2021 UNAIDS Global AIDS Update. UNAIDS report shows that people living with HIV face a double jeopardy, HIV and COVID-19, while key populations and children continue to be left behind in access to HIV services
WHO six-year strategy for the health sector and community capacity development.
It aims to minimize damage to property, reduce injury and lives lost, and normalize the lives of those affected in a timely manner in the case of a damaging earthquake in the country.
It also seeks to contribute to the achievements of Myanmar Sustainable Development Goals as well as respond to Gl...obal and Regional Frameworks which Myanmar has endorsed.
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WHO/HTM/TB/2007.384a
“TB is too often a death sentence for people with AIDS.
It does not have to be this way.”
-Nelson Mandela, International conference on HIV /AIDS, Bangkok, Thailand, July 2004
Working Document Nov. 2020
The COVAX Supply and Logistics workstream lead by UNICEF, Gavi and WHO have released a working copy of the COVID-19 Vaccination, Country Readiness & Delivery: Supply and Logistics Guidance. Countries might find this Guide useful when developing and strengthening their sup...ply chain strategies to receive, store, distribute and manage the COVID-19 vaccines and their ancillary products, in line with their national deployment and vaccination plan (NDVP). The document also provides links to the different tools and resources to aid countries in performing assessment, planning and capacity-building activities.
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Tsetse traps and targets (insecticide-impregnated screens) function by attracting the flies to a device that collects and/or kills them. Traps can be used for entomological surveillance, and also for control. Targets are simpler than traps, but are not used for surveillance. They are impregnated wit...h biodegradable insecticides in order to kill any flies that alight on them. Traps can also be impregnated with insecticides. Traps and targets can both be used to eliminate a fraction of the tsetse population.
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Plan stratégique 2022-2030 de l’ICTC
This report investigates the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014. In addition to visual inspection of laboratory results, we examined how accounting for potential misclassification of HIV status through Bayesian latent ...class models affected the prevalence estimates. Two types of Bayesian models were specified: a model that only uses the individual dichotomous test results and a continuous model that uses the quantitative information of the EIA (i.e., the signal-to-cutoff values). Overall, we found that adjusted prevalence estimates matched the surveys’ original results, with overlapping uncertainty intervals. This suggested that misclassification of HIV status should not affect the prevalence estimates in most surveys. However, our analyses suggested that two surveys may be problematic. The prevalence could have been overestimated in the Uganda AIDS Indicator Survey 2011 and the Zambia Demographic and Health Survey 2013-14, although the magnitude of overestimation remains difficult to ascertain. Interpreting results from the Uganda survey is difficult because of the lack of internal quality control and potential violation of the multivariate normality assumption of the continuous Bayesian latent class model. In conclusion, despite the limitations of our latent class models, our analyses suggest that prevalence estimates from most of the surveys reviewed are not affected by sample misclassification.
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The frequency of infectious disease epidemics is increasing, and the role of the health sector in the management of epidemics is crucial in terms of response. In the context of infectious disease epidemics, the use of climate-informed early warning systems (EWS) has the potential to increase the eff...ectiveness of disease control by intervening before or at the beginning of the epidemic curve, instead of during the downward slope.
Currently, the initiation of interventions is heavily reliant on routine disease surveillance systems – data that often arrive too late for preventative response. However, forecasting of disease outbreaks using surveillance and weather information shows promising potential – there also remains further scope to examine seasonal climate forecasts. By combining these elements in new EWS based on computational models, it will be possible to improve both the timeliness and impact of disease control. The World Health Organization (WHO) is strengthening existing surveillance systems for infectious diseases to enable the development of more robust and timely EWS, which has resulted in the rapid development and innovation of EWS for disease outbreaks.
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