ORCID Profile
0000-0001-6525-101X
Current Organisations
UK Health Security Agency
,
Imperial College London
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Publisher: Oxford University Press (OUP)
Date: 24-08-2020
DOI: 10.1093/JTM/TAAA135
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 17-02-2021
DOI: 10.1038/S41467-021-21358-2
Abstract: In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27–77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation from a median adjusted R-squared of 74% (IQR across countries [49–91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12–48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
Publisher: Springer Science and Business Media LLC
Date: 27-08-2021
DOI: 10.1038/S41467-021-25207-0
Abstract: Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.
Publisher: F1000 Research Ltd
Date: 17-07-2020
DOI: 10.12688/WELLCOMEOPENRES.15997.1
Abstract: 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.
Publisher: Springer Science and Business Media LLC
Date: 06-07-2021
DOI: 10.1038/S41598-021-92500-9
Abstract: SARS-CoV-2 infections have been reported in all age groups including infants, children, and adolescents. However, the role of children in the COVID-19 pandemic is still uncertain. This systematic review of early studies synthesises evidence on the susceptibility of children to SARS-CoV-2 infection, the severity and clinical outcomes in children with SARS-CoV-2 infection, and the transmissibility of SARS-CoV-2 by children in the initial phases of the COVID-19 pandemic. A systematic literature review was conducted in PubMed. Reviewers extracted data from relevant, peer-reviewed studies published up to July 4th 2020 during the first wave of the SARS-CoV-2 outbreak using a standardised form and assessed quality using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. For studies included in the meta-analysis, we used a random effects model to calculate pooled estimates of the proportion of children considered asymptomatic or in a severe or critical state. We identified 2775 potential studies of which 128 studies met our inclusion criteria data were extracted from 99, which were then quality assessed. Finally, 29 studies were considered for the meta-analysis that included information of symptoms and/or severity, these were further assessed based on patient recruitment. Our pooled estimate of the proportion of test positive children who were asymptomatic was 21.1% (95% CI: 14.0–28.1%), based on 13 included studies, and the proportion of children with severe or critical symptoms was 3.8% (95% CI: 1.5–6.0%), based on 14 included studies. We did not identify any studies designed to assess transmissibility in children and found that susceptibility to infection in children was highly variable across studies. Children’s susceptibility to infection and onward transmissibility relative to adults is still unclear and varied widely between studies. However, it is evident that most children experience clinically mild disease or remain asymptomatically infected. More comprehensive contact-tracing studies combined with serosurveys are needed to quantify children’s transmissibility relative to adults. With children back in schools, testing regimes and study protocols that will allow us to better understand the role of children in this pandemic are critical.
Publisher: F1000 Research Ltd
Date: 02-04-2020
DOI: 10.12688/WELLCOMEOPENRES.15808.1
Abstract: Background : Several non-pharmaceutical interventions (NPIs) have been implemented across the world to control the coronavirus disease (COVID-19) pandemic. Social distancing (SD) interventions applied so far have included school closures, remote working and quarantine. These measures have been shown to have large impacts on pandemic influenza transmission. However, there has been comparatively little examination of such measures for COVID-19. Methods : We examined the existing literature, and collated data, on implementation of NPIs to examine their effects on the COVID-19 pandemic so far. Data on NPIs were collected from official government websites as well as from media sources. Results : Measures such as travel restrictions have been implemented in multiple countries and appears to have slowed the geographic spread of COVID-19 and reduced initial case numbers. We find that, due to the relatively sparse information on the differences with and without interventions, it is difficult to quantitatively assess the efficacy of many interventions. Similarly, whilst the comparison to other pandemic diseases such as influenza can be helpful, there are key differences that could affect the efficacy of similar NPIs. Conclusions : The timely implementation of control measures is key to their success and must strike a balance between early enough application to reduce the peak of the epidemic and ensuring that they can be feasibly maintained for an appropriate duration. Such measures can have large societal impacts and they need to be appropriately justified to the population. As the pandemic of COVID-19 progresses, quantifying the impact of interventions will be a vital consideration for the appropriate use of mitigation strategies.
