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1.
9th International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2021 ; 267:429-439, 2022.
Article in English | Scopus | ID: covidwho-1844314

ABSTRACT

Outliers, or outlying observations, are values in data, which appear unusual. It is quite essential to analyze various unexpected events or anomalies in economic domain like sudden crash of stock market, mismatch between country’s per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest to find the insights for the benefit of humankind. These situations can arise due to several reasons, out of which pandemic is a major one. The present COVID-19 pandemic also disrupted the global economy largely as various countries faced various types of difficulties. This motivates the present researchers to identify a few such difficult areas in economic domain, arises due to the pandemic situation and identify the countries, which are affected most under each bucket. Two well-known machine-learning techniques DBSCAN (density based clustering approach) and Z-score (statistical technique) are utilized in this analysis. The results can be used as suggestive measures to the administrative bodies, which show the effectiveness of the study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

2.
Sustainability ; 14(9):4891, 2022.
Article in English | ProQuest Central | ID: covidwho-1843196

ABSTRACT

The Chinese government regards the night-time economy as one of the essential means to expand domestic demand and enhance sustainable economic development. Scientifically choosing the night-time economic development path of the suburban counties of the Chinese metropolis (SCCM) and proposing a reasonable spatial matching planning strategy is an urgent problem for Chinese local governments. This study takes Anning county, a suburban Kunming metropolitan area, as the research area. Using Python to capture multi-spatial data, such as POI and Baidu heatmap, we use ArcGIS spatial analysis and statistical tools to show the spatial distribution characteristics of the night-time economic formats in Anning County. At the same time, the spatial coupling coordination model is used to calculate the coupling coordination degree of the night-time economic formats distribution and comprehensive traffic distribution (D1), night-time economic formats distribution and night-crowd vitality (D2), and the spatial coupling coordination of the three (D3). It is divided into five spatial matching levels and analyzes the shortage of night-time economic development in each subdistrict. The research results show that the spatial development of the night-time economy in Anning county is unbalanced at the current stage. The northern part of the county has a good development trend, and the Lianran subdistrict has the highest coupling coordination degree (0.995). In contrast, the southern part of the county has the lowest coupling coordination degree due to a lack of economic formats and traffic restrictions (0.115). According to the subdistricts’ differences, the sustainable development strategy of the county’s night-time economy should be formulated from the perspective of the long-term development of metropolitan areas. We hope that this research can provide valuable inspiration and a development reference for relevant countries and regions to stimulate the sustainable power of the night-time economy.

3.
BMJ Open ; 11(9), 2021.
Article in English | ProQuest Central | ID: covidwho-1843045

ABSTRACT

ObjectivesThe widespread use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) by patients with chronic conditions raised early concerns on the potential exacerbation of COVID-19 severity and fatality. Previous studies addressing this question have used standard methods that may lead to biased estimates when analysing hospital data because of the presence of competing events and event-related dependency. We investigated the association of ACEIs/ARBs’ use with COVID-19 disease outcomes using time-to-event data in a multistate setting to account for competing events and minimise bias.SettingNationwide surveillance data from 119 Belgian hospitals.ParticipantsMedical records of 10 866 patients hospitalised from 14 March 2020to 14 June 2020 with a confirmed SARS-CoV-19 infection and information about ACEIs/ARBs’ use.Primary outcome measureMultistate, multivariate Cox-Markov models were used to estimate the hazards of patients transitioning through health states from admission to discharge or death, along with transition probabilities calculated by combining the baseline cumulative hazard and regression coefficients.ResultsAfter accounting for potential confounders, there was no discernable association between ACEIs/ARBs’ use and transfer to intensive care unit (ICU). Contrastingly, for patients without ICU transfer, ACEIs/ARBs’ use was associated with a modest increase in recovery (HR 1.07, 95% CI 1.01 to 1.13, p=0.027) and reduction in fatality (HR 0.83, 95% CI 0.75 to 0.93, p=0.001) transitions. For patients transferred to ICU admission, no evidence of an association between ACEIs/ARBs’ use and recovery (HR 1.16, 95% CI 0.97 to 1.38, p=0.098) or in-hospital death (HR 0.91, 95% CI 0.73 to 1.12, p=0.381) was observed. Male gender and older age were significantly associated with higher risk of ICU admission or death. Chronic cardiometabolic comorbidities were also associated with less recovery.ConclusionsFor the first time, a multistate model was used to address magnitude and direction of the association of ACEIs/ARBs’ use on COVID-19 progression. By minimising bias, this study provided a robust indication of a protective, although modest, association with recovery and survival.

