178 results found
Mills HL, Riley S, 2014, The Spatial Resolution of Epidemic Peaks, PLOS COMPUTATIONAL BIOLOGY, Vol: 10
Kwok KO, Jiang C, Tan L, et al., 2014, [An international collaborative study on influenza viruses antibody titers and contact patterns of individuals in rural and urban household of Guangzhou]., Zhonghua Liu Xing Bing Xue Za Zhi, Vol: 35, Pages: 433-436, ISSN: 0254-6450
OBJECTIVE: To describe the influenza viruses antibody levels and contact patterns of individuals in rural and urban regions of Guangzhou and to understand how contact patterns and other factors would correlate with the levels on the titers of antibody. METHODS: "Google Map" was used to randomly select the study points from the administrative areas in Guangzhou region. Each participant was required to provide 5 ml blood serum sample to be tested against different strains of H1N1 and H3N2 influenza viruses. RESULTS: 1) Using "Google map", 50 study points were selected but only 40 study points would meet the inclusion criteria. The cohort of this study consisted 856 households with 2 801 individuals. 1 821 participants (65% of the total number individuals in the cohort) completed the questionnaires. Among the 1 821 participants, 77.3% (1 407/1 821) and 22.7% (414/1 821) of them were from rural and urban areas respectively. There were more male participants in the rural but more female participants in the urban regions. Majority of the participants were from age group 18-59 followed by group 60 with aged 2-17 the least, in both rural and urban areas. 2) 78.1% (1 423/1 821) of the participants provided their serum samples. There appeared a strong correlation between age of the participants and the strength of their antibodies against that strain when a strain first circulated. In particular, seroprevalence was the highest at the age group 2-17. 3) 'Contact' was defined as persons having physical touch or/and conversation within one meter with the participants. Participants reported all having had large number of contacts. The proportion of participants having contacts with ten persons or above was the highest, ranging from 49.8% to 72.6%, particularly in age group 6-17. Compared to weekdays, participants had fewer contact persons on weekends. CONCLUSION: There was a strong correlation between the age of participants at the time when the strains first
Kucharski A, Mills H, Pinsent A, et al., 2014, Distinguishing Between Reservoir Exposure and Human-to-Human Transmission for Emerging Pathogens Using Case Onset Data., PLoS Curr, Vol: 6
Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%-32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.
Pepin KM, Spackman E, Brown JD, et al., 2014, Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America, PREVENTIVE VETERINARY MEDICINE, Vol: 113, Pages: 376-397, ISSN: 0167-5877
Cauchemez S, Fraser C, Van Kerkhove MD, et al., 2014, Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility, LANCET INFECTIOUS DISEASES, Vol: 14, Pages: 50-56, ISSN: 1473-3099
, 2014, Ebola Virus Disease in West Africa — The First 9 Months of the Epidemic and Forward Projections, New England Journal of Medicine, Vol: 371, Pages: 1481-1495
Pepin KM, Lloyd-Smith JO, Webb CT, et al., 2013, Minimizing the threat of pandemic emergence from avian influenza in poultry systems, BMC INFECTIOUS DISEASES, Vol: 13, ISSN: 1471-2334
Riley S, 2013, Complex Disease Dynamics and the Design of Influenza Vaccination Programs, PLOS MEDICINE, Vol: 10, ISSN: 1549-1277
Van Kerkhove MD, Hirve S, Koukounari A, et al., 2013, Estimating age-specific cumulative incidence for the 2009 influenza pandemic: a meta-analysis of A(H1N1)pdm09 serological studies from 19 countries, Influenza and Other Respiratory Viruses, Vol: 7, Pages: 872-886, ISSN: 1750-2640
