scholarly journals Characteristics of the 100 largest modern zoonotic disease outbreaks

2021 ◽  
Vol 376 (1837) ◽  
pp. 20200535 ◽  
Author(s):  
Patrick R. Stephens ◽  
N. Gottdenker ◽  
A. M. Schatz ◽  
J. P. Schmidt ◽  
John M. Drake
Keyword(s):  
Disease Surveillance ◽  
Large Scale ◽  
Disease Outbreaks ◽  
Weather Conditions ◽  
Zoonotic Disease ◽  
Global Scale ◽  
Press Freedom ◽  
Quantitative Studies ◽  
Vector Borne

Zoonotic disease outbreaks are an important threat to human health and numerous drivers have been recognized as contributing to their increasing frequency. Identifying and quantifying relationships between drivers of zoonotic disease outbreaks and outbreak severity is critical to developing targeted zoonotic disease surveillance and outbreak prevention strategies. However, quantitative studies of outbreak drivers on a global scale are lacking. Attributes of countries such as press freedom, surveillance capabilities and latitude also bias global outbreak data. To illustrate these issues, we review the characteristics of the 100 largest outbreaks in a global dataset ( n = 4463 bacterial and viral zoonotic outbreaks), and compare them with 200 randomly chosen background controls. Large outbreaks tended to have more drivers than background outbreaks and were related to large-scale environmental and demographic factors such as changes in vector abundance, human population density, unusual weather conditions and water contamination. Pathogens of large outbreaks were more likely to be viral and vector-borne than background outbreaks. Overall, our case study shows that the characteristics of large zoonotic outbreaks with thousands to millions of cases differ consistently from those of more typical outbreaks. We also discuss the limitations of our work, hoping to pave the way for more comprehensive future studies. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

scholarly journals Random Regret-Minimization Model for Emergency Resource Preallocation at Freeway Accident Black Spots

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Siliang Luan ◽  
Qingfang Yang ◽  
Wei Wang ◽  
Zhongtai Jiang ◽  
Ruru Xing ◽  
...  
Keyword(s):  
Large Scale ◽  
Traffic Accidents ◽  
Weather Conditions ◽  
Time Cost ◽  
Frequency Method ◽  
Regret Minimization ◽  
Accident Probability ◽  
Black Spots ◽  
Random Regret Minimization

The preallocation of emergency resources is a mechanism increasing preparedness for uncertain traffic accidents under different weather conditions. This paper introduces the concept of accident probability of black spots and an improved accident frequency method to identify accident black spots and obtain the accident probability. At the same time, we propose a three-stage random regret-minimization (RRM) model to minimize the regret value of the attribute of overall response time, cost, and demand, which allocates limited emergency resources to more likely to happen accident spots. Due to the computational complexity of our model, a genetic algorithm is developed to solve a large-scale instance of the problem. A case study focuses on three-year rainy accidents’ data in Weifang, Linyi, and Rizhao of China to test the correctness and validity of the application of the model.

scholarly journals Testing predictability of disease outbreaks with a simple model of pathogen biogeography

2019 ◽  
Vol 6 (11) ◽  
pp. 190883 ◽  
Author(s):  
Tad A. Dallas ◽  
Colin J. Carlson ◽  
Timothée Poisot
Keyword(s):  
Simple Model ◽  
Disease Surveillance ◽  
Influenza Pandemic ◽  
Disease Outbreaks ◽  
Success Rates ◽  
Predictive Capacity ◽  
Public Health Professionals ◽  
Single Location ◽  
Vector Borne ◽  
Basic Network

