119 results found
Cori A, Donnelly CA, Dorigatti I, et al., 2017, Key data for outbreak evaluation: building on the Ebola experience., Philos Trans R Soc Lond B Biol Sci, Vol: 372
Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.
Garske T, Cori A, Ariyarajah A, et al., 2017, Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013-2016., Philos Trans R Soc Lond B Biol Sci, Vol: 372
The 2013-2016 Ebola outbreak in West Africa is the largest on record with 28 616 confirmed, probable and suspected cases and 11 310 deaths officially recorded by 10 June 2016, the true burden probably considerably higher. The case fatality ratio (CFR: proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits, if estimated accurately. We analysed individual-level clinical outcome data from Guinea, Liberia and Sierra Leone officially reported to the World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%) among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalized also played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres (TCs), adjusting for known factors influencing survival and identified eight districts and three TCs with a CFR significantly different from the average. From the current dataset, we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors or was caused by differences in reporting practices or case ascertainment.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.
Kucharski A, Riley S, 2017, Reducing uncertainty about flavivirus infections, LANCET INFECTIOUS DISEASES, Vol: 17, Pages: 13-15, ISSN: 1473-3099
Lau MSY, Dalziel BD, Funk S, et al., 2017, Spatial and temporal dynamics of superspreading events in the 2014-2015 West Africa Ebola epidemic, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 114, Pages: 2337-2342, ISSN: 0027-8424
Nouvellet P, Cori A, Garske T, et al., 2017, A simple approach to measure transmissibility and forecast incidence., Epidemics
Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen "future" simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes - other than the widespread depletion of susceptible individuals - that produce non-exponential patterns of incidence.
Pepin KM, Kay SL, Golas BD, et al., 2017, Inferring infection hazard in wildlife populations by linking data across individual and population scales, ECOLOGY LETTERS, Vol: 20, Pages: 275-292, ISSN: 1461-023X
Yuan HY, Baguelin M, Kwok KO, et al., 2017, The impact of stratified immunity on the transmission dynamics of influenza., Epidemics
Although empirical studies show that protection against influenza infection in humans is closely related to antibody titres, influenza epidemics are often described under the assumption that individuals are either susceptible or not. Here we develop a model in which antibody titre classes are enumerated explicitly and mapped onto a variable scale of susceptibility in different age groups. Fitting only with pre- and post-wave serological data during 2009 pandemic in Hong Kong, we demonstrate that with stratified immunity, the timing and the magnitude of the epidemic dynamics can be reconstructed more accurately than is possible with binary seropositivity data. We also show that increased infectiousness of children relative to adults and age-specific mixing are required to reproduce age-specific seroprevalence observed in Hong Kong, while pre-existing immunity in the elderly is not. Overall, our results suggest that stratified immunity in an aged-structured heterogeneous population plays a significant role in determining the shape of influenza epidemics.