BACKGROUND: The global impact of the 2009 influenza A(H1N1) pandemic (H1N1pdm) is not well understood. OBJECTIVES: We estimate overall and age-specific prevalence of cross-reactive antibodies to H1N1pdm virus and rates of H1N1pdm infection during the first year of the pandemic using data from published and unpublished H1N1pdm seroepidemiological studies. METHODS: Primary aggregate H1N1pdm serologic data from each study were stratified in standardized age groups and evaluated based on when sera were collected in relation to national or subnational peak H1N1pdm activity. Seropositivity was assessed using well-described and standardized hemagglutination inhibition (HI titers >/=32 or >/=40) and microneutralization (MN >/= 40) laboratory assays. The prevalence of cross-reactive antibodies to the H1N1pdm virus was estimated for studies using sera collected prior to the start of the pandemic (between 2004 and April 2009); H1N1pdm cumulative incidence was estimated for studies in which collected both pre- and post-pandemic sera; and H1N1pdm seropositivity was calculated from studies with post-pandemic sera only (collected between December 2009-June 2010). RESULTS: Data from 27 published/unpublished studies from 19 countries/administrative regions - Australia, Canada, China, Finland, France, Germany, Hong Kong SAR, India, Iran, Italy, Japan, Netherlands, New Zealand, Norway, Reunion Island, Singapore, United Kingdom, United States, and Vietnam - were eligible for inclusion. The overall age-standardized pre-pandemic prevalence of cross-reactive antibodies was 5% (95%CI 3-7%) and varied significantly by age with the highest rates among persons >/=65 years old (14% 95%CI 8-24%). Overall age-standardized H1N1pdm cumulative incidence was 24% (95%CI 20-27%) and varied significantly by age with the highest in children 5-19 (47% 95%CI 39-55%) and 0-4 years old (36% 95%CI 30-43%). CONCLUSIONS: Our results offer unique insight into the global impact of the H1N1 pandemic a
Strelioff CC, Vijaykrishna D, Riley S, et al., 2013, Inferring patterns of influenza transmission in swine from multiple streams of surveillance data, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 280, ISSN: 0962-8452
Cauchemez S, Van Kerkhove MD, Riley S, et al., 2013, Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart, EUROSURVEILLANCE, Vol: 18, Pages: 7-13, ISSN: 1560-7917
Kwok KO, Leung GM, Mak P, et al., 2013, Antiviral stockpiles for influenza pandemics from the household perspective: Treatment alone versus treatment with prophylaxis, EPIDEMICS, Vol: 5, Pages: 92-97, ISSN: 1755-4365
Cowling BJ, Ho LM, Riley S, et al., 2013, Statistical algorithms for early detection of the annual influenza peak season in Hong Kong using sentinel surveillance data., Hong Kong Med J, Vol: 19 Suppl 4, Pages: 4-5, ISSN: 1024-2708
In Hong Kong, influenza sentinel surveillance systems have been recently established. Methods that compare current data to data from recent weeks may be appropriate to indicate the start of peak influenza activity. These methods can produce reliable and timely alerts at the start of the annual influenza peak season.
Riley S, Cowling BJ, Chan KH, et al., 2013, Viral evolution from one generation of human influenza infection to the next., Hong Kong Med J, Vol: 19 Suppl 4, Pages: 6-10, ISSN: 1024-2708
1. In a sub-tropical epidemic, most of the apparent household secondary cases are actually secondary infections. 2. The consensus sequence for the entire influenza virus genome is not usually identical within the same household sample. Rather, there are commonly one or two nucleotide changes. 3. These results hint at an obvious generational threshold for adaptation at the level of the consensus sequence.
Cowling BJ, Chan KH, Peiris JSM, et al., 2013, Viral shedding, clinical history and transmission of influenza., Hong Kong Med J, Vol: 19 Suppl 4, Pages: 19-23, ISSN: 1024-2708
1. During influenza infections, most viral shedding occurs within a few days of illness onset. 2. Children may be more infectious than adults because they shed more virus. 3. The degree of viral shedding (infectiousness) correlates with symptoms and tympanic temperature.
