Imperial College London

DrIsobelBlake

Faculty of MedicineSchool of Public Health

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 3260isobel.blake

 
 
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Location

 

G26Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

30 results found

Blake IM, Pons Salort M, Molodecky N, Diop O, Chenoweth P, Bandyopadhyay A, Zaffran M, Sutter R, Grassly Net al., 2018, Type 2 Poliovirus Detection After Global Withdrawal of Trivalent Oral Vaccine, New England Journal of Medicine, Vol: 379, Pages: 834-845, ISSN: 0028-4793

BackgroundMass campaigns with oral poliovirus vaccine (OPV) have brought the world close to the eradication of wild poliovirus. However, to complete eradication, OPV must itself be withdrawn to prevent outbreaks of vaccine-derived poliovirus (VDPV). Synchronized global withdrawal of OPV began with serotype 2 OPV (OPV2) in April 2016, which presented the first test of the feasibility of eradicating all polioviruses.MethodsWe analyzed global surveillance data on the detection of serotype 2 Sabin vaccine (Sabin-2) poliovirus and serotype 2 vaccine–derived poliovirus (VDPV2, defined as vaccine strains that are at least 0.6% divergent from Sabin-2 poliovirus in the viral protein 1 genomic region) in stool samples from 495,035 children with acute flaccid paralysis in 118 countries and in 8528 sewage samples from four countries at high risk for transmission; the samples were collected from January 1, 2013, through July 11, 2018. We used Bayesian spatiotemporal smoothing and logistic regression to identify and map risk factors for persistent detection of Sabin-2 poliovirus and VDPV2.ResultsThe prevalence of Sabin-2 poliovirus in stool samples declined from 3.9% (95% confidence interval [CI], 3.5 to 4.3) at the time of OPV2 withdrawal to 0.2% (95% CI, 0.1 to 2.7) at 2 months after withdrawal, and the detection rate in sewage samples declined from 71.0% (95% CI, 61.0 to 80.0) to 13.0% (95% CI, 8.0 to 20.0) during the same period. However, 12 months after OPV2 withdrawal, Sabin-2 poliovirus continued to be detected in stool samples (<0.1%; 95% CI, <0.1 to 0.1) and sewage samples (8.0%; 95% CI, 5.0 to 13.0) because of the use of OPV2 in response to VDPV2 outbreaks. Nine outbreaks were reported after OPV2 withdrawal and were associated with low coverage of routine immunization (odds ratio, 1.64 [95% CI, 1.14 to 2.54] per 10% absolute decrease) and low levels of population immunity (odds ratio, 2.60 [95% CI, 1.35 to 5.59] per 10% absolute decrease) within affected cou

JOURNAL ARTICLE

Molodecky NAL, Blake IM, O'reilly KM, Wadood MZ, Safdar RM, Wesolowski A, Buckee CO, Bandyopadhyay AS, Okayasu H, Grassly NCet al., 2017, Risk-factors and short-term projections for serotype-1 poliomyelitis incidence in Pakistan: a spatio-temporal analysis, Plos Medicine, Vol: 14, ISSN: 1549-1676

