Report #2

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Published on 8/25/2020

Confirmed Cases

The charts above show the progression of COVID-19 in Nepal ever since the first case was detected in January of this year.

The chart on the left shows the growth in the total number of cumulative cases across the country. We note that while the total had started to stabilize around July, in August, the path is back to exponential growth. As of the day of writing 31015 cases have been identified across the country, of which 12847 cases are still active. Identified cases have been growing at a rate of 2.67% over the past week, a relative increase of 0.50% compared to the week before last.  

An exponential resurgence of cases is supported by the chart on the right, which shows the same data (cumulative cases) but in the Logarithmic (natural) scale. Essentially, the scale allows us to see what the “curve” in Nepal looks like. Since a 1-point increase in the log scale is equivalent to about 3000 new cases, the increase in the curve on right shows some cause for concern. 

High Growth Areas

The chart above compares the “curve” for Nepal with that of specific districts. It is evident that the recent spike in cases in the country has been driven by two districts in particular: Kathmandu and Parsa; whereas districts which were previously hot-spots (Rautahat, Dailekh and Kailali) seem to have flattened the curve to a certain extent. We have added two districts to our report last week (Banke & Rupandehi), where we see the cases growing at a faster pace over the past week.

Haripur (Sarlahi district) and Jitpur Simra (Bara district), are experiencing a recent spike in cases
(Rautahat, Dailekh and Kailali) seem to have flattened the curve to a certain extent.

Digging a little deeper, the chart above – which shows the number of cases for specific municipalities since July – highlights areas where the growth in COVID-19 cases has spiked only recently. Official records show that Budhanilkantha (Kathmandu district), Belbari (Morang district) and Paterwa Sugauli (Parsa district) are being affected particularly strongly. To this list, we add Haripur (Sarlahi district) and Jitpur Simra (Bara district), which are also experiencing a recent spike in cases.  

Geographic Breakdown

Nepal saw a significant inflow of people in the bordering areas, where – as the map above shows – the number of identified COVID-19 cases have been the highest to date. Kathmandu is now clearly the most affected district in Nepal.

Unlike other countries where there was steady growth in COVID-19 cases through local transmission, in Nepal the situation seems to have been quite different. The combination of a porous border with neighboring India, where a large proportion of the population live and work, and a phased out lockdown in both countries, Nepal saw a significant inflow of people in the bordering areas, where – as the map above shows – the number of identified COVID-19 cases have been the highest to date. Kathmandu is now clearly the most affected district in Nepal.

Demographic Breakdown

83.2% of the identified cases so far have been Male while only 16.8% have been female.

A demographic analysis of identified COVID-19 cases shows that most cases have been identified in a much younger portion of the population. This holds true regardless of gender and can be hypothesized to be the result of a correlation between the younger and migrant worker populations. In terms of gender breakdown, where data on gender is available, 83.2% of the identified cases so far have been Male while only 16.8% have been female. Once again, this could be the result of migratory factors.

Correlation with Development Indicators and Possible Causation

After our previous report, we were encouraged by some readers to carry our further analysis. Compiling data from numerous sources (see Bibliography), we carried out a correlation analysis of the COVID-19 cases (by Province) with the different indicators we were able to find. Our results can be summarized as follows:

  1. The chart above shows the indicators that have a positive and higher than 70% correlation with COVID-19 cases in all the Provinces. It should be noted that while Student-Teacher ratio (for example) has an over 70% positive correlation with COVID-19 cases, indicating that a higher Student-Teacher ratio leads to higher COVID-19 cases, correlation does not equal causation so some caution is warranted in its interpretation.
  1. Of the variables that we found to be positively correlated to COVID-19 cases, we found two in particular: Municipalities (no.) & Without basic vaccines (%) that could be hypothesized as to have a causal relationship with COVID-19 cases.
  1. Similarly, the chart above shows those indicators that have a high but negative 70% or more correlation with COVID-19 cases. In this case, for example, the higher % budget for transport, fuel and energy, the lower the number of COVID-19 cases. Naturally, the correlation is not equal to causation rule applies in this case as well. Of the variables we found to be negatively correlated to COVID-19 cases, we found one in particular: Public hospitals that could hypothesized as to have a causal relationship with COVID-19 cases.

