Publications
109 results found
Geismar C, Nguyen V, Fragaszy E, et al., 2022, Bayesian reconstruction of household transmissions to infer the serial interval of COVID-19 by variants of concern: analysis from a prospective community cohort study (Virus Watch), LANCET, Vol: 400, Pages: 40-40, ISSN: 0140-6736
Evans B, Jombart T, 2022, Worldwide routine immunisation coverage regressed during the first year of the COVID-19 pandemic, VACCINE, Vol: 40, Pages: 3531-3535, ISSN: 0264-410X
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- Citations: 1
Waites W, Pearson CAB, Gaskell KM, et al., 2022, Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK., Sci Rep, Vol: 12
Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations.
Jarvis CI, Gimma A, Finger F, et al., 2022, Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics., PLoS Comput Biol, Vol: 18
The fraction of cases reported, known as 'reporting', is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018-2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5-10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities.
Lindsey BB, Villabona-Arenas CJ, Campbell F, et al., 2022, Characterising within-hospital SARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves (vol 13, pg 1013, 2022), NATURE COMMUNICATIONS, Vol: 13
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- Citations: 2
Davis EL, Lucas TCD, Borlase A, et al., 2021, Contact tracing is an imperfect tool for controlling COVID-19 transmission and relies on population adherence, NATURE COMMUNICATIONS, Vol: 12
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- Citations: 9
Brooks-Pollock E, Danon L, Jombart T, et al., 2021, Modelling that shaped the early COVID-19 pandemic response in the UK, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 376, ISSN: 0962-8436
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- Citations: 19
Jombart T, Ghozzi S, Schumacher D, et al., 2021, Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 376, ISSN: 0962-8436
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- Citations: 4
Campbell F, Archer B, Laurenson-Schafer H, et al., 2021, Increased transmissibility and global spread of SARS-CoV-2 variants of concern as at June 2021, EUROSURVEILLANCE, Vol: 26, ISSN: 1025-496X
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- Citations: 397
Jombart T, 2021, A public health ethic should inform policies on COVID-19 immunity passports Reply, LANCET INFECTIOUS DISEASES, Vol: 21, Pages: 456-459, ISSN: 1473-3099
Jombart T, 2021, Why development of outbreak analytics tools should be valued, supported, and funded., Lancet Infect Dis, Vol: 21, Pages: 458-459
Leclerc QJ, Nightingale ES, Abbott S, et al., 2021, Analysis of temporal trends in potential COVID-19 cases reported through NHS Pathways England, SCIENTIFIC REPORTS, Vol: 11, ISSN: 2045-2322
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- Citations: 3
Saltz JS, Sutherland A, Jombart T, 2021, Identifying and Addressing 6 Key Questions when Using Data Driven Scrum, 9th IEEE International Conference on Big Data (IEEE BigData), Publisher: IEEE, Pages: 2345-2352, ISSN: 2639-1589
Carter SE, Ahuka-Mundeke S, Zambruni JP, et al., 2021, How to improve outbreak response: a case study of integrated outbreak analytics from Ebola in Eastern Democratic Republic of the Congo, BMJ GLOBAL HEALTH, Vol: 6, ISSN: 2059-7908
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- Citations: 4
Abbas M, Robalo Nunes T, Cori A, et al., 2021, Explosive Nosocomial Outbreak of SARS-CoV-2 in a Rehabilitation Clinic: The Limits of Genomics for Outbreak Reconstruction, SSRN
Parisi A, Tu LTP, Mather AE, et al., 2020, The role of animals as a source of antimicrobial resistant nontyphoidal Salmonella causing invasive and non-invasive human disease in Vietnam, INFECTION GENETICS AND EVOLUTION, Vol: 85, ISSN: 1567-1348
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- Citations: 3
Jit M, Jombart T, Nightingale ES, et al., 2020, Estimating number of cases and spread of coronavirus disease (COVID-19) using critical care admissions, United Kingdom, February to March 2020, Eurosurveillance, Vol: 25, Pages: 6-10, ISSN: 1025-496X
An exponential growth model was fitted to critical care admissions from two surveillance databases to determine likely coronavirus disease (COVID-19) case numbers, critical care admissions and epidemic growth in the United Kingdom before the national lockdown. We estimate, on 23 March, a median of 114,000 (95% credible interval (CrI): 78,000–173,000) new cases and 258 (95% CrI: 220–319) new critical care reports, with 527,000 (95% CrI: 362,000–797,000) cumulative cases since 16 February.
Jombart T, Jarvis CI, Mesfin S, et al., 2020, The cost of insecurity: from flare-up to control of a major Ebola virus disease hotspot during the outbreak in the Democratic Republic of the Congo, 2019, EUROSURVEILLANCE, Vol: 25, Pages: 19-22, ISSN: 1560-7917
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- Citations: 9
Jombart T, van Zandvoort K, Russell TW, et al., 2020, Inferring the number of COVID-19 cases from recently reported deaths., Wellcome Open Res, Vol: 5, ISSN: 2398-502X
We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly, online tool.
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.
Thompson R, Stockwin J, van Gaalen R, et al., 2019, Improved inference of time-varying reproduction numbers during infectious disease outbreaks, Epidemics, Vol: 29, Pages: 1-11, ISSN: 1755-4365
Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.