BackgroundPakistan currently provides a substantial challenge to global polio eradication, having contributed to 73% of reported poliomyelitis in 2015 and 54% in 2016. A better understanding of the risk factors and movement patterns that contribute to poliovirus transmission across Pakistan would support evidence-based planning for mass vaccination campaigns.Methods and findingsWe fit mixed-effects logistic regression models to routine surveillance data recording the presence of poliomyelitis associated with wild-type 1 poliovirus in districts of Pakistan over 6-month intervals between 2010 to 2016. To accurately capture the force of infection (FOI) between districts, we compared 6 models of population movement (adjacency, gravity, radiation, radiation based on population density, radiation based on travel times, and mobile-phone based). We used the best-fitting model (based on the Akaike Information Criterion [AIC]) to produce 6-month forecasts of poliomyelitis incidence. The odds of observing poliomyelitis decreased with improved routine or supplementary (campaign) immunisation coverage (multivariable odds ratio [OR] = 0.75, 95% confidence interval [CI] 0.67–0.84; and OR = 0.75, 95% CI 0.66–0.85, respectively, for each 10% increase in coverage) and increased with a higher rate of reporting non-polio acute flaccid paralysis (AFP) (OR = 1.13, 95% CI 1.02–1.26 for a 1-unit increase in non-polio AFP per 100,000 persons aged <15 years). Estimated movement of poliovirus-infected individuals was associated with the incidence of poliomyelitis, with the radiation model of movement providing the best fit to the data. Six-month forecasts of poliomyelitis incidence by district for 2013–2016 showed good predictive ability (area under the curve range: 0.76–0.98). However, although the best-fitting movement model (radiation) was a significant determinant of poliomyelitis incidence, it did not improve the predictive ability of the multivariable mo

JOURNAL ARTICLE

Cori A, Donnelly CA, dorigatti, ferguson NM, fraser, garske, jombart, Nedjati-Gilani G, Nouvellet, Riley, Van Kerkhove, Mills, Blake IMet 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.

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Garske T, Cori A, Ariyarajah A, Blake I, Dorigatti I, Eckmanns T, Fraser C, Hinsley W, Jombart T, Mills H, Nedjati-Gilani G, Newton E, Nouvellet P, Perkins D, Riley S, Schumacher D, Shah A, Van Kerkhove M, Dye C, Ferguson N, Donnelly Cet 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.

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Nouvellet P, Cori A, Garske T, Blake IM, Dorigatti I, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Van Kerkhove MD, Fraser C, Donnelly CA, Ferguson NM, Riley Set 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.

JOURNAL ARTICLE

International Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Bawo L, Bilivogui P, Blake IM, Brennan RJ, Cawthorne A, Cleary E, Clement P, Conteh R, Cori A, Dafae F, Dahl B, Dangou JM, Diallo B, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Fallah M, Ferguson NM, Fiebig L, Fraser C, Garske T, Gonzalez L, Hamblion E, Hamid N, Hersey S, Hinsley W, Jambei A, Jombart T, Kargbo D, Keita S, Kinzer M, George FK, Godefroy B, Gutierrez G, Kannangarage N, Mills HL, Moller T, Meijers S, Mohamed Y, Morgan O, Nedjati-Gilani G, Newton E, Nouvellet P, Nyenswah T, Perea W, Perkins D, Riley S, Rodier G, Rondy M, Sagrado M, Savulescu C, Schafer IJ, Schumacher D, Seyler T, Shah A, Van Kerkhove MD, Wesseh CS, Yoti Zet 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

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Pons-Salort M, Molodecky NA, O'Reilly KM, Wadood MZ, Safdar RM, Etsano A, Vaz RG, Jafari H, Grassly NC, Blake IMet al., 2016, Population immunity against serotype-2 poliomyelitis Leading up to the global withdrawal of the oral poliovirus vaccine: spatio-temporal modelling of surveillance data, Plos Medicine, Vol: 13, ISSN: 1549-1676

