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Kathmandu, Bagmati Zone, Nepal
I am Basan Shrestha from Kathmandu, Nepal. I use the term 'BASAN' as 'Balancing Actions for Sustainable Agriculture and Natural Resources'. I am a Design, Monitoring & Evaluation professional. I hold 1) MSc in Regional and Rural Development Planning, Asian Institute of Technology, Thailand, 2002; 2) MSc in Statistics, Tribhuvan University (TU), Kathmandu, Nepal, 1995; and 3) MA in Sociology, TU, 1997. I have more than 10 years of professional experience in socio-economic research, monitoring and documentation on agricultural and natural resource management. I had worked in Lumle Agricultural Research Centre, western Nepal from Nov. 1997 to Dec. 2000; CARE Nepal, mid-western Nepal from Mar. 2003 to June 2006 and WTLCP in far-western Nepal from June 2006 to Jan. 2011, Training Institute for Technical Instruction (TITI) from July to Sep 2011, UN Women Nepal from Sep to Dec 2011 and Mercy Corps Nepal from 24 Jan 2012 to 14 August 2016 and CAMRIS International in Nepal commencing 1 February 2017. I have published articles to my credit.

Wednesday, April 29, 2020

How Predictable Death by COVID-19 Incidence?


I downloaded from the European Centre for Disease Prevention and Control website the daily database constituting the number of new cases reported and death worldwide due to COVID-19. The database had 13,623 daily records from December 31, 2019 to April 28, 2020. I summed records by country and filtered the number of incidences avoiding outliers using a statistical formula.  The filtered record shows a list of 136 countries that had an incidence in 314,099 persons, of which 7,866 persons died. It shows that 2.5 percent of infected people had already died and there is a possibility of other deaths too, which the time will tell. Israel had a maximum of 15,598 incidences, and the British Virgin Islands had a minimum of 6 incidences. Algeria had a maximum of 437 deaths and 13 countries each had one death.

Then, I calculated the Pearson correlation coefficient between the number of incidence and deaths for the filtered records.  The purpose of removing outliers was because the Pearson correlation coefficient works when the datasets are close to normal distribution. The analysis shows a correlation coefficient of 0.677 which was significantly different from zero (p=0.01). This suggests that there is a moderate chance of death if one is infected with COVID-19. A regression analysis was also conducted to predict death based on incidence, which gives an R square value of 0.459 indicating that nearly half the variation in the number of deaths is explained by the model. A significant coefficient indicates that the number of incidences significantly contributes to the model. For every 1000 incidences 18 deaths are likely.

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