report of the WHO-China Joint Mission on Coronavirus Disease (COVID-19); 2019.  Back to cited text no. 6
    
7.
Rijksinstituut Voor Volksgezondheid En Milieu. Actuele Informatie over Het Nieuwe Coronavirus (COVID-19); 2020. Available from: https://www.rivm.nl/nieuws/actuele-informatie-over-coronavirus. [Last accessed on 2020 Mar 10].  Back to cited text no. 7
    
8.
Sciensano. Public Health Event Follow Up: COVID-19; 2020. Available from: https://epidemio.wivisp.be/ID/Documents/Covid19/Derni%c3%a8re%20mise%20%c3%a0%20jour%20de%20la%20situation%20%c3%a9pid%c3%a9miologique.pdf. [Last accessed on 2020 Mar 10].  Back to cited text no. 8
    
9.
World Health Organization (WHO). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19); 2020. Available from: https://www.who.int/docs/default-source/coronaviruse/who-china-jointmission-on-covid-19-final-report.pdf. [Last accessed on 1 Mar 2020].  Back to cited text no. 9
    
10.
Tatem A, Smith D, Gething P, Kabaria C, Snow R, Hay S. Ranking elimination feasibility among malaria-endemic countries. Lancet 2010;376:1579-91.  Back to cited text no. 10
    
11.
Project Global Health Group at Malaria Atlas 2011. Atlas of Malaria Eliminating Countries. San Francisco, CA: The Global Health Group, Global Health Sciences, University of California; 2011.  Back to cited text no. 11
    
12.
WHO. International travel and health: Situation as on; 1 January, 2010. Geneva, Switzerland: World Health Organization; 2010.  Back to cited text no. 12
    
13.
Fefferman NH, Naumova EN. Innovation in observation: A vision for early outbreak detection. Emerg Health Threats J 2009;3:e6.  Back to cited text no. 13
    
14.
Brownstein JS, Freifeld CC, Reis BY, Mandl KD. Surveillance sans frontiers: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Med 2008;5:e151.  Back to cited text no. 14
    
15.
Freifeld CC, Mandl KD, Reis BY, Brownstein JS. HealthMap: Global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc 2008;15:150-7.  Back to cited text no. 15
    
16.
Collier N, Doan S, Kawazoe A, Goodwin RM, Conway M, Tateno Y, et al. BioCaster: detecting public health rumors with a Web-based text mining system. Bioinformatics 2008;24:2940-1.  Back to cited text no. 16
    
17.
Collier N, Goodwin RM, McCrae J, Doan S, Kawazoe A, Conway M, et al. An Ontology-Driven System for Detecting Global Health Events. In: Proceeding 23rd International Conference on Computational Linguistics, Beijing: China Association for Computational Linguistics; 2010.  Back to cited text no. 17
    
18.
Torii M, Yin L, Nguyen T, Mazumdar CT, Liu H, Hartley DM, et al. An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics. Int J Med Inf 2011;80:56-66.  Back to cited text no. 18
    
19.
Hartley D, Nelson N, Walters R, Arthur R, Yangarber R, Madoff L, et al. Landscape of international event-based biosurveillance. Emerg Health Threats J 2010;3:e3  Back to cited text no. 19
    
20.
Doherr MG, Audige L. Monitoring and surveillance for rare health-related events: A review from the veterinary perspective. Phil Trans R Soc Lond B 2001;356:1097-106.  Back to cited text no. 20
    
21.
Blazes DL, Russell KL. Joining forces: Civilians and the military must cooperate on global disease control. Nature 2011;477:395-6.  Back to cited text no. 21
    
22.
Khan K, McNabb SJ, Memish ZA, Eckhardt R, Hu W, Kossowsky D, et al. Infectious disease surveillance and modelling across geographic frontiers and scientific specialties. Lancet Infect Dis 2012;12:222-30.  Back to cited text no. 22
    
23.
International Health Regulations Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-nCoV); 2005. Geneva, Switzerland; 30 January, 2020.  Back to cited text no. 23
    