4.
Sustainability ; 14(9):5706, 2022.
Article in English | ProQuest Central | ID: covidwho-1842949

ABSTRACT

Due to the seriousness of COVID-19, masks are considered to be as a key and effective device to cut off the spread of viruses and are widely used by people, such as doctors and patients. Hundreds of millions of masks used worldwide in daily life will inevitably cause huge pollution and damage to the environment. However, existing research has not yet provided a method to simultaneously evaluate the economic, environmental, and social aspects of sustainable design of masks, which brings great barriers and challenges for designers to make sustainability decisions on masks and consumers’ behavioral decisions on mask purchases. Consequently, on the basis of principles of sustainability evaluation of masks, this work evaluates ten masks of different materials (including two newly designed masks) by using a novel hybrid of rank-sum ratio and entropy weight method. The results indicate that some disposable masks also show better sustainability than reusable masks, and in addition, the integrated rank-sum ratio and entropy weight method can effectively realize the sustainability evaluation of masks. The main contribution is to furnish an effective decision-making reference for sustainability evaluation of masks while greatly reducing the negative impacts of masks on the environment during the epidemic.

5.
Bulletin of Agrarian Science ; 1:128-134, 2021.
Article in Russian | CAB Abstracts | ID: covidwho-1841733

ABSTRACT

One of the most important factors of the effective operation of business entities in the new economic circumstances, in particular, under the conditions of preventing spread of the new coronavirus infection (COVID-19), is a diagnosis of the financial condition, which makes it possible to identify unfavorable business development trends timely and ensure the bailout package. In this regard, the methodological basis is of great importance, allowing to conduct analytical research in the context of the application of effective algorithms to identify financial problems and develop practical recommendations for their elimination. This line of research requires improvement, development of the theoretical and methodological foundations of economic analysis and assessment of the effectiveness of financial activities of organizations that meet the needs and characteristics of the modern economy. The purpose of the research is to develop conceptual provisions and guidelines for assessing the financial condition of the agricultural organizations. With the help of general scientific and economic-statistical research methods, an evaluative monitoring of the financial activities of agricultural organizations in the Oryol region was carried out. The author's concept is based on the application of a point-based assessment of financial indicators - autonomy coefficients (concentration of equity capital), current liquidity, provision with own circulating assets, which allows diagnosing and interpreting the level of economic development of an agricultural organization under the modern conditions. The methodology for assessing the financial and property status was developed and tested, based on the use of the absolute value of net assets and the calculation of relative indicators - the ratio of net assets to authorized capital, turnover and profitability of net assets. Proposals to improve and stabilize the financial and property situation of enterprises are formulated. Practical recommendations can be used when conducting a comprehensive financial analysis of business entities.

6.
Journal of Army Medical University ; 44(3):195-202, 2022.
Article in Chinese | CAB Abstracts | ID: covidwho-1841727

ABSTRACT

Objective: To construct an XGBoost prediction model to predict disease severity of COVID-19 based on clinical characteristics dataset of COVID-19 patients.

7.
Discrete Dynamics in Nature & Society ; : 1-6, 2022.
Article in English | Academic Search Complete | ID: covidwho-1840659

ABSTRACT

This paper employs data envelopment analysis (DEA) to determine crop production efficiency in 15 major provinces of China during 2019-2020. The total power of agricultural machinery, the application amount of chemical fertilizer, the irrigation area of cultivated land, the area of grain sowing, and the total capacity of reservoirs in each province are defined as the input items. The production of food, production of oil plants, and production of fruits are considered output items. According to the findings from the DEA, the most efficient crop production is observed in Shandong and Xinjiang provinces. We also discuss the role of farmers' uncertainty perceptions in COVID-19. By cluster analysis, the provinces with large grain sown area and high grain yield are Henan and Heilongjiang, the provinces with moderate grain production in the grain sown area are Hunan, Hubei, Jiangxi, Guizhou, and Yunnan, and Xinjiang, Shandong, Hebei, Anhui, Sichuan, Jiangsu, Inner Mongolia, and Jilin are the provinces with low grain production. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