BackgroundGlobal withdrawal of serotype-2 oral poliovirus vaccine (OPV2) took place in April 2016. This marked a milestone in global polio eradication and was a public health intervention of unprecedented scale, affecting 155 countries. Achieving high levels of serotype-2 population immunity before OPV2 withdrawal was critical to avoid subsequent outbreaks of serotype-2 vaccine-derived polioviruses (VDPV2s).Methods and FindingsIn August 2015, we estimated vaccine-induced population immunity against serotype-2 poliomyelitis for 1 January 2004–30 June 2015 and produced forecasts for April 2016 by district in Nigeria and Pakistan. Population immunity was estimated from the vaccination histories of children <36 mo old identified with non-polio acute flaccid paralysis (AFP) reported through polio surveillance, information on immunisation activities with different oral poliovirus vaccine (OPV) formulations, and serotype-specific estimates of the efficacy of these OPVs against poliomyelitis. District immunity estimates were spatio-temporally smoothed using a Bayesian hierarchical framework. Coverage estimates for immunisation activities were also obtained, allowing for heterogeneity within and among districts. Forward projections of immunity, based on these estimates and planned immunisation activities, were produced through to April 2016 using a cohort model.Estimated population immunity was negatively correlated with the probability of VDPV2 poliomyelitis being reported in a district. In Nigeria and Pakistan, declines in immunity during 2008–2009 and 2012–2013, respectively, were associated with outbreaks of VDPV2. Immunity has since improved in both countries as a result of increased use of trivalent OPV, and projections generally indicated sustained or improved immunity in April 2016, such that the majority of districts (99% [95% uncertainty interval 97%–100%] in Nigeria and 84% [95% uncertainty interval 77%–91%] in Pakistan) had >70

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Pinsent A, Blake IM, Basáñez MG, Gambhir Met al., 2016, Mathematical Modelling of Trachoma Transmission, Control and Elimination., Publisher: Elsevier, Pages: 1-48

The World Health Organization has targeted the elimination of blinding trachoma by the year 2020. To this end, the Global Elimination of Blinding Trachoma (GET, 2020) alliance relies on a four-pronged approach, known as the SAFE strategy (S for trichiasis surgery; A for antibiotic treatment; F for facial cleanliness and E for environmental improvement). Well-constructed and parameterized mathematical models provide useful tools that can be used in policy making and forecasting in order to help to control trachoma and understand the feasibility of this large-scale elimination effort. As we approach this goal, the need to understand the transmission dynamics of infection within areas of different endemicities, to optimize available resources and to identify which strategies are the most cost-effective becomes more pressing. In this study, we conducted a review of the modelling literature for trachoma and identified 23 articles that included a mechanistic or statistical model of the transmission, dynamics and/or control of (ocular) Chlamydia trachomatis. Insights into the dynamics of trachoma transmission have been generated through both deterministic and stochastic models. A large body of the modelling work conducted to date has shown that, to varying degrees of effectiveness, antibiotic administration can reduce or interrupt trachoma transmission. However, very little analysis has been conducted to consider the effect of nonpharmaceutical interventions (and particularly the F and E components of the SAFE strategy) in helping to reduce transmission. Furthermore, very few of the models identified in the literature review included a structure that permitted tracking of the prevalence of active disease (in the absence of active infection) and the subsequent progression to disease sequelae (the morbidity associated with trachoma and ultimately the target of GET 2020 goals). This represents a critical gap in the current trachoma modelling literature, which m

BOOK CHAPTER

Agua-Agum J, Allegranzi B, Ariyarajah A, Aylward RB, Blake IM, Barboza P, Bausch D, Brennan RJ, Clement P, Coffey P, Cori A, Donnelly CA, Dorigatti I, Drury P, Durski K, Dye C, Eckmanns T, Ferguson NM, Fraser C, Garcia E, Garske T, Gasasira A, Gurry C, Gutierrez GJ, Hamblion E, Hinsley W, Holden R, Holmes D, Hugonnet S, Jombart T, Kelley E, Santhana R, Mahmoud N, Mills HL, Mohamed Y, Musa E, Naidoo D, Nedjati-Gilani G, Newton E, Norton I, Nouvellet P, Perkins D, Perkins M, Riley S, Schumacher D, Shah A, Minh T, Varsaneux O, Van Kerkhove MDet 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.

