Publications
73 results found
Ferguson NM, Van Kerkhove MD, 2014, Identification of MERS-CoV in dromedary camels, LANCET INFECTIOUS DISEASES, Vol: 14, Pages: 93-94, ISSN: 1473-3099
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- Citations: 31
Van Kerkhove M, Wood J, 2014, Conclusions of the fourth CONSISE international meeting, EUROSURVEILLANCE, Vol: 19, Pages: 36-37, ISSN: 1560-7917
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- Citations: 1
Van Kerkhove M, Wood J, CONSISE, 2014, Conclusions of the fourth CONSISE international meeting., Euro surveillance : bulletin Européen sur les maladies transmissibles = European communicable disease bulletin, Vol: 19
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- Citations: 2
Laurie KL, Engelhardt OG, Wood J, et al., 2014, Global seroepidemiology: Value and limitations, Clinical Insights: Influenza Surveillance, Pages: 51-66, ISBN: 9781780843629
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- Citations: 2
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
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- Citations: 226
Simonsen L, Spreeuwenberg P, Lustig R, et al., 2013, Global Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study, PLoS Medicine, Vol: 10
Abdallat MM, Abroug F, Al Dhahry SHS, et al., 2013, State of knowledge and data gaps of middle east respiratory syndrome coronavirus (MERS-CoV) in humans, PLoS Currents, Vol: 5
BACKGROUND: Between September 2012 and 22 October 2013, 144 laboratory-confirmed and 17 probable MERS-CoV cases from nine countries were notified to WHO. METHODS: We summarize what is known about the epidemiology, virology, phylogeny and emergence of MERS-CoV to inform public health policies. RESULTS: The median age of patients (n=161) was 50 years (range 14 months to 94 years), 64.5% were male and 63.4% experienced severe respiratory disease. 76.0% of patients were reported to have ≥1 underlying medical condition and fatal cases, compared to recovered or asymptomatic cases were more likely to have an underlying condition (86.8% vs. 42.4%, p<0.001). Analysis of genetic sequence data suggests multiple independent introductions into human populations and modelled estimates using epidemiologic and genetic data suggest R0 is <1, though the upper range of estimates may exceed 1. Index/sporadic cases (cases with no epidemiologic-link to other cases) were more likely to be older (median 59.0 years vs. 43.0 years, p<0.001) compared to secondary cases, although these proportions have declined over time. 80.9% vs. 67.2% of index/sporadic and secondary cases, respectively, reported ≥1 underlying condition. Clinical presentation ranges from asymptomatic to severe pneumonia with acute respiratory distress syndrome and multi-organ failure. Nearly all symptomatic patients presented with respiratory symptoms and 1/3 of patients also had gastrointestinal symptoms. CONCLUSIONS: Sustained human-to-human transmission of MERS-CoV has not been observed. Outbreaks have been extinguished without overly aggressive isolation and quarantine suggesting that transmission of virus may be stopped with implementation of appropriate infection control measures.
Sridhar S, Begom S, Bermingham A, et al., 2013, Incidence of Influenza A(H1N1) pdm09 Infection, United Kingdom, 2009-2011, EMERGING INFECTIOUS DISEASES, Vol: 19, Pages: 1866-1869, ISSN: 1080-6040
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- Citations: 6
Storms AD, Van Kerkhove MD, Azziz-Baumgartner E, et al., 2013, Worldwide transmission and seasonal variation of pandemic influenza A(H1N1)2009 virus activity during the 2009-2010 pandemic, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 7, Pages: 1328-1335, ISSN: 1750-2640
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- Citations: 9
Van Kerkhove MD, 2013, Brief literature review for the WHO global influenza research agenda - highly pathogenic avian influenza H5N1 risk in humans, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 7, Pages: 26-33, ISSN: 1750-2640
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- Citations: 34
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
Gambhir M, Swerdlow DL, Finelli L, et al., 2013, Multiple Contributory Factors to the Age Distribution of Disease Cases: A Modeling Study in the Context of Influenza A(H3N2v), CLINICAL INFECTIOUS DISEASES, Vol: 57, Pages: S23-S27, ISSN: 1058-4838
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- Citations: 10
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
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- Citations: 8
Roddy P, Howard N, Van Kerkhove MD, et al., 2012, Clinical manifestations and case management of ebola haemorrhagic Fever caused by a newly identified virus strain, bundibugyo, Uganda, 2007-2008., Plos One
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.