Publisher: Copernicus GmbH
Date: 02-09-2019
Abstract: Abstract. Detection and quantification of greenhouse-gas emissions is important for both compliance and environment conservation. However, despite several decades of active research, it remains predominantly an open problem, largely due to model errors and assumptions that appear at each stage of the inversion processing chain. In 2015, a controlled-release experiment headed by Geoscience Australia was carried out at the Ginninderra Controlled Release Facility, and a variety of instruments and methods were employed for quantifying the release rates of methane and carbon dioxide from a point source. This paper proposes a fully Bayesian approach to atmospheric tomography for inferring the methane emission rate of this point source using data collected during the experiment from both point- and path-s ling instruments. The Bayesian framework is designed to account for uncertainty in the parameterisations of measurements, the meteorological data, and the atmospheric model itself when performing inversion using Markov chain Monte Carlo (MCMC). We apply our framework to all instrument groups using measurements from two release-rate periods. We show that the inversion framework is robust to instrument type and meteorological conditions. From all the inversions we conducted across the different instrument groups and release-rate periods, our worst-case median emission rate estimate was within 36 % of the true emission rate. Further, in the worst case, the closest limit of the 95 % credible interval to the true emission rate was within 11 % of this true value.
Publisher: Springer Science and Business Media LLC
Date: 11-12-2015
DOI: 10.1007/S00285-014-0852-1
Abstract: Establishing a distance between genomes is a significant problem in computational genomics, because its solution can be used to establish evolutionary relationships including phylogeny. The "double cut and join" (DCJ) model of chromosomal rearrangement proposed by Yancopoulos et al. (Bioinformatics 21:3340-3346, 2005) has received attention as it can model inversions, translocations, fusion and fission on a multichromosomal genome that may contain both linear and circular chromosomes. In this paper, we realize the DCJ operator as a group action on the space of multichromosomal genomes. We study this group action, deriving some properties of the group and finding group-theoretic analogues for the key results in the DCJ theory.
Publisher: Cold Spring Harbor Laboratory
Date: 28-11-2021
DOI: 10.1101/2021.11.26.21266899
Abstract: Recent months have demonstrated that emerging variants may set back the global COVID-19 response. The ability to rapidly assess the threat of new variants in real-time is critical for timely optimisation of control strategies. We extend the EpiEstim R package, designed to estimate the time-varying reproduction number ( R t ), to estimate in real-time the effective transmission advantage of a new variant compared to a reference variant. Our method can combine information across multiple locations and over time and was validated using an extensive simulation study, designed to mimic a variety of real-time epidemic contexts. We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29, (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We further estimate that Beta and Gamma combined are 1.25 (95% CrI 1.24-1.27) times more transmissible than the wildtype (France data). All results are in line with previous estimates from literature, but could have been obtained earlier and more easily with our off-the-shelf open-source tool. Our tool can be used as an important first step towards quantifying the threat of new variants in real-time. Given the popularity of EpiEstim, this extension will likely be used widely to monitor the co-circulation and/or emergence of multiple variants of infectious pathogens. Early assessment of the transmissibility of new variants of an infectious pathogen is critical for anticipating their impact and designing appropriate interventions. However, this often requires complex and bespoke analyses relying on multiple data streams, including genomic data. Here we present a novel method and software to rapidly quantify the transmission advantage of new variants. Our method is fast and requires only routinely collected disease surveillance data, making it easy to use in real-time. The ongoing high level of SARS-CoV-2 circulation in a number of countries makes the emergence of new variants highly likely. Our work offers a powerful tool to help public health bodies monitor such emerging variants and rapidly detect those with increased transmissibility.
Publisher: Elsevier BV
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 02-10-2020
DOI: 10.1101/2020.10.02.20198663
Abstract: As in many countries, quantifying COVID-19 spread in Indonesia remains challenging due to testing limitations. In Java, non-pharmaceutical interventions (NPIs) were implemented throughout 2020. However, as a vaccination c aign launches, cases and deaths are rising across the island. We used modelling to explore the extent to which data on burials in Jakarta using strict COVID-19 protocols (C19P) provide additional insight into the transmissibility of the disease, epidemic trajectory, and the impact of NPIs. We assess how implementation of NPIs in early 2021 will shape the epidemic during the period of likely vaccine roll-out. C19P burial data in Jakarta suggest a death toll approximately 3.3 times higher than reported. Transmission estimates using these data suggest earlier, larger, and more sustained impact of NPIs. Measures to reduce sub-national spread, particularly during Ramadan, substantially mitigated spread