Global circulation patterns of seasonal influenza viruses vary with antigenic drift

Trevor Bedford, Steven Riley, Ian G. Barr, Shobha Broor, Mandeep Chadha, Nancy J. Cox, Rodney S. Daniels, C. Palani Gunasekaran, Aeron C. Hurt, Anne Kelso, Alexander Klimov, Nicola S. Lewis, Xiyan Li, John W. McCauley, Takato Odagiri, Varsha Potdar, Andrew Rambaut, Yuelong Shu, Eugene Skepner, Derek J. Smith, Marc A. Suchard, Masato Tashiro, Dayan Wang, Xiyan Xu, Philippe Lemey, Colin A. Russell

Nature

June 8, 2015

Understanding the spatiotemporal patterns of emergence and circulation of new human seasonal influenza virus variants is a key scientific and public health challenge. The global circulation patterns of influenza A/H3N2 viruses are well characterized but the patterns of A/H1N1 and B viruses have remained largely unexplored. Here we show that the global circulation patterns of A/H1N1 (up to 2009), B/Victoria, and B/Yamagata viruses differ substantially from those of A/H3N2 viruses, on the basis of analyses of 9,604 haemagglutinin sequences of human seasonal influenza viruses from 2000 to 2012. Whereas genetic variants of A/H3N2 viruses did not persist locally between epidemics and were reseeded from East and Southeast Asia, genetic variants of A/H1N1 and B viruses persisted across several seasons and exhibited complex global dynamics with East and Southeast Asia playing a limited role in disseminating new variants. The less frequent global movement of influenza A/H1N1 and B viruses coincided with slower rates of antigenic evolution, lower ages of infection, and smaller, less frequent epidemics compared to A/H3N2 viruses. Detailed epidemic models support differences in age of infection, combined with the less frequent travel of children, as probable drivers of the differences in the patterns of global circulation, suggesting a complex interaction between virus evolution, epidemiology, and human behaviour.

Dynamics of Pertussis Transmission in the United States

F. M. G. Magpantay and P. Rohani

American Journal of Epidemiology

May 27, 2015

Past patterns of infectious disease transmission set the stage on which modern epidemiologic dynamics are played out. Here, we present a comprehensive account of pertussis (whooping cough) transmission in the United States during the early vaccine era. We analyzed recently digitized weekly incidence records from Morbidity and Mortality Weekly Reports from 1938 to 1955, when the whole-cell pertussis vaccine was rolled out, and related them to contemporary patterns of transmission and resurgence documented in monthly incidence data from the National Notifiable Diseases Surveillance System. We found that, during the early vaccine era, pertussis epidemics in US states could be categorized as 1) annual, 2) initially annual and later multiennial, or 3) multiennial. States with predominantly annual cycles tended to have higher per capita birth rates, more household crowding, more children per family, and lower rates of school attendance than the states with multiennial cycles. Additionally, states that exhibited annual epidemics during 1938–1955 have had the highest recent (2001–2010) incidence, while those states that transitioned from annual cycles to multiennial cycles have had relatively low recent incidence. Our study provides an extensive picture of pertussis epidemiology in the United States dating back to the onset of vaccination, a back-story that could aid epidemiologists in understanding contemporary transmission patterns.

Software for the analysis and visualization of deep mutational scanning data

Jesse D. Bloom

BMC Bioinformatics

May 20, 2015

Abstract

Background: Deep mutational scanning is a technique to estimate the impacts of mutations on a gene by using deep sequencing to count mutations in a library of variants before and after imposing a functional selection. The impacts of mutations must be inferred from changes in their counts after selection.

Results: I describe a software package, dms_tools, to infer the impacts of mutations from deep mutational scanning data using a likelihood-based treatment of the mutation counts. I show that dms_tools yields more accurate inferences on simulated data than simply calculating ratios of counts pre- and post-selection. Using dms_tools, one can infer the preference of each site for each amino acid given a single selection pressure, or assess the extent to which these preferences change under different selection pressures. The preferences and their changes can be intuitively visualized with sequence-logo-style plots created using an extension to weblogo.

Conclusions: dms_tools implements a statistically principled approach for the analysis and subsequent visualization of deep mutational scanning data.