JOURNAL ARTICLE

Pons-Salort M, Burns CC, Lyons H, Blake IM, Jafari H, Oberste MS, Kew OM, Grassly NCet al., 2016, Preventing Vaccine-Derived Poliovirus Emergence during the Polio Endgame, PLOS Pathogens, Vol: 12, ISSN: 1553-7366

Reversion and spread of vaccine-derived poliovirus (VDPV) to cause outbreaks of poliomyelitis is a rare outcome resulting from immunisation with the live-attenuated oral poliovirus vaccines (OPVs). Global withdrawal of all three OPV serotypes is therefore a key objective of the polio endgame strategic plan, starting with serotype 2 (OPV2) in April 2016. Supplementary immunisation activities (SIAs) with trivalent OPV (tOPV) in advance of this date could mitigate the risks of OPV2 withdrawal by increasing serotype-2 immunity, but may also create new serotype-2 VDPV (VDPV2). Here, we examine the risk factors for VDPV2 emergence and implications for the strategy of tOPV SIAs prior to OPV2 withdrawal. We first developed mathematical models of VDPV2 emergence and spread. We found that in settings with low routine immunisation coverage, the implementation of a single SIA increases the risk of VDPV2 emergence. If routine coverage is 20%, at least 3 SIAs are needed to bring that risk close to zero, and if SIA coverage is low or there are persistently "missed" groups, the risk remains high despite the implementation of multiple SIAs. We then analysed data from Nigeria on the 29 VDPV2 emergences that occurred during 2004-2014. Districts reporting the first case of poliomyelitis associated with a VDPV2 emergence were compared to districts with no VDPV2 emergence in the same 6-month period using conditional logistic regression. In agreement with the model results, the odds of VDPV2 emergence decreased with higher routine immunisation coverage (odds ratio 0.67 for a 10% absolute increase in coverage [95% confidence interval 0.55-0.82]). We also found that the probability of a VDPV2 emergence resulting in poliomyelitis in >1 child was significantly higher in districts with low serotype-2 population immunity. Our results support a strategy of focused tOPV SIAs before OPV2 withdrawal in areas at risk of VDPV2 emergence and in sufficient number to raise population immuni

JOURNAL ARTICLE

Blake IM, Chenoweth P, Okayasu H, Donnelly C, Aylward RB, Grassly NCet al., 2016, Faster Detection of Poliomyelitis Outbreaks to Support Polio Eradication, Emerging Infectious Diseases, Vol: 22, Pages: 449-456, ISSN: 1080-6040

As the global eradication of poliomyelitis approaches the final stages, prompt detection of new outbreaks is critical to enable a fast and effective outbreak response. Surveillance relies on reporting of acute flaccid paralysis (AFP) cases and laboratory confirmation through isolation of poliovirus from stool. However, delayed sample collection and testing can delay outbreak detection. We investigated whether weekly testing for clusters of AFP by location and time, using the Kulldorff scan statistic, could provide an early warning for outbreaks in 20 countries. A mixed-effects regression model was used to predict background rates of nonpolio AFP at the district level. In Tajikistan and Congo, testing for AFP clusters would have resulted in an outbreak warning 39 and 11 days, respectively, before official confirmation of large outbreaks. This method has relatively high specificity and could be integrated into the current polio information system to support rapid outbreak response activities.

JOURNAL ARTICLE

Agua-Agum J, Ariyarajah A, Blake IM, Cori A, Donnelly CA, Dorigatti I, Dye C, Eck-Manns T, Ferguson NM, Fraser C, Garske T, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Newton E, Nouvellet P, Perkins D, Riley S, Schumacher D, Shah A, Thomas LJ, Van Kerkhove MDet 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

JOURNAL ARTICLE

Gambhir M, Grassly NC, Burton MJ, Solomon AW, Taylor HR, Mabey DC, Blake IM, Basanez M-Get al., 2015, Estimating the Future Impact of a Multi-Pronged Intervention Strategy on Ocular Disease Sequelae Caused by Trachoma: A Modeling Study, Ophthalmic Epidemiology, Vol: 22, Pages: 394-402, ISSN: 1744-5086