Cowling B, Desenclos J-C, Riley S, et al., 2013, PLOS Currents: Outbreaks --- For findings that the world just can't wait to see., PLoS Curr, Vol: 5
Riley P, Ben-Nun M, Armenta R, et al., 2013, Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations, PLOS COMPUTATIONAL BIOLOGY, Vol: 9
Pepin KM, Wang J, Webb CT, et al., 2013, Anticipating the Prevalence of Avian Influenza Subtypes H9 and H5 in Live-Bird Markets, PLOS ONE, Vol: 8, ISSN: 1932-6203
Pepin KM, Riley S, Grenfell BT, 2013, Effects of influenza antivirals on individual and population immunity over many epidemic waves, EPIDEMIOLOGY AND INFECTION, Vol: 141, Pages: 366-376, ISSN: 0950-2688
Pepin KM, Wang J, Webb CT, et al., 2013, Multiannual patterns of influenza A transmission in Chinese live bird market systems, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 7, Pages: 97-107, ISSN: 1750-2640
Van Kerkhove MD, Broberg E, Engelhardt OG, et al., 2012, The consortium for the standardization of influenza seroepidemiology (CONSISE): a global partnership to standardize influenza seroepidemiology and develop influenza investigation protocols to inform public health policy., Influenza Other Respi Viruses
CONSISE - The consortium for the Standardization of Influenza Seroepidemiology - is a global partnership to develop influenza investigation protocols and standardize seroepidemiology to inform health policy. This international partnership was formed in 2011 and was created out of a need, identified during the 2009 H1N1 pandemic, for timely seroepidemiological data to better estimate pandemic virus infection severity and attack rates to inform policy decisions. CONSISE has developed into a consortium of two interactive working groups: epidemiology and laboratory, with a steering committee composed of individuals from several organizations. CONSISE has had two international meetings with more planned for 2013. We seek additional members from public health agencies, academic institutions and other interested parties.
Read JM, Edmunds WJ, Riley S, et al., 2012, Close encounters of the infectious kind: methods to measure social mixing behaviour, EPIDEMIOLOGY AND INFECTION, Vol: 140, Pages: 2117-2130, ISSN: 0950-2688
Laurie KL, Huston P, Riley S, et al., 2012, Influenza serological studies to inform public health action: best practices to optimise timing, quality and reporting, Influenza and Other Respiratory Viruses
Lessler J, Riley S, Read JM, et al., 2012, Evidence for Antigenic Seniority in Influenza A (H3N2) Antibody Responses in Southern China, PLOS PATHOGENS, Vol: 8, ISSN: 1553-7374
Van Kerkhove MD, Riley S, Lipsitch M, et al., 2012, Comment on "Seroevidence for H5N1 Influenza Infections in Humans: Meta-Analysis", SCIENCE, Vol: 336, ISSN: 0036-8075
Riley R, Leung GM, Cowling BJ, 2012, Public health interventions to control the spread of a directly transmitted human pathogen within and between Hong Kong and Guangzhou., Hong Kong Med J, Vol: 18 Suppl 2, Pages: 37-38, ISSN: 1024-2708
The ability to detect and differentiate between fast and slow spatial spread of infectious disease depends on the density of the surveillance network. 2. The results of this study suggest that more concentrated surveillance networks are required in Guangzhou compared with other regions, such as Thailand and Europe, as long-distance travel is less frequent.
Riley S, Leung GM, Ho LM, et al., 2012, Transmission of Japanese encephalitis virus in Hong Kong., Hong Kong Med J, Vol: 18 Suppl 2, Pages: 45-46, ISSN: 1024-2708
1. Pigs are likely to be the main amplifying host for Japanese encephalitis virus. 2. The success of a swine vaccination programme depends on the timing of the loss of maternal antibody protection and seasonal dynamics of the vector population. 3. Vaccination may be ineffective in the face of strong natural infection because of the variability in timing of the loss of maternal antibody protection.4. Evidence in support of swine vaccination as a human health intervention was not found.
Bahl J, Nelson MI, Chan KH, et al., 2011, Temporally structured metapopulation dynamics and persistence of influenza A H3N2 virus in humans., Proc Natl Acad Sci USA
Perera RAPM, Riley S, Ma SK, et al., 2011, Seroconversion to Pandemic (H1N1) 2009 Virus and Cross-Reactive Immunity to Other Swine Influenza Viruses., Emerging Infect Dis, Vol: 17, Pages: 1897-1899
Lipsitch M, Finelli L, Heffernan RT, et al., 2011, IMPROVING THE EVIDENCE BASE FOR DECISION MAKING DURING A PANDEMIC: THE EXAMPLE OF 2009 INFLUENZA A/H1N1, BIOSECURITY AND BIOTERRORISM-BIODEFENSE STRATEGY PRACTICE AND SCIENCE, Vol: 9, Pages: 89-115, ISSN: 1538-7135
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