key: cord-0426397-dzzezm7u authors: Joshi, G.; Pande, A.; Gupta, O.; Nautiyal, A.; Jasola, S.; Gahtori, P. title: UTTARAKHAND COVID-19 CASES AND DEATHS: COMPARING THE DEMOGRAPHICS AND SUGGESTIVE STRATEGY TO PREVENT SIMILAR PANDEMICS IN FUTURE date: 2021-09-06 journal: nan DOI: 10.1101/2021.09.03.21263064 sha: 4ba54504d3d0c436deb5a8a955213301cf3bff3b doc_id: 426397 cord_uid: dzzezm7u Coronavirus disease 19 (Covid-19) is causing a dramatic impact on human life worldwide. As of June 11 2021, later one has attributed more than 174 million confirmed cases and over 3.5 million deaths globally. Nonetheless, a World Bank Group flagship report features Covid-19 induced global crisis as the strongest post-recession since World WarII. Currently, all approved therapeutics or vaccines are strictly allowed for emergency use. Hence, in the absence of pharmaceutical interventions, it is vital to analyze data set covering the growth rates of positive human cases, number of recoveries, other factors, and future strategies to manage the growth of fatal Covid-19 effectively. The Uttarakhand state of India is snuggled in the lap of the Himalayas and occupies more people than Israel, Switzerland, Hong Kong, etc. This study analyzed state Covid-19 data, fetched from an authenticated government repository using Python 3.9 from April 1, 2020, to February 28, 2021. The highest recovery rate was attributed to the hilly district Rudraprayag. The analysis also revealed that a very high doubling rate was seen during the last week of May to the first week of Jun 2020. At last, based on this blueprint, we have suggested 6-points solutions for preventing the next pandemic. Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is an "evil genius" that invades the host cell like a lamb, tearing the organ system, resulting in death and stalling the world economy causing widespread unemployment, poverty, and hunger 1 . SARS-CoV-2 is responsible for Covid-19 diseases and the seventh coronavirus strain overall infecting Homo sapiens 2 . The situation is even critical due to non-availability of approved therapeutics against SARS-CoV-2 3 . Above all, the invasiveness of SARS-CoV-2 is further influenced by numerous intrinsic and extrinsic factors 4 . An analysis of Covid-19 impact on the country or multiple states may lead to a different picture considering the different timelines of the first case, infection rates, mortality, and many other parameters. Further measures took by different state governments might also vary from state to state and may differently affect the outcome of the disease. Thus, addressing each state will enable the Government to follow innovative measures to curb the pandemic growth with the limited number of resources they already possess. Although, all Indian states have foreseen the distress of Covid-19 in the year 2020 and followed by the second wave that just emerged during the writing of this manuscript. To foresee the actual effect of these underlying factors in Covid-19 cases and death, we proposed a statistical analysis in the 'Uttarakhand' state based on positivity, recovery, and death rate and comparing it with numerous intrinsic and extrinsic factors to overlay a clear impact of the current pandemic in the state. In the northern states of India, Uttarakhand is nestled in the lap of the Himalayas and crossed by the sacred Ganges River 5 . The Indian Population Census 2011 proposed that current population of state is about 11.9 million and has become home to more people than countries like Israel, Switzerland, Hong Kong, etc. The rugged terrains of Uttarakhand, with crippled health care infrastructure, have further worsened the pandemic. The recent surge of 446 COVID-19 cases in a day and 23 deaths as of Sunday, 06-Jun-2021, according to Uttarakhand's Government Covid19 Health Bulletin, even after imposing a state lockdown from 11-May-2021. Overall, Uttarakhand reports a All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in infrastructure but also ensure to relieve the economic burdens from its citizens to get away fromthe ongoing and similar pandemic in the future. Python 3.9 programming language was used to build an epidemic model, and all Covid-19 data was retrieved from authenticated and free to use resource data from Department of Medical Health and Family Welfare, Uttarakhand Government for about 12 months w.e.f. 15-Mar-2020 to 28-Feb-2021. The epidemic area in Uttarakhand was divided into thirteen parts covering all districts Almora, Bageshwar, Chamoli, Champawat, Dehradun, Haridwar, Nainital, Pauri Garhwal, Pithoragarh, Rudraprayag, Tehri Garhwal, U. S. Nagar, and Uttarkashi. The methodology consists of three steps, (i) Data collection, (ii) Data Conversion, and (iii) Information Extraction, as reported below. All authenticated and free to use resource data is retrieved from Uttarakhand Government Covid-19 health bulletin for 12 months from 15-Mar-2020 to 28-Feb-2021 (https://health.uk.gov.in/pages/display/140-novel-corona-virus-guidelines-and-advisory-). pdf2txt.py was used to extract text contents and stored them in CSVfile format. All.csv files were converted to .xls format for analytical purposes. The final excel file consists of twelve fields for all 13 districts Almora, Bageshwar, Chamoli, Champawat, Dehradun, Haridwar, Nainital, Pauri Garhwal, Pithoragarh, Rudraprayag, Tehri Garhwal, U. S. Nagar, and Uttarkashi, for analytical purpose. In The cumulative analysis of