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Monitoring and Epidemiological Trends of Coronavirus Disease (COVID-19) Around The World Arnab Saha 1, Komal Gupta 2, Manti Patil 3 1 Indian Institute of Technology, Kharagpur, West Bengal, India 2 Banasthali University, Vanasthali, Rajasthan, India 3 Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India ABSTRACT COVID-19 has struck fear into populaces all through the world and shocked the worldwide restorative community, with the World Health Organization (WHO) pronouncing it a widespread as it were approximately three months after the flare-up of the infection. A new different virus (primarily called ‘Novel Coronavirus 2019-nCoV’) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China in December 2019 and rapidly spread to other parts of China and other countries around the world. The outbreak of the novel coronavirus disease (COVID-19) has caused more than 850,000 people infected and approx. 40000 of deaths in more than 190 countries up to March 2020, extremely affecting economic and social development. Presently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects. This paper describes the usage of practical GIS and mapping dashboards and applications for monitoring the coronavirus epidemic and related activities as they spread around the world. At the facts level, in the generation of massive data, information no longer come on the whole from the authorities but are gathered from greater diverse enterprises. As of now and for a long time in the future, the improvement of GIS should be fortified to create a data-driven framework for fast information securing, which implies that GIS ought to be utilized to fortify the social operation parameterization of models and methods, particularly when giving back for social administration. Keywords: COVID-19, GIS, Disease, Monitoring
Introduction Rising infectious diseases are remarkable threats to public health worldwide. The new coronavirus pneumonia has been named as Coronavirus Disease (COVID-19) by the World Health Organization (WHO) and declared a pandemic on March 11, 2020 (WHO, 2020). This coronavirus was at to begin with called the 2019 novel coronavirus (2019-nCoV) by WHO on 12 January 2020. WHO authoritatively referred to as coronavirus disease 2019 (COVID-19) and coronavirus study group of the Worldwide committee recommended that the current coronavirus be named SARS CoV-2 on 11 February 2020 (WHO, 2020; Murugesan et al., 2020). In 1930s Coronaviruses were first discovered (Estola et al., 1970) and Human coronaviruses were first recognized in the 1960s and concern in these viruses advanced significantly in 2002 from the emergence of Severe Acute Respiratory syndrome CoV (SARS-CoV) (Drosten et al., 2003; Ksiazek et al., 2003; Peiris et al., 2003). The outbreak of 2019 novel coronavirus disease (COVID-19) is a public health emergency of global difficulty that had triggered greater than 860,000 infections and more than 40000 deaths in more than 190 countries by March 31, 2020 (WHO, 2020; Worldometer, 2020), critically affecting financial and social improvement. United Nations (UN) Secretary General referred to as on governments to take action to do the entirety feasible to control the COVID-19 epidemic on February 28 (New.cn, 2020; Zhou et al., 2020). China stated a cluster of pneumonia cases in humans related to the Huanan Seafood Wholesale marketplace in Wuhan, Hubei Province on December 31, 2020 (WHO, 2020). Moreover, there is no current evidence that the source of COVID-19 originated in the Huanan seafood market. Various researches have proposed that the bat might be a potential pool of COVID-19 (Giovanetti et al., 2020; Paraskevis et al., 2020). Bats are considered to be the common store for numerous infections, of which a few are conceivable human pathogens, which infect respiratory, gastrointestinal and neurologic diseases. (Yadav et al., 2020). Somehow, bats are natural pollutants of a wide extend of CoVs, such as SARS-CoV and MERS-CoV viruses in 2003 and 2012, respectively, have accepted the transmission from animal to animal, and human to human (Hampton, 2005; Banerjee et al., 2019; Li et al., 2005; Cauchemez et al., 2013; Al-qaness et al., 2020). However, the source of the COVID-19 is not confirmed yet, and it requires more investigations and researches. Chinese health authorities showed that this cluster was related to a unique coronavirus, 2019- nCoV on January 7, 2020 (WHO, 2020; Holshue et al., 2020). A total of 9976 confirmed cases have been stated in at least 21 nations on January 30, 2020 (John Hopkins University report, 2020), 7 such as the primary confirmed case of Coronavirus infection in the United States, stated on January 20, 2020 (Holshue et al., 2020). Because of rapid pandemic ability and the absence of vaccines and drugs, the infectious COVID-19 disease devastated the everyday lifestyles throughout the globe. According to the observation of early study of disease transmission of COVID-19, the incubation duration of COVID-19 extends from 1 to 14 days (Li et al, 2020; Lin
