Publications
82 results found
Wardle J, Bhatia S, Cori A, et al., 2024, Temporal variations in international air travel: implications for modelling the spread of infectious diseases., J Travel Med
BACKGROUND: The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics. METHODS: We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected in outbreaks of MERS, Zika, and SARS-CoV-2 over the past decade. We then used a stochastic discrete time SEIR metapopulation model to simulate global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data. RESULTS: We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruption occurred during the SARS-CoV-2 pandemic.For outbreak events that were accompanied by local, small, and short-term changes in air travel, epidemic models using historical flight data gave similar projections of timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2 in which there were durable and extensive levels of global travel disruption. CONCLUSIONS: The use of historical flight data as a proxy in epidemic models is an acceptable practice except in rare, large epidemics that lead to substantial disruptions to international travel.
Hao L, Boehnke N, Elledge SK, et al., 2024, Targeting and monitoring ovarian cancer invasion with an RNAi and peptide delivery system., Proc Natl Acad Sci U S A, Vol: 121
RNA interference (RNAi) therapeutics are an emerging class of medicines that selectively target mRNA transcripts to silence protein production and combat disease. Despite the recent progress, a generalizable approach for monitoring the efficacy of RNAi therapeutics without invasive biopsy remains a challenge. Here, we describe the development of a self-reporting, theranostic nanoparticle that delivers siRNA to silence a protein that drives cancer progression while also monitoring the functional activity of its downstream targets. Our therapeutic target is the transcription factor SMARCE1, which was previously identified as a key driver of invasion in early-stage breast cancer. Using a doxycycline-inducible shRNA knockdown in OVCAR8 ovarian cancer cells both in vitro and in vivo, we demonstrate that SMARCE1 is a master regulator of genes encoding proinvasive proteases in a model of human ovarian cancer. We additionally map the peptide cleavage profiles of SMARCE1-regulated proteases so as to design a readout for downstream enzymatic activity. To demonstrate the therapeutic and diagnostic potential of our approach, we engineered self-assembled layer-by-layer nanoparticles that can encapsulate nucleic acid cargo and be decorated with peptide substrates that release a urinary reporter upon exposure to SMARCE1-related proteases. In an orthotopic ovarian cancer xenograft model, theranostic nanoparticles were able to knockdown SMARCE1 which was in turn reported through a reduction in protease-activated urinary reporters. These LBL nanoparticles both silence gene products by delivering siRNA and noninvasively report on downstream target activity by delivering synthetic biomarkers to sites of disease, enabling dose-finding studies as well as longitudinal assessments of efficacy.
Bhatia SN, Dahlman JE, 2024, RNA delivery systems., Proc Natl Acad Sci U S A, Vol: 121
Cuomo-Dannenburg G, McCain K, McCabe R, et al., 2023, Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review, Lancet Infectious Diseases, ISSN: 1473-3099
Recent Marburg virus disease (MVD) outbreaks in Equatorial Guinea and Tanzania highlighted the importance of better understanding this highly lethal infectious pathogen. We conducted a systematic review (PROSPERO CRD42023393345), reported according to PRISMA guidelines, of peer-reviewed papers reporting historical outbreaks, modelling studies and epidemiological parameters focused on MVD. We searched PubMed and Web of Science until 31/03/2023. 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 for risk of bias assessment. We present detailed information on 478 reported cases and 385 deaths from MVD. Analysis of historical outbreaks and seroprevalence 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 important epidemiological parameters relating to transmission and natural history for which there are few estimates. This review and the accompanying database provide a comprehensive overview of MVD epidemiology, and identify key knowledge gaps, contributing crucial information for mathematical models to support future MVD epidemic responses.
Bhatia S, Parag KV, Wardle J, et al., 2023, Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts, PLOS ONE, Vol: 18, ISSN: 1932-6203
Bhatia S, Imai N, Watson OJ, et al., 2023, Lessons from COVID-19 for re-scalable data collection, Lancet Infectious Diseases, Vol: 23, Pages: E383-E388, ISSN: 1473-3099
Novel data and analyses have played an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances, new systems developed to meet the challenges posed by the magnitude of the pandemic. Here, we describe the routine and novel data that were used to address urgentpublic health questions during the pandemic, underscore challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision-making during a public health crisis.As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, as SARS-CoV-2 resurgence remains a threat to global health security, it is important that a minimal cost-effective system remains active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.
Bhatia S, Wardle J, Nash R, et al., 2023, Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study, Epidemics: the journal of infectious disease dynamics, Vol: 44, Pages: 1-8, ISSN: 1755-4365
The evolution of SARS-CoV-2 has demonstrated that emerging variants can set back the global COVID-19 response. The ability to rapidly assess the threat ofnew variants is critical for timely optimisation of control strategies.We present a novel method to estimate the effective transmission advantage of a new variant compared to a reference variant combining information across multiple locations and over time. Through an extensive simulation study designed to mimic real-time epidemic contexts, we show that our method performs well across a range of scenarios and provide guidance on its optimal useand interpretation of results. We also provide an open-source software implementation of our method. The computational speed of our tool enables users torapidly explore spatial and temporal variations in the estimated transmission advantage.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 furtherestimate that Delta is 1.77 (95% CrI: 1.69-1.85) times more transmissible than Alpha (England data).Our approach can be used as an important first step towards quantifying the threat of emerging or co-circulating variants of infectious pathogens in real-time.