to more vulnerable rural areas. Given current trajectory, daily cases and deaths are likely to increase in most regions as the vaccine is rolled-out. Transmission may peak in early 2021 in Jakarta if current levels of control are maintained. However, relaxation of control measures is likely to lead to a subsequent resurgence in the absence of an effective vaccination c aign. Syndromic measures of mortality provide a more complete picture of COVID-19 severity upon which to base decision-making. The high potential impact of the vaccine in Java is attributable to reductions in transmission to date and dependent on these being maintained. Increases in control in the relatively short-term will likely yield large, synergistic increases in vaccine impact. In many settings, limited SARS-CoV-2 testing makes it difficult to estimate the true trajectory and associated burden of the virus. Non-pharmaceutical interventions (NPIs) are key tools to mitigate SARS-CoV-2 transmission. Vaccines show promise but effectiveness depends upon prioritization strategies, roll-out and uptake. This study gives evidence of the value of syndrome-based mortality as a metric, which is less dependent upon testing capacity with which to estimate transmission trends and evaluate intervention impact. NPIs implemented in Java earlier in the pandemic have substantially slowed the course of the epidemic with movement restrictions during Ramadan preventing spread to more vulnerable rural populations. Population-level immunity remains below proposed herd-immunity thresholds for the virus, though it is likely substantially higher in Jakarta. Given current levels of control, upwards trends in deaths are likely to continue in many provinces while the vaccine is scheduled to be rolled out. A key exception is Jakarta where population-level immunity may increase to a level where the epidemic begins to decline before the vaccine c aign has reached high coverage. Further relaxation of measures would lead to more rapidly progressing epidemics, depleting the eventual incremental effectiveness of the vaccine. Maintaining adherence to control measures in Jakarta may be particularly challenging if the epidemic enters a decline phase but will remain necessary to prevent a subsequent large wave. Elsewhere, higher levels of control with NPIs are likely to yield high synergistic vaccine impact.
Publisher: Cold Spring Harbor Laboratory
Date: 10-03-2022
DOI: 10.1101/2022.03.07.22272001
Abstract: Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and, in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.
Publisher: Springer Science and Business Media LLC
Date: 19-08-2019
DOI: 10.1038/S41559-019-0958-3
Abstract: Direct quantification of terrestrial biosphere responses to global change is crucial for projections of future climate change in Earth system models. Here, we synthesized ecosystem carbon-cycling data from 1,119 experiments performed over the past four decades concerning changes in temperature, precipitation, CO
Publisher: Mary Ann Liebert Inc
Date: 08-2019
DOI: 10.1089/HS.2019.0022
Publisher: Elsevier BV
Date: 07-2018
Publisher: Springer Science and Business Media LLC
Date: 16-04-2021
DOI: 10.1038/S41746-021-00442-3
Abstract: Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013–2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1–4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.
Publisher: Cold Spring Harbor Laboratory
Date: 26-02-2021
DOI: 10.1101/2021.02.24.21252339
Abstract: Contact tracing, where exposed in iduals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an in idual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 – 16.0%) but depending on assumptions 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.7 – 80.9%).
Publisher: Elsevier BV
Date: 03-2018
Publisher: F1000 Research Ltd
Date: 10-06-2021
DOI: 10.12688/WELLCOMEOPENRES.16466.2
Abstract: State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user’s needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.
Publisher: The Journal of Rheumatology
Date: 15-07-2023
Abstract: Obesity is an important comorbidity in axial spondyloarthritis (axSpA) however, the prevalence of obesity in axSpA compared with the general population and associated socioeconomic factors remain unknown. This repeated cross-sectional study compared BMI (kg/m 2 ) groups of patients with axSpA to the Swiss population at 3 timepoints (2007, 2012, and 2017). BMI categories were compared by different age, sex, and education categories using the chi-square goodness of fit test. Unpaired, 1-sided t tests were used to compare the BMI in patients with axSpA between the different timepoints. Compared to the general population, patients with axSpA had a higher proportion of overweight and obesity: 18.9% of all patients with axSpA were obese, compared to 11.3% of the Swiss population in 2017. Comparison of BMI groups within sex, age, and education groups consistently showed a trend toward higher rates of overweight and obesity in axSpA. Further, patients with axSpA, especially females, showed a trend of increasing BMI over the studied 10 years. At every time point, overweight and obese patients were significantly more likely to be male, were older, and had higher disease activity than patients with normal weight. Obesity was associated with a deprived socioeconomic status as indicated by a higher proportion of patients with manual labor jobs and lower levels of education. The prevalence of obesity was significantly higher among patients with axSpA compared to the Swiss population, with socially disadvantaged in iduals being the most affected. There is an urgent need to initiate prevention strategies for obesity in patients with axSpA.