8.
13th International Conference on E-Education, E-Business, E-Management, and E-Learning, IC4E 2022 ; : 90-96, 2022.
Article in English | Scopus | ID: covidwho-1840634

ABSTRACT

The study demonstrates the learning losses in remote online learning during the Covid-19 pandemic and how online teaching can mitigate these losses. The findings were analyzed using descriptive statistics and theme analysis based on online surveys and focus groups collected. Significant learning losses occurred in an online learning environment due to reduced curricular content, a lack of student engagement, and holistic performance assessment. Unique and varied teaching tactics, such as optimizing the use of online tools and platforms, online teacher presence, and tailored evaluations, were discovered to assure optimal student learning. The University's e-readiness contributes to the e-learning success of the University's curriculum delivery. The findings provided in this paper have policy implications for curriculum review, performance assessment and evaluation in online learning, and intervention and development programs for online teachers. © 2022 ACM.

9.
Teaching Statistics ; : 1, 2022.
Article in English | Academic Search Complete | ID: covidwho-1840532

ABSTRACT

Real‐world data are fundamental to modern teaching methodologies that aim to improve statistical knowledge and reasoning in students. Statistical information is encountered in everyday life, such as media articles and involves real‐world contexts. However, information could be biased or (mis)represented and students should be concerned about the validity of such articles, as well as the nature and trustworthiness of the evidence presented, while considering alternative interpretations of the findings conveyed to them. Statistics educators could make use of media articles to create opportunities for students to reflect on such (mis)representations and build statistical literacy. The purpose of this article is to show how information and data on the recently discovered Omicron COVID‐19 variant have been (mis)represented in the media and by government entities. I also demonstrate how these examples may be utilized in the statistics classroom as they relate to concepts covered in most basic statistics courses. [ FROM AUTHOR] Copyright of Teaching Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

10.
IEEE Spectrum ; 59(5):20-22, 2022.
Article in English | ProQuest Central | ID: covidwho-1840285

ABSTRACT

The World Health Organization (WHO) declared the outbreak of the COVID-19 pandemic on 11 March 2020. Two years later, it put the cumulative number of cases at about 452 million, more than 5 percent of the world's population, and the number of new infections was still averaging more than a million a day.

11.
IEEE Access ; 2022.
Article in English | Scopus | ID: covidwho-1840228

ABSTRACT

The coronavirus disease (COVID-19) outbreak has become a global public health threat. The influx of COVID-19 patients has prolonged the length of stay (LOS) in the emergency department (ED) in the United States. Our objective is to develop a reliable prediction model for COVID-19 patient ED LOS and identify clinical factors, such as age and comorbidities, associated with LOS within a “4-hour target.”Data were collected from an urban, demographically diverse hospital in Detroit for all COVID-19 patients’ED presentations from March 16 to December 29, 2020. We trained four machine learning models, namely logistic regression (LR), gradient boosting (GB), decision tree (DT), and random forest (RF), across different data processing stages to predict COVID-19 patients with an ED LOS of less than or greater than 4 hours. The analysis is inclusive of 3,301 COVID-19 patients with known ED LOS, and 17 significant clinical factors were incorporated. The GB model outperformed the baseline classifier (LR) and tree-based classifiers (DT and RF) with an accuracy of 85% and F1-score of 0.88 for predicting ED LOS in the testing data. No significant accuracy gains were achieved through further splitting. This study identified key independent factors from a combination of patient demographics, comorbidities, and ED operational data that predicted ED stay in patients with prolonged COVID-19. The prediction framework can serve as a decision-support tool to improve ED and hospital resource planning and inform patients about better ED LOS estimations. Author

12.
International Journal of Environmental Research and Public Health ; 19(9):4959, 2022.
Article in English | ProQuest Central | ID: covidwho-1837528

