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Dorigatti I, Hamlet A, Aguas R, et al., 2017, International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017, EUROSURVEILLANCE, Vol: 22, Pages: 1-4, ISSN: 1560-7917
States in south-eastern Brazil were recently affected by the largest Yellow Fever (YF) outbreak seen in a decade in Latin America. Here we provide a quantitative assessment of the risk of travel-related international spread of YF indicating that the United States, Argentina, Uruguay, Spain, Italy and Germany may have received at least one travel-related YF case capable of seeding local transmission. Mitigating the risk of imported YF cases seeding local transmission requires heightened surveillance globally.
Ozawa S, Clark S, Portnoy A, et al., 2017, Estimated economic impact of vaccinations in 73 low- and middle-income countries, 2001-2020, Bulletin of the World Health Organization, Vol: 95, Pages: 629-638, ISSN: 0042-9686
Objective To estimate the economic impact likely to be achieved by efforts to vaccinate against 10 vaccine-preventable diseases between 2001 and 2020 in 73 low- and middle-income countries largely supported by Gavi, the Vaccine Alliance.Methods We used health impact models to estimate the economic impact of achieving forecasted coverages for vaccination against Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, rubella, Streptococcus pneumoniae and yellow fever. In comparison with no vaccination, we modelled the costs – expressed in 2010 United States dollars (US$) – of averted treatment, transportation costs, productivity losses of caregivers and productivity losses due to disability and death. We used the value-of-a-life-year method to estimate the broader economic and social value of living longer, in better health, as a result of immunization.Findings We estimated that, in the 73 countries, vaccinations given between 2001 and 2020 will avert over 20 million deaths and save US$ 350 billion in cost of illness. The deaths and disability prevented by vaccinations given during the two decades will result in estimated lifelong productivity gains totalling US$ 330 billion and US$ 9 billion, respectively. Over the lifetimes of the vaccinated cohorts, the same vaccinations will save an estimated US$ 5 billion in treatment costs. The broader economic and social value of these vaccinations is estimated at US$ 820 billion.Conclusion By preventing significant costs and potentially increasing economic productivity among some of the world’s poorest countries, the impact of immunization goes well beyond health.
Cori A, Donnelly CA, dorigatti, et al., 2017, Key data for outbreak evaluation: building on the Ebola experience, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 372, ISSN: 1471-2970
Following the detection of an infectious disease outbreak, rapid epidemiological assessmentis critical to guidean effectivepublic 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 inthe West AfricanEbolaepidemic and prior emerging infectious disease outbreaksto set out a checklist of data needed to: 1) quantify severity and transmissibility;2) characterise heterogeneities in transmission and their determinants;and 3) assess the effectiveness of different interventions.We differentiate data needs into individual-leveldata (e.g. a detailed list of reported cases), exposure data(e.g.identifying where / howcases may have been infected) and populationlevel data (e.g.size/demographicsof the population(s)affected andwhen/where interventions were implemented). A remarkable amount of individual-level and exposuredata 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 whenand where)posed challenges to the assessment of (3).Herewehighlight recurrent data issues, give practical suggestions for addressingthese issues and discuss priorities for improvements in data collection in future outbreaks.
Garske T, Cori A, Ariyarajah A, et al., 2017, Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013 – 2016, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 372, ISSN: 1471-2970
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.
Nouvellet P, Cori A, Garske T, et al., 2017, A simple approach to measure transmissibility and forecast incidence, Epidemics, Vol: 22, Pages: 29-35, ISSN: 1755-4365
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.
Hamlet A, Jean K, Ferguson N, et al., 2017, POLICI: AN ONLINE TOOL FOR VISUALIZATION OF POPULATION-LEVEL YELLOW FEVER IMMUNIZATION COVERAGE IN AFRICA, 66th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 257-257, ISSN: 0002-9637
International Ebola Response Team, Agua-Agum J, Ariyarajah A, et al., 2016, Exposure patterns driving Ebola transmissions in West Africa: a retrospective observational study, PLOS Medicine, Vol: 13, ISSN: 1549-1277
BACKGROUND: The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.METHODS AND FINDINGS: Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the
Jean K, Donnelly C, Ferguson N, et al., 2016, A meta-analysis of serological response associated with yellow fever vaccination, American Journal of Tropical Medicine and Hygiene, Vol: 95, Pages: 1435-1439, ISSN: 1476-1645
Despite previous evidence of high level of efficacy, no synthetic metric of yellow fever (YF) vaccine efficacy is currently available. Based on the studies identified in a recent systematic review, we conducted a random-effects meta-analysis of the serological response associated with YF vaccination. Eleven studies conducted between 1965 and 2011 representing 4,868 individual observations were included in the meta-analysis. The pooled estimate of serological response was 97.5% (95% confidence interval [CI] = 82.9–99.7%). There was evidence of between-study heterogeneity (I2 = 89.1%), but this heterogeneity did not appear to be related to study size, study design, seroconversion measurement, or definition. Pooled estimates were significantly higher (P & 0.0001) among studies conducted in nonendemic settings (98.9%, 95% CI = 98.2–99.4%) than among those conducted in endemic settings (94.2%, 95% CI = 83.8–98.1%). These results provide background information against which to evaluate the efficacy of fractional doses of YF vaccine that may be used in outbreak situations.
