Publications
47 results found
Fitzjohn R, Ainslie K, 2020, mrc-ide/china-exit-covid-19: Second release
This release includes supplemental material from the manuscript.
Ainslie K, Walters C, Fu H, et al., 2020, Report 11: Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment
The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. As of 20 March 2020, over 254,000 cases and 10,000 deaths had been reported worldwide. The outbreak began in the Chinese city of Wuhan in December 2019. In response to the fast-growing epidemic, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. At the peak of the outbreak in China (early February), there were between 2,000 and 4,000 new confirmed cases per day. For the first time since the outbreak began there have been no new confirmed cases caused by local transmission in China reported for five consecutive days up to 23 March 2020. This is an indication that the social distancing measures enacted in China have led to control of COVID-19 in China. These interventions have also impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic is not yet clear. Here, we estimate transmissibility from reported cases and compare those estimates with daily data on within-city movement, as a proxy for economic activity. Initially, within-city movement and transmission were very strongly correlated in the 5 provinces most affected by the epidemic and Beijing. However, that correlation is no longer apparent even though within-city movement has started to increase. A similar analysis for Hong Kong shows that intermediate levels of local activity can be maintained while avoiding a large outbreak. These results do not preclude future epidemics in China, nor do they allow us to estimate the maximum proportion of previous within-city activity that will be recovered in the medium term. However, they do suggest that after very intense social distancing which resulted in containment, China has successfully exited their stringent social distancing policy to some degree. Globally, China is at a more advanced stage of the pandemic. Policies implemented to reduce the spread of CO
Gaythorpe K, Imai N, Cuomo-Dannenburg G, et al., 2020, Report 8: Symptom progression of COVID-19
The COVID-19 epidemic was declared a Public Health Emergency of International Concern (PHEIC) by WHO on 30th January 2020 [1]. As of 8 March 2020, over 107,000 cases had been reported. Here, we use published and preprint studies of clinical characteristics of cases in mainland China as well as case studies of individuals from Hong Kong, Japan, Singapore and South Korea to examine the proportional occurrence of symptoms and the progression of symptoms through time.We find that in mainland China, where specific symptoms or disease presentation are reported, pneumonia is the most frequently mentioned, see figure 1. We found a more varied spectrum of severity in cases outside mainland China. In Hong Kong, Japan, Singapore and South Korea, fever was the most frequently reported symptom. In this latter group, presentation with pneumonia is not reported as frequently although it is more common in individuals over 60 years old. The average time from reported onset of first symptoms to the occurrence of specific symptoms or disease presentation, such as pneumonia or the use of mechanical ventilation, varied substantially. The average time to presentation with pneumonia is 5.88 days, and may be linked to testing at hospitalisation; fever is often reported at onset (where the mean time to develop fever is 0.77 days).
Thompson H, Imai N, Dighe A, et al., 2020, Report 7: Estimating infection prevalence in Wuhan City from repatriation flights
Since the end of January 2020, in response to the growing COVID-19 epidemic, 55 countries have repatriated over 8000 citizens from Wuhan City, China. In addition to quarantine measures for returning citizens, many countries implemented PCR screening to test for infection regardless of symptoms. These flights therefore give estimates of infection prevalence in Wuhan over time. Between 30th January and 1st February (close to the peak of the epidemic in Wuhan), infection prevalence was 0.87% (95% CI: 0.32% - 1.89%). As countries now start to repatriate citizens from Iran and northern Italy, information from repatriated citizens could help inform the level of response necessary to help control the outbreaks unfolding in newly affected areas.
Bhatia S, Imai N, Cuomo-Dannenburg G, et al., 2020, Report 6: Relative sensitivity of international surveillance, Report 6: Relative sensitivity of international surveillance
Since the start of the COVID-19 epidemic in late 2019, there are now 29 affected countries with over 1000 confirmed cases outside of mainland China. In previous reports, we estimated the likely epidemic size in Wuhan City based on air traffic volumes and the number of detected cases internationally. Here we analysed COVID-19 cases exported from mainland China to different regions and countries, comparing the country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different countries. 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 about two thirds of COVID-19 cases exported from mainland China have remained undetected worldwide, potentially resulting in multiple chains of as yet undetected human-to-human transmission outside mainland China.