Keywords: Deep mutational scanning, Sequence logo, Amino-acid preferences

The dengue vaccine pipeline: Implications for the future of dengue control

Lauren M. Schwartz, M. Elizabeth Halloran, Anna P. Durbin, Ira M. Longini Jr

Vaccine

May 16, 2015

Abtract

Dengue has become the most rapidly expanding mosquito-borne infectious disease on the planet, surpassing malaria and infecting at least 390 million people per year. There is no effective treatment for dengue illness other than supportive care, especially for severe cases. Symptoms can be mild or life-threatening as in dengue hemorrhagic fever and dengue shock syndrome. Vector control has been only partially successful in decreasing dengue transmission. The potential use of safe and effective tetravalent dengue vaccines is an attractive addition to prevent disease or minimize the possibility of epidemics. There are currently no licensed dengue vaccines. This review summarizes the current status of all dengue vaccine candidates in clinical evaluation. Currently five candidate vaccines are in human clinical trials. One has completed two Phase III trials, two are in Phase II trials, and three are in Phase I testing.

The contribution of neighbours to an individual's risk of typhoid outcome

D. L. CHAO, J. K. PARK, F. MARKS , R. L. OCHIAI , I. M. LONGINI JR., AND M. E. HALLORAN

Epidemiology & Infection

May 4, 2015

Summary

An individual’s risk of infection from an infectious agent can depend on both the individual’s own risk and protective factors and those of individuals in the same community. We hypothesize that an individual’s exposure to an infectious agent is associated with the risks of infection of those living nearby, whether their risks are modified by pharmaceutical interventions or by other factors, because of the potential for transmission from them. For example, unvaccinated individuals living in a highly vaccinated community can benefit from indirect protection, or living near more children in a typhoid-endemic region (where children are at highest risk) might result in more exposure to typhoid. We tested this hypothesis using data from a cluster-randomized typhoid vaccine trial. We first estimated each individual’s relative risk of confirmed typhoid outcome using their vaccination status and age. We defined a new covariate, potential exposure, to be the sum of the relative risks of all who live within 100 m of each person. We found that potential exposure was significantly associated with an individual’s typhoid outcome, and adjusting for potential exposure affected estimates of vaccine efficacy. We suggest that it is useful and feasible to adjust for spatially heterogeneous distributions of individual-level risk factors, but further work is required to develop and test such approaches.

Vaccine Testing: Ebola and beyond

Marc Lipsitch, Nir Eyal, M. Elizabeth Halloran, Miguel A. Hernán, Ira M. Longini , Eli N. Perencevich, Rebecca F. Grais  

 

Science

April 3, 2015

Recent experiences in confronting the Ebola epidemic suggest principles for vaccine efficacy trials in challenging environments.

Many epidemic-prone infectious diseases present challenges that the current West African Ebola outbreak brings into sharp relief. Specifically, the urgency to evaluate vaccines, initially limited vaccine supplies, and large and unpredictable spatial and temporal fluctuations in incidence have presented huge logistical, ethical, and statistical challenges to trial design.

Household Transmissibility of Avian Influenza A (H7N9) Virus, China, February to May 2013 and October 2013 to March 2014

Y. Yang, Y. Zhang, L. Fang, M.E. Halloran, M. Ma, S. Liang, E. Kenah, T. Britton, E. Chen, J. Hu, F. Tang, W. Cao, Z. Feng, I.M. Longini Jr.

Eurosurveillance

March 12, 2015

Abstract

To study human-to-human transmissibility of the avian influenza A (H7N9) virus in China, household contact information was collected for 125 index cases during the spring wave (February to May 2013), and for 187 index cases during the winter wave (October 2013 to March 2014). Using a statistical model, we found evidence for human-to-human transmission, but such transmission is not sustainable. Under plausible assumptions about the natural history of disease and the relative transmission frequencies in settings other than household, we estimate the household secondary attack rate (SAR) among humans to be 1.4% (95% CI: 0.8 to 2.3), and the basic reproductive number R0 to be 0.08 (95% CI: 0.05 to 0.13). The estimates range from 1.3% to 2.2% for SAR and from 0.07 to 0.12 for R0 with reasonable changes in the assumptions. There was no significant change in the human-to-human transmissibility of the virus between the two waves, although a minor increase was observed in the winter wave. No sex or age difference in the risk of infection from a human source was found. Human-to-human transmissibility of H7N9 continues to be limited, but it needs to be closely monitored for potential increase via genetic reassortment or mutation.

