pharmaceutical interventions
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Yong Ge ◽  
Wen-Bin Zhang ◽  
Haiyan Liu ◽  
Corrine W Ruktanonchai ◽  
Maogui Hu ◽  

2022 ◽  
Vol 16 (1) ◽  
pp. e0010101
Hao Li ◽  
Luqi Wang ◽  
Mengxi Zhang ◽  
Yihan Lu ◽  
Weibing Wang

Many countries implemented measures to control the COVID-19 pandemic, but the effects of these measures have varied greatly. We evaluated the effects of different policies, the prevalence of dominant variants (e.g., Delta), and vaccination on the characteristics of the COVID-19 pandemic in eight countries. We quantified the lag times of different non-pharmaceutical interventions (NPIs) and vaccination using a distributed lag non-linear model (DLNM). We also tested whether these lag times were reasonable by analyzing changes in daily cases and the effective reproductive number (Rt)over time. Our results indicated that the response to vaccination in countries with continuous vaccination programs lagged by at least 40 days, and the lag time for a response to NPIs was at least 14 days. A rebound was most likely to occur during the 40 days after the first vaccine dose. We also found that the combination of school closure, workplace closure, restrictions on mass gatherings, and stay-at-home requirements were successful in containing the pandemic. Our results thus demonstrated that vaccination was effective, although some regions were adversely affected by new variants and low vaccination coverage. Importantly, relaxation of NPIs soon after implementation of a vaccination program may lead to a rebound.

2022 ◽  
Vol 12 (1) ◽  
Elizabeth Goult ◽  
Shubha Sathyendranath ◽  
Žarko Kovač ◽  
Christina Eunjin Kong ◽  
Petar Stipanović ◽  

AbstractIn the absence of an effective vaccine or drug therapy, non-pharmaceutical interventions are the only option for control of the outbreak of the coronavirus disease 2019, a pandemic with global implications. Each of the over 200 countries affected has followed its own path in dealing with the crisis, making it difficult to evaluate the effectiveness of measures implemented, either individually, or collectively. In this paper we analyse the case of the south Indian state of Kerala, which received much attention in the international media for its actions in containing the spread of the disease in the early months of the pandemic, but later succumbed to a second wave. We use a model to study the trajectory of the disease in the state during the first four months of the outbreak. We then use the model for a retrospective analysis of measures taken to combat the spread of the disease, to evaluate their impact. Because of the differences in the trajectory of the outbreak in Kerala, we argue that it is a model worthy of a place in the discussion on how the world might best handle this and other, future, pandemics.

Sara Saadatmand ◽  
Khodakaram Salimifard ◽  
Reza Mohammadi

2022 ◽  
John Harvey ◽  
Bryan Chan ◽  
Tarun Srivastava ◽  
Alexander E. Zarebski ◽  
Pawel Dlotko ◽  

Introduction: A discussion of 'waves' of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous. Methods: We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as 'observed waves'. This provides an objective means of describing observed waves in time series. Results: The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of non-pharmaceutical interventions correlates with a reduced number of observed waves and reduced mortality burden in those waves. Conclusion: It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.

2022 ◽  
Vol 27 (1) ◽  
Lloyd A C Chapman ◽  
Rosanna C Barnard ◽  
Timothy W Russell ◽  
Sam Abbott ◽  
Kevin van Zandvoort ◽  

We estimate the potential remaining COVID-19 hospitalisation and death burdens in 19 European countries by estimating the proportion of each country’s population that has acquired immunity to severe disease through infection or vaccination. Our results suggest many European countries could still face high burdens of hospitalisations and deaths, particularly those with lower vaccination coverage, less historical transmission and/or older populations. Continued non-pharmaceutical interventions and efforts to achieve high vaccination coverage are required in these countries to limit severe COVID-19 outcomes.

2022 ◽  
Vol 22 (1) ◽  
Matthew J. Silk ◽  
Simon Carrignon ◽  
R. Alexander Bentley ◽  
Nina H. Fefferman

Abstract Background Individual behavioural decisions are responses to a person’s perceived social norms that could be shaped by both their physical and social environment. In the context of the COVID-19 pandemic, these environments correspond to epidemiological risk from contacts and the social construction of risk by communication within networks of friends. Understanding the circumstances under which the influence of these different social networks can promote the acceptance of non-pharmaceutical interventions and consequently the adoption of protective behaviours is critical for guiding useful, practical public health messaging. Methods We explore how information from both physical contact and social communication layers of a multiplex network can contribute to flattening the epidemic curve in a community. Connections in the physical contact layer represent opportunities for transmission, while connections in the communication layer represent social interactions through which individuals may gain information, e.g. messaging friends. Results We show that maintaining focus on awareness of risk among each individual’s physical contacts promotes the greatest reduction in disease spread, but only when an individual is aware of the symptoms of a non-trivial proportion of their physical contacts (~ ≥ 20%). Information from the social communication layer without was less useful when these connections matched less well with physical contacts and contributed little in combination with accurate information from physical contacts. Conclusions We conclude that maintaining social focus on local outbreak status will allow individuals to structure their perceived social norms appropriately and respond more rapidly when risk increases. Finding ways to relay accurate local information from trusted community leaders could improve mitigation even where more intrusive/costly strategies, such as contact-tracing, are not possible.

2022 ◽  
Nicolò Gozzi ◽  
Matteo Chinazzi ◽  
Jessica T. Davis ◽  
Kunpeng Mu ◽  
Ana Pastore y Piontti ◽  

We develop a stochastic, multi-strain, compartmental epidemic model to estimate the relative transmissibility and immune escape of the Omicron variant of concern (VOC) in South Africa. The model integrates population, non-pharmaceutical interventions, vaccines, and epidemiological data and it is calibrated in the period May 1st, 2021 - November 23rd, 2021. We explore a parameter space of relative transmissibility with respect to the Delta variant and immune escape for Omicron by assuming an initial seeding, from unknown origin, in the first week of October 2021. We identify a region of the parameter space where combinations of relative transmissibility and immune escape are compatible with the growth of the epidemic wave. We also find that changes in the generation time associated with Omicron infections strongly affect the results concerning its relative transmissibility. The presented results are informed by current knowledge of Omicron and subject to changes.

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