COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions

Willis, M.J.; Wright, A.R.; Bramfitt, Victoria; Díaz, Víctor Hugo Grisales

doi:10.1016/j.ces.2020.116330

Cited by 10 publications

(16 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the outbreak of COVID-19, researchers worldwide have been carrying out a lot of research works on it. These researches can be mainly divided into the following six categories: (1) to study the impact of COVID-19 on human physical and mental health from a biomedical perspective ( Tsamakis et al, 2020 , Xiong et al, 2020 , Pascoal et al, 2021 ); (2) to study the impact of COVID-19 on human production, life, and social and economic development from a sociological perspective ( Takyi and Bentum-Ennin, 2020 , Qian et al, 2021 , Shang et al, 2021 , Beiderbeck et al, 2021 , Jiang et al, 2021 ); (3) to creatively propose new mathematical models or revise some existing models based on relevant data for predicting and analyzing the development of the epidemic in a specific area ( Vianello et al, 2021 , Willis et al, 2021 , Mun and Geng, 2021 , Al-qaness et al, 2021 , Manenti et al, 2020 , Hu et al, 2020 , Cao et al, 2020 , Mojjada et al, 2020 , Yang et al, 2020 ); (4) to analyze the spatial-temporal characteristics of the epidemic in a specific area ( Lv and Cheng, 2020 , Feng et al, 2020 ); (5) to explore related factors which may affect the development of the epidemic ( Hu et al, 2021 ); (6) to evaluate the effects of different epidemic prevention measures ( Leung et al, 2020 , Hasnain et al, 2020 ). In terms of the research purpose and content, the third, the fourth, and the fifth categories are more relevant to the work carried out in this paper.…”

Section: Related Workmentioning

confidence: 99%

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Jin

Yang

et al. 2021

Process Safety and Environmental Protection

View full text Add to dashboard Cite

COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial( Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.

show abstract

Section: Related Workmentioning

confidence: 99%

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Jin

Yang

et al. 2021

Process Safety and Environmental Protection

View full text Add to dashboard Cite

show abstract

“…In our earlier work (Willis, Diaz, Prado-Rubio, & von Stosch, 2020) (Willis, Wright, Bramfitt, & Diaz, 2021) and other previous work (Linka, Peirlinck, Sahli Costabal, & Kuhl, 2020) (Manenti, et al, 2020), the data selected for the objective function used cumulative figures for dead, infected and recovered individuals. We have observed that there were limitations in the accuracy of reported data, in particular for the number of recovered individuals to the extent that there was an apparent imbalance in the system.…”

Section: Reported Case Data Used For Model Calibrationmentioning

confidence: 99%

“…The optimisation procedure uses jacketing of the optimisation algorithm to perturb the solution to allow optimisation to progress when local minima are encountered. As the value of in equation ( 13) is a scaling factor for temperature in all model calibrations we fixed this value as, = 5.859 × 10 3 , a value found in our previous modelling work (Willis, Wright, Bramfitt, & Diaz, 2021) when calibrating a model to reported case data.…”

Section: Model Calibration Sequencementioning

confidence: 99%

“…In this paper we extend our modelling work (Willis, Wright, Bramfitt, & Diaz, 2021) examining the effects of COVID-19 in the UK. We apply our modelling approach to calibrate the characteristic rates of infection and removal of individuals and estimate the basic and effective reproduction numbers using historical UK data.…”

Section: Introductionmentioning

confidence: 95%

“…In recent work (Willis, Wright, Bramfitt, & Diaz, 2021) we demonstrated that the well-known susceptible -infected -removed (…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mechanistic model calibration and the dynamics of the COVID-19 epidemic in the UK (the past, the present and the future)

Willis

Wright

Bramfitt

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We augment the well-known susceptible - infected - recovered - deceased (SIRD) epidemiological model to include vaccination dynamics, implemented as a piecewise continuous simulation. We calibrate this model to reported case data in the UK at a national level. Our modelling approach decouples the inherent characteristics of the infection from the degree of human interaction (as defined by the effective reproduction number, Re). This allows us to detect and infer a change in the characteristic of the infection, for example the emergence of the Kent variant, We find that that the infection rate constant (k) increases by around 89% as a result of the B.1.1.7 (Kent) COVID-19 variant in England. Through retrospective analysis and modelling of early epidemic case data (between March 2020 and May 2020) we estimate that ~1.2M COVID-19 infections were unreported in the early phase of the epidemic in the UK. We also obtain an estimate of the basic reproduction number as, R0=3.23. We use our model to assess the UK Government's roadmap for easing the third national lockdown as a result of the current vaccination programme. To do this we use our estimated model parameters and a future forecast of the daily vaccination rates of the next few months. Our modelling predicts an increased number of daily cases as NPIs are lifted in May and June 2021. We quantify this increase in terms of the vaccine rollout rate and in particular the percentage vaccine uptake rate of eligible individuals, and show that a reduced take up of vaccination by eligible adults may lead to a significant increase in new infections.

show abstract

Two chemical engineers look at the COVID‐19 pandemic

Visscher

Patrício

2022

Can J Chem Eng

View full text Add to dashboard Cite

Chemical engineering involves a skill set that is transferrable to a broad range of other areas. A case in point is the work that is being done by chemical engineers to better understand and fight the COVID‐19 epidemic. In this study, we consider a problem that has eluded the COVID‐19 research community, which is nevertheless very tractable with a chemical engineering mindset: the true or intrinsic mortality rate of COVID‐19, i.e., the fraction or percentage of COVID‐19 infected people that die of the disease. We solve this problem in two locations (Spain and the state of New York) for the epidemic's first wave with a combination of daily death data, a fit of a computer simulation of an epidemiological model with adjustable parameters, and independent results of immunological blood testing on a random sample of the population. Parallels are drawn with the problem of determining the turnover frequency of a catalyst based on a similar combination of data and approaches. It is concluded from the study that the intrinsic mortality rate of COVID‐19 was 1.45 ± 0.45 % during the first wave, a number that reflects OECD countries. By incorporating data on the age dependence of the mortality rate, a relationship f mort = (3.0 ± 0.7)×10 −5 exp(0.1 a ), where a is the age in years, is tentatively put forward for the mortality rate as a fraction. This article is protected by copyright. All rights reserved.

show abstract

COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions

Cited by 10 publications

References 12 publications

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

Mechanistic model calibration and the dynamics of the COVID-19 epidemic in the UK (the past, the present and the future)

Two chemical engineers look at the COVID‐19 pandemic

Contact Info

Product

Resources

About