Publisher: F1000 Research Ltd
Date: 11-12-2020
DOI: 10.12688/WELLCOMEOPENRES.16466.1
Abstract: State space models, including compartmental models, are used to model physical, biological and social phenomena in a broad range of scientific fields. A common way of representing the underlying processes in these models is as a system of stochastic processes which can be simulated forwards in time. Inference of model parameters based on observed time-series data can then be performed using sequential Monte Carlo techniques. However, using these methods for routine inference problems can be made difficult due to various engineering considerations: allowing model design to change in response to new data and ideas, writing model code which is highly performant, and incorporating all of this with up-to-date statistical techniques. Here, we describe a suite of packages in the R programming language designed to streamline the design and deployment of state space models, targeted at infectious disease modellers but suitable for other domains. Users describe their model in a familiar domain-specific language, which is converted into parallelised C++ code. A fast, parallel, reproducible random number generator is then used to run large numbers of model simulations in an efficient manner. We also provide standard inference and prediction routines, though the model simulator can be used directly if these do not meet the user’s needs. These packages provide guarantees on reproducibility and performance, allowing the user to focus on the model itself, rather than the underlying computation. The ability to automatically generate high-performance code that would be tedious and time-consuming to write and verify manually, particularly when adding further structure to compartments, is crucial for infectious disease modellers. Our packages have been critical to the development cycle of our ongoing real-time modelling efforts in the COVID-19 pandemic, and have the potential to do the same for models used in a number of different domains.
Publisher: Public Library of Science (PLoS)
Date: 18-10-2023
Publisher: Cold Spring Harbor Laboratory
Date: 22-07-2021
DOI: 10.1101/2021.07.19.21260746
Abstract: As of July 2021, more than 180,000,000 cases of COVID-19 have been reported across the world, with more than 4 million deaths. Mathematical modelling and forecasting efforts have been widely used to inform policy-making and to create situational awareness. From 8 th March to 29 th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for countries with evidence of sustained transmission. The estimates and forecasts were based on an ensemble model comprising of three models that were calibrated using only the reported number of COVID-19 cases and deaths in each country. We also developed a novel heuristic to combine weekly estimates of transmissibility and potential changes in population immunity due to infection to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, we produced short- and medium-term forecasts for 81 countries. Both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax stringent public health measures that were implemented to contain the pandemic.
Publisher: F1000 Research Ltd
Date: 06-12-2021
DOI: 10.12688/WELLCOMEOPENRES.15805.3
Abstract: 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.
Publisher: F1000 Research Ltd
Date: 13-09-2021
DOI: 10.12688/WELLCOMEOPENRES.15805.2
Abstract: 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.
Publisher: F1000 Research Ltd
Date: 15-06-2020
DOI: 10.12688/WELLCOMEOPENRES.15805.1
Abstract: Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China. 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 more than two thirds (70%, 95% CI: 54% - 80%, compared to Singapore 75%, 95% CI: 66% - 82%, compared to multiple countries) of cases exported from mainland China have remained undetected. Conclusions: These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.
Publisher: Springer Science and Business Media LLC
Date: 09-07-2020
DOI: 10.1038/S41597-020-0534-3
Abstract: The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
Publisher: F1000 Research Ltd
Date: 10-2020
DOI: 10.12688/WELLCOMEOPENRES.15843.2
Abstract: Background : The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. By 24 March 2020, over 440,000 cases and almost 20,000 deaths had been reported worldwide. In response to the fast-growing epidemic, which began in the Chinese city of Wuhan, Hubei, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. These interventions have impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic was not clear. Methods : Using daily reported cases from mainland China and Hong Kong SAR, we estimated transmissibility over time and compared it to daily within-city movement, as a proxy for economic activity. Results : Initially, within-city movement and transmission were very strongly correlated in the five mainland provinces most affected by the epidemic and Beijing. However, that correlation decreased rapidly after the initial sharp fall in transmissibility. In general, towards the end of the study period, the correlation was no longer apparent, despite substantial increases in within-city movement. A similar analysis for Hong Kong shows that intermediate levels of local activity were maintained while avoiding a large outbreak. At the very end of the study period, when China began to experience the re-introduction of a small number of cases from Europe and the United States, there is an apparent up-tick in transmission. Conclusions: Although these results do not preclude future substantial increases in incidence, they suggest that after very intense social distancing (which resulted in containment), China successfully exited its lockdown to some degree. Elsewhere, movement data are being used as proxies for economic activity to assess the impact of interventions. The results presented here illustrate how the eventual decorrelation between transmission and movement is likely a key feature of successful COVID-19 exit strategies.