Predicting disease emergence and outbreak events is a critical task for public health professionals and epidemiologists. Advances in global disease surveillance are increasingly generating datasets that are worth more than their component parts for prediction-oriented work. Here, we use a trait-free approach which leverages information on the global community of human infectious diseases to predict the biogeography of pathogens through time. Our approach takes pairwise dissimilarities between countries’ pathogen communities and pathogens’ geographical distributions and uses these to predict country–pathogen associations. We compare the success rates of our model for predicting pathogen outbreak, emergence and re-emergence potential as a function of time (e.g. number of years between training and prediction), pathogen type (e.g. virus) and transmission mode (e.g. vector-borne). With only these simple predictors, our model successfully predicts basic network structure up to a decade into the future. We find that while outbreak and re-emergence potential are especially well captured by our simple model, prediction of emergence events remains more elusive, and sudden global emergences like an influenza pandemic are beyond the predictive capacity of the model. However, these stochastic pandemic events are unlikely to be predictable from such coarse data. Together, our model is able to use the information on the existing country–pathogen network to predict pathogen outbreaks fairly well, suggesting the importance in considering information on co-occurring pathogens in a more global view even to estimate outbreak events in a single location or for a single pathogen.

scholarly journals Leishmaniasis and Climate Change—Case Study: Argentina

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Oscar Daniel Salomón ◽  
María Gabriela Quintana ◽  
Andrea Verónica Mastrángelo ◽  
María Soledad Fernández
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Large Scale ◽  
Local Level ◽  
Point Of View ◽  
Mitigation Strategies ◽  
Vector Borne Diseases ◽  
Scale Models ◽  
Vector Borne

Vector-borne diseases closely associated with the environment, such as leishmaniases, have been a usual argument about the deleterious impact of climate change on public health. From the biological point of view interaction of different variables has different and even conflicting effects on the survival of vectors and the probability transmission of pathogens. The results on ecoepidemiology of leishmaniasis in Argentina related to climate variables at different scales of space and time are presented. These studies showed that the changes in transmission due to change or increase in frequency and intensity of climatic instability were expressed through changes in the probability of vector-human reservoir effective contacts. These changes of contact in turn are modulated by both direct effects on the biology and ecology of the organisms involved, as by perceptions and changes in the behavior of the human communities at risk. Therefore, from the perspective of public health and state policy, and taking into account the current nonlinear increased velocity of climate change, we concluded that discussing the uncertainties of large-scale models will have lower impact than to develop-validate mitigation strategies to be operative at local level, and compatibles with sustainable development, conservation biodiversity, and respect for cultural diversity.

scholarly journals From qualitative to quantitative insect metabarcoding: an in tandem multilocus mosquito identification methodology

2020 ◽  
Author(s):  
Katerina Kassela ◽  
Adamantia Kouvela ◽  
Michael de Courcy Williams ◽  
Konstantinos Konstantinidis ◽  
Maria Goreti Rosa Freitas ◽  
...  
Keyword(s):  
Large Scale ◽  
Disease Outbreaks ◽  
Small Animal ◽  
Individual Species ◽  
Vector Borne Diseases ◽  
Animal Populations ◽  
Dna Metabarcoding ◽  
Vector Borne ◽  
28S Ribosomal Dna ◽  
Identification And Quantification

AbstractIn the era of emergence and re-emergence of vector-borne diseases, a high throughput trap-based insect monitoring is essential for the identification of invasive species, study of mosquito populations and risk assessment of disease outbreaks. Insect DNA metabarcoding technology has emerged as a highly promising methodology for unbiased and large-scale surveillance. Despite significant attempts to introduce DNA metabarcoding in mosquito or other insect surveillance qualitative and quantitative metabarcoding remains a challenge. In the present study, we have developed a methodology of in-tandem identification and quantification using cytochrome oxidase subunit I (COI) combined with a secondary multilocus identification and quantification involving three loci of 28S ribosomal DNA. The presented methodology was able to identify individual species in pools of mosquitoes with 95.94% accuracy and resolve with high accuracy (p = 1, χ2 = 2.55) mosquito population composition providing a technology capable of revolutionizing mosquito surveillance through metabarcoding. The methodology, given the respective dataset, has the potential to be applied to various small animal populations.

