Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models

Lin, Yi; Duan, Qibin; Zhou, Yiguo; Yuan, Tanwei; Li, Peiyang; Fitzpatrick, Thomas; Fu, Leiwen; Feng, Anping; Luo, Ganfeng; Zhan, Yuewei; Liang, Bowen; Fan, Saijun; Lü, Yong; Wang, Bingyi; Wang, Zhenyu; Zhao, Heping; Gao, Yanxiao; Li, Meijuan; Chen, Dahui; Rothenberg, Marc E.; Ao, Yunlong; Li, Linghua; Cai, Weiping; Du, Xiangjun; Shu, Yuelong; Zou, Huachun

doi:10.3389/fmed.2020.00321

Cited by 49 publications

(57 citation statements)

References 59 publications

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“…This new coronavirus has been found to have 88%-96% sequence similarity to bat coronaviruses in its genome, according to other pathogenic coronaviruses such as SARS-CoV and MERS-CoV. 3,4 This new SARS-CoV-2 coronavirus is a pathogen that has shown a high potential for contagion, 5,6 with personto-person transmission. The primary way the virus disseminates 7 seems to be close contact between individuals, with transmission by respiratory droplets spread by coughing or sneezing.…”

Section: Introductionmentioning

confidence: 95%

Comparison of Growth Patterns of COVID-19 Cases through the ARIMA and Gompertz Models. Case Studies: Austria, Switzerland, and Israel

Pérez

Otin

Martín

et al. 2020

Rambam Maimonides Med J

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On May 19, 2020, data confirmed that coronavirus 2019 disease (COVID-19) had spread worldwide, with more than 4.7 million infected people and more than 316,000 deaths. In this article, we carry out a comparison of the methods to calculate and forecast the growth of the pandemic using two statistical models: the autoregressive integrated moving average (ARIMA) and the Gompertz function growth model. The countries that have been chosen to verify the usefulness of these models are Austria, Switzerland, and Israel, which have a similar number of habitants. The investigation to check the accuracy of the models was carried COVID-19 Growth Patterns Comparison: ARIMA and Gompertz Models Rambam Maimonides Medical Journal 2 July 2020  Volume 11  Issue 3  e0022 out using data on confirmed, non-asymptomatic cases and confirmed deaths from the period February 21-May 19, 2020. We use the root mean squared error (RMSE), the mean absolute percentage error (MAPE), and the regression coefficient index R 2 to check the accuracy of the models. The experimental results provide promising adjustment errors for both models (R 2 >0.99), with the ARIMA model being the best for infections and the Gompertz best for mortality. It has also been verified that countries are affected differently, which may be due to external factors that are difficult to measure quantitatively. These models provide a fast and effective system to check the growth of pandemics that can be useful for health systems and politicians so that appropriate measures are taken and countries' health care systems do not collapse.

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Section: Introductionmentioning

confidence: 95%

Comparison of Growth Patterns of COVID-19 Cases through the ARIMA and Gompertz Models. Case Studies: Austria, Switzerland, and Israel

Pérez

Otin

Martín

et al. 2020

Rambam Maimonides Med J

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“…The copyright holder for this preprint this version posted August 1, 2020. ; https://doi.org/10.1101/2020.07.28.20164087 doi: medRxiv preprint Lin et al [6] aim to summarize the mathematical models that have been developed to understand and predict the infectiousness of COVID-19 to inform efforts to manage the outbreak in China. The authors searched various article bases for relevant studies published between 1 st December, 2019 and 21 st , February, 2020, as well as built a Gaussian simulation for the evolution of the epidemic in Wuhan specifically.…”

Section: Absolute Percentage Error (Mape) Mean Absolute Deviation (Mmentioning

confidence: 99%

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling

Carvalho

2020

Preprint

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Background: Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. Objective: This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. Methods: The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. Results: Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. Conclusions: We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent Reproduction Numbers (Rt), as well as we demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

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“…This reflects the fact that without non-pharmaceutical mitigation measures (NPMM) such as masks, physical distance between individuals, better air ventilation and hygiene, and without contact tracing, the infection rates would return to the initial exponential increase. This was reflected in a reproduction rate of 3.3 to 3.8, as observed at the beginning of the pandemic (Lin et al (2020), and Alimohamadi et al (2020), RKI (2020)). However, the presence of NPMM also mitigates the effect of the increase in contacts due to the return to office, opening of shops, restaurants, as well as schools, and venues visited by young adults, leaving it far from the initial impact.…”

Section: Discussionmentioning

confidence: 91%

The Effect of Gender on Covid-19 Infections and Mortality in Germany: Insights From Age- and Sex-Specific Modelling of Contact Rates, Infections, and Deaths

Dörre

Doblhammer

2020

Preprint

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Background: Recent research points towards age- and sex-specific transmission of COVID-19 infections and their outcomes. The effect of sex, however, has been overlooked in past modelling approaches of COVID-19 infections. Aim: The aim of our study is to develop an age- and sex-specific model of COVID-19 transmission and to explore how contact changes effect COVID-19 infection and death rates. Method: We consider a compartment model to establish forecasts of the COVID-19 epidemic, in which the compartments are subdivided into different age groups and genders. Estimated contact patterns, based on other studies, are incorporated to account for age- and sex-specific social behaviour. The model is fitted to real data and used for assessing hypothetical scenarios with regard to lockdown measures. Results: Under current mitigation measures as of mid-August, active COVID-19 cases will double by the end of October 2020. Infection rates will be highest among the young and working ages, but will also rise among the old. Sex ratios reveal higher infection risks among women than men at working ages; the opposite holds true at old age. Death rates in all age groups are twice as high among men as women. Small changes in contact rates at working and young ages may have a considerable effect on infections and mortality at old age, with elderly men being always at higher risk of infection and mortality. Discussion: Our results underline the high importance of the non-pharmaceutical mitigation measures in the current phase of the pandemic to prevent that an increase in contact rates leads to higher mortality among the elderly. Gender differences in contact rates, in addition to biological mechanisms related to the immune system, may contribute to sex-specific infection rates and their mortality outcome. To further explore possible pathways, more data on COVID-19 transmission is needed which includes socio-demographic information.

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Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models

Cited by 49 publications

References 59 publications

Comparison of Growth Patterns of COVID-19 Cases through the ARIMA and Gompertz Models. Case Studies: Austria, Switzerland, and Israel

Comparison of Growth Patterns of COVID-19 Cases through the ARIMA and Gompertz Models. Case Studies: Austria, Switzerland, and Israel

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling

The Effect of Gender on Covid-19 Infections and Mortality in Germany: Insights From Age- and Sex-Specific Modelling of Contact Rates, Infections, and Deaths

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