Publications
433 results found
Cattarino L, Rodriguez-Barraquer I, Imai N, et al., 2020, Mapping global variation in dengue transmission intensity, Science Translational Medicine, Vol: 12, Pages: eaax4144-eaax4144, ISSN: 1946-6234
Intervention planning for dengue requires reliable estimates of dengue transmission intensity. However, currentmaps of dengue risk provide estimates of disease burden or the boundaries of endemicity rather than transmissionintensity. We therefore developed a global high-resolution map of dengue transmission intensity by fittingenvironmentally driven geospatial models to geolocated force of infection estimates derived from cross-sectionalserological surveys and routine case surveillance data. We assessed the impact of interventions on dengue transmission and disease using Wolbachia-infected mosquitoes and the Sanofi-Pasteur vaccine as specific examples.We predicted high transmission intensity in all continents straddling the tropics, with hot spots in South America(Colombia, Venezuela, and Brazil), Africa (western and central African countries), and Southeast Asia (Thailand,Indonesia, and the Philippines). We estimated that 105 [95% confidence interval (CI), 95 to 114] million dengueinfections occur each year with 51 (95% CI, 32 to 66) million febrile disease cases. Our analysis suggests thattransmission-blocking interventions such as Wolbachia, even at intermediate efficacy (50% transmission reduction), might reduce global annual disease incidence by up to 90%. The Sanofi-Pasteur vaccine, targeting onlyseropositive recipients, might reduce global annual disease incidence by 20 to 30%, with the greatest impact inhigh-transmission settings. The transmission intensity map presented here, and made available for download,may help further assessment of the impact of dengue control interventions and prioritization of global publichealth efforts.
Imai N, Cori A, Dorigatti I, et al., 2020, Report 3: Transmissibility of 2019-nCoV
Self-sustaining human-to-human transmission of the novel coronavirus (2019-nCov) is the only plausible explanation of the scale of the outbreak in Wuhan. We estimate that, on average, each case infected 2.6 (uncertainty range: 1.5-3.5) other people up to 18th January 2020, based on an analysis combining our past estimates of the size of the outbreak in Wuhan with computational modelling of potential epidemic trajectories. This implies that control measures need to block well over 60% of transmission to be effective in controlling the outbreak. It is likely, based on the experience of SARS and MERS-CoV, that the number of secondary cases caused by a case of 2019-nCoV is highly variable – with many cases causing no secondary infections, and a few causing many. Whether transmission is continuing at the same rate currently depends on the effectiveness of current control measures implemented in China and the extent to which the populations of affected areas have adopted risk-reducing behaviours. In the absence of antiviral drugs or vaccines, control relies upon the prompt detection and isolation of symptomatic cases. It is unclear at the current time whether this outbreak can be contained within China; uncertainties include the severity spectrum of the disease caused by this virus and whether cases with relatively mild symptoms are able to transmit the virus efficiently. Identification and testing of potential cases need to be as extensive as is permitted by healthcare and diagnostic testing capacity – including the identification, testing and isolation of suspected cases with only mild to moderate disease (e.g. influenza-like illness), when logistically feasible.