ABSTRACT

Australia is a federation of six states and two territories (the States). These eight governmental entities share responsibility for health and health services with the Australian Government. Mortality statistics, including causes of death, have been collected since the late 19th century, with national data produced by the (now) Australian Bureau of Statistics (ABS) from 1907. Each State introduced hospital in-patient statistics, assisted by State offices of the ABS. Beginning in the 1970s, the ABS conducts regular health surveys, including specific collections on Aboriginal and Torres Strait Islander peoples. Overall, Australia now has a comprehensive array of health statistics, published regularly without political or commercial interference. Privacy and confidentiality are guaranteed by legislation. Data linkage has grown and become widespread. However, there are gaps, as papers in this issue demonstrate. Most notably, data on primary care patients and encounters reveal stark gaps. This paper accompanies a range of papers from expert authors across the health statistics spectrum in Australia. It is hoped that the collection of papers will inform interested readers and stand as a comprehensive review of the strengths and weaknesses of Australian health statistics in the early 2020s.

13.
Arab Gulf Journal of Scientific Research ; 39(Special Issue (2):79-137, 2021.
Article in English | CAB Abstracts | ID: covidwho-1837421

ABSTRACT

Purpose: Evolving technologies allow us to measure human molecular data in a wide reach. Those data are extensively used by researchers in many studies and help in advancements of medical field. Transcriptome, proteome, metabolome, and epigenome are few such molecular data. This study utilizes the transcriptome data of COVID-19 patients to uncover the dysregulated genes in the SARS-COV-2. Method: Selected genes are used in machine learning models to predict various phenotypes of those patients. Ten different phenotypes are studied here such as time since onset, COVID-19 status, connection between age and COVID-19, hospitalization status and ICU status, using classification models. Further, this study compares molecular characterization of COVID-19 patients with other respiratory diseases.

14.
Int J Popul Data Sci ; 5(4): 1710, 2020.
Article in English | MEDLINE | ID: covidwho-1836338

ABSTRACT

Introduction: The COVID-19 pandemic revealed an urgent need for analytic tools to help health system leaders plan for surges in hospital capacity. Our objective was to develop a practical and locally informed Tool to help explore the effects of public health interventions on SARS-CoV-2 transmission and create scenarios to project potential surges in hospital admissions and resource demand. Methods: Our Excel-based Tool uses a modified S(usceptible)-E(xposed)-I(nfected)-R(emoved) model with vaccination to simulate the potential spread of COVID-19 cases in the community and subsequent demand for hospitalizations, intensive care unit beds, ventilators, health care workers, and personal protective equipment. With over 40+ customizable parameters, planners can adapt the Tool to their jurisdiction and changes in the pandemic. Results: We showcase the Tool using data for Ontario, Canada. Using healthcare utilization data to fit hospitalizations and ICU cases, we illustrate how public health interventions influenced the COVID-19 reproduction number and case counts. We also demonstrate the Tool's ability to project a potential epidemic trajectory and subsequent demand for hospital resources. Using local data, we built three planning scenarios for Ontario for a 3-month period. Our worst-case scenario accurately projected the surge in critical care demand that overwhelmed hospital capacity in Ontario during Spring 2021. Conclusions: Our Tool can help different levels of health authorities plan their response to the pandemic. The main differentiators between this Tool and other existing tools include its ease of use, ability to build scenarios, and that it provides immediate outcomes that are ready to share with executive decision makers. The Tool is used by provincial health ministries, public health departments, and hospitals to make operational decisions and communicate possible scenarios to the public. The Tool provides educational value for the healthcare community and can be adapted for existing and emerging diseases.


Subject(s)
COVID-19 , COVID-19/epidemiology , Hospitals , Humans , Ontario/epidemiology , Pandemics/prevention & control , Personal Protective Equipment , SARS-CoV-2
15.
Journal of Economic and Social Development ; 9(1):10-18, 2022.
Article in English | ProQuest Central | ID: covidwho-1836718

ABSTRACT

The deterioration of the economic situation during Covid-19 has raised the issue of the quality of banks' assets and in particular the growth of non-performing loans (NPL). This is a topical issue not only for banks that, in this context, incur additional costs for allowances and capital requirements but also for society as a whole, as credit availability is likely to be reduced. The Baltic States experienced a particularly severe financial crisis in 2008-2009, resulting in a rapid increase in NPLs. This study analyses the factors affecting NPLs in the Baltic States, using information available from WB, Eurostat, and econometrical modeling methods. The results of the study allow conclusions to be drawn on the necessary actions to mitigate credit risk.

