- Review: found

Ananth Govind Rajan4

An Engineering Model of the COVID-19 Trajectory to Predict Success of Isolation InitiativesCrossrefScienceOpen

Average rating: | Rated 3.5 of 5. |

Level of importance: | Rated 4 of 5. |

Level of validity: | Rated 3 of 5. |

Level of completeness: | Rated 4 of 5. |

Level of comprehensibility: | Rated 3 of 5. |

Competing interests: | None |

- Record: found
- Abstract: found
- Article: found

Steven King, Alberto Striolo (2020)

ScienceOpen disciplines: | Earth & Environmental sciences, Engineering |

Keywords: | The Environment, Engineering model, Covid-19, Recommendations |

**Review of “An Engineering Model of the COVID-19 Trajectory to Predict Success of Isolation Initiatives” by King and Striolo**

In this article, King and Striolo have developed a simple engineering model to understand the trajectory of COVID-19 cases. The model is based on three population-related variables and two rate constants. The model also takes into account the effect of intervention strategies applied by the government by scaling down the growth rate of cases. The authors applied this simple model to WHO data from three countries around the world, namely China, Singapore, and South Korea. The authors have also applied the model to the COVID trajectory in the U.K. during the revisions. Although the model is simple and cannot be used to inform complex and far-reaching public health decisions, as the authors rightly caution, the model results in some notable conclusions regarding the pandemic and lockdown strategies to contain it. Over the course of the previous two rounds of review, the authors have improved the manuscript based on the suggestions made. Nevertheless, the manuscript may be improved further by implementing the following revisions, prior to publication:

- On page 2, the authors introduce SIR-type models and provide a brief overview of such models. The authors should explain the abbreviation SIR and also how such models work. The authors should also briefly describe any alternative or superior strategies for modeling the spread of infectious diseases, which may already have been explored or could be explored in the future.
- On page 4, the authors mention that “Fitting is not shown here because abundant analysis is reported on the news as well as on specialist literature.” However, the authors should report the values of k
_{1}and k_{2}used in their model and how these values were obtained/chosen, along with their 95% confidence intervals. This should be done for all the cases considered in the manuscript. - In Figure 1, the authors mention a R
^{2}value of 1.0 for the exponential fit during the initial stage of the pandemic; however, they do not indicate till what time point the data was included in the fit. - Although the authors provide R
^{2}values for their fits, they do not provide 95% confidence intervals for any of their parameters and error bars for their predictions. These should be added to the manuscript, to quantify the robustness of the model and the predictions. - The authors should clearly mention and indicate that in Figure 1 and all other figures, the vertical axis refers to the percentage of cases, rather than the absolute number of cases. Further, what is the unit of time in the plots shown in the manuscript? This should be mentioned in the manuscript. In Figure 1, the maximum time considered is less than 10, whereas in Figure 2, the maximum time considered is 500. Could the authors explain this choice?
- In Figure 3, it appears that the top two panels plot the absolute number of cases; however, the bottom plot seems to depict the cases as a fraction of the population. The authors must clarify this point.
- On page 11, the authors mention that the “fit of the South Korean data is more promising…, as shown by the data in Table 2”. The authors should explain how the ratios A/B and C/D that they have defined can be used to infer the quality of the fit. The authors should also provide concise physical interpretations of these ratios.
- Regarding the proposed expression for
*dX/dt*on page 3, could other types of functional expressions (e.g., power laws) in terms of*X*and*Y*work better in modeling the spread of disease? - On page 4, the authors mention that
*N=X+Y+Po*, but in so doing they have not considered the individuals labeled as*REC*. Why shouldn’t*N = X + Y + REC*? - On pages 4-5, the authors should fix the spellings ‘Hetohcote’ to ‘Hethcote’, ‘mode’ to ‘model’, and ‘out’ to ‘our’.
- On page 6, the authors include the effects of government intervention in the simplified model where
*X*, i.e., the number of uninfected individuals, is assumed to be equal to*Po*. This seems to be a major simplification, which may not be true as the pandemic progresses. What would be the effect of not making this simplification on the model predictions? - In Figure 3, the authors refer to the ‘left’ panel, when they should in fact refer to the ‘top’ panel.
- On page 11, it would be interesting to see the effect on the model upon addition of another term to
*K**step-down*that models a reduction in the intervention strength or a reduction in compliance to the intervention measures, which set in after a certain amount of time.