Publisher: Cold Spring Harbor Laboratory
Date: 12-07-2023
DOI: 10.1101/2023.07.10.23292424
Abstract: Recent Marburg virus disease (MVD) outbreaks in Equatorial Guinea and Tanzania highlighted the importance of better understanding this highly lethal infectious pathogen. Past epidemics of Ebola, COVID-19, and other pathogens have re-emphasised the usefulness of mathematical models in guiding public health responses during outbreaks. We conducted a systematic review, registered with PROSPERO (CRD42023393345) and reported according to PRISMA guidelines, of peer-reviewed papers reporting historical out-breaks, modelling studies and epidemiological parameters focused on MVD, including contextual information. We searched PubMed and Web of Science until 31st March 2023. Two reviewers evaluated all titles and abstracts, with consensus-based decision-making. To ensure agreement, 31% (13/42) of studies were double-extracted and a custom-designed quality assessment questionnaire was used to assess the risk of bias. We present detailed outbreak, model and parameter information on 970 reported cases and 818 deaths from MVD until 31 March 2023. Analysis of historical outbreaks and sero-prevalence estimates suggests the possibility of undetected MVD outbreaks, asymptomatic transmission and/or cross-reactivity with other pathogens. Only one study presented a mathematical model of MVD transmission. We estimate an unadjusted, pooled total random effect case fatality ratio for MVD of 61.9% (95% CI: 38.8-80.6%, I 2 =93%). We identify key epidemiological parameters relating to transmission and natural history for which there are few estimates. This review provides a comprehensive overview of the epidemiology of MVD, identifying key knowledge gaps about this pathogen. The extensive collection of knowledge gathered here will be crucial in developing mathematical models for use in the early stages of future outbreaks of MVD. All data are published alongside this article with functionality to easily update the database as new data become available. MRC Centre for Global Infectious Disease Analysis Evidence before this study We searched Web of Science and PubMed up to 31 March 2023 using the search terms Marburg virus, epidemiology, outbreaks, models, transmissibility, severity, delays, risk factors, mutation rates and seroprevalence. We found five systematic reviews, all of which considered MVD alongside Ebola virus disease (EVD). One modelling study of Marburg virus disease (MVD) focused on animals, and not on computational models to understand past or project future disease transmission. One systematic review collated risk factors for transmission based on four MVD studies, but did not report attack rates due to missing underlying MVD estimates another systematic review pooled estimates of MVD case fatality ratios (CFR): 53.8% (95% CI: 26.5–80.0%) and seroprevalence: 1.2% (95% CI: 0.5–2.0%). No systematic review covered transmission models of MVD, and the impact of public health and social measures is unknown. Added value of this study We provide a comprehensive summary of the available, peer-reviewed literature of historical outbreaks, transmission models and parameters for MVD. Meta-analysis of existing estimates of CFRs, and our original estimates based on historical outbreak information, illustrate the severity of MVD with our pooled random effect estimated CFR of 61.9% (95% CI: 38.8-80.6%, I 2 =93%). We demonstrate the sparsity of evidence on MVD transmission and disease dynamics, particularly on transmissibility and natural history, which are key input parameters for computational models supporting outbreak response. Our work highlights key areas where further disease characterization is necessary. Implications of all the available evidence Previous outbreaks of infectious pathogens emphasized the usefulness of computational modelling in assessing epidemic trajectories and the impact of mitigation strategies. Our study provides necessary information for using mathematical models in future outbreaks of MVD, identifies uncertainties and knowledge gaps in MVD transmission and natural history, and highlights the severity of MVD.
Publisher: Springer Science and Business Media LLC
Date: 25-05-2022
DOI: 10.1186/S12879-022-07469-1
Abstract: Understanding the characteristics and natural history of novel pathogens is crucial to inform successful control measures. Japan was one of the first affected countries in the COVID-19 pandemic reporting their first case on 14 January 2020. Interventions including airport screening, contact tracing, and cluster investigations were quickly implemented. Here we present insights from the first 3 months of the epidemic in Japan based on detailed case data. We conducted descriptive analyses based on information systematically extracted from in idual case reports from 13 January to 31 March 2020 including patient demographics, date of report and symptom onset, symptom progression, travel history, and contact type. We analysed symptom progression and estimated the time-varying reproduction number, R t , correcting for epidemic growth using an established Bayesian framework. Key delays and the age-specific probability of transmission were estimated using data on exposures and transmission pairs. The corrected fitted mean onset-to-reporting delay after the peak was 4 days (standard deviation: ± 2 days). Early transmission was driven primarily by returning travellers with R t peaking at 2.4 (95% CrI: 1.6, 3.3) nationally. In the final week of the trusted period (16–23 March 2020), R t accounting for importations erged from overall R t at 1.1 (95% CrI: 1.0, 1.2) compared to 1.5 (95% CrI: 1.3, 1.6), respectively. Household (39.0%) and workplace (11.6%) exposures were the most frequently reported potential source of infection. The estimated probability of transmission was assortative by age with in iduals more likely to infect, and be infected by, contacts in a similar age group to them. Across all age groups, cases most frequently onset with cough, fever, and fatigue. There were no reported cases of patients 20 years old developing pneumonia or severe respiratory symptoms. Information collected in the early phases of an outbreak are important in characterising any novel pathogen. The availability of timely and detailed data and appropriate analyses is critical to estimate and understand a pathogen’s transmissibility, high-risk settings for transmission, and key symptoms. These insights can help to inform urgent response strategies.
