Introduction

This review follows specific guidelines provided by the Publons team to secure rapid critical appraisals of papers related to the COVID-19 outbreak. My assessment scrutinizes this paper for sound scientific practices, in the hope that this will help researchers working on the COVID-19 outbreak.

Reviewer type

I am not currently working directly on the COVID-19 response, but I consider myself an expert in the methods and core concepts used in this paper

COVID-19 Topic

Modelling

Author/s’ experience

The author appears to be a senior researcher (graduate student) with a background in mathematics. Specifically, the author’s area of research includes agglomeration of nanoparticles, cohesive DEM, CFD analysis of chemical reactors, and fluidisation.

Brief overview of the paper and its main findings

The paper models the growth of COVID-19 cases in India. Specifically, it claims that the actual infectious population is 10 times that of the reported cases. Some of the indicators are compared with other behavior in other countries. Finally, the paper predicts the future trend, especially, it claims that the number of cases will peak at 22,000 by end of April, taking up to July for the epidemic to end in India.

Major and minor points

The paper’s model is mathematically accurate. However, the outcome of the model is dependent on some key assumptions that are not justified. If technically valid justifications could be presented for the following, the paper’s results could be noteworthy. Specifically, estimation of eta (rate of detection of new case) has a myriad of unjustified assumptions, which eventually leads to the core results of the paper, such as R0, doubling rate, I/Q ratio, etc.

Major points

1. Total infected population that has been quarantined was taken to be 10% (mu) without any justification. The sentences that precede this assumption has no relationship to the assumption of 10% for mu: “It has been reported that in Japan 50% of the population is asymptotic [9]. Considering the domination of young population in Indian age distribution [7] and low testing per million of the population [10].”

2. Part of the assumptions comes from China and some from Italy, whose applicability to India is not well known. Assuming rate of detection, eta = 0.2 x mu, where 0.2 was taken from China’s data. Gamma, the rate of removal of quarantine cases, was taken from Italy to be 25 days.

3. I would recommend having an error bound for eta and gamma, which will give a range: worst case scenario – best case scenario.

Minor points:

1. The susceptible people set is taken to be N(l-1) which is the total population not following lockdown. However, S= N-Q-I-R, also noted in the reference [3]. The outcome of this assumption is that, it may be true only as long as Q, I, R are much smaller than N. This may be true for India, but definitely not for any other country. Please state this assumption clearly.

2. Equation 9 is missing a "t"

Research integrity

No issues.

Conflicts of interest

No conflicts of interest