Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt.

Human outbreaks with avian influenza have been, so far, constrained by poor viral adaptation to non-avian hosts. This could be overcome via co-infection, whereby two strains share genetic material, allowing new hybrid strains to emerge. Identifying areas where co-infection is most likely can help target spaces for increased surveillance. Ecological niche modeling using remotely-sensed data can be used for this purpose. H5N1 and H9N2 influenza subtypes are endemic in Egyptian poultry. From 2006 to 2015, over 20,000 poultry and wild birds were tested at farms and live bird markets. Using ecological niche modeling we identified environmental, behavioral, and population characteristics of H5N1 and H9N2 niches within Egypt. Niches differed markedly by subtype. The subtype niches were combined to model co-infection potential with known occurrences used for validation. The distance to live bird markets was a strong predictor of co-infection. Using only single-subtype influenza outbreaks and publicly available ecological data, we identified areas of co-infection potential with high accuracy (area under the receiver operating characteristic (ROC) curve (AUC) 0.991)

Landscape Epidemiology and Machine Learning: A Geospatial Approach to Modeling West Nile Virus Risk in the United States

The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is a sub-discipline of artificial intelligence that can be used to create predictive models from large and complex datasets. West Nile virus (WNV) is a relatively new infectious disease in the United States, and has a fairly well-understood transmission cycle that is believed to be highly dependent on environmental conditions. This study takes a geospatial approach to the study of WNV risk, using both landscape epidemiology and machine learning techniques. A combination of remotely sensed and in situ variables are used to predict WNV incidence with a correlation coefficient as high as 0.86. A novel method of mitigating the small numbers problem is also tested and ultimately discarded. Finally a consistent spatial pattern of model errors is identified, indicating the chosen variables are capable of predicting WNV disease risk across most of the United States, but are inadequate in the northern Great Plains region of the US

Using internet search data to predict new HIV diagnoses in China: a modelling study.

ObjectivesInternet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.DesignWe identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).ResultsSearch query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.ConclusionsBaidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention

Signature Changing Spacetimes and WKB Approximations in General Relativity

Some properties of spacetimes that change signature from Riemannian (positive definite metric) to Lorentzian (metric has a single negative eigenvalue) are investigated. Specifically, the form of geodesics and solutions to the Klein-Gordon equation are calculated. Geodesics behave as expected since they are equally well defined for Lorentzian and Riemannian manifolds, though null geodesics cease to have meaning in the Riemannian region. Solutions to the Klein-Gordon equation exhibit oscillatory behavior in the Lorentzian region and exponential behavior in the Riemannian region. In an effort to further interpret these results, approximate wave solutions are found for a generic spacetime using WKB approximations in the large momentum limit. This approximation encompasses traditional, non-degenerate spacetimes as well as those that change signature. This solution is shown to break down near regions where the metric becomes degenerate, except in the 1 + 1 dimensional case. Further, these solutions can define a vector field with the gradient of their phase. The integral curves of the resulting field are shown to be geodesics, parametrized by an affine parameter.Physic

Redesigning design education: the next Bauhaus?

This chapter, following an invitation to deliver a keynote address at the inaugural ICSID Educational Seminar 2001 Seongnam, Korea, examines the theme of emerging service design thinking for education. This was also the subject of Young’s collaborative student learning project; ‘Review of a Design Practice Learning Project to Pilot Heightened Social Responsibility and Engagement,’ (with Hilton K). This was presented at EAD, Barcelona in 2003, and further developed in keynote addresses by Young at International Service Design Northumbria conference (ISDn1) at the Sage, Gateshead, March 2006 and ISDn2 at the Centre for Life, Newcastle, November 2006. The subject of new design paradigms and emerging methods is now a co-sponsored PhD between the Design Council and Northumbria’s CfDR (Young & Siodmok supervisors – research funding £32k to support the studentship). This includes a review of the Dott 07 public commission projects. Young’s service design research led to a commission with the ONE NorthEast; Design Innovation Education Centre project in 2003, to develop service design expertise and resources within NE England. Also, to join the AHRC/EPSRC Designing for the 21st Century project; Service Design for Science and Technology SMEs, 2006, based in SAID Business School, Oxford University. Practice-based research using service design methods were deployed to improve the experience of patients of the NHS; this led to Dott 07 sponsoring the Design and Sexual Health project developed by Young with Gateshead PCT and the Strategic Health Authority. Related Northumbria-funded PhD student Lauren Tan working on the future development of design thinking in area of service design and linking to Dott07

The strong geometric lemma in the Heisenberg group

We prove that in the first Heisenberg group, unlike Euclidean spaces and higher dimensional Heisenberg groups, the best possible exponent for the strong geometric lemma for intrinsic Lipschitz graphs is $4$ instead of $2$. Combined with earlier work from arXiv:2004.11447 and arXiv:2207.03013, our result completes the proof of the strong geometric lemma in Heisenberg groups. One key tool in our proof, and possibly of independent interest, is a suitable refinement of the foliated coronizations which first appeared in arXiv:2004.12522.Comment: 29 page