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Open AccessJournal ArticleDOI

Strategies for mitigating an influenza pandemic

TLDR
It is found that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2–3 weeks unless more than 99% effective, and vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy.
Abstract
Development of strategies for mitigating the severity of a new influenza pandemic is now a top global public health priority. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine and non-pharmaceutical (case isolation, household quarantine, school or workplace closure, restrictions on travel) measures. Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options. Here we use a large-scale epidemic simulation to examine intervention options should initial containment of a novel influenza outbreak fail, using Great Britain and the United States as examples. We find that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2-3 weeks unless more than 99% effective. School closure during the peak of a pandemic can reduce peak attack rates by up to 40%, but has little impact on overall attack rates, whereas case isolation or household quarantine could have a significant impact, if feasible. Treatment of clinical cases can reduce transmission, but only if antivirals are given within a day of symptoms starting. Given enough drugs for 50% of the population, household-based prophylaxis coupled with reactive school closure could reduce clinical attack rates by 40-50%. More widespread prophylaxis would be even more logistically challenging but might reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy. Estimates of policy effectiveness will change if the characteristics of a future pandemic strain differ substantially from those seen in past pandemics.

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Social contacts and mixing patterns relevant to the spread of infectious diseases.

TL;DR: This study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.
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Pandemics, tourism and global change: a rapid assessment of COVID-19

TL;DR: Why COVID-19 is an analogue to the ongoing climate crisis, and why there is a need to question the volume growth tourism model advocated by UNWTO, ICAO, CLIA, WTTC and other tourism organizations are discussed.
Journal ArticleDOI

Natural Scales in Geographical Patterns

TL;DR: The detection of phase transitions constitutes the first objective method of characterising endogenous, natural scales of human movement and allows us to draw discrete multi-scale geographical boundaries, potentially capable of providing key insights in fields such as epidemiology or cultural contagion.
References
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Book

Infectious Diseases of Humans: Dynamics and Control

TL;DR: This book discusses the biology of host-microparasite associations, dynamics of acquired immunity heterogeneity within the human community indirectly transmitted helminths, and the ecology and genetics of hosts and parasites.
Journal ArticleDOI

The scaling laws of human travel

TL;DR: It is shown that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and concluded that human travel on geographical scales is an ambivalent and effectively superdiffusive process.
Book

Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation

TL;DR: The Basic Reproduction Ratio (RBR) as mentioned in this paper is a variant of the Kermack-McKendrick ODE model, and it has been used to model population dynamics at the Demographic Time Scale.
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