Purpose: Trachoma control programs are underway in endemic regions worldwide. They are based on the SAFE strategy (Surgery for trichiasis, Antibiotic distribution, Facial cleanliness, and Environmental improvement). Although much is known about the effect of community-wide treatment with antibiotics on the prevalence of Chlamydia trachomatis, the impact of the SAFE strategy on severe ocular disease sequelae (the main focus of the Global Elimination of blinding Trachoma by 2020 program) remains largely unknown.Methods: We use a mathematical model to explore the impact of each of the components of the SAFE strategy, individually and together, on disease sequelae, arising from repeat infection and subsequent conjunctival scarring. We ask whether two elimination goals, to reduce the prevalence of trachomatous trichiasis to 1 per 1000 persons, and the incidence of corneal opacity to 1 per 10,000 persons per annum, are achievable, and which combinations of interventions have the greatest impact on these indicators.Results: In high prevalence communities (here, >20% infection of children aged 1–9 years), a combination of efforts is needed to bring down sustainably the prevalence and incidence of ocular disease sequelae.Conclusion: The mass delivery of antibiotics is highly beneficial for the clearance of infection, inflammation and prevention of subsequent scarring, but needs to be supplemented with sustained reductions in transmission and surgery to consider realistically the elimination of blindness by the year 2020.

JOURNAL ARTICLE

Nouvellet P, Garske T, Mills HL, Nedjati-Gilani G, Hinsley W, Blake IM, Van Kerkhove MD, Cori A, Dorigatti I, Jombart T, Riley S, Fraser C, Donnelly CA, Ferguson NMet 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.

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Lipsitch M, Donnelly CA, Fraser C, Blake IM, Cori A, Dorigatti I, Ferguson NM, Garske T, Mills HL, Riley S, Van Kerkhove MD, Hernan MAet al., 2015, Potential biases in estimating absolute and relative case-fatality risks during outbreaks, PLOS Neglected Tropical Diseases, Vol: 9, ISSN: 1935-2735

JOURNAL ARTICLE

Agua-Agum J, Ariyarajah A, Blake IM, Cori A, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Ferguson NM, Fowler RA, Fraser C, Garske T, Hinsley W, Jombart T, Mills HL, Murthy S, Nedjati-Gilani G, Nouvellet P, Pelletier L, Riley S, Schumacher D, Shah A, Van Kerkhove MDet al., 2015, Ebola virus disease among children in West Africa, New England Journal of Medicine, Vol: 372, Pages: 1274-1277, ISSN: 1533-4406

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Agua-Agum J, Ariyarajah A, Aylward B, Blake IM, Brennan R, Cori A, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Ferguson NM, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills HL, Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van Kerkhove MD, Varsaneux O, Kannangarage NWet 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

JOURNAL ARTICLE

Turner HC, Walker M, French MD, Blake IM, Churcher TS, Basanez M-Get al., 2014, Neglected tools for neglected diseases: mathematical models in economic evaluations, TRENDS IN PARASITOLOGY, Vol: 30, Pages: 562-570, ISSN: 1471-4922

JOURNAL ARTICLE

Pinsent A, Blake IM, White MT, Riley Set al., 2014, Surveillance of low pathogenic novel H7N9 avian influenza in commercial poultry barns: detection of outbreaks and estimation of virus introduction time, BMC INFECTIOUS DISEASES, Vol: 14, ISSN: 1471-2334

