Health and climate – opportunities

Rogers, D.; Ma, Shapiro; Brunet, Gilbert; Cohen, J-C; Connor, Stephen J.; Diallo, Abdourahmane; Elliott, Wayne; Kan, Haidong; Hales, Simon; Hemming, Deborah; Jeanne, Isabelle; Lafaye, Murielle; Mumba, Zilore; Raholijao, Nirivololona; Rakotomanana, Fanjasoa; Teka, Hiwot; Trtanj, J.; Whung, Pai-Yei

doi:10.1016/j.proenv.2010.09.005

Cited by 21 publications

(26 citation statements)

References 20 publications

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“…HWI is able to provide an early warning forecast for heat waves with 5 days up to 9 days forecast. It can be reliable information for decision makers to provide efficacious and timely actions to prepare for imminent heat waves (Rogers et al , ). The timing and duration of heat waves are important factors in terms of forecasting because they seem to have an impact on health and well‐being.…”

Section: Resultsmentioning

confidence: 99%

Assessment of medium‐range ensemble forecasts of heat waves

Lee

Yoo

2015

Atmospheric Science Letters

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This study assesses the predictability of heat wave occurrence over Korea applying The Observing system Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data for the Heat Wave Index (HWI) associated with large-scale circulation. HWI shows higher predictability of heat waves than those of maximum temperature (TMAX) and bias corrected TMAX. The verification scores using HWI are compared to other cases of forecasts. It is concluded that the proposed HWI can be useful for heat wave forecasting, which may reduce the health impacts from heat waves through appropriate and timely mitigation efforts.

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Section: Resultsmentioning

confidence: 99%

Assessment of medium‐range ensemble forecasts of heat waves

Lee

Yoo

2015

Atmospheric Science Letters

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“…Health forecasting is predicting health situations or disease episodes and forewarning future events. It is also a form of preventive medicine or preventive care that engages public health planning and is aimed at facilitating health care service provision in populations [8,10,29,30]. Health forecasting has been commonly applied to emergency department visits, daily hospital attendance and admissions [27,[31][32][33].…”

Section: Defining Health Forecasting and Related Terminologiesmentioning

confidence: 99%

“…The birth of forecasting as a science, however, is associated with weather forecasting and, is credited to Francis Beaufort, who developed the popularly known scale for measuring wind force (the Beaufort scale) and Robert Fitzroy, who developed the Fitzroy barometer for measuring atmospheric pressure [6]. Forecasting has advanced over time and has increased in sophistication in many specialised areas, including the fields of health [7][8][9][10], economics and commerce [1,11], sports [12,13], environment (including meteorology) [14,15], technology and politics [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

An overview of health forecasting

Soyiri

Reidpath

2012

Environ Health Prev Med

120

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Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.

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“…Environmental conditions around water bodies are important factors in the risk of microbial contamination [20]- [23]. The description and monitoring of these conditions can help target recreational waters most at risk to optimize resources of monitoring programs and reduce population exposure to potentially contaminated water [24].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Earth Observation Systems for Estimating Environmental Determinants of Microbial Contamination in Recreational Waters

Kotchi

Brazeau

Turgeon

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Public health risks related to the microbial contamination of recreational waters are increased by global environmental change. Intensification of agriculture, urban sprawl, and climate change are some of the changes which can lead to favorable conditions for the emergence of waterborne diseases. Earth observation (EO) images have several advantages for the characterization and monitoring of environmental determinants that could be associated with the risk of microbial contamination of recreational waters in vast territories like Canada. There are a large number of EO systems characterized by different spatial, temporal, spectral, and radiometric resolutions. Also, they have different levels of accessibility. In this study, we compared several EO systems for the estimation of environmental determinants to assess their usefulness and their added value in monitoring programs of recreational waters. Satellite images from EO systems WorldView-2, GeoEye-1, SPOT-5/HRG, Landsat-5/TM, Envisat/MERIS, Terra/MODIS, NOAA/AVHRR, and Radarsat-2 were acquired in 2010 and 2011 in southern Quebec, Canada. A supervised classification of these images with a maximum likelihood algorithm was used to estimate five key environmental determinants (agricultural land, impervious surfaces, water, forest, and wetlands) within the area of influence of 78 beaches. Logistic regression models were developed to establish the relationship between fecal contamination of beaches and environmental determinants derived from satellite images. The power prediction of these models and criteria such as accuracy of classified images, the ability of the sensor to detect environmental determinants in the area of influence of beaches, the correlation between the estimated environmental determinants in the area of influence by the sensor with those estimated by very high spatial resolution reference sensors (WorldView-2 and GeoEye-1), and general criteria of accessibility (cost of the images, imaging swath, satellite revisit interval, hours of work, and expertise and material required to process the images) were used to evaluate the EO systems. The logistic regression model establishing the relationship between environmental determinants Manuscript

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Health and climate – opportunities

Cited by 21 publications

References 20 publications

Assessment of medium‐range ensemble forecasts of heat waves

Assessment of medium‐range ensemble forecasts of heat waves

An overview of health forecasting

Evaluation of Earth Observation Systems for Estimating Environmental Determinants of Microbial Contamination in Recreational Waters

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