Agua-Agum J, Allegranzi B, Ariyarajah A, et al., 2016, After Ebola in West Africa - Unpredictable Risks, Preventable Epidemics, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 375, Pages: 587-596, ISSN: 0028-4793
Agua-Agum J, Ariyarajah A, Aylward B, et al., 2016, Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study, PLOS MEDICINE, Vol: 13, ISSN: 1549-1676
Agua-Agum J, Ariyarajah A, Blake IM, et al., 2016, Ebola Virus Disease among Male and Female Persons in West Africa, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 374, Pages: 96-98, ISSN: 0028-4793
Cauchemez S, Nouvellet P, Cori A, et al., 2016, Unraveling the drivers of MERS-CoV transmission, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 113, Pages: 9081-9086, ISSN: 0027-8424
Jiang CQ, Lessler J, Kim L, et al., 2016, Cohort Profile: A study of influenza immunity in the urban and rural Guangzhou region of China: the Fluscape Study., Int J Epidemiol
Lipsitch M, Barclay W, Raman R, et al., 2016, Viral factors in influenza pandemic risk assessment, ELIFE, Vol: 5, ISSN: 2050-084X
Pinsent A, Fraser C, Ferguson NM, et al., 2016, A systematic review of reported reassortant viral lineages of influenza A, BMC INFECTIOUS DISEASES, Vol: 16, ISSN: 1471-2334
Pitzer VE, Aguas R, Riley S, et al., 2016, High turnover drives prolonged persistence of influenza in managed pig herds, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 13, ISSN: 1742-5689
Riley P, Cost AA, Riley S, 2016, Intra-Weekly Variations of Influenza-Like Illness in Military Populations, MILITARY MEDICINE, Vol: 181, Pages: 364-368, ISSN: 0026-4075
Riley S, 2016, Making high-res Zika maps, NATURE MICROBIOLOGY, Vol: 1
Truelove S, Zhu H, Lessler J, et al., 2016, A comparison of hemagglutination inhibition and neutralization assays for characterizing immunity to seasonal influenza A, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 10, Pages: 518-524, ISSN: 1750-2640
Wu KM, Riley S, 2016, Estimation of the Basic Reproductive Number and Mean Serial Interval of a Novel Pathogen in a Small, Well-Observed Discrete Population, PLOS ONE, Vol: 11, ISSN: 1932-6203
Bedford T, Riley S, Barr IG, et al., 2015, Global circulation patterns of seasonal influenza viruses vary with antigenic drift, NATURE, Vol: 523, Pages: 217-U206, ISSN: 0028-0836
Britton T, House T, Lloyd AL, et al., 2015, Five challenges for stochastic epidemic models involving global transmission, EPIDEMICS, Vol: 10, Pages: 54-57, ISSN: 1755-4365
Chretien J-P, Riley S, George DB, 2015, Mathematical modeling of the West Africa Ebola epidemic, eLife, Vol: 4, ISSN: 2050-084X
As of November 2015, the Ebola virus disease (EVD) epidemic that began in West Africa in late 2013 is waning. The human toll includes more than 28,000 EVD cases and 11,000 deaths in Guinea, Liberia, and Sierra Leone, the most heavily-affected countries. We reviewed 66 mathematical modeling studies of the EVD epidemic published in the peer-reviewed literature to assess the key uncertainties models addressed, data used for modeling, public sharing of data and results, and model performance. Based on the review, we suggest steps to improve the use of modeling in future public health emergencies.
Eames K, Bansal S, Frost S, et al., 2015, Six challenges in measuring contact networks for use in modelling, EPIDEMICS, Vol: 10, Pages: 72-77, ISSN: 1755-4365
Kucharski AJ, Lessler J, Read JM, et al., 2015, Estimating the Life Course of Influenza A (H3N2) Antibody Responses from Cross-Sectional Data, PLOS BIOLOGY, Vol: 13, ISSN: 1545-7885
Kucharski AJ, Mills HL, Donnelly CA, et al., 2015, Transmission Potential of Influenza A(H7N9) Virus, China, 2013-2014, EMERGING INFECTIOUS DISEASES, Vol: 21, Pages: 852-855, ISSN: 1080-6040
Kwok KO, Davoudi B, Riley S, et al., 2015, Early real-time estimation of the basic reproduction number of emerging or reemerging infectious diseases in a community with heterogeneous contact pattern: Using data from Hong Kong 2009 H1N1 Pandemic Influenza as an illustrative example, PLOS ONE, Vol: 10, ISSN: 1932-6203
Lau MSY, Cowling BJ, Cook AR, et al., 2015, Inferring influenza dynamics and control in households, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 112, Pages: 9094-9099, ISSN: 0027-8424
Lipsitch M, Donnelly CA, Fraser C, et al., 2015, Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks, PLOS NEGLECTED TROPICAL DISEASES, Vol: 9, ISSN: 1935-2735
Lloyd-Smith JO, Funk S, McLean AR, et al., 2015, Nine challenges in modelling the emergence of novel pathogens, EPIDEMICS, Vol: 10, Pages: 35-39, ISSN: 1755-4365
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