International audience ; In the winter 2016-2017 the largest epidemic of highly pathogenic avian influenza (HPAI) ever recorded in the European Union spread to all 28 member states. France was hit particularly hard and reported a total of 484 infected premises (IPs) by March 2017. We developed a mathematical model to analyze the spatiotemporal evolution of the epidemic and evaluate the impact of control strategies. We estimated that farms rearing ducks were on average 2.5 times more infectious and 5.0 times more susceptible to HPAI than farms rearing other avian species. The implementation of surveillance zones around IPs reduced transmission by a factor of 1.8 on average. Compared to the strengthening of pre-emptive culling measures enforced by French authorities in February 2017, we found that a faster depopulation of diagnosed IPs would have had a larger impact on the total number of infections. For example, halving the time delay from detection to slaughter of infected animals would have reduced the total number of IPs by 52% and total cull numbers by 50% on average. This study showcases the possible contribution of modeling to inform and optimize control strategies during an outbreak.
International audience ; In the winter 2016-2017 the largest epidemic of highly pathogenic avian influenza (HPAI) ever recorded in the European Union spread to all 28 member states. France was hit particularly hard and reported a total of 484 infected premises (IPs) by March 2017. We developed a mathematical model to analyze the spatiotemporal evolution of the epidemic and evaluate the impact of control strategies. We estimated that farms rearing ducks were on average 2.5 times more infectious and 5.0 times more susceptible to HPAI than farms rearing other avian species. The implementation of surveillance zones around IPs reduced transmission by a factor of 1.8 on average. Compared to the strengthening of pre-emptive culling measures enforced by French authorities in February 2017, we found that a faster depopulation of diagnosed IPs would have had a larger impact on the total number of infections. For example, halving the time delay from detection to slaughter of infected animals would have reduced the total number of IPs by 52% and total cull numbers by 50% on average. This study showcases the possible contribution of modeling to inform and optimize control strategies during an outbreak.
International audience ; In the winter 2016-2017 the largest epidemic of highly pathogenic avian influenza (HPAI) ever recorded in the European Union spread to all 28 member states. France was hit particularly hard and reported a total of 484 infected premises (IPs) by March 2017. We developed a mathematical model to analyze the spatiotemporal evolution of the epidemic and evaluate the impact of control strategies. We estimated that farms rearing ducks were on average 2.5 times more infectious and 5.0 times more susceptible to HPAI than farms rearing other avian species. The implementation of surveillance zones around IPs reduced transmission by a factor of 1.8 on average. Compared to the strengthening of pre-emptive culling measures enforced by French authorities in February 2017, we found that a faster depopulation of diagnosed IPs would have had a larger impact on the total number of infections. For example, halving the time delay from detection to slaughter of infected animals would have reduced the total number of IPs by 52% and total cull numbers by 50% on average. This study showcases the possible contribution of modeling to inform and optimize control strategies during an outbreak.
In the winter 2016–2017 the largest epidemic of highly pathogenic avian influenza (HPAI) ever recorded in the European Union spread to all 28 member states. France was hit particularly hard and reported a total of 484 infected premises (IPs) by March 2017. We developed a mathematical model to analyze the spatiotemporal evolution of the epidemic and evaluate the impact of control strategies. We estimated that farms rearing ducks were on average 2.5 times more infectious and 5.0 times more susceptible to HPAI than farms rearing other avian species. The implementation of surveillance zones around IPs reduced transmission by a factor of 1.8 on average. Compared to the strengthening of pre-emptive culling measures enforced by French authorities in February 2017, we found that a faster depopulation of diagnosed IPs would have had a larger impact on the total number of infections. For example, halving the time delay from detection to slaughter of infected animals would have reduced the total number of IPs by 52% and total cull numbers by 50% on average. This study showcases the possible contribution of modeling to inform and optimize control strategies during an outbreak. ; ISSN:1878-0067 ; ISSN:1755-4365
International audience ; Background : The 2018–2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces in the Democratic Republic of the Congo (DRC) is the largest ever recorded in the DRC. It has been declared a Public Health Emergency of International Concern. The outbreak emerged in a region of chronic conflict and insecurity, and directed attacks against health care workers may have interfered with disease response activities. Our study characterizes and quantifies the broader conflict dynamics over the course of the outbreak by pairing epidemiological and all available spatial conflict data.Methods : We build a set of conflict variables by mapping the spatial locations of all conflict events and their associated deaths in each of the affected health zones in North Kivu and Ituri, eastern DRC, before and during the outbreak. Using these data, we compare patterns of conflict before and during the outbreak in affected health zones and those not affected. We then test whether conflict is correlated with increased EVD transmission at the health zone level.Findings : The incidence of conflict events per capita is ~ 600 times more likely in Ituri and North Kivu than for the rest of the DRC. We identified 15 time periods of substantial uninterrupted transmission across 11 health zones and a total of 120 bi-weeks. We do not find significant short-term associations between the bi-week reproduction numbers and the number of conflicts. However, we do find that the incidence of conflict per capita was correlated with the incidence of EVD per capita at the health zone level for the entire outbreak (Pearson's r = 0.33, 95% CI 0.05–0.57). In the two provinces, the monthly number of conflict events also increased by a factor of 2.7 in Ebola-affected health zones ( p value < 0.05) compared to 2.0 where no transmission was reported and 1.3 in the rest of the DRC, in the period between February 2019 and July 2019.Conclusion : We characterized the association between variables documenting broad conflict ...