et al., 2020). Most of the COVID-19 infected people, respiratory indications ought to be mild to direct and improve without the required for medical treatment. At the starting of the epidemic, the restorative and investigate communities reacted rapidly. The Chinese government took conclusive measures to lock down the city of Wuhan and to shut the outside routes to all cities in Hubei Territory on January 23, 2020 (State council, 2020; Zhou et al., 2020). From January 23, 2020 to April 8, 2020, Wuhan, the capital city of Hubei Province, was in lockdown. China has embraced colossal individual and financial misfortunes and has won important time for the Chinese and for worldwide avoidance and control of the epidemic (Zhou et al., 2020). Amid this period, utilization of GIS and spatial enormous information innovation, which have a high degree of logical and innovative display (Zhou et al., 2016; Zhou et al., 2020), to supply imperative scientific and specialized back to permit the government to judge the epidemic circumstance and define anticipation and control measures (Health Commission of Hubei Province, 2020). Access to real-time GIS data is essential to the public, scientists, and public health officials. During the battle against epidemic, GIS and spatial enormous information innovation have played a critical part in distinguishing the spatial transmission of the epidemic, in spatial avoidance and control of the epidemic, in spatial allotment of assets, and in spatial discovery of social assumption, among other things (Zhou et al., 2020). With the development of GIS generation, a statistics system for a relevant situation can be built rapidly, specifically in terms of database control, spatial evaluation tools, and mapping. Disease mapping and environmental hazard evaluation utilizing advanced geospatial information assets are presently built up expository analytical tools in both human and veterinary public health (Bergquist and Rinaldi, 2010; Richardson et al, 2013; Cicalò and Valentino, 2019). The World Health Organization, detecting this potential, has begun to gather this spatial information all over the world to encourage the moderation and control of the spread of certain infections. Since the World Health Organization (WHO) is the specialist, inside the United Countries framework of observation and coordination for public health, it must provide technical assist to international locations, monitoring and to evaluate health developments globally (Cicalò and Valentino, 2019). Since the outbreak of COVID-19, in expansion to the news distributed by the governments, epidemic data has too been broadly disseminated through web platforms such and other channels. ESRI’s expert Kenneth Field appealed that coronavirus mapping should be responsible (Field, 2020). For Multi-city modeling of COVID-19 epidemics using spatial networks, Pujari and Shekatkar, (2020), propose a computationally efficient hybrid method that makes use of SIR model for individual cities which are in turn coupled through experimental transportation systems that encourage movement among them. This model disseminates the overall population into compartments for Vulnerable, Tainted and Recuperated people, and a set of coupled differential equations describes the movement of population from one compartment to another. The results
extend that through the domestic transportation, the significant population is balanced to be uncovered inside 90 days of the onset of epidemic. Kumar et al., (2020) predicted some trajectories of COVID-19 till April 30, 2020 using the most advanced Auto-Regressive Integrated Moving Average Model (ARIMA) for the top 15 most infectious countries. Based on forecasts, public health authorities ought to tailor aggressive mediations to get a handle on the control exponential development, and quick infection control measures at hospital levels are critically required to reduce the COVID-19 pandemic and the United States of America (USA) will come as a surprise and going to become the epicenter for new cases during the mid-April 2020. The spatiotemporal spread of irresistible infections in expansive populaces could be an exceptionally expansive and complex framework that postures awesome challenges to numerical modeling (Grassly and Fraser, 2008; Riley, 2007). When a major epidemic happens, the negative affect of instability and freeze on social operations may surpass that of the viral infections. Subsequently, this investigates connected enormous social media information to track and assess the spatial spread of open estimation (Miller and Goodchild, 2015). Methodology This paper will discuss the role of data visualization in the relationships between health research and geospatial information sciences. We considered maps for the multidimensional dynamic appearance of the epidemic situation, such as the cumulative distribution map of confirmed cases of 860,000 people and the distribution map of places. We strictly tracked the global official websites to collect the epidemiological information about COVID-19 pandemic. The number of total new confirmed cases and total deaths of COVID-19 was presented to exemplify the trend of this epidemic. Spatial analysis will be executed and geostatistical maps on the predominance of indications and positive test probability will be developed. Results An overview of epidemic trends of new cases and deaths of COVID-19 across 180 countries and territories from January to March 31, 2020, is showed in Figure 1 and 2. Thirty-one days of the March 2020, the world is not likely to soon forget. It was the month that a new coronavirus disease that had infected tens of thousands in China becomes a global pandemic. On March 1, nearly 89000 cases of COVID-19 disease caused by the virus had been reported worldwide (WHO, 2020). Then, the vast majority of the cases were still in China, the original epicenter of the disease. By end of this month, infections worldwide increased nearly 10 times, to nearly 860000 cases, and deaths soared from over 3000 to more than 40000 (WHO, 2020).
