Existential threats to the Summer Olympic and Paralympic Games? a review of emerging environmental health risks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Annear ◽  
Tetsuhiro Kidokoro ◽  
Yasuo Shimizu
Keyword(s):  
Infectious Disease ◽  
Health Risks ◽  
Disease Surveillance ◽  
Disease Outbreaks ◽  
Systematic Change ◽  
Heat Injury ◽  
Vector Borne Diseases ◽  
Infectious Disease Outbreaks ◽  
Sports Events ◽  
Paralympic Games

Abstract This review highlights two intersecting environmental phenomena that have significantly impacted the Tokyo Summer Olympic and Paralympic Games: infectious disease outbreaks and anthropogenic climate change. Following systematic searches of five databases and the gray literature, 15 studies were identified that addressed infectious disease and climate-related health risks associated with the Summer Games and similar sports mega-events. Over two decades, infectious disease surveillance at the Summer Games has identified low-level threats from vaccine-preventable illnesses and respiratory conditions. However, the COVID-19 pandemic and expansion of vector-borne diseases represent emerging and existential challenges for cities that host mass gathering sports competitions due to the absence of effective vaccines. Ongoing threats from heat injury among athletes and spectators have also been identified at international sports events from Asia to North America due to a confluence of rising Summer temperatures, urban heat island effects and venue crowding. Projections for the Tokyo Games and beyond suggest that heat injury risks are reaching a dangerous tipping point, which will necessitate relocation or mitigation with long-format and endurance events. Without systematic change to its format or staging location, the Summer Games have the potential to drive deleterious health outcomes for athletes, spectators and host communities.

scholarly journals Critical transitions in malaria transmission models are consistently generated by superinfection

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180275 ◽  
Author(s):  
David Alonso ◽  
Andy Dobson ◽  
Mercedes Pascual
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Distribution Patterns ◽  
Past Century ◽  
Theme Issue ◽  
Vector Borne Diseases ◽  
Infectious Disease Outbreaks ◽  
Critical Transitions ◽  
Vector Borne ◽  
And Control

The history of modelling vector-borne infections essentially begins with the papers by Ross on malaria. His models assume that the dynamics of malaria can most simply be characterized by two equations that describe the prevalence of malaria in the human and mosquito hosts. This structure has formed the central core of models for malaria and most other vector-borne diseases for the past century, with additions acknowledging important aetiological details. We partially add to this tradition by describing a malaria model that provides for vital dynamics in the vector and the possibility of super-infection in the human host: reinfection of asymptomatic hosts before they have cleared a prior infection. These key features of malaria aetiology create the potential for break points in the prevalence of infected hosts, sudden transitions that seem to characterize malaria’s response to control in different locations. We show that this potential for critical transitions is a general and underappreciated feature of any model for vector-borne diseases with incomplete immunity, including the canonical Ross–McDonald model. Ignoring these details of the host’s immune response to infection can potentially lead to serious misunderstanding in the interpretation of malaria distribution patterns and the design of control schemes for other vector-borne diseases.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

scholarly journals Global rise in human infectious disease outbreaks

2014 ◽  
Vol 11 (101) ◽  
pp. 20140950 ◽  
Author(s):  
Katherine F. Smith ◽  
Michael Goldberg ◽  
Samantha Rosenthal ◽  
Lynn Carlson ◽  
Jane Chen ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Surveillance ◽  
Temporal Trends ◽  
Disease Outbreaks ◽  
Zoonotic Diseases ◽  
Internet Usage ◽  
Disease Emergence ◽  
Infectious Disease Outbreaks ◽  
Per Capita ◽  
Biased Reporting

To characterize the change in frequency of infectious disease outbreaks over time worldwide, we encoded and analysed a novel 33-year dataset (1980–2013) of 12 102 outbreaks of 215 human infectious diseases, comprising more than 44 million cases occuring in 219 nations. We merged these records with ecological characteristics of the causal pathogens to examine global temporal trends in the total number of outbreaks, disease richness (number of unique diseases), disease diversity (richness and outbreak evenness) and per capita cases. Bacteria, viruses, zoonotic diseases (originating in animals) and those caused by pathogens transmitted by vector hosts were responsible for the majority of outbreaks in our dataset. After controlling for disease surveillance, communications, geography and host availability, we find the total number and diversity of outbreaks, and richness of causal diseases increased significantly since 1980 ( p < 0.0001). When we incorporate Internet usage into the model to control for biased reporting of outbreaks (starting 1990), the overall number of outbreaks and disease richness still increase significantly with time ( p < 0.0001), but per capita cases decrease significantly ( p = 0.005). Temporal trends in outbreaks differ based on the causal pathogen's taxonomy, host requirements and transmission mode. We discuss our preliminary findings in the context of global disease emergence and surveillance.

scholarly journals Big Data, Algorithmic Governmentality and the Regulation of Pandemic Risk

2019 ◽  
Vol 10 (1) ◽  
pp. 94-115
Author(s):  
Stephen L ROBERTS
Keyword(s):  
Infectious Disease ◽  
Big Data ◽  
Disease Surveillance ◽  
Disease Outbreaks ◽  
Surveillance Systems ◽  
Infectious Disease Surveillance ◽  
Novel Technologies ◽  
Infectious Disease Outbreaks ◽  
The Government ◽  
Processing Techniques