Imai N, Dorigatti I, Cori A, et al., 2020, Report 2: Estimating the potential total number of novel Coronavirus cases in Wuhan City, China
We estimate that a total of 4,000 cases of 2019-nCoV in Wuhan City (uncertainty range: 1,000 – 9,700) had onset of symptoms by 18th January 2020 (the last reported onset date of any case) [15].Our estimates should not be interpreted as implying the outbreak has doubled in size in the period 12th January to 18th January – delays in confirming and reporting exported cases and incomplete information about dates of symptom onset together with the still very small numbers of exported cases mean we are unable to estimate the epidemic growth rate at the current time.This estimate is based on the following assumptions:• Wuhan International Airport has a catchment population of 19 million individuals [1].• There is a mean 10-day delay between infection and detection, comprising a 5-6 day incubation period [16,17] and a 4-5 day delay from symptom onset to detection/hospitalisation of a case (the cases detected in Thailand and Japan were hospitalised 3 and 7 days after onset, respectively) [4,18].• Total volume of international travel from Wuhan over the last two months has been 3,301 passengers per day. This estimate is derived from the 3,418 foreign passengers per day in the top 20 country destinations based on 2018 IATA data [19], and uses 2016 IATA data held by Imperial College London to correct for the travel surge at Chinese New Year present in the latter data (which has not happened yet this year) and for travel to countries outside the top 20 destination list.• Exit screening (which reportedly came into force on the 15th January [13]) had no impact on exported cases reported up to 16th January. Exit screening may have reduced exports in recent days, in which case our baseline prediction may be an underestimate of the true number of cases in Wuhan.• We assume all cases in travellers flying to destinations outside mainland China are being detected at those destinations. This may well not be the case. If cases are being missed in other
Imai N, Dorigatti I, Cori A, et al., 2020, Report 1: Estimating the potential total number of novel Coronavirus cases in Wuhan City, China
We estimate that a total of 1,723 cases of 2019-nCoV in Wuhan City (95% CI: 427 – 4,471) had onset of symptoms by 12th January 2020 (the last reported onset date of any case).This estimate is based on the following assumptions:• Wuhan International Airport has a catchment population of 19 million individuals [1].• There is a mean 10-day delay between infection and detection, comprising a 5-6 day incubation period [8,9] and a 4-5 day delay from symptom onset to detection/hospitalisation of a case (the cases detected in Thailand and Japan were hospitalised 3 and 7 days after onset, respectively) [4,10].• Total volume of international travel from Wuhan over the last two months has been 3,301 passengers per day. This estimate is derived from the 3,418 foreign passengers per day in the top 20 country destinations based on 2018 IATA data [11], and uses 2016 IATA data held by Imperial College to correct for the travel surge at Chinese New Year present in the latter data (which has not happened yet this year) and for travel to countries outside the top 20 destination list.
Sangkaew S, Tam LK, Ng LC, et al., 2020, Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore, Parasites and Vectors, Vol: 13, Pages: 1-10, ISSN: 1756-3305
BackgroundDengue is a mosquito-borne viral disease caused by one of four serotypes (DENV1-4). Infection provides long-term homologous immunity against reinfection with the same serotype. Plaque reduction neutralization test (PRNT) is the gold standard to assess serotype-specific antibody levels. We analysed serotype-specific antibody levels obtained by PRNT in two serological surveys conducted in Singapore in 2009 and 2013 using cluster analysis, a machine learning technique that was used to identify the most common histories of DENV exposure.MethodsWe explored the use of five distinct clustering methods (i.e. agglomerative hierarchical, divisive hierarchical, K-means, K-medoids and model-based clustering) with varying number (from 4 to 10) of clusters for each method. Weighted rank aggregation, an evaluating technique for a set of internal validity metrics, was adopted to determine the optimal algorithm, comprising the optimal clustering method and the optimal number of clusters.ResultsThe K-means algorithm with six clusters was selected as the algorithm with the highest weighted rank aggregation. The six clusters were characterised by (i) dominant DENV2 PRNT titres; (ii) co-dominant DENV1 and DENV2 titres with average DENV2 titre > average DENV1 titre; (iii) co-dominant DENV1 and DENV2 titres with average DENV1 titre > average DENV2 titre; (iv) low PRNT titres against DENV1-4; (v) intermediate PRNT titres against DENV1-4; and (vi) dominant DENV1-3 titres. Analyses of the relative size and age-stratification of the clusters by year of sample collection and the application of cluster analysis to the 2009 and 2013 datasets considered separately revealed the epidemic circulation of DENV2 and DENV3 between 2009 and 2013.ConclusionCluster analysis is an unsupervised machine learning technique that can be applied to analyse PRNT antibody titres (without pre-established cut-off thresholds to indicate protection) to explore common patterns
Bhatia S, Imai N, Cuomo-Dannenburg G, et al., 2020, Estimating the number of undetected COVID-19 cases among travellers from mainland China., Wellcome open research, Vol: 5, Pages: 143-143, ISSN: 2398-502X
Background: As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide.Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries.Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries.Conclusions: Our analysis shows that a large number of COVID-19 cases remain undetected across the world. These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.