BackgroundBoth high and low pathogenic subtype A avian influenza remain ongoing threats to the commercial poultry industry globally. The emergence of a novel low pathogenic H7N9 lineage in China presents itself as a new concern to both human and animal health and may necessitate additional surveillance in commercial poultry operations in affected regions.MethodsSampling data was simulated using a mechanistic model of H7N9 influenza transmission within commercial poultry barns together with a stochastic observation process. Parameters were estimated using maximum likelihood. We assessed the probability of detecting an outbreak at time of slaughter using both real-time polymerase chain reaction (rt-PCR) and a hemagglutinin inhibition assay (HI assay) before considering more intense sampling prior to slaughter. The day of virus introduction and R 0 were estimated jointly from weekly flock sampling data. For scenarios where R 0 was known, we estimated the day of virus introduction into a barn under different sampling frequencies.ResultsIf birds were tested at time of slaughter, there was a higher probability of detecting evidence of an outbreak using an HI assay compared to rt-PCR, except when the virus was introduced <2 weeks before time of slaughter. Prior to the initial detection of infection N s a m p l e = 50 (1%) of birds were sampled on a weekly basis once, but after infection was detected, N s a m p l e = 2000 birds (40%) were sampled to estimate both parameters. We accurately estimated the day of virus introduction in isolation with weekly and 2-weekly sampling.ConclusionsA strong sampling effort would be required to infer both the day of virus introduction and R 0. Such a sampling effort would not be required to estimate the day of virus introduction alone once R 0 was known, and sampling N s a m p l e = 50 of birds in the flock on a weekly or 2 weekly basis would be sufficient.

JOURNAL ARTICLE

Blake IM, Martin R, Goel A, Khetsuriani N, Everts J, Wolff C, Wassilak S, Aylward RB, Grassly NCet al., 2014, The role of older children and adults in wild poliovirus transmission, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 111, Pages: 10604-10609, ISSN: 0027-8424

JOURNAL ARTICLE

Blake IM, Donnelly CA, 2014, A simple incidence-based method to avoid misinterpretation of bovine tuberculosis incidence trends in great britain., PLoS Curr, Vol: 6

The incidence of bovine tuberculosis (TB) in Great Britain has generally been increasing in recent decades. Routine ante-mortem testing of cattle herds is required for disease surveillance and control, due to the asymptomatic nature of the infection. The Department for Environment, Food and Rural Affairs (Defra) publishes TB incidence trends as the percentage of officially TB-free (OTF) herds tested per month with OTF status withdrawn due to post-mortem evidence of infection. This method can result in artefactual fluctuations. We have previously demonstrated an alternative method, that distributes incidents equally over the period of risk, provides a more accurate representation of underlying risk. However, this method is complex and it may not be sufficiently straightforward for use in the national statistics. Here we present a simple incidence-based method that adjusts for the time between tests and show it can provide a reasonable representation of the underlying risk without artefactual fluctuations.

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, 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

JOURNAL ARTICLE

Koukounari A, Moustaki I, Grassly NC, Blake IM, Basanez M-G, Gambhir M, Mabey DCW, Bailey RL, Burton MJ, Solomon AW, Donnelly CAet al., 2013, Using a Nonparametric Multilevel Latent Markov Model to Evaluate Diagnostics for Trachoma, American Journal of Epidemiology, Vol: 177, Pages: 913-922, ISSN: 0002-9262

In disease control or elimination programs, diagnostics are essential for assessing the impact of interventions, refining treatment strategies, and minimizing the waste of scarce resources. Although high-performance tests are desirable, increased accuracy is frequently accompanied by a requirement for more elaborate infrastructure, which is often not feasible in the developing world. These challenges are pertinent to mapping, impact monitoring, and surveillance in trachoma elimination programs. To help inform rational design of diagnostics for trachoma elimination, we outline a nonparametric multilevel latent Markov modeling approach and apply it to 2 longitudinal cohort studies of trachoma-endemic communities in Tanzania (2000–2002) and The Gambia (2001–2002) to provide simultaneous inferences about the true population prevalence of Chlamydia trachomatis infection and disease and the sensitivity, specificity, and predictive values of 3 diagnostic tests for C. trachomatis infection. Estimates were obtained by using data collected before and after mass azithromycin administration. Such estimates are particularly important for trachoma because of the absence of a true “gold standard” diagnostic test for C. trachomatis. Estimated transition probabilities provide useful insights into key epidemiologic questions about the persistence of disease and the clearance of infection as well as the required frequency of surveillance in the postelimination setting.