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: 1533-4406
Between December 2013 and April 2016, the largest epidemic of Ebola virus disease (EVD) to date generated more than 28,000 cases and more than 11,000 deaths in the large, mobile populations of Guinea, Liberia, and Sierra Leone. Tracking the rapid rise and slower decline of the West African epidemic has reinforced some common understandings about the epidemiology and control of EVD but has also generated new insights. Despite having more information about the geographic distribution of the disease, the risk of human infection from animals and from survivors of EVD remains unpredictable over a wide area of equatorial Africa. Until human exposure to infection can be anticipated or avoided, future outbreaks will have to be managed with the classic approach to EVD control — extensive surveillance, rapid detection and diagnosis, comprehensive tracing of contacts, prompt patient isolation, supportive clinical care, rigorous efforts to prevent and control infection, safe and dignified burial, and engagement of the community. Empirical and modeling studies conducted during the West African epidemic have shown that large epidemics of EVD are preventable — a rapid response can interrupt transmission and restrict the size of outbreaks, even in densely populated cities. The critical question now is how to ensure that populations and their health services are ready for the next outbreak, wherever it may occur. Health security across Africa and beyond depends on committing resources to both strengthen national health systems and sustain investment in the next generation of vaccines, drugs, and diagnostics.
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: 1091-6490
With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was >1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.
Lessler J, Salje H, van Kerkhove M, et al., 2016, Estimating the Severity and Subclinical Burden of Middle East Respiratory Syndrome Coronavirus Infection in the Kingdom of Saudi Arabia, American Journal of Epidemiology, Vol: 183, Pages: 657-663, ISSN: 1476-6256
Not all persons infected with Middle East respiratory syndrome coronavirus (MERS-CoV) develop severe symptoms, which likely leads to an underestimation of the number of people infected and an overestimation of the severity. To estimate the number of MERS-CoV infections that have occurred in the Kingdom of Saudi Arabia, we applied a statistical model to a line list describing 721 MERS-CoV infections detected between June 7, 2012, and July 25, 2014. We estimated that 1,528 (95% confidence interval (CI): 1,327, 1,883) MERS-CoV infections occurred in this interval, which is 2.1 (95% CI: 1.8, 2.6) times the number reported. The probability of developing symptoms ranged from 11% (95% CI: 4, 25) in persons under 10 years of age to 88% (95% CI: 72, 97) in those 70 years of age or older. An estimated 22% (95% CI: 18, 25) of those infected with MERS-CoV died. MERS-CoV is deadly, but this work shows that its clinical severity differs markedly between groups and that many cases likely go undiagnosed.
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: 1533-4406
Nouvellet P, Garske T, Mills HL, et al., 2015, The role of rapid diagnostics in managing Ebola epidemics, Nature, Vol: 528, Pages: S109-S116, ISSN: 0028-0836
Ebola emerged in West Africa around December 2013 and swept through Guinea, Sierra Leone and Liberia, giving rise to 27,748 confirmed, probable and suspected cases reported by 29 July 2015. Case diagnoses during the epidemic have relied on polymerase chain reaction-based tests. Owing to limited laboratory capacity and local transport infrastructure, the delays from sample collection to test results being available have often been 2 days or more. Point-of-care rapid diagnostic tests offer the potential to substantially reduce these delays. We review Ebola rapid diagnostic tests approved by the World Health Organization and those currently in development. Such rapid diagnostic tests could allow early triaging of patients, thereby reducing the potential for nosocomial transmission. In addition, despite the lower test accuracy, rapid diagnostic test-based diagnosis may be beneficial in some contexts because of the reduced time spent by uninfected individuals in health-care settings where they may be at increased risk of infection; this also frees up hospital beds. We use mathematical modelling to explore the potential benefits of diagnostic testing strategies involving rapid diagnostic tests alone and in combination with polymerase chain reaction testing. Our analysis indicates that the use of rapid diagnostic tests with sensitivity and specificity comparable with those currently under development always enhances control, whether evaluated at a health-care-unit or population level. If such tests had been available throughout the recent epidemic, we estimate, for Sierra Leone, that their use in combination with confirmatory polymerase chain-reaction testing might have reduced the scale of the epidemic by over a third.