Publisher: Springer Science and Business Media LLC
Date: 04-10-2018
DOI: 10.1007/S11538-018-0514-3
Abstract: Modellers of large-scale genome rearrangement events, in which segments of DNA are inverted, moved, swapped, or even inserted or deleted, have found a natural syntax in the language of permutations. Despite this, there has been a wide range of modelling choices, assumptions and interpretations that make navigating the literature a significant challenge. Indeed, even authors of papers that use permutations to model genome rearrangement can struggle to interpret each others' work, because of subtle differences in basic assumptions that are often deeply ingrained (and consequently sometimes not even mentioned). In this paper, we describe the different ways in which permutations have been used to model genomes and genome rearrangement events, presenting some features and limitations of each approach, and show how the various models are related. This paper will help researchers navigate the landscape of permutation-based genome rearrangement models and make it easier for authors to present clear and consistent models.
Publisher: F1000 Research Ltd
Date: 28-04-2020
DOI: 10.12688/WELLCOMEOPENRES.15843.1
Abstract: Background : The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. By 24 March 2020, over 440,000 cases and almost 20,000 deaths had been reported worldwide. In response to the fast-growing epidemic, which began in the Chinese city of Wuhan, Hubei, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. These interventions have impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic was not clear. Methods : Using daily reported cases from mainland China and Hong Kong SAR, we estimated transmissibility over time and compared it to daily within-city movement, as a proxy for economic activity. Results : Initially, within-city movement and transmission were very strongly correlated in the five mainland provinces most affected by the epidemic and Beijing. However, that correlation decreased rapidly after the initial sharp fall in transmissibility. In general, towards the end of the study period, the correlation was no longer apparent, despite substantial increases in within-city movement. A similar analysis for Hong Kong shows that intermediate levels of local activity were maintained while avoiding a large outbreak. At the very end of the study period, when China began to experience the re-introduction of a small number of cases from Europe and the United States, there is an apparent up-tick in transmission. Conclusions: Although these results do not preclude future substantial increases in incidence, they suggest that after very intense social distancing (which resulted in containment), China successfully exited its lockdown to some degree. Elsewhere, movement data are being used as proxies for economic activity to assess the impact of interventions. The results presented here illustrate how the eventual decorrelation between transmission and movement is likely a key feature of successful COVID-19 exit strategies.
Publisher: Cold Spring Harbor Laboratory
Date: 15-11-2019
DOI: 10.1101/19011940
Abstract: In our increasingly interconnected world, it is crucial to understand the risk of an outbreak originating in one country or region and spreading to the rest of the world. Digital disease surveillance tools such as ProMED and HealthMap have the potential to serve as important early warning systems as well as complement the field surveillance during an ongoing outbreak. Here we present a flexible statistical model that uses data produced from digital surveillance tools (ProMED and HealthMap) to forecast short term incidence trends in a spatially explicit manner. The model was applied to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic. The model was able to predict each instance of international spread 1 to 4 weeks in advance. Our study highlights the potential and limitations of using publicly available digital surveillance data for assessing outbreak dynamics in real-time.
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 2020
DOI: 10.1016/J.MEEGID.2019.104043
Abstract: Molecular epidemiology uses genetic information from bacterial isolates to shed light on the population structure and dynamics of pathogens. Bacterial pathogens can now be studied by whole genome sequencing, but for some well-studied pathogens such as Mycobacterium tuberculosis a wealth of information is also available from other sources such as spoligotyping and multi-locus variable-number-tandem-repeats (VNTR). Isolates are also frequently tested for susceptibility to antibiotics. Methods of analysis are available for each type of data but it would be informative to combine multiple sources of information into a single analysis or visualisation. Here, we propose and implement a simple way to visualise genotypes along with drug resistance profiles for multiple drugs. We also present a way to combine information from different markers to aid in visualising relationships among isolates. These methods help to reveal the origins and spread of multi-drug resistant lineages of pathogens. We introduce a new computational package, MERCAT (Molecular Epidemiology Researcher's Collection of Analytical Tools), for analysing genotypic data from bacterial isolates. The software is available as an open source package in the statistical language R with a user-friendly interface using R Shiny. Although we focus on tuberculosis and the major molecular markers used to understand tuberculosis transmission - multilocus VNTR-typing (MLVA or MIRU) and spoligotyping - the methods and tools can be applied to other bacteria and can be easily tailored to other genetic markers such as SNP data from whole genome sequencing.
