Research Article

The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak

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Science  24 Apr 2020:
Vol. 368, Issue 6489, pp. 395-400
DOI: 10.1126/science.aba9757

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  • RE: Evidences of reduction in SARS-C0V-2 transmission due to local measures and policies in early stages of the pandemic
    • Thiago B. Murari, Researcher, Programa de Gestão e Tecnologia Industrial – Centro Universitário SENAI CIMATEC. Salvador, BA, Brazil.
    • Other Contributors:
      • Aloisio S. Nascimento Filho, Researcher, Programa de Gestão e Tecnologia Industrial – Centro Universitário SENAI CIMATEC. Salvador, BA, Brazil.
      • Tarcísio M. Rocha Filho, Professor, International Center for Condensed Matter Physics - UnB
      • Carla A. Scorza, Professor, 3Disciplina de Neurociência, Escola Paulista de Medicina/Universidade Federal de São Paulo (EPM/UNIFESP). São Paulo, SP, Brazil
      • Fulvio A. Scorza, Professor, 3Disciplina de Neurociência, Escola Paulista de Medicina/Universidade Federal de São Paulo (EPM/UNIFESP). São Paulo, SP, Brazil
      • Antônio-Carlos G. de Almeida, Professor, Universidade Federal de São João del-Rei (UFSJ). São João del-Rei, MG, Brazil
      • Marcelo A. Moret, Professor, Programa de Modelagem Computacional – Centro Universitário SENAI CIMATEC. Salvador, BA, Brazil.

    We read with great interest the article by Chinazzi et al. who used a global meta-population disease transmission model to demonstrate that travel limitations could be extremely beneficial on the national and international spread of the COVID-19 pandemic (1). Intensive control measures, including travel restrictions to and from major outbreak areas that act as disease source, were useful to limit the spread of the COVID-19 in China. Also, social isolation and hygiene, rather than long-distance travel restrictions, played the largest part in controlling SARS-CoV-2 spread (2). But Xiao and Torok says there is no value for the prevention and control of COVID-19 in lookdown areas and blocking the traffic (3).
    First, the quick action of some Brazilian mayors and governors contributed positively to the delay in the death rate growth, resulting in even better results for the attack rate (the proportion of individuals that has been infected) when compared to most countries in Europe and North America. Most of the Brazilian states presented an attack rate below 2.3%, while it was much higher in São Paulo (3.3%), Rio de Janeiro (3.35%), Ceará (4.46%), Pernambuco (3%), Pará (5.05%) and Amazonas (10.6%) (4). On the other hand, the attack rates in Bahia (0.4%) was as low as in Germany (0.72%) (5). We note that the average attack rate in the US was 4.1%, including New York with 16.6% and New Jersey with 16.1% attack rates (6), comparable to those of Spain (15%) and Italy (9.8%) (...

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    Competing Interests: None declared.
  • RE: A Fatal Error of COVID-19 Epidemiological Model Data Presentation and Interpretation: Social Distancing is Effective and Must Strictly be Enforced

    No one can escape the fact that worldwide data presentation and epidemiological modeling on COVID-19’s pandemic are expressed in terms of the absolute total new confirmed cases on that specific date such as the work of Matteo et al. (1). This presentation of data and its corresponding interpretations are erroneous and actually not indicative regarding the epidemiology of COVID-19, mitigation measures and flattening the curve (1). This is because the data need to be normalized over the total number of cases tested on that specific date; viz., the larger the pool number of subjects tested the more new confirmed cases will be reported. Hence, all infectious data have to be normalized over the total cases tested on that specific date.
    This can be substantiated by examining the data collected, for example, on New York State (NYS). The raw data were obtained from the New York State Statewide COVID-19 Testing Data Collection (2). Figure1A depicts a plot of the daily new confirmed cases of COVID-19 “Y” in NYS while Figure1B represents its daily corresponding calculated percentage normalized data “%Z” with respect to the total cases tested starting from 12 March until 22 May (3). These numbers were calculated as the ratio of total number of confirmed cases Y to the total number of cases tested at that specific date multiplied by 100%.
    The significance of the shape of the two profiles of Figure1 is that from 12-30 March, the two profiles mimic each other and both are...

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    Competing Interests: None declared.
  • RE: Travel Restriction Confines COVID-19 Outbreak
    • Madiha Zahra Syeda, Postdoctoral Fellow, Zhejiang University, School of Medicine, Hangzhou, China
    • Other Contributors:
      • Liujian Ouyang, MD student, Zhejiang University, School of Medicine, Hangzhou, China
      • Zhihua Chen, Professor, Zhejiang University, School of Medicine, Hangzhou, China
      • Songmin ying, Professor, Zhejiang University, School of Medicine, Hangzhou, China
      • Shen Huahao, Professor, Zhejiang University, School of Medicine, Hangzhou, China

    Travel Restriction Confines COVID-19 Outbreak
    Madiha Zahra Syeda1#, Liujian Ouyang#1,2, Zhihua Chen3, Songmin Ying1*, Huahao Shen3,4*

    1. Department of Pharmacology & Department of Respiratory and Critical Care Medicine of the Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory of Respiratory Disease of Zhejiang Province, Hangzhou 310009,
    2. Chu Kochen Honors College of Zhejiang University
    3. Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China
    4. State Key Laboratory of Respiratory Diseases, Guangzhou, Guangdong 510120, China

    edited
    #These authors have contributed equally
    *Correspondence should be addressed to:
    yings@zju.edu.cn (S.Y.), huahaoshen@zju.edu.cn (H.S.)

    In their publication, Matteo et al. evaluated “The effect of travel restrictions on spread of COVID-19 outbreak” and concluded that travel ban was only modestly-effective in slowing down the spread of virus. Although reasonably designed, their analysis and model is probably overestimated and the conclusion is misleading. Available data on the spread of COVID-19 across China and other countries clearly indicates the effectiveness of travel restrictions by China. For instance...

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    Competing Interests: None declared.
  • RE: Questions about methodology

    Having lived in China for many years, I'm curious about how you arrived at your conclusion that this only delayed the spread of the virus to the rest of China by a few days (or, as you put it, "the model output shows no noticeable differences in the epidemic trajectory of Wuhan, while it shows a delay of about 3 days in the rest of mainland China"). Maybe in general terms, yes, it was already spreading. China waited too long to ban travel to and from Wuhan if it wanted to completely control the spread of the disease. It was too late. It was already going to spread because infected people were already moving throughout the country.

    So far (March 12), Wuhan has had just short of 50,000 confirmed infections and more than 2,436 deaths (https://ncov.dxy.cn/ncovh5/view/pneumonia). But other cities are nowhere near that. Eight of the 436 infected people in Beijing died. Three of 346 in Shanghai, two of 120 Xi'an. Kunming had 0 out of 53. Changchun had 0 of 45. And in the city where I live, Yinchuan, zero of 36. Another nearby city, Guyuan had zero of five. Actually, there were no reported deaths in the entire province. But if these and hundreds more similar cities are all considered "no noticible differences in the epidemic trajectory," I question the model. Is it only used to identify that each city is likely to have cases of the disease? Granted, maybe there are 4 times as many unrepor...

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    Competing Interests: None declared.

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