Jeffrey B, Walters CE, Ainslie KEC, et al., 2020, Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK., Wellcome Open Res, Vol: 5, ISSN: 2398-502X
Background: Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. Methods: Here, we use two mobile phone-based datasets (anonymised and aggregated crowd level data from O2 and from the Facebook app on mobile phones) to assess changes in average mobility, both overall and broken down into high and low population density areas, and changes in the distribution of journey lengths. Results: We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister's announcement of an enforced lockdown. The reduction in mobility was highly synchronized across the UK. Although mobility has remained low since 26th March 2020, we detect a gradual increase since that time. We also show that the two different datasets produce similar trends, albeit with some location-specific differences. We see slightly larger reductions in average mobility in high-density areas than in low-density areas, with greater variation in mobility in the high-density areas: some high-density areas eliminated almost all mobility. Conclusions: These analyses form a baseline from which to observe changes in behaviour in the UK as social distancing is eased and inform policy towards the future control of SARS-CoV-2 in the UK.
Dighe A, Jombart T, Van Kerkhove MD, et al., 2019, A systematic review of MERS-CoV seroprevalence and RNA prevalence in dromedary camels: implications for animal vaccination, Epidemics, Vol: 29, ISSN: 1755-4365
Human infection with Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is driven by recurring dromedary-to-human spill-over events, leading decision-makers to consider dromedary vaccination. Dromedary vaccine candidates in the development pipeline are showing hopeful results, but gaps in our understanding of the epidemiology of MERS-CoV in dromedaries must be addressed to design and evaluate potential vaccination strategies. We aim to bring together existing measures of MERS-CoV infection in dromedary camels to assess the distribution of infection, highlighting knowledge gaps and implications for animal vaccination. We systematically reviewed the published literature on MEDLINE, EMBASE and Web of Science that reported seroprevalence and/or prevalence of active MERS-CoV infection in dromedary camels from both cross-sectional and longitudinal studies. 60 studies met our eligibility criteria. Qualitative syntheses determined that MERS-CoV seroprevalence increased with age up to 80–100% in adult dromedaries supporting geographically widespread endemicity of MERS-CoV in dromedaries in both the Arabian Peninsula and countries exporting dromedaries from Africa. The high prevalence of active infection measured in juveniles and at sites where dromedary populations mix should guide further investigation – particularly of dromedary movement – and inform vaccination strategy design and evaluation through mathematical modelling.
Watson C, Ferguson N, Donnelly C, et al., 2019, Design of Vaccine Efficacy Trials for Priority Emerging and Epidemic Diseases, Publisher: BMC
McCormack CP, Ghani AC, Ferguson NM, 2019, Fine-scale modelling finds that breeding site fragmentation can reduce mosquito population persistence, Communications Biology, Vol: 2, ISSN: 2399-3642
Fine-scale geographic variation in the transmission intensity of mosquito-borne diseases is primarily caused by variation in the density of female adult mosquitoes. Therefore, an understanding of fine-scale mosquito population dynamics is critical to understanding spatial heterogeneity in disease transmission and persistence at those scales. However, mathematical models of dengue and malaria transmission, which consider the dynamics of mosquito larvae, generally do not account for the fragmented structure of larval breeding sites. Here, we develop a stochastic metapopulation model of mosquito population dynamics, and explore the impact of accounting for breeding site fragmentation when modelling fine-scale mosquito population dynamics. We find that, when mosquito population densities are low, fragmentation can lead to a reduction in population size, with population persistence dependent on mosquito dispersal and features of the underlying landscape. We conclude that using non-spatial models to represent fine-scale mosquito population dynamics may substantially underestimate the stochastic volatility of those populations.
Aguas R, Dorigatti I, Coudeville L, et al., 2019, Cross-serotype interactions and disease outcome prediction of dengue infections in Vietnam, Scientific Reports, Vol: 9, ISSN: 2045-2322
Dengue pathogenesis is extremely complex. Dengue infections are thought to induce life-long immunity from homologous challenges as well as a multi-factorial heterologous risk enhancement. Here, we use the data collected from a prospective cohort study of dengue infections in schoolchildren in Vietnam to disentangle how serotype interactions modulate clinical disease risk in the year following serum collection. We use multinomial logistic regression to correlate the yearly neutralizing antibody measurements obtained with each infecting serotype in all dengue clinical cases collected over the course of 6 years (2004-2009). This allowed us to extrapolate a fully discretised matrix of serotype interactions, revealing clear signals of increased risk of clinical illness in individuals primed with a previous dengue infection. The sequences of infections which produced a higher risk of dengue fever upon secondary infection are: DEN1 followed by DEN2; DEN1 followed by DEN4; DEN2 followed by DEN3; and DEN4 followed by DEN3. We also used this longitudinal data to train a machine learning algorithm on antibody titre differences between consecutive years to unveil asymptomatic dengue infections and estimate asymptomatic infection to clinical case ratios over time, allowing for a better characterisation of the population's past exposure to different serotypes.