JOURNAL ARTICLE

Blake IM, Donnelly CA, 2012, Estimating risk over time using data from targeted surveillance systems: Application to bovine tuberculosis in Great Britain, EPIDEMICS, Vol: 4, Pages: 179-186, ISSN: 1755-4365

JOURNAL ARTICLE

Solomon AW, Engels D, Bailey RL, Blake IM, Brooker S, Chen JX, Chen JH, Churcher TS, Drakely CJ, Edwards T, Fenwick A, French MD, Gabrielli AF, Grassly NC, Harding-Esch E, Holland MJ, Koukounari A, Lammie PJ, Leslie J, Mabey DCW, Rhajaoui M, Secor WE, Stothard JR, Wei H, Willingham AL, Zhou XN, Peeling RWet al., 2012, A Diagnostics Platform for the Integrated Mapping, Monitoring and Surveillance of Neglected Tropical Diseases: Rationale and Target Product Profiles, PLoS Neglected Tropical Diseases

JOURNAL ARTICLE

Gambhir M, Basanez MG, Blake IM, Grassly Net al., 2010, Modelling trachoma for control programmes, Modelling Parasite Transmission and Control, Editors: Michael, Spear, Publisher: Austin, Texas: Landes Bioscience, ISBN: 9781441960641

BOOK CHAPTER

Blake IM, Burton MJ, Solomon AW, West SK, Basanez M-G, Gambhir M, Bailey RL, Mabey DCW, Grassly NCet al., 2010, Targeting Antibiotics to Households for Trachoma Control, PLOS NEGLECTED TROPICAL DISEASES, Vol: 4, ISSN: 1935-2735

JOURNAL ARTICLE

Gambhir M, Basanez M-G, Blake IM, Grassly NCet al., 2010, Modelling Trachoma for Control Programmes, MODELLING PARASITE TRANSMISSION AND CONTROL, Vol: 673, Pages: 141-156, ISSN: 0065-2598

JOURNAL ARTICLE

Gambhir M, Basanez M-G, Burton MJ, Solomon AW, Bailey RL, Holland MJ, Blake IM, Donnelly CA, Jabr I, Mabey DC, Grassly NCet al., 2009, The Development of an Age-Structured Model for Trachoma Transmission Dynamics, Pathogenesis and Control, PLOS Neglected Tropical Diseases, Vol: 3, ISSN: 1935-2735

Background: Trachoma, the worldwide leading infectious cause of blindness, is due to repeated conjunctival infection with Chlamydia trachomatis. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular sequelae require long-term monitoring. We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma.Methodology/Principal Findings: The model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness. It also includes aspects of trachoma natural history, such as an increasing rate of recovery from infection and a decreasing chlamydial load with subsequent infections that depend upon a (presumed) acquired immunity that clears infection with age more rapidly. Parameters were estimated using maximum likelihood by fitting the model to pre-control infection prevalence data from hypo-, meso- and hyperendemic communities from The Gambia and Tanzania. The model reproduces key features of trachoma epidemiology: 1) the age-profile of infection prevalence, which increases to a peak at very young ages and declines at older ages; 2) a shift in this prevalence peak, toward younger ages in higher force of infection environments; 3) a raised overall profile of infection prevalence with higher force of infection; and 4) a rising profile, with age, of the prevalence of the ensuing severe sequelae (trachomatous scarring, trichiasis), as well as estimates of the number of infections that need to occur before these sequelae appear.Conclusions/Significance: We present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease. The suitability of the model for representing population-level patterns of infection and disease sequelae is discussed in

JOURNAL ARTICLE

Blake IM, Burton MJ, Bailey RL, Solomon AW, West S, Munoz B, Holland MJ, Mabey DCW, Gambhir M, Basanez M-G, Grassly NCet al., 2009, Estimating Household and Community Transmission of Ocular Chlamydia trachomatis, PLOS NEGLECTED TROPICAL DISEASES, Vol: 3, ISSN: 1935-2735

JOURNAL ARTICLE

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