Jean K, Ferguson NM, Van Kerkhove MD, et al., 2015, INTEGRATING TRANSMISSION DYNAMICS IN THE MODELLING OF VACCINATION IMPACT AGAINST YELLOW FEVER IN AFRICA, Publisher: AMER SOC TROP MED & HYGIENE, Pages: 187-187, ISSN: 0002-9637
Garske T, Jean K, Van Kerkhove MD, et al., 2015, INFERRING THE YELLOW FEVER FORCE OF INFECTION FROM THE OBSERVED AGE DISTRIBUTION OF CONFIRMED CASES, Publisher: AMER SOC TROP MED & HYGIENE, Pages: 441-441, ISSN: 0002-9637
Garske T, 2015, HETEROGENEITIES IN THE CASE FATALITY RATE IN THE EBOLA OUTBREAK IN WEST AFRICA, Publisher: AMER SOC TROP MED & HYGIENE, Pages: 1-1, ISSN: 0002-9637
Cairns ME, Walker PGT, Okell LC, et al., 2015, Seasonality in malaria transmission: implications for case-management with long-acting artemisinin combination therapy in sub-Saharan Africa, Malaria Journal, Vol: 14, ISSN: 1475-2875
Background: Long-acting artemisinin-based combination therapy (LACT) offers the potential to prevent recurrentmalaria attacks in highly exposed children. However, it is not clear where this advantage will be most important, anddeployment of these drugs is not rationalized on this basis.Methods: To understand where post-treatment prophylaxis would be most beneficial, the relationship betweenseasonality, transmission intensity and the interval between malaria episodes was explored using data from six cohortstudies in West Africa and an individual-based malaria transmission model. The total number of recurrent malariacases per 1000 child-years at risk, and the fraction of the total annual burden that this represents were estimated forsub-Saharan Africa.Results: In settings where prevalence is less than 10 %, repeat malaria episodes constitute a small fraction of thetotal burden, and few repeat episodes occur within the window of protection provided by currently available drugs.However, in higher transmission settings, and particularly in high transmission settings with highly seasonal transmis‑sion, repeat malaria becomes increasingly important, with up to 20 % of the total clinical burden in children estimatedto be due to repeat episodes within 4 weeks of a prior attack.Conclusion: At a given level of transmission intensity and annual incidence, the concentration of repeat malariaepisodes in time, and consequently the protection from LACT is highest in the most seasonal areas. As a result, thedegree of seasonality, in addition to the overall intensity of transmission, should be considered by policy makers whendeciding between ACT that differ in their duration of post-treatment prophylaxis.
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
Agua-Agum J, Ariyarajah A, Blake IM, et al., 2015, Ebola virus disease among children in West Africa, New England Journal of Medicine, Vol: 372, Pages: 1274-1277, ISSN: 1533-4406
Agua-Agum J, Ariyarajah A, Aylward B, et al., 2015, West African Ebola epidemic after one year - slowing but not yet under control, New England Journal of Medicine, Vol: 372, Pages: 584-587, ISSN: 1533-4406
Dye C, 2015, Goal-Directed Resuscitation in Septic Shock, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 372, Pages: 189-189, ISSN: 0028-4793
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Lessler J, Rodriguez-Barraquer I, Cummings T, et al., 2014, Estimating potential incidence of MERS-CoV associated with Hajj pilgrims to Saudi Arabia, 2014, PLoS Currents, Vol: Edition 1, ISSN: 2157-3999
Between March and June 2014 the Kingdom of Saudi Arabia (KSA) had a large outbreak of MERS-CoV, renewing fears of a major outbreak during the Hajj this October. Using KSA Ministry of Health data, the MERS-CoV Scenario and Modeling Working Group forecast incidence under three scenarios. In the expected incidence scenario, we estimate 6.2 (95% Prediction Interval [PI]: 1–17) pilgrims will develop MERS-CoV symptoms during the Hajj, and 4.0 (95% PI: 0–12) foreign pilgrims will be infected but return home before developing symptoms. In the most pessimistic scenario, 47.6 (95% PI: 32–66) cases will develop symptoms during the Hajj, and 29.0 (95% PI: 17–43) will be infected but return home asymptomatic. Large numbers of MERS-CoV cases are unlikely to occur during the 2014 Hajj even under pessimistic assumptions, but careful monitoring is still needed to detect possible mass infection events and minimize introductions into other countries.