Campbell F, Cori A, Ferguson N, et al., 2019, Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data, PLoS Computational Biology, Vol: 15, ISSN: 1553-734X
There exists significant interest in developing statistical and computational tools for inferring ‘who infected whom’ in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should
Hamlet A, Jean K, Yactaco S, et al., 2019, POLICI: A web application for visualising and extracting yellow fever vaccination coverage in Africa, Vaccine, Vol: 37, Pages: 1384-1388, ISSN: 0264-410X
Recent yellow fever (YF) outbreaks have highlighted the increasing global risk of urban spread of the disease. In context of recurrent vaccine shortages, preventive vaccination activities require accurate estimates of existing population-level immunity. We present POLICI (POpulation-Level Immunization Coverage – Imperial), an interactive online tool for visualising and extracting YF vaccination coverage estimates in Africa.We calculated single year age-disaggregated sub-national population-level vaccination coverage for 1950–2050 across the African endemic zone by collating vaccination information and inputting it into a demographic model. This was then implemented on an open interactive web platform.POLICI interactively displays age-disaggregated, population-level vaccination coverages at the first subnational administrative level, through numerous downloadable and customisable visualisations. POLICI is available at https://polici.shinyapps.io/yellow_fever_africa/.POLICI offers an accessible platform for relevant stakeholders in global health to access and explore vaccination coverages. These estimates have already been used to inform the WHO strategy to Eliminate Yellow fever Epidemics (EYE).
Wilder-Smith A, Hombach J, Ferguson N, et al., 2019, Deliberations of the Strategic Advisory Group of Experts on Immunization on the use of CYD-TDV dengue vaccine, LANCET INFECTIOUS DISEASES, Vol: 19, Pages: E31-E38, ISSN: 1473-3099
- Author Web Link
- Cite
- Citations: 87
Hamlet A, Ramos DG, Gaythorpe K, et al., 2019, SEASONALITY OF AGRICULTURAL EXPOSURE MORE IMPORTANT THAN SEASONALITY OF CLIMATE FOR PREDICTING YELLOW FEVER TRANSMISSION IN BRAZIL, 68th Annual Meeting of the American-Society-for-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 207-207, ISSN: 0002-9637
Magombedze G, Ferguson NM, Ghani AC, 2018, A trade-off between dry season survival longevity and wet season high net reproduction can explain the persistence of Anopheles mosquitoes., Parasites & Vectors, Vol: 11, ISSN: 1756-3305
BACKGROUND: Plasmodium falciparum malaria remains a leading cause of death in tropical regions of the world. Despite efforts to reduce transmission, rebounds associated with the persistence of malaria vectors have remained a major impediment to local elimination. One area that remains poorly understood is how Anopheles populations survive long dry seasons to re-emerge following the onset of the rains. METHODS: We developed a suite of mathematical models to explore the impact of different dry-season mosquito survival strategies on the dynamics of vector populations. We fitted these models to an Anopheles population data set from Mali to estimate the model parameters and evaluate whether incorporating aestivation improved the fit of the model to the observed seasonal dynamics. We used the fitted models to explore the impact of intervention strategies that target aestivating mosquitoes in addition to targeting active mosquitoes and larvae. RESULTS: Including aestivation in the model significantly improved our ability to reproduce the observed seasonal dynamics of vector populations as judged by the deviance information criterion (DIC). Furthermore, such a model resulted in more biologically plausible active mosquito survival times (for A. coluzzii median wet season survival time of 10.9 days, 95% credible interval (CrI): 10.0-14.5 days in a model with aestivation versus 38.1 days, 95% CrI: 35.8-42.5 days in a model without aestivation; similar patterns were observed for A. arabiensis). Aestivation also generated enhanced persistence of the vector population over a wider range of both survival times and fecundity levels. Adding vector control interventions that target the aestivating mosquito population is shown to have the potential to enhance the impact of existing vector control. CONCLUSIONS: Dry season survival attributes appear to drive vector population persistence and therefore have implications for vector control. Further research is therefore needed to better u
