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 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 9  |  Issue : 2  |  Page : 177-181

Community mapping of COVID-19 cases admitted from April to June 2020 at a tertiary health care hospital in Raigad district in Maharashtra, India


Department of Community Medicine, MGM Medical College and Hospital, Navi Mumbai, Maharashtra, India

Date of Submission18-Feb-2022
Date of Acceptance18-May-2022
Date of Web Publication17-Jun-2022

Correspondence Address:
Dr. Saili Jadhav
Department of Community Medicine, MGM Medical College and Hospital, Kamothe, Navi Mumbai 410209, Maharashtra
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/mgmj.mgmj_22_22

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  Abstract 

Introduction: At end of 2019, a novel coronavirus caused severe acute respiratory syndrome, which emerged in Wuhan, Hubei province of China. Health professionals have always used conventional mapping (in recent times geographic information systems [GIS] mapping) as a useful tool for better tracking which further facilitated better management of deadly contagions such as SARS-CoV 2. This study aimed to map geographically the positive patients admitted in a dedicated COVID-19 hospital which is a tertiary care hospital from April to June 2020 to gain insight into the local viral transmission and pattern of geographical spread because of ongoing cluster transmission. Objectives: The aims of this study were (1) to locate geographically the COVID-19 cases admitted from April to June 2020 at a tertiary health-care facility, (2) to study trends and patterns of geographical spread, and (3) to identify geographical clustering of cases, if any. Materials and Methods: This was an observational, cross-sectional, secondary data-based study. The study was conducted at MGM Medical College Hospital, Kamothe. The data were collected from existing surveillance and lab data records. The data were analyzed in Excel and Epi info. Specialized GIS software was used for mapping to Taluka level based on patients’ addresses using standard “.shp” files for the local area. Results: There were a total of 968 cases. The majority of which were from Raigad district (839, 87%). The Panvel taluka in Raigad District having Panvel as a major city and the thickly populated urban area has shown clustering of cases extending to neighboring Uran taluka. Conclusion: For better preparedness, we need to keep tracking new outbreaks through GIS and promote further advances in mapping technologies.

Keywords: COVID-19, geographic information systems, mapping outbreak


How to cite this article:
Jadhav S, Waingankar P, Thakur M. Community mapping of COVID-19 cases admitted from April to June 2020 at a tertiary health care hospital in Raigad district in Maharashtra, India. MGM J Med Sci 2022;9:177-81

How to cite this URL:
Jadhav S, Waingankar P, Thakur M. Community mapping of COVID-19 cases admitted from April to June 2020 at a tertiary health care hospital in Raigad district in Maharashtra, India. MGM J Med Sci [serial online] 2022 [cited 2022 Jul 6];9:177-81. Available from: http://www.mgmjms.com/text.asp?2022/9/2/177/347686




  Introduction Top


In December 2019, a new virus (initially called “Novel Coronavirus 2019-nCoV” and later renamed SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease-2019 {COVID-19} emerged in Wuhan, Hubei Province, China,[1] and rapidly spread to other parts of China and other countries around the world, despite China’s massive efforts to contain the disease within Hubei. Compared to the 2002/2003 SARS-CoV and the 2012–2014 Middle East Respiratory syndrome-related coronavirus (MERS-CoV), the COVID-19 coronavirus spread strikingly fast. Although MERS took about two and a half years to infect 1000 people and SARS took roughly 4 months, the novel SARS-CoV-2 reached that figure in just 48 days. On January 30, 2020, the World Health Organization (WHO) declared that the new SARS-CoV-2 coronavirus outbreak constitutes a Public Health Emergency of International Concern (PHEIC).[2]

Health professionals have long considered conventional mapping, and more recently geographic information systems (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.[3] The value of maps as a communication tool blossomed over the next 225 years in the service of understanding and tracking infectious diseases, such as yellow fever, cholera, and the 1918 influenza pandemic. From the 1960s, when computerized GIS were born, the possibilities for analyzing, visualizing, and detecting patterns of disease dramatically increased again. A 2014 review of the health GIS literature found that 248 out of 865 included papers (28.7%) focused on infectious disease mapping.[4]

In India, the initial COVID-19 testing strategy included people who had international travel history with symptoms, symptomatic contacts of laboratory-confirmed COVID-19 patients, and symptomatic healthcare workers managing respiratory distress/severe acute respiratory illness (SARI).[5] All over India by May 14, 2020, a total of 51401 active cases, 27919 cured/discharged, and 2649 deaths were reported.[6] Most people infected with the COVID-19 virus have mild disease and recover. Approximately 80% of laboratory-confirmed patients have had mild disease, 15% required hospitalization, and 5% cases were critical requiring ventilator management. But the identification of each case still was a critical part, to avoid those severe 20% cases which add on as a major burden on the healthcare system. Hence, this study was undertaken to identify the pattern of the geographic distribution of cases [Table 1].
Table 1: Taluka wise distribution of COVID-19 positive cases admitted at hospital

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Objectives

  1. To locate geographically the COVID-19 cases admitted from April to June 2020 at a tertiary health-care facility.


  2. To study the trend and pattern of geographical spread in the neighboring area over the time of 3 months.


  3. To Identify geographical clustering of cases, if any.



  Materials and methods Top


Study type

This was an observational, cross-sectional study, based on secondary data.

