Effectiveness of non-pharmaceutical interventions on COVID-19 transmission in 190 countries from 23 January to 13 April 2020

Bo, Yacong; Guo, Cui; Lin, Changqing; Zeng, Yiqian; Li, Hao Bi; Zhang, Yumiao; Hossain, Shakhaoat; Chan, Jimmy W.M.; Yeung, David W. K.; Kwok, Kin On; Wong, Samuel Yeung Shan; Lau, Alexis K.H.; Lao, Xiang Qian

doi:10.1016/j.ijid.2020.10.066

Cited by 273 publications

(265 citation statements)

References 23 publications

Supporting

Mentioning

252

Contrasting

Unclassified

Order By: Relevance

“…The list does not come as surprise considering the epicenter of the pandemic, data availability as well as the evolution of the pandemic in USA and Europe. However, few models in the sample consider hundreds of countries [22] , [44] , [45] , [45] , [46] , [47] . Second, this body of research has been published by authors with affiliations in

countries.…”

Section: Epidemic Modelsmentioning

confidence: 99%

“…These approaches have been applied to the COVID-19 pandemic to gather information about the key epidemiological parameters and quantify the effects of NPIs by looking at deviations from expected and/or initial trends [24] , [46] , [124] , [125] , [126] , [127] , [129] , [130] , [131] , [132] , [133] , [134] . Let’s dive into some details.…”

Section: Epidemic Modelsmentioning

confidence: 99%

“…Authors in Refs. [46] , [126] , [129] , [131] adopt and/or extend the approach developed in [155] , [156] to estimate

. This method is readily available in Python and R and is called EpiEstim .…”

Section: Epidemic Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

2021

View full text Add to dashboard Cite

Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 of authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.

show abstract

countries.…”

Section: Epidemic Modelsmentioning

confidence: 99%

Section: Epidemic Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

2021

View full text Add to dashboard Cite

show abstract

“…22 This good news does not mean that awareness about transmission of the disease is unnecessary anymore. A study revealed the importance of non-pharmaceutical interventions (NPIs) on reducing the reproduction number of COVID-19 virus 23 but may limit data in Indonesia about it. Understanding KAP is essential to identify and overcome false rumours about a disease that may negatively influence community prevention behaviour, including NPI.…”

Section: Introductionmentioning

confidence: 99%

Knowledge, Attitudes, Practices and Information Needs During the COVID-19 Pandemic in Indonesia

Sulistyawati¹,

Rokhmayanti²,

Aji³

et al. 2021

RMHP

101

View full text Add to dashboard Cite

“…In the absence of specific medicine and vaccine to counter the virus, non-pharmaceutical interventions (NPIs) play an important role to control epidemics. Bo et al 8 divides the implemented NPIs into four categories: mandatory face mask in public, isolation or quarantine, social distancing and traffic restriction. There have been several researches to evaluate the effectiveness of these NPIs.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China

Chen

Karimian

et al. 2021

Sci Rep

View full text Add to dashboard Cite

In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China’s non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = − 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics.

show abstract

Effectiveness of non-pharmaceutical interventions on COVID-19 transmission in 190 countries from 23 January to 13 April 2020

Abstract: Highlights Non-pharmaceutical interventions was effective in reducing COVID-19 transmission. Social distancing is more effective than the other NPIs in containing COVID-19. Two or more synchronous NPIs are more effective than a single type of NPIs.

Cited by 273 publications

References 23 publications

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Knowledge, Attitudes, Practices and Information Needs During the COVID-19 Pandemic in Indonesia

Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China

Contact Info

Product

Resources

About