Transmissibility of tuberculosis among school contacts: An outbreak investigation in a boarding middle school, China

Mai-Juan Ma, Yang Yang, Hai-Bin Wang, Yi-Fan Zhu, Li-Qun Fang, Xiao-Ping An, Kang-Lin Wan, Christopher C. Whalen, Xiao-Xian Yang, Michael Lauzardo, Zhi-Yi Zhang, Jin-Feng Cao, Yi-Gang Tong, Er-Hei Dai, Wu-Chun Cao

Science Direct

March 7, 2015

Abstract

Tuberculosis (TB) outbreak occurred in a boarding middle school of China. We explored its probable sources and quantified the transmissibility and pathogenicity of TB. Clinical evaluation, tuberculin skin testing and chest radiography were conducted to identify TB cases. Mycobacterium tuberculosis isolates underwent genotyping analysis to identify the outbreak source. A chain-binomial transmission model was used to evaluate transmissibility and pathogenicity of TB. A total of 46 active cases were ascertained among 258 students and 15 teachers/staff, an attack rate of 16.8%. Genetic analyses revealed two groups of M. tuberculosis cocirculating during the outbreak and possible importation from local communities. Secondary attack rates among students were 4.1% (2.9%, 5.3%) within grade and 7.9% (4.9%, 11%) within class. An active TB case was estimated to infect 8.4 (7.2, 9.6) susceptible people on average. The smear-positive cases were 28 (8, 101) times as infective as smear-negative cases. Previous BCG vaccination could reduce the probability of developing symptoms after infection by 70% (1.4%, 91%). The integration of clinical evaluation, genetic sequencing, and statistical modeling greatly enhanced our understanding of TB transmission dynamics. Timely diagnosis of smear-positive cases, especially in the early phase of the outbreak, is the key to preventing further spread among close contacts.

Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola

Aaron A. King, Matthieu Domenech de Cellès, Felicia M. G. Magpantay, Pejman Rohani

arXiv

March 3, 2015

Abstract

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely-used modeling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far over-estimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola Virus Disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modeling response to future infectious disease outbreaks.

The Spatiotemporal Expansion of Human Rabies and Its Probable Explanation in Mainland China, 2004-2013

Hong-Wu Yao, Yang Yang, Kun Liu, Xin-Lou Li, Shu-Qing Zuo, Ruo-Xi Sun, Li-Qun Fang , Wu-Chun Cao 

PLOS Neglected Tropical Diseases

February 18, 2015

Background

Human rabies is a significant public health concern in mainland China. However, the neglect of rabies expansion and scarce analyses of the dynamics have made the spatiotemporal spread pattern of human rabies and its determinants being poorly understood.

Methods

We collected geographic locations and timeline of reported human rabies cases, rabies sequences and socioeconomic variables for the years 2004-2013, and integrated multidisciplinary approaches, including epidemiological characterization, hotspots identification, risk factors analysis and phylogeographic inference, to explore the spread pattern of human rabies in mainland China during the last decade.

Results

The results show that human rabies distribution and hotspots were expanding from southeastern regions to north or west regions, which could be associated with the evolution of the virus, especially the clade I-G. A Panel Poisson Regression analysis reveals that human rabies incidences had significant correlation with the education level, GDP per capita, temperature at one-month lag and canine rabies outbreak at two-month lag.

Conclusions

The reduction in the overall human rabies incidence was accompanied by a westward and northward expansion of the circulating region in mainland China. Higher risk of human rabies was associated with lower level of education and economic status. New clades of rabies, especial Clade I-G, played an important role in recent spread. Our findings provide valuable information for rabies control and prevention in the future.

Author Summary

Although the number of human rabies cases has slightly decreased since 2008 in mainland China, the rabies seemed to be gradually expanding to the low-incidence or non-epidemic areas. The neglect of rabies expansion and scarce analyses of the dynamics have made the spatiotemporal spread pattern of human rabies and its determinants poorly understood. Here, we integrate multidisciplinary approaches to explore and describe the spread pattern and evolution dynamic of human rabies in mainland China during the last decade. The results indicated that the reduction in the overall human rabies incidence was accompanied by a westward and northward expansion of the circulating region, which could be associated with the evolution of the virus, especially the clade I-G. And the education level, GDP per capita, temperature at one-month lag and canine rabies outbreak at two-month lag were firstly found to be significant correlation human rabies incidences according to the Panel Poisson Regression analysis. Our findings give a relatively complete picture about the human rabies spatiotemporal dynamics and spread pattern, thus provide new insights on risk factors and control strategies for the disease spread.