Publisher: Springer Science and Business Media LLC
Date: 16-09-2021
Publisher: American Association for the Advancement of Science (AAAS)
Date: 24-07-2020
Publisher: American Association for the Advancement of Science (AAAS)
Date: 14-07-2021
DOI: 10.1126/SCITRANSLMED.ABG4262
Abstract: A SARS-CoV-2 transmission model fitted to surveillance data estimates virus transmissibility, infection severity, and the impact of interventions.
Publisher: Cold Spring Harbor Laboratory
Date: 08-11-2021
DOI: 10.1101/2021.11.05.21265810
Abstract: During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess forecast calibration. The presented work is part of a pre-registered evaluation study and covers the period from January through April 2021. We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods (i.e., combinations of different available forecasts) show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (alpha) variant in March 2021, prove challenging to predict. Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance. The goal of this study is to assess the quality of forecasts of weekly case and death numbers of COVID-19 in Germany and Poland during the period of January through April 2021. We focus on real-time forecasts at time horizons of one and two weeks ahead created by fourteen independent teams. Forecasts are systematically evaluated taking uncertainty ranges of predictions into account. We find that combining different forecasts into ensembles can improve the quality of predictions, but especially case numbers proved very challenging to predict beyond quite short time windows. Additional data sources, in particular genetic sequencing data, may help to improve forecasts in the future.
Publisher: Research Square Platform LLC
Date: 13-08-2021
DOI: 10.21203/RS.3.RS-799162/V1
Abstract: From 8th March to 29th November 2020, we produced weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for 81 countries with evidence of sustained transmission. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. We evaluated the robustness of the forecasts using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3\\% and 45.6\\% of the observations lying in the 50\\% Credible Interval in 1-week and 4-week ahead forecasts respectively. We could accurately characterise the overall phase of the epidemic up to 4-weeks ahead in 84.9\\% of country-days. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.
Publisher: Frontiers Media SA
Date: 24-09-2020
Publisher: Springer Science and Business Media LLC
Date: 28-01-2022
DOI: 10.1186/S40249-022-00935-7
Abstract: Non-pharmaceutical interventions (NPIs) are a crucial suite of measures to prevent and control infectious disease outbreaks. Despite being particularly important for crisis-affected populations and those living in informal settlements, who typically reside in overcrowded and resource limited settings with inadequate access to healthcare, guidance on NPI implementation rarely takes the specific needs of such populations into account. We therefore conducted a systematic scoping review of the published evidence to describe the landscape of research and identify evidence gaps concerning the acceptability, feasibility, and effectiveness of NPIs among crisis-affected populations and informal settlements. We systematically reviewed peer-reviewed articles published between 1970 and 2020 to collate available evidence on the feasibility, acceptability, and effectiveness of NPIs in crisis-affected populations and informal settlements. We performed quality assessments of each study using a standardised questionnaire. We analysed the data to produce descriptive summaries according to a number of categories: date of publication geographical region of intervention typology of crisis, shelter, modes of transmission, NPI, research design study design and study quality. Our review included 158 studies published in 85 peer-reviewed articles. Most research used low quality study designs. The acceptability, feasibility, and effectiveness of NPIs was highly context dependent. In general, simple and cost-effective interventions such as community-level environmental cleaning and provision of water, sanitation and hygiene services, and distribution of items for personal protection such as insecticide-treated nets, were both highly feasible and acceptable. Logistical, financial, and human resource constraints affected both the implementation and sustainability of measures. Community engagement emerged as a strong factor contributing to the effectiveness of NPIs. Conversely, measures that involve potential restriction on personal liberty such as case isolation and patient care and burial restrictions were found to be less acceptable, despite apparent effectiveness. Overall, the evidence base was variable, with substantial knowledge gaps which varied between settings and pathogens. Based on the current landscape, robust evidence-based guidance is not possible, and a research agenda is urgently required that focusses on these specific vulnerable populations. Although implementation of NPIs presents unique practical challenges in these settings, it is critical that such an agenda is put in place, and that the lessons learned from historical and present experiences are documented to build a firm evidence base.