scholarly journals A Bayesian approach to exploring the influence of climate variability modes on fire weather conditions and lightning-ignited wildfires

2021 ◽  
Author(s):  
Bryson C. Bates ◽  
Andrew J. Dowdy ◽  
Lachlan McCaw
Keyword(s):  
Climate Variability ◽  
Network Architecture ◽  
Large Scale ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Fire Weather ◽  
Prescribed Burn ◽  
Data Set ◽  
Climate Modes

AbstractUnderstanding the relationships between large-scale, low-frequency climate variability modes, fire weather conditions and lighting-ignited wildfires has implications for fire-weather prediction, fire management and conservation. This article proposes a Bayesian network framework for quantifying the influence of climate modes on fire weather conditions and occurrence of lightning-ignited wildfires. The main objectives are to describe and demonstrate a probabilistic framework for identifying and quantifying the joint and individual relationships that comprise the climate-wildfire system; gain insight into potential causal mechanisms and pathways; gauge the influence of climate modes on fire weather and lightning-ignition relative to that of local-scale conditions alone; assess the predictive skill of the network; and motivate the use of techniques that are intuitive, flexible and for which user‐friendly software is freely available. A case study illustrates the application of the framework to a forested region in southwest Australia. Indices for six climate variability modes are considered along with two hazard variables (observed fire weather conditions and prescribed burn area), and a 41-year record of lightning-ignited wildfire counts. Using the case study data set, we demonstrate that the proposed framework: (1) is based on reasonable assumptions provided the joint density of the variables is converted to multivariate normal; (2) generates a parsimonious and interpretable network architecture; (3) identifies known or partially known relationships between the variables; (4) has potential to be used in a predictive setting for fire weather conditions; and (5) climate modes are more directly related to fire weather conditions than to lightning-ignition counts.

scholarly journals Social media: A new tool for outbreak surveillance

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Averi E. Wilson ◽  
Christoph U. Lehmann ◽  
Sameh N. Saleh ◽  
John Hanna ◽  
Richard J. Medford
Keyword(s):  
Social Media ◽  
Disease Surveillance ◽  
Disease Incidence ◽  
Disease Outbreaks ◽  
Global Scale ◽  
Social Media Platforms ◽  
Monitoring And Forecasting ◽  
Use Of Social Media ◽  
Scale Data ◽  
Data Processing Methods

Abstract Social media platforms allow users to share news, ideas, thoughts, and opinions on a global scale. Data processing methods allow researchers to automate the collection and interpretation of social media posts for efficient and valuable disease surveillance. Data derived from social media and internet search trends have been used successfully for monitoring and forecasting disease outbreaks such as Zika, Dengue, MERS, and Ebola viruses. More recently, data derived from social media have been used to monitor and model disease incidence during the coronavirus disease 2019 (COVID-19) pandemic. We discuss the use of social media for disease surveillance.

Ensuring Australia's competitiveness in the global oil and gas market

2011 ◽  
Vol 51 (2) ◽  
pp. 707
Author(s):  
Peter Goode
Keyword(s):  
Large Scale ◽  
Oil And Gas ◽  
Capital Expenditure ◽  
Global Scale ◽  
North West ◽  
The People ◽  
Highly Skilled ◽  
Working Together ◽  
Gas Market

There is an estimated $200 billion worth of capital expenditure presently planned for Australian gas projects. These projects provide the potential for $20 billion worth of engineering and maintenance opportunities for Australian companies and an estimated 16,000 ongoing positions in the sector. The scale of these projects has drawn international attention and is increasingly drawing global competition. Australian companies are at risk of the misperception that they don’t have the international know-how or the people to compete for these large-scale projects. We need to ensure that our Australian ingenuity and scale continue to position us as the service provider of choice for construction, project management and maintenance opportunities. Working together with industry, we have shown that we have what it takes to compete on a global scale. We also need to work with government and unions to ensure we have scalable highly-skilled people available to support these projects. This presentation will consider the following case study: Transfield Services delivers services to companies including Woodside Energy, which operates the A$27 billion North West Shelf project, one of the world’s largest LNG production facilities with an output of 16.4 million tonnes of LNG a year. While expansion continues, ongoing brownfield project and maintenance services demand the ongoing support of a highly-skilled workforce of up to 1,000 people. This case study explores: innovative service solutions in a resource-scarce environment through access to global resources innovative scheduling of work; and, the challenges of sourcing and retaining highly-skilled people by improving the opportunities for global and domestic employee mobility and investing in training and developing local people.