Iran and Italy suffer dramatic increases in infection numbers in early March. Iran’s health minister was one of the first public officials to be visibly sickened by the disease (South China Morning Post, 2020). Iran’s epidemic reached the top levels of its government and mass graves were dug in the countryside. In Italy, the virus spread fast, despite early attempts to protect citizens from the novel coronavirus. The nation already declared a state of emergency by the end of January, after a Chinese couple holidaying in Rome tested positive for COVID-19. Italy reported its first local case on February 20, but the virus had already been circulating in the country for some time. During Italy’s peak flu season, people were being diagnosed with influenza, when they may actually had COVID-19. Infections in Italy rose from four cases on February 20 to nearly 106000 by the end of March (WHO, 2020; South china morning post, 2020). By the end of the month nearly 12500 deaths related to COVID-19 were also recorded more than three times the number of fatalities in China. This despite the Italy’s government ordering a lockdown for the entire country on March 9 (South china morning post, 2020). After Italy, Spain has the most COVID-19 related deaths in the world, nearly 8500 people died in March (WHO, 2020) and the infections there jumped more than 1000 times. The Spanish government announced a near-total lockdown on March 15 to try to curb the spread of the virus (South china morning post, 2020). But thousands of new cases are still being reported every day, leaving hospitals overwhelmed and the health services at the breaking point. While much of the rest of Europe restricted people’s movements, the United Kingdom took a vastly different approach at first. Thy hoped to stagger the rate of infections in the British population, so as not to overwhelm the public health care system. The number of new cases accelerated. By then, nearly 4000 people in the UK had tested positive for COVID-19. Over the course of March, infections in the UK went from 36 to over near 25000 (WHO, 2020). And from zero fatalities on March 1st, the country saw nearly 1800 succumb to the disease by the end of the month. At the beginning of the March, there were 158 cases of COVID-19 in the United States. In two weeks, infections had risen to 9197 (WHO, 2020). After that the US government did finally urge people to stay at home if they or a family member showed symptoms of the virus and to limit gathering to no more than 10 people. But in the two weeks following the plea, COVID-19 cases in the US increased to more than 188000 and the death cases shot up to more than 12000 people (in Figure 1 and Figure 2) (WHO, 2020). In India, the primary COVID-19 disease case was detailed on January 30, 2020, according to the Ministry of Health and Family Welfare, Government of India. The various preliminary cases in India came from contact with the individuals having a history of traveling from Iran, Italy, and China. The first COVID-19 death in the country was reported on March 13, 2020 (MoH&FW, India). For the increasing rate of COVID-19 infections, the country declared lockdown from March 22, 2020 to till date. Being a nation of 1.3 billion people the adequacy of giving lockdown may be a major hurdle to the Government. The total number of cases in India is presented in Figure 6. The most affected state of coronavirus infection is Maharashtra, Delhi and Kerala till March 31, 2020 showed in Figure 6. (MoH&FW, India)