data revealed (Figure 1 ) that during the course of analysis (April (Figure 2A) . For instance, among 13 districts, Dehradun, with a total of 29,724 cases, was worst affected during the first COVID-19 wave. This was followed by Haridwar (14, 188) , Nainital (12, 650) , and U. S. Nagar (11, 549) . All these four districts have large areas in the plains and possess larger populations. In terms of percentage infection rate of samples tested district-wise, the highest positive cases was appeared in Dehradun (7.10%), followed by Nainital (6.20%) and Pithoragarh (4.63%). The lowest infection rates were displayed by district Champawat (1.92%), followed by Bageshwar (2.77%) and Uttarkashi (3.16%). In contrast, the major sign of relief was the fast recovery rate at par with the positivity cases. The data analyzed revealed ( Figure 2B ) that among 13 districts, Dehradun, with the highest number of positive cases, revealed the highest recovery (28,218) that accounted for a total 30.72% share of all other districts. This trend was followed by Haridwar (11, 282) , Nainital (12, 281) , and U.S. Nagar In addition, almost every hilly district of Uttarakhand reported an increase in Covid-19 cases during the 34 th to 38 th week. The case reported during this span was maximum compared to their average cases from 1 st to 47 th week (Figure 4 ). Furthermore, the analysis revealed that Uttarakhandhad attaineda high recovery of 96.41% of total positive COVID-19 cases. Here, the district Rudraprayag conferred with the highest recovery (99.21%), followed by Pauri Garhwal (98.49%) and Pithoragarh (98.25%) ( Figure 5 ). Despite the high recovery shown by district Almora (94.89%) during the pandemic time, it appears the lowest than other districts. This is further marked with a doubling interval of Covid-19 cases in different districts of Uttarakhand. A doubling rate portrays the number of days in which virus growth turns 2-folds. Here, All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Tables 2A and 2B ). The doubling rate of 2 -2.9 is graded as 'moderate,' 3 -4.9 is graded as 'high,' and 5 -7 is graded as 'very high.' The analysis revealed that Uttarakhand had attained maximum growth of the virus from 23-May-2020 to 06-Jun-2020 with the lowest doubling rate at district Tehri Garhwal (1.93) last week of May 2020. The U. S. Nagar has seen the lowest virus growth rate with a very high doubling rate of 5.54 as of 25-May-2020, followed by a high doubling rate at Dehradun (3.84) as on 06-Jun-2020,Bageshwar (3.50) as on 26-May-2020, Rudraprayag (3.27) as on 06-Jun-2020 and Haridwar (3.07)) as on 31-May-2020. Overall, Tehri Garhwal was found to worst affected with the highest virus replication as deduced by the analysis as of May 30, 2020. Next, we thought to compare the positivity rate of different districts of Uttarakhand based on their population and density. As the Census is due for Uttarakhand in 2021, we took the recent original data of 2011 when the last census study was last made 6 . However, for ease of correctness and to improve data significance in the gap of 10 years from the last Census, we also took the estimated population of Uttarakhand till the year 2021 7 . The analysis revealed (Figure 6 ) that population was one factor that portrayed a significantly high correlation (avg. r 2 : 0.83). Further, we attempted to find a correlation between geographical area and prevailing density with the number of Covid-19 positive cases around 13 districts of Uttarakhand. Our analysis ( Figure 7 ) revealed a high population density in US Nagar (648), Haridwar (801), Dehradun (550), and Nainital (225) with respect to their respective geographical area in a square kilometer. The high population density with respect to the geographical area is also one of the vital factors for high Covid-19 positive cases in these districts. This has eventually led to the non-implication of social distancing norms which are foremost required in such pandemic situations. The districts like Dehradun, Haridwar, US Nagar, and Nainital were severely affected in contrast to their geographical area, suggesting a high population in these particular districts. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.03.21263064 doi: medRxiv preprint As per Press Information Bureau, latest on February 17, 2021, released a comparative analysis of Covid-19 impact on all States and Union territories of India 8 . As per data, these regions were classified into three groups, viz. green zone (1-30000 cases); orange zone (30001-280000 cases); and red zone (above 280000). The Uttarakhand was well placed in the orange zone with 96722 active cases, 94396 recovered cases,and 1678 people deceaseduntil the press release date. An overlook over the data (Figure 8) Pradesh, was shown to exhibit fewer cases (58142) and deaths (990 cases) in comparison to Uttarakhand during the period of analysis. Moreover, we analyzed (Figure 9 ) to decipher positivity, recovery, and death percentages among Indian states/Union Territories due to Covid-19 during the said period of analysis. The analysis revealed that the positivity and recovery percentage exhibit a high correlation among all the states and union territories of India. Maharashtra experienced a high positivity rate of 18.94%. This was followed by Andhra Pradesh (8.12), Tamil Nadu (7.73), Karnataka (8.65%), Delhi (5.82%), and so on. This was further found overlapping with recovery rates, with Maharashtra