Combating Pertussis Resurgence: One Booster Vaccination Schedule Doesn't Fit All.

Maria A. Riolo and Pejman Rohani

PNAS

January 20, 2015

Abstract

Pertussis has reemerged as a major public health concern in many countries where it was once considered well controlled. Although the mechanisms responsible for continued pertussis circulation and resurgence remain elusive and contentious, many countries have nevertheless recommended booster vaccinations, the timing and number of which vary widely. Here, using a stochastic, age-stratified transmission model, we searched for cost-effective booster vaccination strategies using a genetic algorithm. We did so assuming four hypothesized mechanisms underpinning contemporary pertussis epidemiology: (I) insufficient coverage, (II) frequent primary vaccine failure, (III) waning of vaccine-derived protection, and (IV) vaccine “leakiness.” For scenarios I–IV, successful booster strategies were identified and varied considerably by mechanism. Especially notable is the inability of booster schedules to alleviate resurgence when vaccines are leaky. Critically, our findings argue that the ultimate effectiveness of vaccine booster schedules will likely depend on correctly pinpointing the causes of resurgence, with misdiagnosis of the problem epidemiologically ineffective and economically costly.

Inference for dynamic and latent variable models via iterated, perturbed Bayes maps

Edward L. Ionides, Dao Nguyen, Yves Atchadé, Stilian Stoev, and Aaron A. King

PNAS

January 7, 2015

Abstract

Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process.

Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: a computational modelling analysis

Stefano Merler, Marco Ajelli, Laura Fumanelli, Marcelo F C Gomes, Ana Pastore y Piontti, Luca Rossi, Dennis L Chao, Ira M Longini Jr, M Elizabeth Halloran, Alessandro Vespignani

The Lancet Infectious Diseases

January 6, 2015

Background

The 2014 epidemic of Ebola virus disease in parts of west Africa defines an unprecedented health threat. We developed a model of Ebola virus transmission that integrates detailed geographical and demographic data from Liberia to overcome the limitations of non-spatial approaches in projecting the disease dynamics and assessing non-pharmaceutical control interventions.

Methods

We modelled the movements of individuals, including patients not infected with Ebola virus, seeking assistance in health-care facilities, the movements of individuals taking care of patients infected with Ebola virus not admitted to hospital, and the attendance of funerals. Individuals were grouped into randomly assigned households (size based on Demographic Health Survey data) that were geographically placed to match population density estimates on a grid of 3157 cells covering the country. The spatial agent-based model was calibrated with a Markov chain Monte Carlo approach. The model was used to estimate Ebola virus transmission parameters and investigate the effectiveness of interventions such as availability of Ebola treatment units, safe burials procedures, and household protection kits.

Findings

Up to Aug 16, 2014, we estimated that 38·3% of infections (95% CI 17·4–76·4) were acquired in hospitals, 30·7% (14·1–46·4) in households, and 8·6% (3·2–11·8) while participating in funerals. We noted that the movement and mixing, in hospitals at the early stage of the epidemic, of patients infected with Ebola virus and those not infected was a sufficient driver of the reported pattern of spatial spread. The subsequent decrease of incidence at country and county level is attributable to the increasing availability of Ebola treatment units (which in turn contributed to drastically decreased hospital transmission), safe burials, and distribution of household protection kits.

Interpretation

The model allows assessment of intervention options and the understanding of their role in the decrease in incidence reported since Sept 7, 2014. High-quality data (eg, to estimate household secondary attack rate, contact patterns within hospitals, and effects of ongoing interventions) are needed to reduce uncertainty in model estimates.

Funding

US Defense Threat Reduction Agency, US National Institutes of Health.

Minimal within-host dengue models highlight the specific roles of the immune response in primary and secondary dengue infections