This article investigates the rise of algorithmic disease surveillance systems as novel technologies of risk analysis utilised to regulate pandemic outbreaks in an era of big data. Critically, the article demonstrates how intensified efforts towards harnessing big data and the application of algorithmic processing techniques to enhance the real-time surveillance and regulation infectious disease outbreaks significantly transform practices of global infectious disease surveillance; observed through the advent of novel risk rationalities which underpin the deployment of intensifying algorithmic practices to increasingly colonise and patrol emergent topographies of data in order to identify and govern the emergence of exceptional pathogenic risks. Conceptually, this article asserts further howthe rise of these novel risk regulating technologies within a context of big data transforms the government and forecasting of epidemics and pandemics: illustrated by the rise of emergent algorithmic governmentalties of risk within contemporary contexts of big data, disease surveillance and the regulation of pandemic.

scholarly journals Surveillance of emerging infectious diseases in wild non-human primates in Kenya: Successes and challenges

2021 ◽  
Author(s):  
Mercy Y. Akinyi ◽  
Stanislaus Kivai ◽  
Peris Mbuthia ◽  
David Kiragu ◽  
Tim Wango ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Emerging Infectious Diseases ◽  
Disease Outbreaks ◽  
Primate Species ◽  
Sample Collection ◽  
Health Security ◽  
Infectious Disease Outbreaks ◽  
High Degree

Abstract Emerging infectious diseases (EIDs) originating from wildlife present a significant threat to global health, security, and economic growth, thus combatting their emergence is a public health priority. Humans and non-human primates (NHPs) exhibit a high degree of overlap in their genetic and physiological similarities, hence making them susceptible to majority of pathogens that can cross the primate species boundaries. However, efforts to understand the potential infectious disease-causing pathogens harbored by wild primate populations has lagged and is yet to be fully explored. Disease surveillance in wildlife to identify probable infectious disease outbreaks has remained a challenge especially in developing countries due to logistical and financial constrains associated with both periodic and longitudinal sample collection. Such loopholes have hampered the preparedness to handle the emerging infectious diseases whenever they arise. In this review we focus on successes, challenges, and proposed solutions for EID surveillance in non-human primate populations in Kenya. We discuss,1) mechanisms of cross species transmission of EIDs, 2) the role of NHPS in EID transmission, 3) results from past NHP pathogen surveillance projects in Kenya and 4) challenges and proposed solutions for NHP-EID surveillance. Finally, we propose that more studies need to include investigations into understanding how cross species transmission occurs in diverse NHP populations and how this impacts one health.

scholarly journals Using prediction polling to harness collective intelligence for disease forecasting

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tara Kirk Sell ◽  
Kelsey Lane Warmbrod ◽  
Crystal Watson ◽  
Marc Trotochaud ◽  
Elena Martin ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Disease Surveillance ◽  
Large Scale ◽  
Best Practice ◽  
Collective Intelligence ◽  
Disease Outbreaks ◽  
Infectious Disease Outbreaks ◽  
Health Related ◽  
Long Term Experiment

Abstract Background The global spread of COVID-19 has shown that reliable forecasting of public health related outcomes is important but lacking. Methods We report the results of the first large-scale, long-term experiment in crowd-forecasting of infectious-disease outbreaks, where a total of 562 volunteer participants competed over 15 months to make forecasts on 61 questions with a total of 217 possible answers regarding 19 diseases. Results Consistent with the “wisdom of crowds” phenomenon, we found that crowd forecasts aggregated using best-practice adaptive algorithms are well-calibrated, accurate, timely, and outperform all individual forecasters. Conclusions Crowd forecasting efforts in public health may be a useful addition to traditional disease surveillance, modeling, and other approaches to evidence-based decision making for infectious disease outbreaks.

scholarly journals Infectious disease outbreaks in the African region: overview of events reported to the World Health Organization in 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
F. Mboussou ◽  
P. Ndumbi ◽  
R. Ngom ◽  
Z. Kassamali ◽  
O. Ogundiran ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Onset ◽  
Disease Outbreaks ◽  
Case Fatality ◽  
World Health ◽  
African Region ◽  
Regional Office ◽  
Member States ◽  
Infectious Disease Outbreaks ◽  
The World

Abstract The WHO African region is characterised by the largest infectious disease burden in the world. We conducted a retrospective descriptive analysis using records of all infectious disease outbreaks formally reported to the WHO in 2018 by Member States of the African region. We analysed the spatio-temporal distribution, the notification delay as well as the morbidity and mortality associated with these outbreaks. In 2018, 96 new disease outbreaks were reported across 36 of the 47 Member States. The most commonly reported disease outbreak was cholera which accounted for 20.8% (n = 20) of all events, followed by measles (n = 11, 11.5%) and Yellow fever (n = 7, 7.3%). About a quarter of the outbreaks (n = 23) were reported following signals detected through media monitoring conducted at the WHO regional office for Africa. The median delay between the disease onset and WHO notification was 16 days (range: 0–184). A total of 107 167 people were directly affected including 1221 deaths (mean case fatality ratio (CFR): 1.14% (95% confidence interval (CI) 1.07%–1.20%)). The highest CFR was observed for diseases targeted for eradication or elimination: 3.45% (95% CI 0.89%–10.45%). The African region remains prone to outbreaks of infectious diseases. It is therefore critical that Member States improve their capacities to rapidly detect, report and respond to public health events.

scholarly journals Statistical methods for the prospective detection of infectious disease outbreaks: a review

2011 ◽  
Vol 175 (1) ◽  
pp. 49-82 ◽  
Author(s):  
Steffen Unkel ◽  
C. Paddy Farrington ◽  
Paul H. Garthwaite ◽  
Chris Robertson ◽  
Nick Andrews
Keyword(s):  
Infectious Disease ◽  
Statistical Methods ◽  
Disease Outbreaks ◽  
Infectious Disease Outbreaks
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