Campbell F, Didelot X, Fitzjohn R, et al., 2018, outbreaker2: a modular platform for outbreak reconstruction, BMC Bioinformatics, Vol: 19, ISSN: 1471-2105
Background:Reconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort.Results:Here we present outbreaker2, a flexible framework for outbreak reconstruction. This R package re-implements and extends the original model introduced with outbreaker, but most importantly also provides a modular platform allowing users to specify custom models within an optimised inferential framework. As a proof of concept, we implement the within-host evolutionary model introduced with TransPhylo, which is very distinct from the original genetic model in outbreaker, and demonstrate how even complex model results can be successfully included with minimal effort.Conclusions:outbreaker2provides a valuable starting point for future outbreak reconstruction tools, and represents a unifying platform that promotes customisability and interoperability. Implemented in the R software, outbreaker2joins a growing body of tools for outbreak analysis
Dorigatti I, Donnelly C, Laydon D, et al., 2018, Refined efficacy estimates of the Sanofi Pasteur dengue vaccine CYD-TDV using machine learning, Nature Communications, Vol: 9, ISSN: 2041-1723
CYD-TDV is the first licensed dengue vaccine for individuals 9–45 (or 60) years of age. Using 12% of the subjects enroled in phase-2b and phase-3 trials for which baseline serostatus was measured, the vaccine-induced protection against virologically confirmed dengue during active surveillance (0–25 months) was found to vary with prior exposure to dengue. Because age and dengue exposure are highly correlated in endemic settings, refined insight into how efficacy varies by serostatus and age is essential to understand the increased risk of hospitalisation observed among vaccinated individuals during the long-term follow-up and to develop safe and effective vaccination strategies. Here we apply machine learning to impute the baseline serostatus for subjects with post-dose 3 titres but missing baseline serostatus. We find evidence for age dependence in efficacy independent of serostatus and estimate that among 9–16 year olds, CYD-TDV is protective against serotypes 1, 3 and 4 regardless of baseline serostatus.
Anders K, Cutcher Z, Kleinschmidt I, et al., 2018, Cluster randomized test-negative design (CR-TND) trials: a novel and efficient method to assess the efficacy of community level dengue interventions, American Journal of Epidemiology, Vol: 187, Pages: 2021-2028, ISSN: 1476-6256
Cluster randomized trials are the gold standard for assessing efficacy of community-level interventions, such as vector control strategies against dengue. We describe a novel cluster randomized trial methodology with a test-negative design, which offers advantages over traditional approaches. It utilizes outcome-based sampling of patients presenting with a syndrome consistent with the disease of interest, who are subsequently classified as test-positive cases or test-negative controls on the basis of diagnostic testing. We use simulations of a cluster trial to demonstrate validity of efficacy estimates under the test-negative approach. This demonstrates that, provided study arms are balanced for both test-negative and test-positive illness at baseline and that other test-negative design assumptions are met, the efficacy estimates closely match true efficacy. We also briefly discuss analytical considerations for an odds ratio-based effect estimate arising from clustered data, and outline potential approaches to analysis. We conclude that application of the test-negative design to certain cluster randomized trials could increase their efficiency and ease of implementation.
Ferguson NM, 2018, Challenges and opportunities in controlling mosquito-borne infections, Nature, Vol: 559, Pages: 490-497, ISSN: 0028-0836
Mosquito-borne diseases remain a major cause of morbidity and mortality across the tropical regions. Despite much progress in the control of malaria, malaria-associated morbidity remains high, whereas arboviruses—most notably dengue—are responsible for a rising burden of disease, even in middle-income countries that have almost completely eliminated malaria. Here I discuss how new interventions offer the promise of considerable future reductions in disease burden. However, I emphasize that intervention programmes need to be underpinned by rigorous trials and quantitative epidemiological analyses. Such analyses suggest that the long-term goal of elimination is more feasible for dengue than for malaria, even if malaria elimination would offer greater overall health benefit to the public.
