Evidence from an analysis of over 4 million authors.

Academic research is being published at a greater rate than ever before. In fact, global research output has been doubling every decade. With the tools necessary to conduct research becoming increasingly prevalent, data getting more transparent and accessible, and the growingly rapid dissemination of research itself, this trend will certainly continue. But with this comes the proliferation of low-quality/predatory journals and less-experienced authors publishing. Therefore, it is necessary to step back and understand how we define “experts” in research.

To embark on an exploratory study, I used data including the author’s h-index, total citations among all of an author’s papers, and the number of scientific papers under their name (regardless of the order of authorship) for >100M authors. …


The numbers say it’s very plausible.

Context

A few days ago, the de facto spokesperson of the White House Coronavirus Task Force, Anthony Fauci, drew national attention when he said that “between 100,000 and 200,000” Americans could die from COVID-19. Such a dire prediction from the nation’s foremost infectious disease expert has led many have been wondering: is this really possible? Is it the best or worst-case scenario? In this article, I will address these questions, and discuss the conditions under which this can happen.

Methods

To obtain the number of deaths, we need two things: the range of case fatality rates and the number of cases.

Estimating case fatality rates

The CFR was calculated for each state in the US with at least 25 deaths (as of 4/1) using a cohort-based method that I describe in detail here. We combine a mathematical model with a cohort analysis approach to determine the range of case fatality rates (CFR). We use a logistical function to describe the exponential growth and subsequent flattening of COVID-19 CFR that depends on three parameters: the final CFR (L), the CFR growth rate (k), and the onset-to-death interval (t₀). Using the logistic model with specific parameters (L, k, and t₀), we calculate the number of deaths each day for each cohort. We build an objective function that minimizes the root mean square error between the actual and predicted values of cumulative deaths and run multiple simulations by altering the three parameters. Using all of these values, we find out which set of parameters returns the lowest error when compared to the number of actual deaths. We then find the range of parameters that are within the top 1% of R². By analyzing the Gaussian kernel density estimation (refer to Figure 3,4 here), I determined the most likely CFR estimate and the highest/lowest possible CFR’s. …


Ramping up testing is imperative.

During the course of an infectious disease outbreak such as the COVID-19 pandemic the world is currently experiencing, it is crucial to understand the lethality of the illness— people want to know their likelihood of dying if they catch the disease. Multiple different measures are used to estimate this, and in this article, we will describe these measures, and discuss the difficulties associated with obtaining these metrics.

Measuring lethality

There are three principal measures of disease lethality: the case fatality rate (CFR), infection fatality rate (IFR) and mortality rate (MR). The mortality rate can be calculated by dividing the number of deaths by the total at-risk population. This tells us the probability of a person dying within a population. On the other hand, the CFR uses the same numerator but instead divides it by the number of confirmed cases. That is, it indicates the percentage of people that die given that one is infected (and report it). The IFR is identical to the CFR, except that it divides deaths by the total number of people who have been infected. This takes into account people who are infected regardless of whether they report or not. In a world where no cases of the virus went undetected and all of them were reported, the IFR and CFR would be equivalent. …


We need to act now.

Summary

There is clear evidence that warm weather will not impede the transmission of the virus. This has tremendous implications for federal, state, and local policy decisions. We must act now and enact drastic measures similar to those imposed by California, Illinois, and New York, with the aim of reaching zero cases as soon as possible.

Background

Coronavirus Disease 2019, or COVID-19 was recently classified as a pandemic by the WHO. The United States currently has more than 32,000 active cases of the disease, and New York, California, and Illinois have imposed statewide lockdowns to limit the outbreak. However, no other states have enacted lockdowns or taken swift, stringent actions to contain the disease, possibly because of expectations that the warm weather brought on by summer will slow the transmission of the virus. …


The CFR may very well be as high as 6%.

Introduction

As the Coronavirus contagion nudges towards pandemic status, with its rapid spread inciting concerns over the epidemic’s global impact, it is increasingly important to understand the real threat it poses.