scholarly journals Global biogeography of human infectious diseases

2015 ◽  
Vol 112 (41) ◽  
pp. 12746-12751 ◽  
Author(s):  
Kris A. Murray ◽  
Nicholas Preston ◽  
Toph Allen ◽  
Carlos Zambrana-Torrelio ◽  
Parviez R. Hosseini ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Ebola Virus ◽  
Critical Role ◽  
Global Scale ◽  
Test Case ◽  
Ecological Barriers ◽  
Health Related Research ◽  
Vector Borne

The distributions of most infectious agents causing disease in humans are poorly resolved or unknown. However, poorly known and unknown agents contribute to the global burden of disease and will underlie many future disease risks. Existing patterns of infectious disease co-occurrence could thus play a critical role in resolving or anticipating current and future disease threats. We analyzed the global occurrence patterns of 187 human infectious diseases across 225 countries and seven epidemiological classes (human-specific, zoonotic, vector-borne, non–vector-borne, bacterial, viral, and parasitic) to show that human infectious diseases exhibit distinct spatial grouping patterns at a global scale. We demonstrate, using outbreaks of Ebola virus as a test case, that this spatial structuring provides an untapped source of prior information that could be used to tighten the focus of a range of health-related research and management activities at early stages or in data-poor settings, including disease surveillance, outbreak responses, or optimizing pathogen discovery. In examining the correlates of these spatial patterns, among a range of geographic, epidemiological, environmental, and social factors, mammalian biodiversity was the strongest predictor of infectious disease co-occurrence overall and for six of the seven disease classes examined, giving rise to a striking congruence between global pathogeographic and “Wallacean” zoogeographic patterns. This clear biogeographic signal suggests that infectious disease assemblages remain fundamentally constrained in their distributions by ecological barriers to dispersal or establishment, despite the homogenizing forces of globalization. Pathogeography thus provides an overarching context in which other factors promoting infectious disease emergence and spread are set.

Developing awareness and use of compliments in the Chinese homestay: A longitudinal case study

2017 ◽  
Vol 8 (4) ◽  
pp. 441-467 ◽  
Author(s):  
Sheng-Hsun Lee
Keyword(s):  
Language Learning ◽  
Interpersonal Communication ◽  
Large Scale ◽  
Longitudinal Case Study ◽  
Potential Conflict ◽  
Quantitative Studies ◽  
Qualitative Changes

AbstractThe study abroad homestay enjoys a widespread reputation as a key context for language learning. However, a homestay advantage has been difficult to prove in large-scale quantitative studies. Few qualitative studies examine the long-term developmental processes that can take place in homestays at the micro-ethnographic level. This paper focuses on how one participant, John, gradually developed awareness of the role of compliments and ability to use them for various purposes. Drawing on Vygotskian sociocultural theory, this study analyzes qualitative changes in John’s use of compliments from informal interaction in two homestays across a year. Over time, John learned to use compliments for an array of purposes: expressing appreciation for food, maintaining his hosts’ positive face, developing cordial relationships, and defusing potential conflict. Development in John’s case is evident as he internalized this speech act that he once grappled with, and used it to mediate the behavior of the others when confronted with interactionally and emotionally challenging situations. This process led to John’s ability to participate appropriately as a guest at the Chinese homestay dinner table and to appreciate the subtleties of interpersonal communication that includes unstated expressions of affection.

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.

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