The Ebola Outbreak Epidemiology Team, Bhatia S, Cori A, et al., 2018, Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study, The Lancet, Vol: 392, Pages: 213-221, ISSN: 0140-6736
BackgroundOn May 8, 2018, the Government of the Democratic Republic of the Congo reported an outbreak of Ebola virus disease in Équateur Province in the northwest of the country. The remoteness of most affected communities and the involvement of an urban centre connected to the capital city and neighbouring countries makes this outbreak the most complex and high risk ever experienced by the Democratic Republic of the Congo. We provide early epidemiological information arising from the ongoing investigation of this outbreak.MethodsWe classified cases as suspected, probable, or confirmed using national case definitions of the Democratic Republic of the Congo Ministère de la Santé Publique. We investigated all cases to obtain demographic characteristics, determine possible exposures, describe signs and symptoms, and identify contacts to be followed up for 21 days. We also estimated the reproduction number and projected number of cases for the 4-week period from May 25, to June 21, 2018.FindingsAs of May 30, 2018, 50 cases (37 confirmed, 13 probable) of Zaire ebolavirus were reported in the Democratic Republic of the Congo. 21 (42%) were reported in Bikoro, 25 (50%) in Iboko, and four (8%) in Wangata health zones. Wangata is part of Mbandaka, the urban capital of Équateur Province, which is connected to major national and international transport routes. By May 30, 2018, 25 deaths from Ebola virus disease had been reported, with a case fatality ratio of 56% (95% CI 39–72) after adjustment for censoring. This case fatality ratio is consistent with estimates for the 2014–16 west African Ebola virus disease epidemic (p=0·427). The median age of people with confirmed or probable infection was 40 years (range 8–80) and 30 (60%) were male. The most commonly reported signs and symptoms in people with confirmed or probable Ebola virus disease were fever (40 [95%] of 42 cases), intense general fatigue (37 [90%] of 41 cases), an
Miglianico M, Eldering M, Slater H, et al., 2018, Repurposing isoxazoline veterinary drugs for control of vector-borne human diseases., Proceedings of the National Academy of Sciences, Vol: 115, Pages: E6920-E6926, ISSN: 0027-8424
Isoxazolines are oral insecticidal drugs currently licensed for ectoparasite control in companion animals. Here we propose their use in humans for the reduction of vector-borne disease incidence. Fluralaner and afoxolaner rapidly killed Anopheles, Aedes, and Culex mosquitoes and Phlebotomus sand flies after feeding on a drug-supplemented blood meal, with IC50 values ranging from 33 to 575 nM, and were fully active against strains with preexisting resistance to common insecticides. Based on allometric scaling of preclinical pharmacokinetics data, we predict that a single human median dose of 260 mg (IQR, 177-407 mg) for afoxolaner, or 410 mg (IQR, 278-648 mg) for fluralaner, could provide an insecticidal effect lasting 50-90 days against mosquitoes and Phlebotomus sand flies. Computational modeling showed that seasonal mass drug administration of such a single dose to a fraction of a regional population would dramatically reduce clinical cases of Zika and malaria in endemic settings. Isoxazolines therefore represent a promising new component of drug-based vector control.
Imai N, Ferguson NM, 2018, Targeting vaccinations for the licensed dengue vaccine: considerations for serosurvey design, PLoS ONE, Vol: 13, Pages: 1-15, ISSN: 1932-6203
BackgroundThe CYD-TDV vaccine was unusual in that the recommended target population for vaccination was originally defined not only by age, but also by transmission setting as defined by seroprevalence. WHO originally recommended countries consider vaccination against dengue with CYD-TDV vaccine in geographic settings only where prior infection with any dengue serotype, as measured by seroprevalence, was >170% in the target age group. Vaccine was not recommended in settings where seroprevalence was <50%. Test-and-vaccinate strategies suggested following new analysis by Sanofi will still require age-stratified seroprevalence surveys to optimise age-group targeting. Here we address considerations for serosurvey design in the context of vaccination program planning.MethodsTo explore how the design of seroprevalence surveys affects estimates of transmission intensity, 100 age-specific seroprevalence surveys were simulated using a beta-binomial distribution and a simple catalytic model for different combinations of age-range, survey size, transmission setting, and test sensitivity/specificity. We then used a Metropolis-Hastings Markov Chain Monte-Carlo algorithm to estimate the force of infection from each simulated dataset.ResultsSampling from a wide age-range led to more accurate estimates than merely increasing sample size in a narrow age-range. This finding was consistent across all transmission settings. The optimum test sensitivity and specificity given an imperfect test differed by setting with high sensitivity being important in high transmission settings and high specificity important in low transmission settings.ConclusionsWhen assessing vaccination suitability by seroprevalence surveys, countries should ensure an appropriate age-range is sampled, considering epidemiological evidence about the local burden of disease.