16.
International Journal of Nursing Education ; 14(2):171-176, 2022.
Article in English | CINAHL | ID: covidwho-1836628

ABSTRACT

"Mucor" is a fungus which is normally present in the environment and in soil. It causes disease only when immunity is critically low. Early detection and management of the Mucormycosis is very crucial. Delay reporting symptoms of the infection should be avoided and treatment should be initiated at the earliest AIM: The aim of this study is to assess the Knowledge of Mucormycosis among Undergraduate Nursing Students of AIIMS New Delhi". Method A descriptive cross sectional survey was adopted using online platform as direct contact with the participants is not possible during this period. An online questionnaire was used to assess knowledge of Mucormycosis among the undergraduate Nursing students of AIIMS, New Delhi. Population selected are B.Sc Nursing 2nd, 3rd and 4th year and post basic 1st and 2nd year students who are studying in College of Nursing, AIIMs, New Delhi using smart Phone and Whats app. Result: All the nursing students (N=230) had good and homogenous knowledge about mucormycosis with mean knowledge score 7.99 (minimum 3 and maximum10) and SD±1.1. Most of the students (88.7%) would like to include the topic in the syllabus as they would like to know in detail about the disease. 90% of the Students showed a greater appreciation and willingness to attend seminar/webinar on this topic of Mucormycosis. Conclusion: In the current study, although most of the study participants possess a good knowledge toward the prevention of COVID-19, it is surprising to know that the students are seeking formation from unverified sources such as social media and internet. These results are impactful and should be addressed through standardized training opportunities and distribution of official sources about mucormycosis .There is also a need to Constantly updated refresher training from authentic sources which will contribute to better performance of the student Nurses in clinical areas

17.
International Journal of Nursing Education ; 14(2):118-130, 2022.
Article in English | CINAHL | ID: covidwho-1836626

ABSTRACT

Background: The global coronavirus disease pandemic of 2019 (COVID-19) has caused health care provider to experience extraordinary psychological stress. Objective: This study assessed the psychological well-being of nurses during the COVID-19 outbreak and factors associated with it. Methods: An online survey was sent to all nurses working at the Ministry of Health Hospitals and living in Tabuk city, Saudi Arabia. A total of 219 nurses were completed the survey. The Depression, Anxiety and Stress Scale -- 21 items (DASS-21) assessed the psychological well-being of respondents in the previous week. Results: One -quarter of nurses (24.7%) reported extremely severe symptoms of anxiety, more than one third (37%) reported extremely sever symptoms of stress, less than one quarter (14.1%) reported extremely sever symptoms of depression. Higher anxiety scores were significantly associated with direct contact with confirmed COVID 19 cases (p= 0.08), general health status (p= 0.001) and marital status (p= 0.042). Higher DASS-21 Stress scores were significantly associated with working more than eight hours per shift (p=0.024), marital status(P=0.036) and general health status (p <0.001). Higher DASS-21 Depression scores was significantly associated general health status (p <0.001). Conclusions & implication for practice: The COVID-19 outbreak has had a significant effect on the psychological well-being of Saudis nurses, particularly nurses who were married, had contact with COVID 19 cases, had working more than eight hours per shift, and had poor general health status. Protecting the psychological health of nursing staff is essential, nursing leaders are in charge of providing social support for nurses so that they will be able to cope with their anxiety, stress, and depression.