Rotem Ben-Shachar and Katia Koelle

Journal of the Royal Society, Interface

December 17, 2015

Abstract

In recent years, the within-host viral dynamics of dengue infections have been increasingly characterized, and the relationship between aspects of these dynamics and the manifestation of severe disease has been increasingly probed. Despite this progress, there are few mathematical models of within-host dengue dynamics, and the ones that exist focus primarily on the general role of immune cells in the clearance of infected cells, while neglecting other components of the immune response in limiting viraemia. Here, by considering a suite of mathematical within-host dengue models of increasing complexity, we aim to isolate the critical components of the innate and the adaptive immune response that suffice in the reproduction of several well-characterized features of primary and secondary dengue infections. By building up from a simple target cell limited model, we show that only the innate immune response is needed to recover the characteristic features of a primary symptomatic dengue infection, while a higher rate of viral infectivity (indicative of antibody-dependent enhancement) and infected cell clearance by T cells are further needed to recover the characteristic features of a secondary dengue infection. We show that these minimal models can reproduce the increased risk of disease associated with secondary heterologous infections that arises as a result of a cytokine storm, and, further, that they are consistent with virological indicators that predict the onset of severe disease, such as the magnitude of peak viraemia, time to peak viral load, and viral clearance rate. Finally, we show that the effectiveness of these virological indicators to predict the onset of severe disease depends on the contribution of T cells in fuelling the cytokine storm.

Seven challenges for model-driven data collection in experimental and observational studies

J. Lessler,  W.J. Edmunds, M.E. Halloran, T.D. Hollingsworth,  A.L. Lloyd

Epidemics

December 16, 2014

ABSTRACT

Infectious disease models are both concise statements of hypotheses and powerful techniques for creating tools from hypotheses and theories. As such, they have tremendous potential for guiding data collection in experimental and observational studies, leading to more efficient testing of hypotheses and more robust study designs. In numerous instances, infectious disease models have played a key role in informing data collection, including the Garki project studying malaria, the response to the 2009 pandemic of H1N1 influenza in the United Kingdom and studies of T-cell immunodynamics in mammals. However, such synergies remain the exception rather than the rule; and a close marriage of dynamic modeling and empirical data collection is far from the norm in infectious disease research. Overcoming the challenges to using models to inform data collection has the potential to accelerate innovation and to improve practice in how we deal with infectious disease threats.

Opinion: Mathematical models: A key tool for outbreak response

E. Lofgren, M.E. Halloran, C.M. Rivers, J.M. Drake, T.C. Porco, B. Lewis, W. Yang, A. Vespignani, J. Shaman, J.N.S. Eisenberg, M.C. Eisenberg, M. Marathe, S.V. Scarpino, K.A. Alexander, R. Meza, M.J. Ferrari, J.M. Hyman, L.A. Meyers, S. Eubank 

PNAS

December 10, 2014

Abstract

The 2014 outbreak of Ebola in West Africa is unprecedented in its size and geographic range, and demands swift, effective action from the international community. Understanding the dynamics and spread of Ebola is critical for directing interventions and extinguishing the epidemic; however, observational studies of local conditions have been incomplete and limited by the urgent need to direct resources to patient care.

Mathematical and computational models can help address this deficiency through work with sparse observations, inference on missing data, and incorporation of the latest information. These models can clarify how the disease is spreading and provide timely guidance to policymakers. However, the use of models in public health often meets resistance (1), from doubts in peer review about the utility of such analyses to public skepticism that models can contribute when the means to control an epidemic are already known (2). Even when they are discussed in a positive light, models are often portrayed as arcane and largely inaccessible thought experiments (3). However, the role of models is crucial: they can be used to quantify the effect of mitigation efforts, provide guidance on the scale of interventions required to achieve containment, and identify factors that fundamentally influence the course of an outbreak.

School-Located Influenza Vaccination Reduces Community Risk for Influenza and Influenza-Like Illness Emergency Care Visits

C.H. Tran, J.D. Sugimoto, J.R.C. Pulliam, K.A. Ryan, P.D. Myers, 
J.B. Castleman, R. Doty, J. Johnson, J. Stringfellow, 
N. Kovacevich, J. Brew, L.L. Cheung, B. Caron, G. Lipori, 
C.A. Harle, C. Alexander, Y. Yang, I.M. Longini Jr., 
M.E. Halloran, J.G. Morris Jr., P.A. Small Jr.

PLOS ONE

December 9, 2014

Background

School-located influenza vaccination (SLIV) programs can substantially enhance the sub-optimal coverage achieved under existing delivery strategies. Randomized SLIV trials have shown these programs reduce laboratory-confirmed influenza among both vaccinated and unvaccinated children. This work explores the effectiveness of a SLIV program in reducing the community risk of influenza and influenza-like illness (ILI) associated emergency care visits.

Methods

For the 2011/12 and 2012/13 influenza seasons, we estimated age-group specific attack rates (AR) for ILI from routine surveillance and census data. Age-group specific SLIV program effectiveness was estimated as one minus the AR ratio for Alachua County versus two comparison regions: the 12 county region surrounding Alachua County, and all non-Alachua counties in Florida.