International audience ; Background : The 2018–2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces in the Democratic Republic of the Congo (DRC) is the largest ever recorded in the DRC. It has been declared a Public Health Emergency of International Concern. The outbreak emerged in a region of chronic conflict and insecurity, and directed attacks against health care workers may have interfered with disease response activities. Our study characterizes and quantifies the broader conflict dynamics over the course of the outbreak by pairing epidemiological and all available spatial conflict data.Methods : We build a set of conflict variables by mapping the spatial locations of all conflict events and their associated deaths in each of the affected health zones in North Kivu and Ituri, eastern DRC, before and during the outbreak. Using these data, we compare patterns of conflict before and during the outbreak in affected health zones and those not affected. We then test whether conflict is correlated with increased EVD transmission at the health zone level.Findings : The incidence of conflict events per capita is ~ 600 times more likely in Ituri and North Kivu than for the rest of the DRC. We identified 15 time periods of substantial uninterrupted transmission across 11 health zones and a total of 120 bi-weeks. We do not find significant short-term associations between the bi-week reproduction numbers and the number of conflicts. However, we do find that the incidence of conflict per capita was correlated with the incidence of EVD per capita at the health zone level for the entire outbreak (Pearson's r = 0.33, 95% CI 0.05–0.57). In the two provinces, the monthly number of conflict events also increased by a factor of 2.7 in Ebola-affected health zones ( p value < 0.05) compared to 2.0 where no transmission was reported and 1.3 in the rest of the DRC, in the period between February 2019 and July 2019.Conclusion : We characterized the association between variables documenting broad conflict levels and EVD transmission. Such assessment is important to understand if and how such conflict variables could be used to inform the outbreak response. We found that while these variables can help characterize long-term challenges and susceptibilities of the different regions they provide little insight on the short-term dynamics of EVD transmission.
The authors acknowledge financial support from the UKCRC Translational Infection Research (TIR) Initiative and the Medical Research Council (Grant number G1000803), with contributions to the grant from the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research on behalf of the Department of Health, and the Chief Scientist Office of the Scottish Government Health Directorate (to Professor Peacock); from Wellcome Trust grant number 098051 awarded to the Wellcome Trust Sanger Institute; and the NIHR Cambridge Biomedical Research Centre (to Professor Peacock). S.Y.C.T. is an Australian National Health and Medical Research Council Career Development Fellow (1065736). ; Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of nosocomial infection. Whole-genome sequencing of MRSA has been used to define phylogeny and transmission in well-resourced healthcare settings, yet the greatest burden of nosocomial infection occurs in resource-restricted settings where barriers to transmission are lower. Here, we study the flux and genetic diversity of MRSA on ward and individual patient levels in a hospital where transmission was common. We repeatedly screened all patients on two intensive care units for MRSA carriage over a 3-mo period. All MRSA belonged to multilocus sequence type 239 (ST 239). We defined the population structure and charted the spread of MRSA by sequencing 79 isolates from 46 patients and five members of staff, including the first MRSA-positive screen isolates and up to two repeat isolates where available. Phylogenetic analysis identified a flux of distinct ST 239 clades over time in each intensive care unit. In total, five main clades were identified, which varied in the carriage of plasmids encoding antiseptic and antimicrobial resistance determinants. Sequence data confirmed intra- and interwards transmission events and identified individual patients who were colonized by more than one clade. One patient on each unit was the source of numerous transmission events, and deep sampling of one of these cases demonstrated colonization with a "cloud" of related MRSA variants. The application of whole-genome sequencing and analysis provides novel insights into the transmission of MRSA in under-resourced healthcare settings and has relevance to wider global health. ; Publisher PDF ; Peer reviewed
BACKGROUND: The 2018–2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces in the Democratic Republic of the Congo (DRC) is the largest ever recorded in the DRC. It has been declared a Public Health Emergency of International Concern. The outbreak emerged in a region of chronic conflict and insecurity, and directed attacks against health care workers may have interfered with disease response activities. Our study characterizes and quantifies the broader conflict dynamics over the course of the outbreak by pairing epidemiological and all available spatial conflict data. METHODS: We build a set of conflict variables by mapping the spatial locations of all conflict events and their associated deaths in each of the affected health zones in North Kivu and Ituri, eastern DRC, before and during the outbreak. Using these data, we compare patterns of conflict before and during the outbreak in affected health zones and those not affected. We then test whether conflict is correlated with increased EVD transmission at the health zone level. FINDINGS: The incidence of conflict events per capita is ~ 600 times more likely in Ituri and North Kivu than for the rest of the DRC. We identified 15 time periods of substantial uninterrupted transmission across 11 health zones and a total of 120 bi-weeks. We do not find significant short-term associations between the bi-week reproduction numbers and the number of conflicts. However, we do find that the incidence of conflict per capita was correlated with the incidence of EVD per capita at the health zone level for the entire outbreak (Pearson's r = 0.33, 95% CI 0.05–0.57). In the two provinces, the monthly number of conflict events also increased by a factor of 2.7 in Ebola-affected health zones (p value < 0.05) compared to 2.0 where no transmission was reported and 1.3 in the rest of the DRC, in the period between February 2019 and July 2019. CONCLUSION: We characterized the association between variables documenting broad conflict levels and EVD transmission. ...
International audience ; 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 ...
International audience ; 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
International audience ; 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
This is an Open Access article under the CC BY license. ; 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. ; info:eu-repo/semantics/publishedVersion
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.
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. ; ISSN:1473-3099 ; ISSN:1474-4457