Marshall JM, Wu SL, Sanchez HMC, et al., 2018, Mathematical models of human mobility of relevance to malaria transmission in Africa, Scientific Reports, Vol: 8, ISSN: 2045-2322
As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3–5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns – a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively “absorbed” as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group – namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread.
Hamlet A, Jean K, Perea W, et al., 2018, The seasonal influence of climate and environment on yellow fever transmission across Africa, PLoS Neglected Tropical Diseases, Vol: 12, ISSN: 1935-2727
Background:Yellow fever virus (YFV) is a vector-borne flavivirus endemic to Africa and Latin America. Ninety per cent of the global burden occurs in Africa where it is primarily transmitted by Aedes spp, with Aedes aegypti the main vector for urban yellow fever (YF). Mosquito life cycle and viral replication in the mosquito are heavily dependent on climate, particularly temperature and rainfall. We aimed to assess whether seasonal variations in climatic factors are associated with the seasonality of YF reports.Methodology/Principal findings:We constructed a temperature suitability index for YFV transmission, capturing the temperature dependence of mosquito behaviour and viral replication within the mosquito. We then fitted a series of multilevel logistic regression models to a dataset of YF reports across Africa, considering location and seasonality of occurrence for seasonal models, against the temperature suitability index, rainfall and the Enhanced Vegetation Index (EVI) as covariates alongside further demographic indicators. Model fit was assessed by the Area Under the Curve (AUC), and models were ranked by Akaike’s Information Criterion which was used to weight model outputs to create combined model predictions. The seasonal model accurately captured both the geographic and temporal heterogeneities in YF transmission (AUC = 0.81), and did not perform significantly worse than the annual model which only captured the geographic distribution. The interaction between temperature suitability and rainfall accounted for much of the occurrence of YF, which offers a statistical explanation for the spatio-temporal variability in transmission.Conclusions/Significance:The description of seasonality offers an explanation for heterogeneities in the West-East YF burden across Africa. Annual climatic variables may indicate a transmission suitability not always reflected in seasonal interactions. This finding, in conjunction with forecasted data, could highlight areas of
Pople D, 2018, The effect of social networks on the participation by those with parental responsibility in the baby immunisation programme in the UK
Campbell F, Strang C, Ferguson N, et al., 2018, When are pathogen genome sequences informative of transmission events?, PLoS Pathogens, Vol: 14, ISSN: 1553-7366
Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and
Dorigatti I, McCormack C, Nedjati-Gilani G, et al., 2017, Using Wolbachia for Dengue Control: Insights from Modelling., Trends in Parasitology, Vol: 34, Pages: 102-113, ISSN: 1471-5007
Dengue is the most common arboviral infection of humans, responsible for a substantial disease burden across the tropics. Traditional insecticide-based vector-control programmes have limited effectiveness, and the one licensed vaccine has a complex and imperfect efficacy profile. Strains of the bacterium Wolbachia, deliberately introduced into Aedes aegyptimosquitoes, have been shown to be able to spread to high frequencies in mosquito populations in release trials, and mosquitoes infected with these strains show markedly reduced vector competence. Thus, Wolbachia represents an exciting potential new form of biocontrol for arboviral diseases, including dengue. Here, we review how mathematical models give insight into the dynamics of the spread of Wolbachia, the potential impact of Wolbachia on dengue transmission, and we discuss the remaining challenges in evaluation and development.
Katzelnick LC, Harris E, 2017, Immune correlates of protection for dengue: State of the art and research agenda, Publisher: ELSEVIER SCI LTD, Pages: 4659-4669, ISSN: 0264-410X
- Author Web Link
- Cite
- Citations: 65
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.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.