Moraga P, Dorigatti I, Kamvar ZN, et al., 2019, epiflows: an R package for risk assessment of travel-related spread of disease, F1000Research, Vol: 7, Pages: 1374-1374
<ns4:p>As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present <ns4:italic>epiflows</ns4:italic>, an R package for risk assessment of travel-related spread of disease. <ns4:italic>epiflows</ns4:italic> produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of <ns4:italic>epiflows</ns4:italic> by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.</ns4:p>
Moraga P, Dorigatti I, Kamvar ZN, et al., 2019, epiflows: an R package for risk assessment of travel-related spread of disease, F1000Research, Vol: 7, Pages: 1374-1374
<ns4:p>As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present <ns4:italic>epiflows</ns4:italic>, an R package for risk assessment of travel-related spread of disease. <ns4:italic>epiflows</ns4:italic> produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of <ns4:italic>epiflows</ns4:italic> by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.</ns4:p>
Cori A, Kamvar ZN, Stockwin J, et al., 2019, annecori/EpiEstim: EpiEstim Cran 2.2-1
new CRAN version of EpiEstim including all new features described in Thompson et al. (currently in review in Epidemics journal).
Stockwin J, Thompson R, Cori A, et al., 2019, jstockwin/EpiEstimApp: v1.0.0
Source code for the EpiEstim app.
Polonsky JA, Baidjoe A, Kamvar ZN, et al., 2019, Outbreak analytics: a developing data science for informing the response to emerging pathogens, Philosophical Transactions B: Biological Sciences, Vol: 374, ISSN: 0962-8436
Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
Leiva C, Taboada S, Kenny NJ, et al., 2019, Population substructure and signals of divergent adaptive selection despite admixture in the sponge Dendrilla antarctica from shallow waters surrounding the Antarctic Peninsula, MOLECULAR ECOLOGY, Vol: 28, Pages: 3151-3170, ISSN: 0962-1083
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- Citations: 13
Sewell T, Zhu J, Rhodes J, et al., 2019, Non-random distribution of azole resistance across the global population of Aspergillus fumigatus, mBio, Vol: 10, ISSN: 2150-7511
The emergence of azole resistance in the pathogenic fungus Aspergillus fumigatus has continued to increase, with the dominant resistance mechanisms, consisting of a 34-nucleotide tandem repeat (TR34)/L98H and TR46/Y121F/T289A, now showing a structured global distribution. Using hierarchical clustering and multivariate analysis of 4,049 A. fumigatus isolates collected worldwide and genotyped at nine microsatellite loci using analysis of short tandem repeats of A. fumigatus (STRAf), we show that A. fumigatus can be subdivided into two broad clades and that cyp51A alleles TR34/L98H and TR46/Y121F/T289A are unevenly distributed across these two populations. Diversity indices show that azole-resistant isolates are genetically depauperate compared to their wild-type counterparts, compatible with selective sweeps accompanying the selection of beneficial mutations. Strikingly, we found that azole-resistant clones with identical microsatellite profiles were globally distributed and sourced from both clinical and environmental locations, confirming that azole resistance is an international public health concern. Our work provides a framework for the analysis of A. fumigatus isolates based on their microsatellite profile, which we have incorporated into a freely available, user-friendly R Shiny application (AfumID) that provides clinicians and researchers with a method for the fast, automated characterization of A. fumigatus genetic relatedness. Our study highlights the effect that azole drug resistance is having on the genetic diversity of A. fumigatus and emphasizes its global importance upon this medically important pathogenic fungus.IMPORTANCE Azole drug resistance in the human-pathogenic fungus Aspergillus fumigatus continues to emerge, potentially leading to untreatable aspergillosis in immunosuppressed hosts. Two dominant, environmentally associated resistance mechanisms, which are thought to have evolved through selection by the agricultural application of azole fungic
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
Jombart T, Kamvar ZN, Cai J, et al., 2019, reconhub/incidence: Incidence version 1.7.0
Incidence can now handle standardised weeks starting on any day thanks to the aweek package :tada:library(incidence)library(ggplot2)library(cowplot)d <- as.Date("2019-03-11") + -7:6setNames(d, weekdays(d))#> Monday Tuesday Wednesday Thursday Friday #> "2019-03-04" "2019-03-05" "2019-03-06" "2019-03-07" "2019-03-08" #> Saturday Sunday Monday Tuesday Wednesday #> "2019-03-09" "2019-03-10" "2019-03-11" "2019-03-12" "2019-03-13" #> Thursday Friday Saturday Sunday #> "2019-03-14" "2019-03-15" "2019-03-16" "2019-03-17"imon <- incidence(d, "mon week") # also ISO weekitue <- incidence(d, "tue week")iwed <- incidence(d, "wed week")ithu <- incidence(d, "thu week")ifri <- incidence(d, "fri week")isat <- incidence(d, "sat week")isun <- incidence(d, "sun week") # also MMWR week and EPI weekpmon <- plot(imon, show_cases = TRUE, labels_week = FALSE)ptue <- plot(itue, show_cases = TRUE, labels_week = FALSE)pwed <- plot(iwed, show_cases = TRUE, labels_week = FALSE)pthu <- plot(ithu, show_cases = TRUE, labels_week = FALSE)pfri <- plot(ifri, show_cases = TRUE, labels_week = FALSE)psat <- plot(isat, show_cases = TRUE, labels_week = FALSE)psun <- plot(isun, show_cases = TRUE, labels_week = FALSE)s <- scale_x_date(limits = c(as.Date("2019-02-26"), max(d) + 7L))plot_grid(pmon + s,ptue + s,pwed + s,pthu + s,pfri + s,psat + s,psun + s)multi-weeks/months/years can now be handledlibrary(incidence)library(outbreaks)d <- ebola_sim_clean$linelist$date_of_onseth <- ebola_sim_clean$linelist$hospitalplot(incidence(d, interval = "1 epiweek", group = h))plot(incidence(d, interval = "2 epiweeks", group = h))plot(incide
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