Results

Vaccination of ~50% of 5–17 year-olds in Alachua reduced their risk of ILI-associated visits, compared to the rest of Florida, by 79% (95% confidence interval: 70, 85) in 2011/12 and 71% (63, 77) in 2012/13. The greatest indirect effectiveness was observed among 0–4 year-olds, reducing AR by 89% (84, 93) in 2011/12 and 84% (79, 88) in 2012/13. Among all non-school age residents, the estimated indirect effectiveness was 60% (54, 65) and 36% (31, 41) for 2011/12 and 2012/13. The overall effectiveness among all age-groups was 65% (61, 70) and 46% (42, 50) for 2011/12 and 2012/13.

Conclusion

Wider implementation of SLIV programs can significantly reduce the influenza-associated public health burden in communities.

Comparative Effectiveness of Different Strategies of Oral Cholera Vaccination in Bangladesh: A Modeling Study

Dobromir T. Dimitrov , Christopher Troeger , M. Elizabeth Halloran, Ira M. Longini , Dennis L. Chao

PLOS 
Neglected Tropical Diseases

December 4, 2014

Background

Killed, oral cholera vaccines have proven safe and effective, and several large-scale mass cholera vaccination efforts have demonstrated the feasibility of widespread deployment. This study uses a mathematical model of cholera transmission in Bangladesh to examine the effectiveness of potential vaccination strategies.

Methods & Findings

We developed an age-structured mathematical model of cholera transmission and calibrated it to reproduce the dynamics of cholera in Matlab, Bangladesh. We used the model to predict the effectiveness of different cholera vaccination strategies over a period of 20 years. We explored vaccination programs that targeted one of three increasingly focused age groups (the entire vaccine-eligible population of age one year and older, children of ages 1 to 14 years, or preschoolers of ages 1 to 4 years) and that could occur either as campaigns recurring every five years or as continuous ongoing vaccination efforts. Our modeling results suggest that vaccinating 70% of the population would avert 90% of cholera cases in the first year but that campaign and continuous vaccination strategies differ in effectiveness over 20 years. Maintaining 70% coverage of the population would be sufficient to prevent sustained transmission of endemic cholera in Matlab, while vaccinating periodically every five years is less effective. Selectively vaccinating children 1–14 years old would prevent the most cholera cases per vaccine administered in both campaign and continuous strategies.

Conclusions

We conclude that continuous mass vaccination would be more effective against endemic cholera than periodic campaigns. Vaccinating children averts more cases per dose than vaccinating all age groups, although vaccinating only children is unlikely to control endemic cholera in Bangladesh. Careful consideration must be made before generalizing these results to other regions.

Ebola: Mobility data

M.E. Halloran, A. Vespignani, N. Bharti, L.R. Feldstein, K.A. Alexander, M. Ferrari, J. Shaman, J.M. Drake, T. Porco, J.N.S. Eisenberg, S.Y. Del Valle, E. Lofgren, S.V. Scarpino, M.C. Eisenberg, D. Gao, J.M. Hyman, S. Eubank, I.M. Longini

Science 

October 24, 2014

Understanding human movement and mobility is important for characterizing, forecasting, and controlling the spatial and temporal spread of infectious diseases. Unfortunately, the current West African Ebola outbreak is taking place in a region where mobility has changed considerably in recent years. Efforts must be made to better understand these mobility patterns. For example, mobile phone call records provide insight into how people move within countries, particularly if they move from hotspots of disease. Analyses of Orange Telecom data have produced initial maps of movement in Senegal and Ivory Coast (1, 2), and endeavors are under way to obtain similar data for Sierra Leone, Guinea, and Liberia.

Assessing the impact of travel restrictions on international spread of the 2014 West African Ebola epidemic

C. Poletto, M.F. Gomes, A. Pastore y Piontti, L. Rossi, L. Bioglio, D.L. Chao, I.M. Longini, M.E. Halloran, V. Colizza, A. Vespignani

Euro Surveillance

October 23, 2014
 

Abstract

The quick spread of an Ebola outbreak in West Africa has led a number of countries and airline companies to issue travel bans to the affected areas. Considering data up to 31 Aug 2014, we assess the impact of the resulting traffic reductions with detailed numerical simulations of the international spread of the epidemic. Traffic reductions are shown to delay by only a few weeks the risk that the outbreak extends to new countries.