28 research outputs found
The Social and Political Dimensions of the Ebola Response: Global Inequality, Climate Change, and Infectious Disease
The 2014 Ebola crisis has highlighted public-health vulnerabilities in Liberia, Sierra
Leone, and Guinea – countries ravaged by extreme poverty, deforestation and
mining-related disruption of livelihoods and ecosystems, and bloody civil wars in
the cases of Liberia and Sierra Leone. Ebola’s emergence and impact are grounded
in the legacy of colonialism and its creation of enduring inequalities within African
nations and globally, via neoliberalism and the Washington Consensus. Recent
experiences with new and emerging diseases such as SARS and various strains of
HN influenzas have demonstrated the effectiveness of a coordinated local and
global public health and education-oriented response to contain epidemics. To what
extent is international assistance to fight Ebola strengthening local public health and
medical capacity in a sustainable way, so that other emerging disease threats, which
are accelerating with climate change, may be met successfully? This chapter
considers the wide-ranging socio-political, medical, legal and environmental factors
that have contributed to the rapid spread of Ebola, with particular emphasis on the
politics of the global and public health response and the role of gender, social
inequality, colonialism and racism as they relate to the mobilization and
establishment of the public health infrastructure required to combat Ebola and other
emerging diseases in times of climate change
Utilization of human papillomavirus testing for cervical cancer prevention in a university hospital
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Capability of CAM5.1 in simulating maximum air temperature patterns over West Africa during boreal spring
This study classifies maximum air temperature patterns over West Africa into six groups and evaluates the capability of a global climate model (Community Atmospheric Model version 5.1; CAM) to simulate them. We analyzed 45-year (1961–2005) multi-ensemble (50 members) simulations from CAM and compared the results with those of the Climate Research Unit (CRU) and the twentieth Century Reanalysis data sets. Using Self Organizing Map algorithm to classify the spatial patterns of maximum air temperature during boreal spring, the study reveals the temperature patterns that CAM can simulate well and those the model struggles to reproduce. The results show that the best agreements between the composites of observation and CAM occur in the first temperature pattern group (which features positive temperatures anomalies over the Sahel) and Node 2 (which features near-normal temperature) pattern of the third group. CAM succeeded in reproducing some of the associated regional atmospheric dynamics and thermodynamic features in winds (horizontal and vertical), temperature fields, the cloud fractions, and the mean sea-level pressure. Although CAM struggles to capture the relationship between air temperature patterns and tele-connection indices during the boreal spring season over West Africa, it agrees with observations that temperature patterns over the sub-region cannot be associated with a single climate index. An ensemble member (SIM48) captures the inter-annual variation of the observed temperaure patterns with high sycronization (ɳ > 44%), much better than that of ensembles mean (ɳ < 30%). SIM48 also captures adequately four of the spatial patterns in comparison to three captured by the ensembles mean. This indicates that, for better seasonal forecasts and more reliable future climate projections, the practice whereby an ensemble mean is based on uniformly averaging the members rather than the performance of individual ensemble members needs to be reviewed. The results of the study may be used to improve the perfomance of CAM over West Africa, thereby strengthening the on-going efforts to include CAM as part of multi-model forecasting system over West Africa
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Capability of CAM5.1 in simulating maximum air temperature patterns over West Africa during boreal spring
This study classifies maximum air temperature patterns over West Africa into six groups and evaluates the capability of a global climate model (Community Atmospheric Model version 5.1; CAM) to simulate them. We analyzed 45-year (1961–2005) multi-ensemble (50 members) simulations from CAM and compared the results with those of the Climate Research Unit (CRU) and the twentieth Century Reanalysis data sets. Using Self Organizing Map algorithm to classify the spatial patterns of maximum air temperature during boreal spring, the study reveals the temperature patterns that CAM can simulate well and those the model struggles to reproduce. The results show that the best agreements between the composites of observation and CAM occur in the first temperature pattern group (which features positive temperatures anomalies over the Sahel) and Node 2 (which features near-normal temperature) pattern of the third group. CAM succeeded in reproducing some of the associated regional atmospheric dynamics and thermodynamic features in winds (horizontal and vertical), temperature fields, the cloud fractions, and the mean sea-level pressure. Although CAM struggles to capture the relationship between air temperature patterns and tele-connection indices during the boreal spring season over West Africa, it agrees with observations that temperature patterns over the sub-region cannot be associated with a single climate index. An ensemble member (SIM48) captures the inter-annual variation of the observed temperaure patterns with high sycronization (ɳ > 44%), much better than that of ensembles mean (ɳ < 30%). SIM48 also captures adequately four of the spatial patterns in comparison to three captured by the ensembles mean. This indicates that, for better seasonal forecasts and more reliable future climate projections, the practice whereby an ensemble mean is based on uniformly averaging the members rather than the performance of individual ensemble members needs to be reviewed. The results of the study may be used to improve the perfomance of CAM over West Africa, thereby strengthening the on-going efforts to include CAM as part of multi-model forecasting system over West Africa
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13. The late onset of the 2015 wet season in Nigeria
We find no evidence that the delayed onset of the wet season over Nigeria during April - May 2015 was made more likely by anthropogenic influences or anomalous sea surface temperatures
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13. The late onset of the 2015 wet season in Nigeria
We find no evidence that the delayed onset of the wet season over Nigeria during April - May 2015 was made more likely by anthropogenic influences or anomalous sea surface temperatures