As global COVID-19 cases surged in March, life began to slowly return to normal in Mainland China. The number of new cases and deaths plummeted in the country, with the majority of new cases being those imported from overseas. Officials credit the complete lockdown of Hubei province, as well as tight restrictions and quarantine rules in other cities, as being key factors in containing the virus. For many other countries still struggling to stop the spread of COVID-19 and the next several months are crucial. Figure 3 showing that the numbers of daily cases are increasing in the world and other countries reached a peak of 73620 on March 31, 2020, and it will be continue to increase. Figure 4 also shows that since January 23, 2020, the total numbers of deaths are increasing from March 5, 2020. In Figure 5, we found that the numbers of daily deaths are increasing significantly since March 9, 2020. May be the number of deaths is going to rise exponentially to rising in the future. Conclusions COVID-19 is characterised by means of an extended incubation length, sturdy infectivity and issue of detection, which has caused the sudden outbreak and the speedy development of a deadly disease. This case requires GIS and massive data generation to allow rapid responses and analyses, a short supply of facts about the epidemic dynamics and information of the epidemic improvement rules to offer timely assist for the prevention and manage decisions and movements. In summary, COVID-19 is quite unique from SARS. It is actually more infectious and detrimental. There are still numerous instabilities around the epidemic. The number of detailed cases in the world expanded during March, for the most part due to people with travel records to the influenced locations. Right now, the number of COVID-19 cases is increasing in many other nations. All the local governments should be taking various preventive and control measures to restrict the transmission based totally on the infection stage of each city. References Al-qaness, M. A., Ewees, A. A., Fan, H., & Abd El Aziz, M., 2020. Optimization method for forecasting confirmed cases of COVID-19 in China. Journal of Clinical Medicine, 9(3), 674. doi: 10.3390/jcm9030674 Banerjee, A., Kulcsar, K., Misra, V., Frieman, M. and Mossman, K., 2019. Bats and Coronaviruses. Viruses, 11(1), 41. DOI: https://doi.org/10.3390/v11010041 Bergquist, R., & Rinaldi, L.. 2010. Health research based on geospatial tools: a timely approach in a changing environment. Journal of helminthology, 84.01 (2010), 1-11.
Cauchemez, S.; Van Kerkhove, M.; Riley, S.; Donnelly, C.; Fraser, C.; Ferguson, N., 2013. Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart. Euro Surveill. Bull. Eur. Sur Les Mal. Transm. Eur. Commun. Dis. Bull., 18, 20503. Cicalò, E., & Valentino, M., 2019. Mapping and visualisation on of health data. The contribution on of the graphic sciences to medical research from New York yellow fever to China Coronavirus. DISEGNARECON, 12(23), 12-1. Drosten, C., Günther, S., Preiser, W., Van Der Werf, S., Brodt, H. R., Becker, S., ... & Berger, A., 2003. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. New England journal of medicine, 348(20), 1967-1976. Estola, T., 1970. "Coronaviruses, a New Group of Animal RNA Viruses". Avian Diseases. 14 (2): 330–336. doi: 10.2307/1588476 Field, K, 2020. responsibly. Mapping coronavirus https://www.esri.com/arcgis-blog/ products/product/mapping/mapping-coronavirus-responsibly/. Giovanetti, M., Benvenuto, D., Angeletti, S. and Ciccozzi, M., 2020. The first two cases of 2019- nCoV in Italy: Where they come from?, J. Med Virol, 92(5), 518-521. DOI: https://doi.org/10.1002/jmv.25699 Grassly, N., Fraser, C., 2008. Mathematical models of infectious disease transmission. Nature Reviews Microbiology 6 (6), 477–487. Hampton, T., 2005. Bats may be SARS reservoir. JAMA. 294(18), 2291. DOI: https://doi.org/10.1001/jama.294.18.2291 Health Commission of Hubei Province. http://wjw.hubei.gov.cn/bmdt/ztzl/fkxxgzbdgrfyyq/xxfb/202002/t20200213_2025580.shtml/. 2020. (In Chinese) Holshue, M. L., DeBolt, C., Lindquist, S., Lofy, K. H., Wiesman, J., Bruce, H., ... & Diaz, G. (2020). First case of 2019 novel coronavirus in the United States. New England Journal of Medicine. Johns Hopkins University CSSE. Wuhan coronavirus (2019-nCoV) global cases, 2020. (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467 b48e9ecf6). Ksiazek, T. G., Erdman, D., Goldsmith, C. S., Zaki, S. R., Peret, T., Emery, S., ... & Rollin, P. E., 2003. A novel coronavirus associated with severe acute respiratory syndrome. New England journal of medicine, 348(20), 1953-1966.