24.
Boulos MN. Descriptive review of geographic mapping of severe acute respiratory syndrome (SARS) on the internet. Int J Health Geogr 2004;3:2.  Back to cited text no. 24
    
25.
U.S. Centers for Disease Control and Prevention (CDC). 2019–2020 U.S. Flu Season: Preliminary Burden Estimates. Available from: https://www.cdc.gov/flu/about/burden/preliminary-in-season-estim ates.htm. [Last accessed on 2020 Feb 17].  Back to cited text no. 25
    
26.
Public Health England. Weekly National Flu Reports. Season; 2019 to 2020.  Back to cited text no. 26
    
27.
Lyseen AK, Nøhr C, Sørensen EM, Gudes O, Geraghty EM, Shaw NT, et al. A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies. Yearb Med Inform 2014;9:110-24.  Back to cited text no. 27
    
28.
Boulos MN. Principles and techniques of interactive web cartography and Internet GIS. In: Madden M, editor. Manual of Geographic Information Systems. Bethesda, Maryland: ASPRS – American Society for Photogrammetry and Remote Sensing; 2009. p. 935-74.  Back to cited text no. 28
    
29.
Boulos MN. Web GIS in practice – IJHG (2004–2011). Available from: https://www.biomedcentral.com/collections/1476-072X-Gis. [Last accessed on 2020 Feb 17].  Back to cited text no. 29
    
30.
Hay SI, Graham AJ, Rogers DJ, editors. Global Mapping of Infectious Diseases: Methods, Examples and Emerging Applications. Advances in Parasitology. Vol. 62. London, UK: Academic Press; 2006.  Back to cited text no. 30
    
31.
Rogers DJ, Randolph SE, Snow RW, Hay SI. Satellite imagery in the study and forecast of malaria. Nature 2002;415:710-5.  Back to cited text no. 31
    
32.
Cromley EK, McLafferty SL. GIS and Public Health. New York, NY: The Guildford Press; 2002.  Back to cited text no. 32
    
33.
Pfeiffer DU, Robinson TP, Stevenson M, Stevens KB, Rogers DJ, Clements ACA. Spatial analysis in epidemiology. Oxford, UK: Oxford University Press; 2008.  Back to cited text no. 33
    
34.
Hay SI. An overview of remote sensing and geodesy for epidemiology and public health application. Adv Parasitol 2000;47:1-35.  Back to cited text no. 34
    
35.
Rogers DJ, Randolph SE. Studying the global distribution of infectious diseases using GIS and RS. Nat Rev Microbiol 2003;1:231-7.  Back to cited text no. 35
    
36.
Hay SI, Snow RW. The malaria atlas project: Developing global maps of malaria risk. PLoS Med 2006;3:e473.  Back to cited text no. 36
    
37.
Riley S. Large-scale spatial-transmission models of infectious disease. Science 2007;316:1298-301.  Back to cited text no. 37
    
38.
Stevens KB, Pfeiffer DU. Spatial modelling of disease using data- and knowledge-driven approaches. Spat Spatio Temporal Epidemiol 2011;2:125-33.  Back to cited text no. 38
    
39.
Braden CR, Dowell SF, Jernigan DB, Hughes JM. Progress in global surveillance and response capacity 10 years after severe acute respiratory syndrome. Emerg Infect Dis 2013;19:864-9. Available from: https://wwwnc.cdc.gov/eid/article/19/6/13-0192-f1. [Last accessed on 2020 Feb 17].  Back to cited text no. 39
    
40.
Boulos MN, Roudsari AV, Carson ER. Health geomatics: An enabling suite of technologies in health and healthcare (methodolical review). J Biomed Inform 2001;34:3195-219.  Back to cited text no. 40
    


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  In this article
Abstract
Introduction
Symptoms, Incuba...
Case Fatality
Geographical Map...
Geoinformatics&#...
Johns Hopkins Un...
Coronavirus Dise...
Surveillance Das...
the World Health...
Situation Dashbo...
European Centre ...
Discussion
Conclusion
References
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