18.
International Journal of Nursing Education ; 14(2):50-57, 2022.
Article in English | CINAHL | ID: covidwho-1836625

ABSTRACT

Background-Covid-19 Pandemic has proved the Nurse's crucial role in health care delivery system and providing nursing care to critically ill patients. It is a challenge for nurses as they need to be astute, competent, compassionate and critical thinker when they have to take care of patients on mechanical ventilator. Aim-To assess knowledge and practices regarding care of patients on mechanical ventilator among nursing personnel before and after administration ofNursing Care Bundle (NCB) in experimental and comparison group. Material and method. A Quasi Experimental non Equivalent comparison group pretest post test design used in thus study. 65 nursing personnels (30 experimental and 35 comparison groups) were selected from hospitals of North India using convenience sampling technique. NCB was administered in experimental group. Structured knowledge questionairre, Structured Observation Checklist for practices was used to collect data before and after intervention. Results-The mean post test knowledge and practices scores of nursing personnel in experimental and comparison groups were (21.6 ± 3.84, 30.83 ± 4.51) and (17.54 ± 2.76, 19.54 ± 4.17) respectively. There was significant difference between mean pre test and post test knowledge and practices scores (p=0.00).There was statistically no significant correlation between post test knowledge and practices score [r=0.16 (0.39)] among nursing personnel in experimental group at the level of significance 0.05.There was significant association of selected variable in area of gender (0.02) in experimental and education (0.02) in comparison group with pre test knowledge scores , also there was a significant association of selected variable in area of gender in experimental (0.03) and present area of working (0.03) in comparison group with pre test practices score. Conclusion-Nursing Care Bundle was effective in improving knowledge and practices of nursing personnel.

19.
Iranian Journal of Medical Microbiology ; 16(3):259-266, 2022.
Article in English | CINAHL | ID: covidwho-1836483

ABSTRACT

Background and Aim: In December 2019, a new type of Coronavirus (SARS-CoV-2) pneumonia (COVID-19) was reported in Wuhan and quickly spread worldwide. This study was designed to investigate the clinical symptoms of the COVID-19 patients. Materials and Methods: In this retrospective study, we collected data of 132 COVID-19 dead patients. Demographic, epidemiological, and clinical data and laboratory test results were analyzed on days 1, 3, and 6 of admission. Results: Most cases were in the 66-75 age group, 64.39% of which were males. Three days after admission, 55.3% of patients died. The most frequent clinical manifestations were dry cough (70.45%) and fever (54.54%), which increased during hospitalization. Diabetes and blood pressure were reported as the most prevalent underlying diseases. Lymphopenia and an increase in leucocyte number were observed in most patients. ESR (92.5%) and LDH (94.64%) levels were above normal. Furthermore, 42.85% and 44.73% of patients had elevated ALT and AST levels, respectively. Conclusion: The results of this study revealed that males are more likely to be infected with SARS-CoV-19. Underlying diseases were common among patients and clinical and laboratory symptoms aggravated with a rise in hospitalization time.

20.
Peer Community Journal ; 2(e6), 2022.
Article in English | CAB Abstracts | ID: covidwho-1836344

ABSTRACT

The SARS-CoV-2 epidemic in France has focused a lot of attention as it has had one of the largest death tolls in Europe. It provides an opportunity to examine the effect of the lockdown and of other events on the dynamics of the epidemic. In particular, it has been suggested that municipal elections held just before lockdown was ordered may have helped spread the virus. In this manuscript we use Bayesian models of the number of deaths through time to study the epidemic in 13 regions of France. We found that the models accurately predict the number of deaths 2 to 3 weeks in advance, and recover estimates that are in agreement with recent models that rely on a different structure and different input data. In particular, the lockdown reduced the viral reproduction number by 80%. However, using a mixture model, we found that the lockdown had had different effectiveness depending on the region, and that it had been slightly more effective in decreasing the reproduction number in denser regions. The mixture model predicts that 2.08 (95% CI: 1.85-2.47) million people had been infected by May 11, and that there were 2567 (95% CI: 1781-5182) new infections on May 10. We found no evidence that the reproduction numbers differ between week-ends and week days, and no evidence that the reproduction numbers increased on the election day. Finally, we evaluated counterfactual scenarios showing that ordering the lockdown 1 to 7 days sooner would have resulted in 19% to 76% fewer deaths, but that ordering it 1 to 7 days later would have resulted in 21% to 266% more deaths. Overall, the predictions of the model indicate that holding the elections on March 15 did not have a detectable impact on the total number of deaths, unless it motivated a delay in imposing the lockdown.

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