Spread and control of COVID-19 in China and their associations with population movement, public health emergency measures, and medical resources

Ying, Songmin; Li, Fēi; Geng, Xinwei; Li, Zhouyang; Du, Xufei; Chen, Haixia; Chen, Sisi; Zhang, Min; Shao, Zhehua; Yin-fang, WU; Syeda, Madiha Zahra; Yan, Fugui; Che, Luanqing; Zhang, Bin; Lou, Jian; Wang, Shaobin; Chen, Zhengming; Wen, Li; Shen, Ye; Chen, Zhihua; Shen, Huahao

doi:10.1101/2020.02.24.20027623

Cited by 20 publications

(19 citation statements)

References 16 publications

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“…The best available scientific evidence is therefore required to design effective NPI strategies and disseminate this knowledge urgently to help policy makers assess the potential benefits and costs of NPIs to contain COVID-19 outbreaks. Some previous studies have preliminarily explored the lockdown of Wuhan, [25][26][27] travel restrictions, [28][29][30] airport screening, 31,32 and the isolation of cases and contact tracing for containing virus transmission, respectively. 33,34 The conclusions of these studies are persuasive, there are still key knowledge gaps on the effectiveness of different interventions.…”

Section: Introductionmentioning

confidence: 99%

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China

Lai

Ruktanonchai

Zhou

et al. 2020

Preprint

228

231

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BackgroundThe COVID-19 outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective. Quantitative research is still needed however to assess the efficacy of different candidate NPIs and their timings to guide ongoing and future responses to epidemics of this emerging disease across the World. MethodsWe built a travel network-based susceptible-exposed-infectious-removed (SEIR) model to simulate the outbreak across cities in mainland China. We used epidemiological parameters estimated for the early stage of outbreak in Wuhan to parameterise the transmission before NPIs were implemented. To quantify the relative effect of various NPIs, daily changes of delay from illness onset to the first reported case in each county were used as a proxy for the improvement of case identification and isolation across the outbreak. Historical and near-real time human movement data, obtained from Baidu location-based service, were used to derive the intensity of travel restrictions and contact reductions across China. The model and outputs were validated using daily reported case numbers, with a series of sensitivity analyses conducted. FindingsWe estimated that there were a total of 114,325 COVID-19 cases (interquartile range [IQR] 76,776 -164,576) in mainland China as of February 29, 2020, and these were highly correlated (p<0.001, R 2 =0.86) with reported incidence.Without NPIs, the number of COVID-19 cases would likely have shown a 67-fold increase (IQR: 44 -94), with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to prevent more infections than travel restrictions and contact reductions, but integrated NPIs would achieve the strongest and most rapid effect. If NPIs could have been conducted one week, two weeks, or three weeks earlier in China, cases could have been reduced by 66%, 86%, and 95%, respectively, together with significantly reducing the number of affected areas. However, if NPIs were conducted one week, two weeks, or three weeks later, the number of cases . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.is the (which was not peer-reviewed) The copyright holder for this preprint .

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Section: Introductionmentioning

confidence: 99%

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China

Lai

Ruktanonchai

Zhou

et al. 2020

Preprint

228

231

View full text Add to dashboard Cite

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“…Furthermore, it was also the earliest date when systematic data for all provinces were available. In the second stage after February 2020, the number of covid-19 cases largely represents further local diffusion in various provinces and the number would be largely determined by the initial cases transferred from Hubei The spread of covid-19 is related to both the spatial configuration and the population flows between Wuhan and other provinces (Alirol et al 2010;Chen et al 2020;Ying et al 2020;Kraemer et al 2020). It may also be related to population and economic size of a province, population density, urbanization level, income level and economic growth rate (Coker et al 2011;Tong et al 2015;Wu et al 2017).…”

Section: Research Methodology and Datamentioning

confidence: 99%

“…They indicated that population movement might be one important trigger of covid-19 infection transmission in China. Ying et al (2020) conducted Spearman's correlation analysis between official data of confirmed covid-19 cases, and real-time travel data and health resources data for the period 20 January to 19 February 2020 for provinces and cities, reporting high correlation coefficients. Kraemer et al (2020) found positive correlation between the growth rate of covid-19 cases and the population flow from Wuhan in the period 9-22 January 2020.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the determinants of the spread of covid-19 among the provincial regions in China

Shen

2020

Preprint

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China has experienced the spatial diffusion of covid-19 from Wuhan since December 2019. This research examines the relationship between the geographical, social and economic factors and the number of covid-19 cases on 26 January, 7 February, 20 February and 6 March 2020 in mainland China. Both correlation and regression analyses show that the migrants who moved into Hubei in 2005-2010 is a good indicator of the population flow from Hubei to other provinces that caused the spread of covid-19 in early 2020. Many migrants travelled back to hometown just before the traffic ban of Wuhan city for the spring holiday. Thus the migration flow from a province to Hubei in the period 2005-2010 had the highest correlation coefficient with the number of covid-19 cases in four selected dates. The population flow data from the Baidu map on 20 January 2020 were also highly correlated with the number of cases, but not as good as above migration data. The regression equation for the number of cases on 26 January 2020 had the highest adj R2 as it was mainly determined by the population flow from Hubei. The numbers of cases in subsequent days were also affected by the local diffusion and the control measure in various provinces. Income and economic variables became additional explanatory variables indicating their complicated impacts on the mitigation measures at various provinces in China. The results of this research have important policy implications to respond to the covid-19 pandemic.

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“…However, we also see good examples like Shanghai, Fujian and Shanxi, which were able to reduce the growth rate consistently with a low variance. These provinces benefited from experience obtained in the fight against the 2003 SARS outbreak or enjoy richer local medical resources [34]. This enabled the government to identify as many infected/suspected cases as possible in order to contain continuously the local transmissions.…”

Section: Heterogeneity Of the Development Of The Epidemic And Responsmentioning

confidence: 99%

Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world

et al. 2020

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Started in Wuhan, China, the COVID-19 has been spreading all over the world. We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 33 countries and regions that have been or are undergoing major outbreaks. We dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogeneous situations across provinces. The extreme containment measures implemented by China were very effective with some instructive variations across provinces. Borrowing from the experience of China, we made scenario projections on the development of the outbreak in other countries. We identified that outbreaks in 14 countries (mostly in western Europe) have ended, while resurgences of cases have been identified in several among them. The modeling results clearly show longer after-peak trajectories in western countries, in contrast to most provinces in China where the after-peak trajectory is characterized by a much faster decay. We identified three groups of countries in different level of outbreak progress, and provide informative implications for the current global pandemic.

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Spread and control of COVID-19 in China and their associations with population movement, public health emergency measures, and medical resources

Cited by 20 publications

References 16 publications

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China

Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China

Analyzing the determinants of the spread of covid-19 among the provincial regions in China

Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world

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