scholarly journals Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a Case Study

2019 ◽  
Author(s):  
Paul Birrell ◽  
Xu-Sheng Zhang ◽  
Alice Corbella ◽  
Edwin van Leeuwen ◽  
Nikolaos Panagiotopoulos ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Added Value ◽  
Disease Spread ◽  
Influenza Epidemic ◽  
Care Model ◽  
Short Term ◽  
Susceptibility Data ◽  
The Uk ◽  
Transmission Models

Abstract Background: Since the 2009 A/H1N1 pandemic, the UK has a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested. Methods: Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored. Results: Estimates of disease spread were consistent over time and across models. The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3–4 of 2018. Estimates and predictions varied according to pre-season immunity levels. Conclusions: This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.

scholarly journals Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a Case Study

2020 ◽  
Author(s):  
Paul Birrell ◽  
Xu-Sheng Zhang ◽  
Alice Corbella ◽  
Edwin van Leeuwen ◽  
Nikolaos Panagiotopoulos ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Added Value ◽  
Influenza Epidemic ◽  
Care Model ◽  
Short Term ◽  
Susceptibility Data ◽  
Primary Care Model ◽  
Transmission Models ◽  
Important Activity

Abstract Background: Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested. Methods: Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored. Results: The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3–4 of 2018. Estimates for R0 were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R0 across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity. Conclusions: This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.

scholarly journals Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Paul J. Birrell ◽  
Xu-Sheng Zhang ◽  
Alice Corbella ◽  
Edwin van Leeuwen ◽  
Nikolaos Panagiotopoulos ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Influenza Epidemic ◽  
Transmission Models

Voluntary sector responses to food poverty: responding in the short term and working for longer-term change

2019 ◽  
Vol 10 (3) ◽  
pp. 361-369 ◽  
Author(s):  
Stephanie Denning
Keyword(s):  
Voluntary Sector ◽  
Short Term ◽  
Term Change ◽  
The Uk ◽  
Do So

In this paper I respond to the question of whether voluntary sector responses to food poverty in the UK are a sticking plaster without addressing the causes of food poverty. I do so by drawing on a case study of a holiday hunger project and I reflect on three principles: being relational, encouraging participation and working for justice. I conclude with three recommendations for how voluntary sector organisations can work towards both short- and longer-term responses to food poverty.

scholarly journals Epidemic predictions in an imperfect world: modelling disease spread with partial data

2015 ◽  
Vol 282 (1808) ◽  
pp. 20150205 ◽  
Author(s):  
Peter M. Dawson ◽  
Marleen Werkman ◽  
Ellen Brooks-Pollock ◽  
Michael J. Tildesley
Keyword(s):  
Disease Spread ◽  
Spatial Spread ◽  
Full Dataset ◽  
Movement Data ◽  
Partial Data ◽  
The Usa ◽  
Access To Data ◽  
The Uk ◽  
Transmission Models ◽  
The Eu

‘Big-data’ epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around the world, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements.

scholarly journals Multisource Data Framework for Road Traffic State Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Josep Maria Salanova Grau ◽  
Evangelos Mitsakis ◽  
Panagiotis Tzenos ◽  
Iraklis Stamos ◽  
Luigi Selmi ◽  
...  
Keyword(s):  
Road Traffic ◽  
Road Networks ◽  
Added Value ◽  
Short Term ◽  
The Road ◽  
Data Framework ◽  
Traffic Volumes ◽  
Short Term Forecasting ◽  
The City

This paper presents a framework for data collection, filtering, and fusion, together with a set of operational tools to validate, analyze, utilize, and highlight the added value of probe data. Data is collected by both conventional (loops, radars, and cameras) and innovative (Floating Car Data, detectors of Bluetooth devices) technologies and refers to travel times and traffic flows on road networks. The city of Thessaloniki, Greece, serves as a case study for the implementation of the proposed framework. The methodology includes the estimation of traffic flow based on measured travel time along predefined routes and short-term forecasting of traffic volumes and their spatial expansion in the road network. The proposed processes and the framework itself have the potential of being implemented in urban road networks.

