Browsing by Author "Faria, Nuno R."
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- Spread of Yellow Fever Virus outbreak in Angola and the Democratic Republic Congo 2015-2016: a modelling studyPublication . Kraemer, Moritz U. G.; Faria, Nuno R.; Reiner Jr, Robert C.; Golding, Nick; Nikolay, Birgit; Stasse, Stephanie; Johansson, Michael A.; Salje, Henrik; Faye, Ousmane; Wint, G. R. William; Niedrig, Matthias; Shearer, Freya M.; Hill, Sarah C.; Thompson, Robin N.; Bisanzio, Donal; Taveira, Nuno; Nax, Heinrich H.; Pradelski, Bary S. R.; Nsoesie, Elaine O.; Murphy, Nicholas R.; Bogoch, Isaac I.; Khan, Kamran; Brownstein, John S.; Tatem, Andrew J.; Oliveira, Tulio de; Smith, David L.; Sall, Amadou A.; Pybus, Oliver G. Pybus; Hay, Simon I.; Cauchemez, SimonBACKGROUND: 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.
- Tracing the impact of public health interventions on HIV-1 transmission in Portugal using molecular epidemiologyPublication . Vasylyeva, Tetyana I.; Plessis, Louis du; Pineda-Peña, Andrea C.; Kühnert, Denise; Lemey, Philippe; Vandamme, Anne-Mieke; Gomes, Perpétua; Camacho, Ricardo J.; Pybus, Oliver G.; Abecasis, Ana B.; Faria, Nuno R.Background Estimation of temporal changes in human immunodeficiency virus (HIV) transmission patterns can help to elucidate the impact of preventive strategies and public health policies. Methods Portuguese HIV-1 subtype B and G pol genetic sequences were appended to global reference data sets to identify country-specific transmission clades. Bayesian birth-death models were used to estimate subtype-specific effective reproductive numbers (Re). Discrete trait analysis (DTA) was used to quantify mixing among transmission groups. Results We identified 5 subtype B Portuguese clades (26–79 sequences) and a large monophyletic subtype G Portuguese clade (236 sequences). We estimated that major shifts in HIV-1 transmission occurred around 1999 (95% Bayesian credible interval [BCI], 1998–2000) and 2000 (95% BCI, 1998–2001) for subtypes B and G, respectively. For subtype B, Re dropped from 1.91 (95% BCI, 1.73–2.09) to 0.62 (95% BCI,.52–.72). For subtype G, Re decreased from 1.49 (95% BCI, 1.39–1.59) to 0.72 (95% BCI, .63–.8). The DTA suggests that people who inject drugs (PWID) and heterosexuals were the source of most (>80%) virus lineage transitions for subtypes G and B, respectively. Conclusions The estimated declines in Re coincide with the introduction of highly active antiretroviral therapy and the scale-up of harm reduction for PWID. Inferred transmission events across transmission groups emphasize the importance of prevention efforts for bridging populations.