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
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
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- Citations: 28
Nicoll A, Ciancio BC, Chavarrias VL, et al., 2012, Influenza-related deaths - available methods for estimating numbers and detecting patterns for seasonal and pandemic influenza in Europe, EUROSURVEILLANCE, Vol: 17, Pages: 18-30, ISSN: 1560-7917
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- Citations: 38
Van Kerkhove MD, Ferguson NM, 2012, Epidemic and intervention modelling – a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic, Bulletin of the World Health Organization, Vol: 90
ProblemOutbreak analysis and mathematical modelling are crucial for planning public health responses to infectious disease outbreaks, epidemics and pandemics. This paper describes the data analysis and mathematical modelling undertaken during and following the 2009 influenza pandemic, especially to inform public health planning and decision-making.ApproachSoon after A(H1N1)pdm09 emerged in North America in 2009, the World Health Organization convened an informal mathematical modelling network of public health and academic experts and modelling groups. This network and other modelling groups worked with policy-makers to characterize the dynamics and impact of the pandemic and assess the effectiveness of interventions in different settings.SettingThe 2009 A(H1N1) influenza pandemic.Relevant changesModellers provided a quantitative framework for analysing surveillance data and for understanding the dynamics of the epidemic and the impact of interventions. However, what most often informed policy decisions on a day-to-day basis was arguably not sophisticated simulation modelling, but rather, real-time statistical analyses based on mechanistic transmission models relying on available epidemiologic and virologic data.Lessons learntA key lesson was that modelling cannot substitute for data; it can only make use of available data and highlight what additional data might best inform policy. Data gaps in 2009, especially from low-resource countries, made it difficult to evaluate severity, the effects of seasonal variation on transmission and the effectiveness of non-pharmaceutical interventions. Better communication between modellers and public health practitioners is needed to manage expectations, facilitate data sharing and interpretation and reduce inconsistency in results.
Borchert M, Mutyaba I, Van Kerkhove MD, et al., 2011, Ebola haemorrhagic fever outbreak in Masindi District, Uganda: outbreak description and lessons learned, BMC Infectious Diseases
BackgroundEbola haemorrhagic fever (EHF) is infamous for its high case-fatality proportion (CFP) and the ease with which it spreads among contacts of the diseased. We describe the course of the EHF outbreak in Masindi, Uganda, in the year 2000, and report on response activities.MethodsWe analysed surveillance records, hospital statistics, and our own observations during response activities. We used Fisher's exact tests for differences in proportions, t-tests for differences in means, and logistic regression for multivariable analysis.ResultsThe response to the outbreak consisted of surveillance, case management, logistics and public mobilisation. Twenty-six EHF cases (24 laboratory confirmed, two probable) occurred between October 21st and December 22nd, 2000. CFP was 69% (18/26). Nosocomial transmission to the index case occurred in Lacor hospital in Gulu, outside the Ebola ward. After returning home to Masindi district the index case became the origin of a transmission chain within her own extended family (18 further cases), from index family members to health care workers (HCWs, 6 cases), and from HCWs to their household contacts (1 case). Five out of six occupational cases of EHF in HCWs occurred after the introduction of barrier nursing, probably due to breaches of barrier nursing principles. CFP was initially very high (76%) but decreased (20%) probably due to better case management after reinforcing the response team. The mobilisation of the community for the response efforts was challenging at the beginning, when fear, panic and mistrust had to be countered by the response team.ConclusionsLarge scale transmission in the community beyond the index family was prevented by early case identification and isolation as well as quarantine imposed by the community. The high number of occupational EHF after implementing barrier nursing points at the need to strengthen training and supervision of local HCWs. The difference in CFP before and after reinforcing the respon
Van Kerkhove MD, Mounts AW, Mall S, et al., 2011, Epidemiologic and virologic assessment of the 2009 influenza A (H1N1) pandemic on selected temperate countries in the Southern Hemisphere: Argentina, Australia, Chile, New Zealand and South Africa, INFLUENZA AND OTHER RESPIRATORY VIRUSES, Vol: 5, Pages: R487-R498, ISSN: 1750-2640
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- Citations: 21
Opatowski L, Fraser C, Griffin J, et al., 2011, Transmission Characteristics of the 2009 H1N1 Influenza Pandemic: Comparison of 8 Southern Hemisphere Countries, PLOS PATHOGENS, Vol: 7, ISSN: 1553-7366