scholarly journals Using Digital Surveillance Tools for Near Real-Time Mapping of the Risk of International Infectious Disease Spread: Ebola as a Case Study

2019 ◽  
Author(s):  
Sangeeta Bhatia ◽  
Britta Lassmann ◽  
Emily Cohn ◽  
Malwina Carrion ◽  
Moritz U. G. Kraemer ◽  
...  
Keyword(s):  
Real Time ◽  
Disease Surveillance ◽  
Early Warning Systems ◽  
West African ◽  
Disease Spread ◽  
Warning Systems ◽  
Short Term ◽  
Incidence Trends ◽  
Digital Surveillance

AbstractIn our increasingly interconnected world, it is crucial to understand the risk of an outbreak originating in one country or region and spreading to the rest of the world. Digital disease surveillance tools such as ProMED and HealthMap have the potential to serve as important early warning systems as well as complement the field surveillance during an ongoing outbreak. Here we present a flexible statistical model that uses data produced from digital surveillance tools (ProMED and HealthMap) to forecast short term incidence trends in a spatially explicit manner. The model was applied to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic. The model was able to predict each instance of international spread 1 to 4 weeks in advance. Our study highlights the potential and limitations of using publicly available digital surveillance data for assessing outbreak dynamics in real-time.

Emplacing Berlin: Digital Itineraries and Short-term Study Abroad in the City

2018 ◽  
Vol 12 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Richard B. Apgar
Keyword(s):  
Study Abroad ◽  
Urban Landscape ◽  
Physical Space ◽  
Short Term ◽  
Study Abroad Programs ◽  
Term Study ◽  
Digital Maps ◽  
Short Term Study ◽  
The City

As destination of choice for many short-term study abroad programs, Berlin offers students of German language, culture and history a number of sites richly layered with significance. The complexities of these sites and the competing narratives that surround them are difficult for students to grasp in a condensed period of time. Using approaches from the spatial humanities, this article offers a case study for enhancing student learning through the creation of digital maps and itineraries in a campus-based course for subsequent use during a three-week program in Berlin. In particular, the concept of deep mapping is discussed as a means of augmenting understanding of the city and its history from a narrative across time to a narrative across the physical space of the city. As itineraries, these course-based projects were replicated on site. In moving from the digital environment to the urban landscape, this article concludes by noting meanings uncovered and narratives formed as we moved through the physical space of the city.

The impact of Airbnb on a non-touristic city. A Case study of short-term rentals in Santa Cruz de Tenerife (Spain)

Erdkunde ◽  
2020 ◽  
Vol 74 (3) ◽  
pp. 191-204
Author(s):  
Marcus Hübscher ◽  
Juana Schulze ◽  
Felix zur Lage ◽  
Johannes Ringel
Keyword(s):  
Santa Cruz ◽  
Short Term ◽  
Rental Market ◽  
Rural Space ◽  
Use Of Data ◽  
Web Scraping ◽  
Per Capita ◽  
Per Se ◽  
The Impact

Short-term rentals such as Airbnb have become a persistent element of today’s urbanism around the globe. The impacts are manifold and differ depending on the context. In cities with a traditionally smaller accommodation market, the impacts might be particularly strong, as Airbnb contributes to ongoing touristification processes. Despite that, small and medium-sized cities have not been in the centre of research so far. This paper focuses on Santa Cruz de Tenerife as a medium-sized Spanish city. Although embedded in the touristic region of the Canary Islands, Santa Cruz is not a tourist city per se but still relies on touristification strategies. This paper aims to expand the knowledge of Airbnb’s spatial patterns in this type of city. The use of data collected from web scraping and geographic information systems (GIS) demonstrates that Airbnb has opened up new tourism markets outside of the centrally established tourist accommodations. It also shows that the price gap between Airbnb and the housing rental market is broadest in neighbourhoods that had not experienced tourism before Airbnb entered the market. In the centre the highest prices and the smallest units are identified, but two peripheral quarters stand out. Anaga Mountains, a natural and rural space, has the highest numbers of Airbnb listings per capita. Suroeste, a suburban quarter, shows the highest growth rates on the rental market, which implies a linkage between Airbnb and suburbanization processes.

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