Kumar, P., Kalita, H., Patairiya, S., Sharma, Y. D., Nanda, C., Rani, M., ... & Bhagavathula, A. S., 2020. Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning Approach. medRxiv. doi: 10.1101/2020.03.30.20046227 Li, W., Shi, Z., Yu, M., Ren, W., Smith, C., Epstein, J. H., Wang, H., Crameri, G., Hu, Z., Zhang, H., Zhang, J., McEachern, J., Field, H., Daszak, P., Eaton, B., T., Zhang, S. and Wang, L.-F., 2005. Bats are natural reservoirs of SARS-like coronaviruses. Science. 310(5748), 676-9. DOI: https://doi.org/10.1126/science.1118391 Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al., 2020, Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med: 10.1056/ NEJMoa 2001316. doi: 10.1056/NEJMoa2001316. PubMed PMID: 31995857. Lin, B., Zhong, H., Ni, Y., Liu, L., Zhong, J., & Su, X., 2020. Epidemiological Trends of Coronavirus Disease 2019 in China. medRxiv. Miller, H., Goodchild, M., 2015. Data-driven geography. Geojournal 80 (4), 449–461. MoH&FW: Ministry of Health and Family Welfare, Govt. of India, Coronavirus disease situation status, 2020, https://www.mohfw.gov.in/, (accessed 1st April 2020) Murugesan, B., Karuppannan, S., Mengistie, A.T., Ranganathan, M., Gopalakrishnan, G., 2020. Distribution and Trend Analysis of COVID-19 in India: Geospatial Approach. J. Geographical Studies, 4(1), 1-9. doi: 10.21523/gcj5.20040101 New.cn, 2020. COVID-19 is urged by the UN Secretary General to do everything possible to contain the outbreak, http://www.xinhuanet.com/2020-02/29/c_1125642849.htm/. Paraskevis, D., Kostaki, E. G., Magiorkinis, G., Panayiotakopoulos, G., Sourvinos, G. and Tsiodras, S., 2020. Full-genome evolutionary analysis of the novel corona virus (2019-nCoV) rejects the hypothesis of emergence as a result of a recent recombination event. Infect Genet Evol. 79, 104212. DOI: https://doi.org/10.1016/j.meegid.2020.104212 Peiris, J. S. M., Lai, S. T., Poon, L. L. M., Guan, Y., Yam, L. Y. C., Lim, W., ... & Cheng, V. C. C., 2003. Coronavirus as a possible cause of severe acute respiratory syndrome. The Lancet, 361(9366), 1319-1325. Pujari, B. S., & Shekatkar, S. M., 2020. Multi-city modeling of epidemics using spatial networks: Application to 2019-nCov (COVID-19) coronavirus in India. medRxiv. Richardson, D.B., et al., 2013. Spatial turn in health research. Science (New York, NY), 339.6126 (2013), 1390. Riley, S., 2007. Large-Scale Spatial-Transmission Models of Infectious Disease. Science 316 (5829), 1298–1301.
South China Morning Post, 2020. March 2020: the month Covid-19 changed the world. State council, http://www.gov.cn/xinwen/2020-01/23/content_5471751.htm/ . 2020 (accessed 23 January 2020). (In Chinese) WHO: World Health Organization, Coronavirus disease (COVID-2019) situation Reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/, 2020 (accessed 1st April 2020). WHO: World Health Organization. Pneumonia of unknown cause — China. 2020 (https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/). WHO Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020 Geneva, Switzerland: World Health Organization (2020). Available from: https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-atthe-media briefing-on- 2019-ncov-on-11-february-2020. Worldometer websites, Coronavirus disease reports. https://www.worldometers.info/coronavirus/, 2020 (accessed 1st April 2020) Yadav, P. D., Shete-Aich, A., Nyayanit, D. A., Pardeshi, P., Majumdar, T., Balasubramanian, R., ... & Patil, S., 2020. Detection of coronaviruses in Pteropus & Rousettus species of bats from different States of India. Indian J Med Res, DOI: 10.4103/ijmr.IJMR_795_20 Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., ... & Song, C. (2020). COVID-19: challenges to GIS with big data. Geography and Sustainability. Zhou, C., Su, F., Harvey, F., Xu, J., 2016. Spatial Data Handling in Big Data Era. Springer, Beijing.
Figure 1: COVID-19 epidemic spatial pattern of total number of confirmed cases in each nations of the world. Figure 2: COVID-19 epidemic spatial pattern of total number of confirmed deaths in each nations of the world.
Figure 3: The number of daily confirmed cases of COVID-19 in the world Figure 4: The total number of daily deaths of COVID-19 in the world Figure 5: The number of daily deaths of COVID-19 in the world
Figure 6: Distribution of the total numbers of confirmed cases of COVID-19 in India till March 31, 2020
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