portraying a recovery rate of 18.62, followed again by Andhra Pradesh (8.28), Tamil Nadu (7.79), Karnataka (8.72%), Delhi (5.87%). Moreover,a high death percentage was also associated with these states, where Maharashtra exhibited a high of 33.10%. This was followed by Tamil Nadu (7.79), Karnataka (7.87%), Delhi (6.99%), West All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Further, the population of a particular district was found to be directly correlated with Covid-19 infective cases. However, this was inversely correlated to the geographical area. Moreover, the major sigh of relief from the current analysis was a 100% correlation between infective cases and recovery cases which have kept mortality under excellent control. We are of the particular opinion that underlying are the plausible reasons. Lockdown is one primary parameter strictly imposed during the first wave experienced by the State of Uttarakhand. The strict lockdown around the state ensured a low number of cases and deaths All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.03.21263064 doi: medRxiv preprint compared to other states. The lockdown also led to the strict following of the health ministry's guidelines and created a social distance that breaks the infectivity chain 9 . Uttarakhand recorded the highest SARS-CoV-2 growth during the last week of May 2020. We viewed the high doubling rates of infections once the lockdown was slowly uplifted in the state post-May 3, 2020. The current analysis also revealed the importance of lockdown, social distancing, and advisory on Covid-19 impact. Viral infections follow seasonal outbreaks, for instance, influenza central prominent peak during the winter (January to March) and a minor but significant peak in the post-monsoon season (August to October). Dengue (August-September), and Chickenpox usually after winter season (Jan-April), etc. Last year, the Covid-19 peak was reported on September 19, 2020, in Uttarakhand with 2078 cases in a day, and the second-largest single-day spike in fresh 1946 Covid-19 cases was appeared as on November 19, 2020. At this moment, massive vaccination drive is being carried all over India including Uttarakhand state. Hence, it is also expected that after November 2021, Covid-19 cases may likely decrease. In terms of doubling rate ranges from 4.94-6.70, maximum growth of the virus was obtained in first week of Jun 2020 in Uttarakhand. Here district Bageshwar has experienced the highest doubling rate 6.70 as on 07th Jun, 2020. The year 2021 is also experiencing the same growth pattern and the active cases were reducing day by day after Jun 2021, i.e. 76232 active cases as on 18 May, 43520 active cases as on 26 May, 3471 active cases as on 17 June, 2627 active cases as on 25 June. This evidence from the previous COVID-19 pandemic suggests that exposure to crucial seasonal time may likely increase COVID-19 cases in 2021 as well. Hence, COVID appropriate behavior/activities/initiatives need to strictly follow up till Nov 2021 for effective tackling of Covid-19. More than half of the Uttarakhand population dwells in villages. The only source of income in the households is agriculture, small handloom industries, or tourism. The educated youth of Uttarakhand All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.03.21263064 doi: medRxiv preprint do not have enough facilities or job opportunities in his nearby vicinity to meet his and his family's daily bread 10, 11 . Due to lack of job opportunities and fewer earning sources, there is a high migration rate from villages to cities of Uttarakhand and the different parts of countries for a better livelihood 12 . Uttarakhand's districts having job opportunities majorly include Dehradun, Haridwar, U. S. Nagar, and Nainital. These were the districts that were worst affected by the Covid-19, and the only plausible reason we explore was high population density inversely impacting the social distances norms during such pandemics aroused by the viral diseases. This is also evident with current analysis that portrays a high positivity rate among districts with high density (US Nagar (648), Haridwar (801), Dehradun (550), and Nainital (225) with respect to their respective geographical area. Secondly, the rise in the cases, particularly in hilly terrains of Uttarakhand districts, is attributed to the movement of natives of Uttarakhand that migrated from their different states where they were initially employed once the restrictions were relaxed in the borders of Uttarakhand state. The local spread has only been confined to the hilly regions 13 . The first wave has seen a high upsurge in unemployment. This will again impact the major big cities of Uttarakhand, equipped with better job opportunities by increasing the population density. This hilly, geographically diverse state is considered the most vulnerable in healthcare facilities. The healthcare facilities of hilly districts are very much crippled. The majority of them are unequipped to handle the pandemics such as Covid-19. The lack of health infrastructure of Government aided hospital further adds to the agony 14 . This can be directly visualized because prominent well-to-do's once infected were airlifted to some well-equipped hospitals of other states 15, 16 . This is an exemplary saying that if the Government itself does not have faith in their aided hospitals, what example will they set for their native population. This is a debatable