We use three metrics (among others) to help effectively assess the overall threat presented by the outbreak of COVID-19: 1) the initial number of people that get infected, 2) the number of days from developing the disease to death, and 3) the percentage of people that die.

Recently, I wrote a piece on how the current method of calculating the mortality rate is wrong, due to something called the lag effect — essentially, due to the nature of an ongoing contagion, the mortality rate needs to be determined on a cohort level, rather than using the overall #deaths/#cases. I also showed a condition under which the modeled number of deaths closely match the reported figures. However, I did not describe the range of possibilities under different conditions where there is a match between model and data. In this post, I simulate the spread and mortality rate of the Coronavirus under different sets of conditions to ascertain the variance of the case fatality rate. …


The mortality rate is likely much higher than we thought.

As you probably know, the coronavirus, or COVID-19, is a new virus that causes respiratory ailments. Its origins can be traced back to Wuhan, China, in late 2019. It is comparable to other animal-based coronaviruses such as SARS and MERS. Although most cases have been confined to the Hubei Province, which Wuhan is part of, it has spread to HK, Taiwan, Japan, Germany, the US, and possibly even North Korea. Although new details about the virus’s nature and its growth are still emerging, we know that it has a 2–14 day incubation period and the average #/transmissions by each person who has it is roughly in the range of 2–2.5. …


Introduction

Hinduism and Islam are closely knit and have shaped India into the country it is today. Both are deep-rooted in Indian culture and society, having existed on the subcontinent for millennia. As well as fostering mutual acceptance and understanding in the diverse and pluralistic society, their confluence has also led to conflict. British colonialism played a tremendous role in the religious polarization and the tragic Partition marked the beginning of most modern tensions between Muslims and Hindus. The BJP’s advancement of their Hindu nationalist agenda has further intensified tensions in recent years. …


In the increasingly competitive atmosphere of college admissions, students increasingly turn to rankings to determine whether to apply or enroll at certain colleges. Perhaps the unspoken leader in the field, now notoriously synonymous with college admissions itself, is the annual US News ranking. How much of a role do universities’ “prestige” influence students’ tastes?

In this blog, I use US News ranking as a proxy for College prestige. Moreover, I use students’ propensity to attend given admission by yield rates (the #/applicants who enroll at a college ÷ the #/applicants who are accepted).

Above is the scatterplot of college rankings and corresponding yield rates; it is immediately clear that there is a strong correlation between the two. Institutions with higher US News rankings also had higher yield rates and vice versa. From here, the extent to which students base their college preferences on rankings is clear. Although many confounding variables are at play, it is safe to say that students do make college decisions based on rankings. So, given a US News Ranking, one can roughly predict the yield rate. Therefore, colleges have very little direct control over the yield rates and try hard to move up the US News Ranking leaderboard. …


In the 21st century, college applications and admissions are growing increasingly competitive and complex. The total number of students applying to college has doubled in the last decade alone. Students apply to a median of 4 colleges. Rising GPAs (the national average was 2.68 in 1990; in 2009, it was 3.00) and SAT scores demonstrate an increasingly competitive applicant pool. Overall, this signals that colleges face tremendous competition in securing first-rate applicants.

Colleges ultimately want to enroll students they feel will enrich the community and will achieve success as the school brand ambassadors. Depending on the school’s capacity of resources, their ideal student body composition, the brand they want to cultivate, etc, each college will have to carefully select their students. …


You have probably heard of TikTok. It has quickly emerged as one of the world’s most popular social media apps. But, is the product really doing that well? I will analyze the product’s influence across the world and look deeply into the US and India — two of their largest countries.

TikTok is increasingly being used across the world

Above are the 8 countries that use TikTok the most. China boasts the largest share of users, followed by India, Pakistan, and America. Few key insights:

  • Douyin is huge in China but is less ticky than top social apps — TikTok is available through an entirely different app (Douyin) in China. Despite its heavy presence, it is less sticky (DAU/MAU) than the best products. Products such as Whatsapp, Instagram and Facebook have much higher DAU/MAU ratios (> 65%). …

About

Charit Narayanan

I am a high school junior from the Bay Area. charit.info

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store