Volz E, Baguelin M, Bhatia S, et al., 2020, Report 5: Phylogenetic analysis of SARS-CoV-2
Genetic diversity of SARS-CoV-2 (formerly 2019-nCoV), the virus which causes COVID-19, provides information about epidemic origins and the rate of epidemic growth. By analysing 53 SARS-CoV-2 whole genome sequences collected up to February 3, 2020, we find a strong association between the time of sample collection and accumulation of genetic diversity. Bayesian and maximum likelihood phylogenetic methods indicate that the virus was introduced into the human population in early December and has an epidemic doubling time of approximately seven days. Phylodynamic modelling provides an estimate of epidemic size through time. Precise estimates of epidemic size are not possible with current genetic data, but our analyses indicate evidence of substantial heterogeneity in the number of secondary infections caused by each case, as indicated by a high level of over-dispersion in the reproduction number. Larger numbers of more systematically sampled sequences – particularly from across China – will allow phylogenetic estimates of epidemic size and growth rate to be substantially refined.
Dorigatti I, Okell L, Cori A, et al., 2020, Report 4: Severity of 2019-novel coronavirus (nCoV)
We present case fatality ratio (CFR) estimates for three strata of 2019-nCoV infections. For cases detected in Hubei, we estimate the CFR to be 18% (95% credible interval: 11%-81%). For cases detected in travellers outside mainland China, we obtain central estimates of the CFR in the range 1.2-5.6% depending on the statistical methods, with substantial uncertainty around these central values. Using estimates of underlying infection prevalence in Wuhan at the end of January derived from testing of passengers on repatriation flights to Japan and Germany, we adjusted the estimates of CFR from either the early epidemic in Hubei Province, or from cases reported outside mainland China, to obtain estimates of the overall CFR in all infections (asymptomatic or symptomatic) of approximately 1% (95% confidence interval 0.5%-4%). It is important to note that the differences in these estimates does not reflect underlying differences in disease severity between countries. CFRs seen in individual countries will vary depending on the sensitivity of different surveillance systems to detect cases of differing levels of severity and the clinical care offered to severely ill cases. All CFR estimates should be viewed cautiously at the current time as the sensitivity of surveillance of both deaths and cases in mainland China is unclear. Furthermore, all estimates rely on limited data on the typical time intervals from symptom onset to death or recovery which influences the CFR estimates.
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.
Stockwin J, Thompson R, Cori A, et al., 2019, jstockwin/EpiEstimApp: v1.0.0
Source code for the EpiEstim app.
Watson O, FitzJohn R, Eaton J, 2019, rdhs: an R package to interact with The Demographic and Health Surveys (DHS) Program datasets [version 1; peer review: 1 approved, 1 approved with reservations], Wellcome Open Research, Vol: 4, Pages: 1-13, ISSN: 2398-502X
Since 1985, the Demographic and Health Surveys (DHS) Program has conducted more than 400 surveys in over 90 countries. These surveys provide decision markers with key measures of population demographics, health and nutrition, which allow informed policy evaluation to be made. Though standard health indicators are routinely published in survey final reports, much of the value of DHS is derived from the ability to download and analyse standardised microdata datasets for subgroup analysis, pooled multi-country analysis, and extended research studies. We have developed an open-source freely available R package ‘rdhs’ to facilitate management and processing of DHS survey data. The package provides a suite of tools to (1) access standard survey indicators through the DHS Program API, (2) identify all survey datasets that include a particular topic or indicator relevant to a particular analysis, (3) directly download survey datasets from the DHS website, (4) load datasets and data dictionaries into R, and (5) extract variables and pool harmonised datasets for multi-survey analysis. We detail the core functionality of ‘rdhs’ by demonstrating how the package can be used to firstly compare trends in the prevalence of anaemia among women between countries before conducting secondary analysis to assess for the relationship between education and anemia.
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
Jombart T, Kamvar ZN, Cai J, et al., 2019, reconhub/incidence 1.5
☣:chart_with_upwards_trend::chart_with_downwards_trend:☣ Compute and visualise incidence
Jombart T, Kamvar Z, Cai J, et al., 2018, reconhub/incidence: Incidence version 1.5.3
This is a patch release that fixes an issue with handling single-group incidence curves.You can install this version like so:remotes::install_github("reconhub/incidence@1.5.3")
Camac JS, Condit R, FitzJohn RG, et al., 2018, Partitioning mortality into growth-dependent and growth-independent hazards across 203 tropical tree species, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 115, Pages: 12459-12464, ISSN: 0027-8424
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- Citations: 20
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
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
Falster DS, Duursma RA, Ishihara MI, et al., 2015, BAAD: a Biomass And Allometry Database for woody plants, Ecology, Vol: 96, ISSN: 0012-9658
Understanding how plants are constructed; i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals; is essential for modeling plant growth, estimating carbon stocks, and mapping energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass and allometry database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at time of publication. Thus the BAAD contains individual level data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) inclusion of plants from 0.01-100 m in height; and (iii) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed subsampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem crosssection including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood d
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