issue, and the Government should look at this unmet fulfilment by investing in improving the infrastructures of all the hospitals in the state. In our personal All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.03.21263064 doi: medRxiv preprint opinion, state Uttarakhand needs minimum of 2 airlift ambulances, one each for Uttarakhand's Kumaun and Garhwal covering the entire region. The one drawback of the Uttarakhand state is that it lacks some major research institutes or scientific centers that indulge in drug discovery or development. The laboratory setups and funding received by Government institutes of higher learning are very meagre to carry out sound translational research, mainly focusing on the bench to bedside discoveries. This will assist in creating awareness and interest in science among youth and create a high intellect workforce having out of box thinking that may lead to some ground-breaking research in the area. Even the state of Uttarakhand, which is rich in terms of Flora, cannot provide a medicinally active and more comprehensive acceptable product to the market so far. This has done more harm than doing good in current Covid-19 scenarios, particularly in the village areas with limited access to the internet and televisions. The scientific guidance's are least prioritized over rumors, conspiracy theories, misinformation fuelled by rumors, stigma, and conspiracy theories can have potentially severe inverse implications on public health if prioritized over scientific guidelines 17 . The first wave has shown immense signs of lack of information and awareness about the severity of Covid-19 infections among the state population. The Government here did their best to aware people but overspread rumor was much high compared to the factual news 18 To address these pertaining issues, immediate action on following suggestions that may curb Covid-19 or even other severe pandemics in the state in the future. We fully understand that lockdown is a haunt for small vendors, Private organization employees, and other daily wagers. This not cripples them economically, mentally, and emotionally but also impacts the development of the country. To All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.03.21263064 doi: medRxiv preprint plan lockdown more effectively, the a. government should focus on the job security of its said class of employees during such pandemics and work on making them financially independent by launching various schemes; b. should plan to develop more employment opportunities in hilly terrains and other smaller district, so the youth find competent employment near their dwelling places thus relieving the strain of migration to big cities; c. more entrepreneurship modules should be developed with better awareness and start-ups grants to popularise them among youth and prevent their migration. The health infrastructure is one key area of significant concern, and these should be a. Further, internet connectivity, electricity, and proper roads should be there to connect with the rural areas of Uttarakhand. The people should be provided basic knowledge on 'digital India' schemes to make them aware and trained to use the internet in a much better way. The awareness will help curb the myths and rumors that were most prominent during the spread of Covid-19. The better roads will help connect with hospitals easily and in quick time andthus, will be a life-saving effort. Last but not least, the policymakers should work in a much better way to develop some scientific institutes especially concerned with high-end and World-class research in pharmaceutical and biological sciences. The institutes already working in high-thrust areas of health sciences should be funded to carry out the high-end research, which not only in other states but could be recognized internationally. An important parameter of academia-industry collaborations should also be looked upon. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in Finally, as we are that Covid-19 is not the last pandemic which the World is witnessing right now. Thus, this is high time to think constructively to mitigate such pandemics in the future with significantly less mortality andsafeguarding economic and socialrequirements. The driving motive behind current work is to correlate the underlying factors and improve preparedness. Moreover, similar studies may assist other states and country(s) re-evaluating the shortcomings of the current pandemic in a much better way and act much better in the future. Authors are thankful to Prof. Kamal Ghansala, Chancellor Graphic Era Hill University for providing all necessary facilities. Authors are also thankful to Uttarakhand Government for free access of Covid-19 Uttarakhand data. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.03.21263064 doi: medRxiv preprint All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted September 6, 2021. ; https://doi.org/10.1101/2021.09.03.21263064 doi: medRxiv preprint Figure 3(A-M) . Bar graphs reveal the effect of Covid-19 restrictions on the outcome of Covid-19 cases, recovered cases, and existing cases per week among 13 districts.The graph revealed that the positive cases were directly correlated with recovered cases. 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Figure 9 . Graph representing positivity, recovery, and death percentages among Indian states/Union Territories due to Covid-19 during the period of analysis