Knowing that one of the ways to establish causation is to establish a linear relationship between the variables, we then carried out simple linear regressions using each of the above variables as an independent variable and the COVID-19 cases as a dependent variable.

Our results (presented in the Appendix) seem to indicate that Municipalities (no.) and Without basic vaccines (%) have a statistically significant (at 95% confidence level) causal relationship with COVID-19 cases while the number of Public hospitals does not.

In addition, we also carried out a combined linear regression using all three variables above as independent variables and COVID-19 cases as the dependent variable. An initial regression with all three variables showed none to be statistically significant. However, we identified that in this case, we had a multicollinearity problem (two independent variables being correlated to each other) as Municipalities (no) and Without basic vaccinations (%) had a 77% correlation. After we removed one of them, we were able to reestablish statistical significance once again for the two positively correlated variables whereas the negatively correlated variable remained statistically insignificant.

We recognize one obvious caveat to this analysis that could nullify our conclusions: the number of observations being only 7 provinces. Having said that, the goal of our work was to identify potential points of discussion and so we encourage any readers with domain specific expertise to reach out to us and help us carry out further analysis. We, as authors, would also like to ask readers to get in touch with us either to provide feedback so that future reports can cover aspects that you would like to see or point us to data that we could integrate into our analysis.
Thank you for reading through our work!

Key Takeaways

COVID-19 is rising exponentially at national Level   We are seeing an average rise of around 2.67% confirmed cases per day over the past week. Death rates are increasing by around 3.05% per day over the past week with the highest at 21 deaths on the 20th of August 2020. Paterwa Sugauli (29), Belbari (13), and Budhanilkantha (11) municipalities, show the highest death rates so far. In Kathmandu, Parsa, Banke and Rupandehi, the number of cases is increasing while in prior hotspots of Rautahat, Dailkeh and Kailali, the cases are stabilized. The new municipal hotspots for the COVID-19 are Budhanilkantha, Belbari, Paterwa Sugauli, Harihar and Jitpur Simara. From a district perspective, Kathmandu is now the most affected area in all of Nepal.  Correlation and Causation Analysis We found some social and economic indicators to be correlated with COVID-19 cases on a Provincial level. Of these, (no. of) Municipalities, Without all basic vaccines (%) and Public hospitals per 1000 people were both highly correlated (70%+) and could also be hypothesized to have a causal relationship with COVID-19 cases. To establish causation i.e to prove that that these variables had a statistical impact on COVID-19 cases, we carried out linear regressions. The results indicated that Municipalities and % without basic vaccines were statistically significant (at 95% confidence level and bar obvious caveats such as sample size) while the number of public hospitals were not. We acknowledge the above analysis to be rudimentary in nature and encourage further/ expert analysis from our readers.   

If you have any feedback on the report or believe we can be of any help to your organization, please get in touch with us at


  1. Physical Violence, Access to health, Vaccination, Domestic Violence: 2016 Demographic and Health Survey Key Findings
  • Multidimensional Poverty Index : National Planning Commission, Government of Nepal, Oxford Poverty and Human Development Initiative, University of Oxford
  • All Education Indicators, GER  etc: Flash report , CEHRD
  • Education in Figures 2017 (At A Glance)
  • Financial indicators and budget data: Red Book

  • Distribution of Health Services

  • Progress of the Health, Sector in FY 2017/18, NATIONAL ANNUAL REVIEW REPORT – 2018 (2075 BS)


The authors of this report work as a part of the team that started the Cope initiative. Cope was formed in early May 2020 by a group of volunteers with experience in Data Analytics and Social Media Management. This report has been prepared voluntarily through Cope Nepal, for information purposes only and without any commercial interests

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