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• W3037353436 abstract "To the Editor: The international spread of the novel human coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] or COVID-19)1 and subsequent pandemic has resulted in a global societal response unprecedented in the modern era. The resultant decrease in operative volume coincident with the strong social-distancing interventions undertaken by the majority of the United States has necessitated significant logistical adjustments to daily neurosurgical practice.2,3 The scientific basis for these efforts to “flatten the curve” is largely found in epidemiological models that seek to forecast potential case volume, mortality, and medical resource usage.4 As such, these epidemiological models have had a significant impact on neurosurgical practice and will likely continue to play an important role in the continued resumption of elective surgical operations in the upcoming months. Given this high degree of influence and the strong emphasis placed by the neurosurgical community on sound scientific methodologies,5 we felt that a brief discussion of some of the more widely used models would be of practical value to the neurosurgical community. THE “SIR MODEL” A commonly used epidemiological technique is called the “SIR model,” in which the entire population is compartmentalized into three categories: susceptible (S), infected (I), or removed (R). At the initiation of a pandemic, S = 100% and no member is yet infected or removed. Gradually individuals transition to “infected” and ultimately either recover or succumb. In both scenarios, they transition to the “removed” group given that they are no longer infected or capable of spreading infection to others. When R = 100% of the population, the pandemic ends. The driving force for the velocity at which new infectious cases occur is the “base reproduction rate” or R0 (known as “R naught”). Simply put, R0 reflects the total number of people that a newly infected individual will, on average, secondarily infect. In reality, the estimation of R0 requires a complex infrastructure of disease surveillance and epidemiological analytics to produce a range of working values, which are still, at best, estimations. However, the foundational principle remains that R0 is affected by 3 main variables: (1) duration of contagious period per infected individual—with longer contagious periods increasing the R0; (2) probability of spread with each contact between a contagious individual and a susceptible individual—with higher likelihood increasing the R0; and (3) contact rate. On March 16, 2020 the Imperial College in London published their influential report, which is at its core a variant of the “SIR model.”6 Using an individual-based simulation,7 high-resolution population-density mapping was utilized to simulate and model human. In our opinion, this represents a mathematically rigorous approach to estimating various outcomes of the pandemic. As with all models, the SIR model is limited by the assumptions that must be made regarding early disease spread, healthcare resource utilization, and pathogen characteristics.8 THE INSTITUTE FOR HEALTH METRICS AND EVALUATION (IHME) MODEL The University of Washington IHME model has been widely publicized throughout the pandemic and has been used by all governmental levels. While its ease of use provides multiple advantages, it should be noted that the methodology is significantly different from the rigorous mathematical simulations described above and has several important limitations. Data for the IHME model were compiled by searching websites, the World Health Organization (WHO) along with government agencies, for updated data regarding deaths from SARS-CoV-2. After a region reached a specific threshold of 0.31 daily deaths per million persons, a normally distributed curve was fit to forecast the ongoing death rate over the ensuing weeks. The slope and peak of the regional curve were estimated based solely on the correlation between mortality and patient age in reports published in China,9 South Korea,10 Italy,11 and the United States.12 Assuming that the same relationship between age and mortality applies in all settings, the region-specific pandemic curve was constructed based on the age distribution of that region's population. The daily death rate was utilized to project an implied estimate for hospitalization and intensive care unit (ICU) admission rates.9 Thresholds for maximal hospital and critical care capacity were based on data collected by the American Hospital Association.13 Lastly, state websites were searched for announcements of social-distancing mandates. The curve was modified based on the number of days between reaching the threshold death rate and implementation of mandated social-distancing measures. The presence of these interventions resulted in a standardized decrease in the projected daily death rate within the model, with further incremental decreases after the addition of each accumulative measure. Thus, large swings can be seen in regional projections surrounding the timing of when public health measures are implemented. In comparison to the mathematical simulation models discussed above, the IHME model is more of a “top-down” approach working backward from a single reported value—the daily death rate. We propose that while this design allows for a dynamic region-specific model, it is also prone to inaccuracy due to its heavy reliance on mortality rates having been correctly reported. CONCLUSION Certainly, not all epidemiological models are equal in terms of methodology and should not be accepted or rejected as a single category of studies. As neurosurgical services begin to return to a more “normal” practice in the upcoming months, an understanding of the workings of these epidemiological models will be of great importance for the scientific and critical interpretation of their disparate forecasts. Disclosures This study was completed while Jacob R. Lepard, MD, was a Wilson Family Clinical Scholar. The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article." @default.
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• W3037353436 date "2020-06-25" @default.
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• W3037353436 title "Letter: Neurosurgeons and Curves: The Need for Critical Appraisal of Modeling in the Post-COVID Era" @default.
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• W3037353436 doi "https://doi.org/10.1093/neuros/nyaa298" @default.
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