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10.1016/S1473-3099(16)30513-8 http://scihub22266oqcxt.onion/10.1016/S1473-3099(16)30513-8 C5332542!5332542!28017559     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Lancet+Infect+Dis 2017 ; 17 (3): 330-8 Nephropedia Template TP {{tp|p=28017559|t=2017. Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015?16: a modelling study |pdf=|usr=}}{{28017559}} gab.com Text Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015 16: a modelling study JO: Lancet Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=28017559 #FOAvip Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5?7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34?0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52?0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13?0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92?0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. Funding: Wellcome Trust. Twit Text FOAVip Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015 16: a modelling study ????Lancet Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=28017559 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28017559 #FOAvip English Wikipedia <ref>{{Cite pmid|28017559}}</ref> Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015?16: a modelling study #MMPMID28017559 Kraemer MUG; Faria NR; Reiner RC; Golding N; Nikolay B; Stasse S; Johansson MA; Salje H; Faye O; Wint GRW; Niedrig M; Shearer FM; Hill SC; Thompson RN; Bisanzio D; Taveira N; Nax HH; Pradelski BSR; Nsoesie EO; Murphy NR; Bogoch II; Khan K; Brownstein JS; Tatem AJ; de Oliveira T; Smith DL; Sall AA; Pybus OG; Hay SI; Cauchemez S Lancet Infect Dis 2017[Mar]; 17 (3): 330-8 PMID28017559show ga Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5?7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34?0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52?0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13?0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92?0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. Funding: Wellcome Trust. äSpread of yellow fever virus outbreak in Angola and the Democratic Rep(epmc).pdf DeepDyve Pubget Overpricing