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- Citations: 55
Van Kerkhove MD, Vandemaele KAH, Shinde V, et al., 2011, Risk Factors for Severe Outcomes following 2009 Influenza A (H1N1) Infection: A Global Pooled Analysis, PLOS Medicine, Vol: 8, ISSN: 1549-1277
Background: Since the start of the 2009 influenza A pandemic (H1N1pdm), the World Health Organization and its member states have gathered information to characterize the clinical severity of H1N1pdm infection and to assist policy makers to determine risk groups for targeted control measures.Methods and Findings: Data were collected on approximately 70,000 laboratory-confirmed hospitalized H1N1pdm patients, 9,700 patients admitted to intensive care units (ICUs), and 2,500 deaths reported between 1 April 2009 and 1 January 2010 from 19 countries or administrative regions—Argentina, Australia, Canada, Chile, China, France, Germany, Hong Kong SAR, Japan, Madagascar, Mexico, the Netherlands, New Zealand, Singapore, South Africa, Spain, Thailand, the United States, and the United Kingdom—to characterize and compare the distribution of risk factors among H1N1pdm patients at three levels of severity: hospitalizations, ICU admissions, and deaths. The median age of patients increased with severity of disease. The highest per capita risk of hospitalization was among patients <5 y and 5–14 y (relative risk [RR] = 3.3 and 3.2, respectively, compared to the general population), whereas the highest risk of death per capita was in the age groups 50–64 y and ≥65 y (RR = 1.5 and 1.6, respectively, compared to the general population). Similarly, the ratio of H1N1pdm deaths to hospitalizations increased with age and was the highest in the ≥65-y-old age group, indicating that while infection rates have been observed to be very low in the oldest age group, risk of death in those over the age of 64 y who became infected was higher than in younger groups. The proportion of H1N1pdm patients with one or more reported chronic conditions increased with severity (median = 31.1%, 52.3%, and 61.8% of hospitalized, ICU-admitted, and fatal H1N1pdm cases, respectively). With the exception of the risk factors asthma, pregnancy, and obesity, the proportion of patients
Van Kerkhove MD, Mounts AW, 2011, 2009 versus 2010 comparison of influenza activity in southern hemisphere temperate countries, Influenza and Other Respiratory Viruses, Vol: Early View available at http://onlinelibrary.wiley.com/doi/10.1111/j.1750-2659.2011.00241.x/abstract
The 2009 influenza season in temperate countries in the southern hemisphere has been well documented as moderately severe in terms of impact on health care systems (1-3). However, experience with previous pandemics has demonstrated that in some cases the second season of transmission can be worse than the initial one(4). There has recently been evidence of this occurring in the U.K. where a large number of severe cases has been reported with the start of the 2011 influenza season(5). As the second season has already come and gone in the temperate countries of the southern hemisphere, there is a unique opportunities to look back for evidence of changes in behavior or severity of the pandemic in two completed transmission seasons. Using data from FluNet(6) and ministries of health in Argentina, Chile, South Africa, Australia and New Zealand, we compare influenza virus circulation, the time course and geographic distribution of the peaks in activity; and the impact of influenza on health care systems during the 2010 winter compared to the 2009 pandemic season.
Van Kerkhove MD, Mumford E, Mounts AW, et al., 2011, Highly Pathogenic Avian Influenza (H5N1): Pathways of Exposure at the Animal-Human Interface, a Systematic Review, PLOS ONE, Vol: 6, ISSN: 1932-6203
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- Citations: 113
van Kerkhove MD, Asikainen T, Becker NG, et al., 2010, Studies needed to address public health challenges of the 2009 H1N1 influenza pandemic: insights from modeling., PLoS Med, Vol: 7, Pages: e1000275-e1000275
Van Kerkhove MD, Asikainen T, Becker N, et al., 2009, for the WHO Informal Network for Mathematical Modelling Pandemic Influenza H1N1 2009 (Working Group on Data Needs). Studies needed to address public health challenges of the 2009 H1N1 influenza pandemic: insights from modeling, PLoS Currents: Influenza
Van Kerkhove MD, Vong S, Guitian J, et al., 2009, Poultry movement networks in Cambodia: Implications for surveillance and control of highly pathogenic avian influenza (HPAI/H5N1), VACCINE, Vol: 27, Pages: 6345-6352, ISSN: 0264-410X
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- Citations: 77
Fraser C, Donnelly CA, Cauchemez S, et al., 2009, Influenza: Making Privileged Data Public Response, SCIENCE, Vol: 325, Pages: 1072-1073, ISSN: 0036-8075
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- Citations: 2
Van Kerkhove MD, Ly S, Guitian J, et al., 2009, Changes in Poultry Handling Behavior and Poultry Mortality Reporting among Rural Cambodians in Areas Affected by HPAI/H5N1, PLOS ONE, Vol: 4, ISSN: 1932-6203
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- Citations: 13
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