Publisher: Springer Science and Business Media LLC
Date: 09-10-2020
DOI: 10.1186/S12916-020-01791-8
Abstract: After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their “test, trace, isolate” strategy. However, it is important to understand the epidemiological peculiarities of South Korea’s outbreak and characterise their response before attempting to emulate these measures elsewhere. We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, R t , using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources. We estimated that after the initial rapid growth in cases, R t dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64–2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, R t was back below one where it remained until the end of our study (July 13th). Despite less stringent “lockdown” measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, in idual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%. Whilst early adoption of testing and contact tracing is likely to be important for South Korea’s successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.
Publisher: Springer Science and Business Media LLC
Date: 31-10-2022
DOI: 10.1038/S43856-022-00191-8
Abstract: During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.
Publisher: Springer Science and Business Media LLC
Date: 18-06-2021
DOI: 10.1186/S12916-021-02016-2
Abstract: As in many countries, quantifying COVID-19 spread in Indonesia remains challenging due to testing limitations. In Java, non-pharmaceutical interventions (NPIs) were implemented throughout 2020. However, as a vaccination c aign launches, cases and deaths are rising across the island. We used modelling to explore the extent to which data on burials in Jakarta using strict COVID-19 protocols (C19P) provide additional insight into the transmissibility of the disease, epidemic trajectory, and the impact of NPIs. We assess how implementation of NPIs in early 2021 will shape the epidemic during the period of likely vaccine rollout. C19P burial data in Jakarta suggest a death toll approximately 3.3 times higher than reported. Transmission estimates using these data suggest earlier, larger, and more sustained impact of NPIs. Measures to reduce sub-national spread, particularly during Ramadan, substantially mitigated spread to more vulnerable rural areas. Given current trajectory, daily cases and deaths are likely to increase in most regions as the vaccine is rolled out. Transmission may peak in early 2021 in Jakarta if current levels of control are maintained. However, relaxation of control measures is likely to lead to a subsequent resurgence in the absence of an effective vaccination c aign. Syndromic measures of mortality provide a more complete picture of COVID-19 severity upon which to base decision-making. The high potential impact of the vaccine in Java is attributable to reductions in transmission to date and dependent on these being maintained. Increases in control in the relatively short-term will likely yield large, synergistic increases in vaccine impact.
Publisher: Cold Spring Harbor Laboratory
Date: 13-02-2022
DOI: 10.1101/2022.02.10.22270735
Abstract: Understanding the characteristics and natural history of novel pathogens is crucial to inform successful control measures. Japan was one of the first affected countries in the COVID-19 pandemic reporting their first case on 14 January 2020. Interventions including airport screening, contact tracing, and cluster investigations were quickly implemented. Here we present insights from the first 3 months of the epidemic in Japan based on detailed case data. We conducted descriptive analyses based on information systematically extracted from in idual case reports from 13 January to 31 March 2020 including patient demographics, date of report and symptom onset, symptom progression, travel history, and contact type. We analysed symptom progression and estimated the time-varying reproduction number, R t , correcting for epidemic growth using an established Bayesian framework. Key delays and the age-specific probability of transmission were estimated using data on exposures and transmission pairs. The corrected fitted mean onset-to-reporting delay after the peak was 4 days (standard deviation: ±2 days). Early transmission was driven primarily by returning travellers with R t peaking at 2.4 (95%CrI:1.6, 3.3) nationally. In the final week of the trusted period, R t accounting for importations erged from overall R t at 1.1 (95% CrI: 1.0, 1.2) compared to 1.5 (95% CrI: 1.3, 1.6) respectively. Household (39.0%) and workplace (11.6%) exposures were the most frequently reported potential source of infection. The estimated probability of transmission was assortative by age. Across all age groups, cases most frequently onset with cough, fever, and fatigue. There were no reported cases of patients years old developing pneumonia or severe respiratory symptoms. Information collected in the early phases of an outbreak are important in characterising any novel pathogen. Timely recognition of key symptoms and high-risk settings for transmission can help to inform response strategies. The data analysed here were the result of robust and timely investigations and demonstrate the improvements to epidemic control as a result of such surveillance.
Publisher: Cambridge University Press (CUP)
Date: 29-01-2019
Publisher: Cold Spring Harbor Laboratory
Date: 26-12-2020
DOI: 10.1101/2020.12.24.20248826
Abstract: We report insights from ten weeks of collaborative COVID-19 forecasting for Germany and Poland (12 October – 19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sangeeta Bhatia.