مقاله انگلیسی کووید-19: کالیبراسیون مدل مکانیکی مداخلات غیر دارویی
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مقاله انگلیسی کووید-19: کالیبراسیون مدل مکانیکی مداخلات غیر دارویی

عنوان فارسی مقاله: کووید-19: کالیبراسیون مدل مکانیکی منوط به مداخلات غیر دارویی فعال و متفاوت
عنوان انگلیسی مقاله: COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions
مجله/کنفرانس: علوم مهندسی شیمی - Chemical Engineering Science
رشته های تحصیلی مرتبط: مهندسی شیمی و داروشازی
گرایش های تحصیلی مرتبط: بیوتکنولوژی دارویی
کلمات کلیدی فارسی: کالیبراسیون مدل ، COVID-19 ، مدل سازی و مدیریت رویداد گسسته ، سرعت انتقال بیماری متغیر با زمان
کلمات کلیدی انگلیسی: Model calibration, COVID-19, Discrete event modelling and management, Time varying disease transmission rate
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ces.2020.116330
دانشگاه: Newcastle University, UK
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2021
ایمپکت فاکتور: 3.871 در سال 2020
شاخص H_index: 175 در سال 2021
شاخص SJR: 0.998 در سال 2020
شناسه ISSN: 0009-2509
شاخص Quartile (چارک): Q1 در سال 2020
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E15387
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
نوع رفرنس دهی: vancouver
فهرست انگلیسی مطالب

Highlights


Abstract


Keywords


1. Introduction


2. Methods


3. Results


4. Discussion and conclusions


CRediT authorship contribution statement


Declaration of Competing Interest


References

نمونه متن انگلیسی مقاله

Abstract


Mathematical models are useful in epidemiology to understand COVID-19 contagion dynamics. We aim to demonstrate the effectiveness of parameter regression methods to calibrate an established epidemiological model describing infection rates subject to active, varying non-pharmaceutical interventions (NPIs). We assess the potential of established chemical engineering modelling principles and practice applied to epidemiological systems. We exploit the sophisticated parameter regression functionality of a commercial chemical engineering simulator with piecewise continuous integration, event and discontinuity management. We develop a strategy for calibrating and validating a model. Our results using historic data from 4 countries provide insights into on-going disease suppression measures, while visualisation of reported data provides up-to-date condition monitoring of the pandemic status. The effective reproduction number response to NPIs is non-linear with variable response rate, magnitude and direction. Our purpose is developing a methodology without presenting a fully optimised model, or attempting to predict future course of the COVID-19 pandemic.


 


1. Introduction


COVID-19 is currently a global pandemic affecting around 213 countries around the world. As of 31 August 2020, 25.6 million cases, with 17.9 million recovered patients and 859,550 deaths have been reported (Worldometer, 2020). To control the pandemic, most governments have issued recommendations such as intensified hand hygiene and have taken measures such as closing borders, enforcing lockdowns, etc. These NPIs reduce infection rates, keeping the number of severe cases below hospital capacity limit, a strategy popularly referred to as ‘flattening the curve’. A significant challenge is to identify and efficiently evaluate the effect that active and varying NPIs have on the disease transmission rate. This is particularly important as countries begin to relax NPIs after successfully flattening the curve of active cases.


1.1. The effective reproduction number


Key parameters used to quantify contagion are the basic and effective reproduction numbers. These dimensionless numbers describe the average number of expected secondary infections generated by each infected person in the absence and presence of controlled interventions. Current opinion suggests that the COVID-19 has a basic reproduction number ~2–3. Although a recent review (Liu et al., 2020) compared twelve studies published from the 1st of January to the 7th of February 2020 which reported a range of values for the COVID-19 basic reproduction number between 1.5 and 6.68. This apparent disparity arises because the reported number depends on country, culture, the stage of the outbreak and calculation method used. NPIs aim to slow the spread of the virus and reduce the effective reproduction number to a sustained value less than one so that the pandemic will eventually die out. Scientists and governments in many countries around the world use the effective reproduction number as an illustrative metric to explain and justify the introduction and relaxation of NPIs (Fauci et al., 2020).

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