Data collection

The study was conducted at MGM Medical College Hospital, Kamothe, Navi Mumbai, India. The data were collated from lab data records of the college by existing surveillance.

Data sampling

Universal sample. No sampling was done. All patients tested positive for COVID-19 by real-time reverse transcription-polymerase chain reaction (RT-PCR) or Gene Expert technique, admitted at MGM Hospital, Kamothe, Navi Mumbai, India from April 1, 2020 to June 30, 2020.

Inclusion criteria

  1. Patients admitted to MGM Hospital, Kamothe, Navi Mumbai, India from April 1, 2020 to June 30, 2020 as COVID-19 cases with positive swab test results on RT-PCR or Gene expert testing.


  2. Residing in Mumbai city/Mumbai Suburban/Thane/Raigad district at the time of onset of symptoms.


Exclusion criteria

None.

Data analysis

Data were collated from surveillance/hospital/lab records and entered in the spreadsheet, Microsoft Excel using the standard coding procedure. The data were analyzed in Excel and Epi info. Specialized GIS software was used for mapping to Taluka level based on patients’ addresses using standard “.shp” files for the local area.

Quality control

The process of data collation was supervised closely by all co-investigators using standard cross-checking methods. The Director, MGM School of Biomedical Sciences, Navi Mumbai, India acted as a coordinator for Data Collation. Department of Community Medicine, MGM Medical College, and Hospital, Navi Mumbai, India conducted the study. The principal investigator and all co-investigator acted as guarantors for the integrity of the process of data collection to the publication of results.


  Results Top


There were a total of 968 cases; the majority of which were from the Raigad District (839, 87%) [Table 1]. The Panvel taluka in Raigad District having Panvel as a major city and a thickly populated urban area has shown clustering of cases extending to neighboring Uran taluka [Figure 1].
Figure 1: The Panvel taluka in Raigad district having Panvel as a major city and the thickly populated urban area has shown clustering of cases extending to neighboring Uran taluka

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In comparison, there were few patients from Thane and Mumbai Districts seeking treatment at this particular hospital, however neighboring Navi Mumbai Corporation, being a thickly populated urban area has shown relatively more cases [Figure 2]. Cases from the M/E ward were seen seeking treatment in this hospital, mostly due to road connectivity and possibly being a catchment area [Figure 3].
Figure 2: Navi Mumbai Corporation, being a thickly populated urban area has shown relatively more cases

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Figure 3: Cases from the M/E (Maharashtra East) ward were seen seeking treatment in the hospital, mostly due to road connectivity and possibly being a catchment area. Ward-ME of Municipal Corporation of Greater Mumbai

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  Discussion Top


To contain the COVID-19 epidemic, one of the nonpharmacological epidemic control measures is reducing the transmission rate of SARS-COV-2 in the population through social distancing.[7] To maintain social distancing and breaking the transmission chain, early identification and prompt isolation of individuals is necessary, where mapping proves its benefit as the disease is shown to be spreading rapidly in thick urban populations.

A similar study done by Zahra Arab-Mazar in March–April 2020 in Iran stated that the highest number of COVID-19 cases were reported in the capital city, using GIS and estimating the incidence/attack rates per province, that one is placed as the seventh, having more cases per population at Qom, Semnan, and Markaz, among other provinces.[8] Similarly in our study with the use of GIS mapping, we were able to conclude that, the maximum number of cases were from Raigad District (Panvel)

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 coronavirus cases and the total number of deaths by country and Chinese province, with informational panels about the map and its data resources.[9]

John Snow (1813–1858) was able to trace the source of a cholera outbreak in Soho, London, in 1854, thanks to his well-known manual spatial analysis exercise using hand-drawn paper maps of cholera cases and water pumps/water companies supplying them with water. Today, more advanced computerized spatial analyses integrating phyloepidemiological methods are used to identify the likely sources of new outbreaks.[10]

A study by Kamel Boulos and Geraghty[11] in 2020 suggested that dashboards and web maps bring together location and time-sensitive events in relationship to spreading disease and give travelers and officials the potential to reduce exposure by avoiding public gatherings in case of a public event. During the period of our study, it was observed that during the peak phases of the pandemic in the Raigad district, the cases were increasing, and the lockdown rules were intensified, leading to a more strict lockdown.