WHO Ebola Response Team, 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, ISSN: 0028-4793
BACKGROUNDOn March 23, 2014, the World Health Organization (WHO) was notified of an outbreak of Ebola virus disease (EVD) in Guinea. On August 8, the WHO declared the epidemic to be a “public health emergency of international concern.”METHODSBy September 14, 2014, a total of 4507 probable and confirmed cases, including 2296 deaths from EVD (Zaire species) had been reported from five countries in West Africa — Guinea, Liberia, Nigeria, Senegal, and Sierra Leone. We analyzed a detailed subset of data on 3343 confirmed and 667 probable Ebola cases collected in Guinea, Liberia, Nigeria, and Sierra Leone as of September 14.RESULTSThe majority of patients are 15 to 44 years of age (49.9% male), and we estimate that the case fatality rate is 70.8% (95% confidence interval [CI], 69 to 73) among persons with known clinical outcome of infection. The course of infection, including signs and symptoms, incubation period (11.4 days), and serial interval (15.3 days), is similar to that reported in previous outbreaks of EVD. On the basis of the initial periods of exponential growth, the estimated basic reproduction numbers (R0) are 1.71 (95% CI, 1.44 to 2.01) for Guinea, 1.83 (95% CI, 1.72 to 1.94) for Liberia, and 2.02 (95% CI, 1.79 to 2.26) for Sierra Leone. The estimated current reproduction numbers (R) are 1.81 (95% CI, 1.60 to 2.03) for Guinea, 1.51 (95% CI, 1.41 to 1.60) for Liberia, and 1.38 (95% CI, 1.27 to 1.51) for Sierra Leone; the corresponding doubling times are 15.7 days (95% CI, 12.9 to 20.3) for Guinea, 23.6 days (95% CI, 20.2 to 28.2) for Liberia, and 30.2 days (95% CI, 23.6 to 42.3) for Sierra Leone. Assuming no change in the control measures for this epidemic, by November 2, 2014, the cumulative reported numbers of confirmed and probable cases are predicted to be 5740 in Guinea, 9890 in Liberia, and 5000 in Sierra Leone, exceeding 20,000 in total.CONCLUSIONSThese data indicate that without drastic improvements in control measures, the numbers of
Walker PGT, ter Kuile FO, Garske T, et al., 2014, Estimated risk of placental infection and low birthweight attributable to Plasmodium falciparum malaria in Africa in 2010: a modelling study, Lancet Global Health, Vol: 2, Pages: E460-E467, ISSN: 2214-109X
Garske T, Van Kerkhove MD, Yactayo S, et al., 2014, Yellow Fever in Africa: Estimating the Burden of Disease and Impact of Mass Vaccination from Outbreak and Serological Data, PLoS Medicine, Vol: 11, ISSN: 1548-7091
BackgroundYellow fever is a vector-borne disease affecting humans and non-human primates in tropical areas of Africa and South America. While eradication is not feasible due to the wildlife reservoir, large scale vaccination activities in Africa during the 1940s to 1960s reduced yellow fever incidence for several decades. However, after a period of low vaccination coverage, yellow fever has resurged in the continent. Since 2006 there has been substantial funding for large preventive mass vaccination campaigns in the most affected countries in Africa to curb the rising burden of disease and control future outbreaks. Contemporary estimates of the yellow fever disease burden are lacking, and the present study aimed to update the previous estimates on the basis of more recent yellow fever occurrence data and improved estimation methods.Methods and FindingsGeneralised linear regression models were fitted to a dataset of the locations of yellow fever outbreaks within the last 25 years to estimate the probability of outbreak reports across the endemic zone. Environmental variables and indicators for the surveillance quality in the affected countries were used as covariates. By comparing probabilities of outbreak reports estimated in the regression with the force of infection estimated for a limited set of locations for which serological surveys were available, the detection probability per case and the force of infection were estimated across the endemic zone.The yellow fever burden in Africa was estimated for the year 2013 as 130,000 (95% CI 51,000–380,000) cases with fever and jaundice or haemorrhage including 78,000 (95% CI 19,000–180,000) deaths, taking into account the current level of vaccination coverage. The impact of the recent mass vaccination campaigns was assessed by evaluating the difference between the estimates obtained for the current vaccination coverage and for a hypothetical scenario excluding these vaccination campaigns. Vaccination campaign
Garske T, Ferguson NM, Ghani AC, 2013, Estimating Air Temperature and Its Influence on Malaria Transmission across Africa, PLOS ONE, Vol: 8, ISSN: 1932-6203
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Cairns M, Roca-Feltrer A, Garske T, et al., 2012, Estimating the potential public health impact of seasonal malaria chemoprevention in African children, NATURE COMMUNICATIONS, Vol: 3, ISSN: 2041-1723
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Griffin JT, Garske T, Ghani AC, et al., 2011, Joint estimation of the basic reproduction number and generation time parameters for infectious disease outbreaks, BIOSTATISTICS, Vol: 12, Pages: 303-312, ISSN: 1465-4644
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Garske T, Yu H, Peng Z, et al., 2011, Travel Patterns in China, PLOS ONE, Vol: 6, ISSN: 1932-6203
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Garske T, Yu H, Peng Z, et al., 2011, Correction: travel patterns in china., PLoS One, Vol: 6, ISSN: 1932-6203
[This corrects the article on p. e16364 in vol. 6.].
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