Understanding of the burden on the health care system can be facilitated by GIS mapping facing treatment facility shortages in Wuhan, government officials commissioned in late January the emergency construction of two new hospitals, which together provide an additional 2600 beds. This increase in requirement for hospital facilities was predicted using GIS mapping.[12]

Residents in affected areas can use publicly available apps to find critical assistance and resources. Apps and maps can provide directions to hospitals with available beds, clinics that provide medical assistance with current wait times, open grocery stores and pharmacies, and places to buy personal protective equipment, among other things. This information could significantly improve outcomes and save lives in severely afflicted cities.[11]


  Conclusion Top


Modern GIS technology relies on web-based applications, increased data sharing, and real-time data to assist users to make better decisions. These values are represented via dashboards, which have proven to be quite useful for sharing and studying SARS-CoV-2 coronavirus spread.[11]

COVID-19 outbreak awareness has surely been centered on dashboards. Another outbreak is not a question of if, but when and where it will occur. Viruses such as SARS-CoV-2 are unconcern ed with national or continental boundaries.[11]

GIS mapping is a useful tool and has scope for continued mapping of COVID. It can help in the prediction of disease spread. Using GIS mapping the extent of the spread of the disease can be tracked and specific measures for control of spread can be implemented in a targeted manner.

Therefore, for better preparedness, we need to keep tracking new outbreaks through GIS and promote further advances in Mapping technologies.

Limitations

Data show positive COVID-19 cases reported at only one tertiary care hospital. A broader picture can be painted if more COVID hospitals data are used.

Ethical consideration

Institutional Ethics Committee (IEC) of MGM Medical College, Navi Mumbai, Maharashtra, India reviewed and approved the research study entitled: “Community mapping of COVID-19 cases admitted during April to June 2020 at a Tertiary Health Care Hospital in Raigad District in Maharashtra, India” in the IEC meeting held on 10 July 2020 communicated vide their letter no. N-EC/2020/07/41 dated July 14, 2020.

Financial support and sponsorship

Study data were partially presented at the Annual National Conference of Epidemiological Association of India, December 19–20, 2020, EFICON 2020, at AIIMS, Rishikesh, Uttarakhand, India.

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.chinacdc.cn/en/article/id/e53946e2-c6c4-41e9-9a9b-fea8db1a8f5117 Feb 2020. [Accessed on 2022 Mar 15].  Back to cited text no. 1
    
2.
World Health Organization (WHO). WHO Director-General’s opening remarks at the media briefing on COVID-19-1 April 2020. Geneva: WHO; 2020. Available from: https://prais.paho.org/es/28863-2/.   Back to cited text no. 2
    
3.
Koch T Plague: Bari, Naples 1690–1692. In: Koch T, editor. Cartographies of Disease: Maps, Mapping, and Medicine. Redlands: Esri Press; 2005. pp. 19-24.  Back to cited text no. 3
    
4.
Lyseen AK, Nøhr C, Sørensen EM, Gudes O, Geraghty EM, Shaw NT, et al; IMIA Health GIS Working Group. 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. 4
    
5.
Indian Council of Medical Research, Department of Health Research. The strategy of COVID19 testing in India (17/03/2020). New Delhi: ICMR; 2020. Available from: https://www.icmr.gov.in/pdf/covid/strategy/Strategy_COVID19_testing_India.pdf. [Accessed on 2020 Mar 30].  Back to cited text no. 5
    
6.
India: Ministry of Health and Family Welfare. Containment plan: Novel coronavirus diseases 2019 (COVID-19). Version 2 (Updated 16.05.2020). New Delhi: MoH & FW; 2020. p. 22. Available from: https://www.mohfw.gov.in.pdf. [Accessed on 2022 Mar 15].  Back to cited text no. 6
    
7.
Gao S, Rao J, Kang Y, Liang Y, Kruse J Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL 2020;12:16-26.   Back to cited text no. 7
    
8.
Aldeer M, Hilli AA, Ismail IS Projecting the short-term trend of COVID-19 in Iraq. Digital Government: Research and Practice 2021;21:1-7.  Back to cited text no. 8
    
9.
World Health Organization. COVID-19 dashboards. Geneva: WHO; 2020. Available from: http://healthcybermap.org/WHO_COVID19. [Accessed on 2022 Mar 15].  Back to cited text no. 9
    
10.
Yu WB, Tang GD, Zhang L, Corlett RT Decoding the evolution and transmissions of the novel pneumonia coronavirus (SARS-cov-2 / hcov-19) using whole genomic data. Zool Res 2020;41:247-57.  Back to cited text no. 10
    
11.
Kamel Boulos MN, Geraghty EM Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-cov-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 2020;19:8.  Back to cited text no. 11
    
12.
BBC News. Coronavirus:10 days of the hospital building in 60 seconds. China: BBC New; 2020. https://www.bbc.co.uk/news/av/world-asia-china-51348297/coronavirus10-days-of-hospital-building-in-60-seconds. [Accessed on 2022 Mar 15].  Back to cited text no. 12
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1]



 

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