Micheroli R, Bhatia S, Vallejo-Yagüe E, et al., 2023, Obesity Represents a Persisting Health Issue in Axial Spondyloarthritis, Particularly Affecting Socially Disadvantaged Patients., J Rheumatol
OBJECTIVE: 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. METHODS: This repeated cross-sectional study compared BMI (kg/m2) 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. RESULTS: 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. CONCLUSION: The prevalence of obesity was significantly higher among patients with axSpA compared to the Swiss population, with socially disadvantaged individuals being the most affected. There is an urgent need to initiate prevention strategies for obesity in patients with axSpA.
Bhatia S, Imai N, Watson OJ, et al., 2023, Lessons from COVID-19 for rescalable data collection (May, 10.1016/S1473-3099(23)00121-4, 2023), LANCET INFECTIOUS DISEASES, Vol: 23, Pages: E227-E227, ISSN: 1473-3099
Herr D, Bhatia S, Breuer F, et al., 2023, Increasing emergency number utilisation is not driven by low-acuity calls: an observational study of 1.5 million emergency calls (2018 – 2021) from Berlin, BMC Medicine, Vol: 21, ISSN: 1741-7015
Background:The Emergency Medical Service (EMS) in Germany is increasingly challenged by strongly rising demand. Speculations about a greater utilisation for minor cases have led to intensive media coverage, but empirical evidence is lacking. We investigated the development of low-acuity calls from 2018 to 2021 in the federal state of Berlin and its correlations with sociodemographic characteristics.Methods:We analysed over 1.5 million call documentations including medical dispatch codes, age, location and time using descriptive and inferential statistics and multivariate binary logistic regression. We defined a code list to classify low-acuity calls and merged the dataset with sociodemographic indicators and data on population density.Results:The number of emergency calls (phone number 112 in Germany) increased by 9.1% from 2018 to 2021; however, the proportion of low-acuity calls did not increase. The regression model shows higher odds of low-acuity for young to medium age groups (especially for age 0–9, OR 1.50 [95% CI 1.45–1.55]; age 10–19, OR 1.77 [95% CI 1.71–1.83]; age 20–29, OR 1.64 [95% CI 1.59–1.68] and age 30–39, OR 1.40 [95% CI 1.37–1.44]; p < 0.001, reference group 80–89) and for females (OR 1.12 [95% CI 1.1–1.13], p < 0.001). Odds were slightly higher for calls from a neighbourhood with lower social status (OR 1.01 per index unit increase [95% CI 1.0–1.01], p < 0.05) and at the weekend (OR 1.02 [95% CI 1.0–1.04, p < 0.05]). No significant association of the call volume with population density was detected.Conclusions:This analysis provides valuable new insights into pre-hospital emergency care. Low-acuity calls were not the primary driver of increased EMS utilisation in Berlin. Younger age is the strongest predictor for low-acuity calls in the model. The association with female gender is significant, while socially
Wardle J, Bhatia S, Kraemer MUG, et al., 2023, Gaps in mobility data and implications for modelling epidemic spread: a scoping review and simulation study, Epidemics: the journal of infectious disease dynamics, Vol: 42, Pages: 1-11, ISSN: 1755-4365
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.
Unwin H, Cori A, Imai N, et al., 2022, Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak, Epidemics: the journal of infectious disease dynamics, Vol: 41, ISSN: 1755-4365
Contact tracing, where exposed individuals 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 individual-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%) if 80% of contacts were under active surveillance but depending on assumptions about the ratio of contacts not under active surveillance versus contacts under active surveillance 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.70 – 80.9%).
Bracher J, Wolffram D, Deuschel J, et al., 2022, National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021, COMMUNICATIONS MEDICINE, Vol: 2, ISSN: 2730-664X
Cramer EY, Huang Y, Wang Y, et al., 2022, The United States COVID-19 Forecast Hub dataset., Sci Data, Vol: 9
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.
Bekdemir A, Tanner EEL, Kirkpatrick J, et al., 2022, Ionic Liquid-Mediated Transdermal Delivery of Thrombosis-Detecting Nanosensors, ADVANCED HEALTHCARE MATERIALS, Vol: 11, ISSN: 2192-2640
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Imai N, Gaythorpe K, Bhatia S, et al., 2022, COVID-19 in Japan, January – March 2020: insights from the first three months of the epidemic, BMC Infectious Diseases, Vol: 22, ISSN: 1471-2334
Background: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. Methods:We conducted descriptive analyses based on information systematically extracted from individual 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, Rt, 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. Results: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 Rt peaking at 2.4 (95%CrI:1.6, 3.3) nationally. In the final week of the trusted period (16 – 23 March 2020), Rt accounting for importations diverged from overall Rt 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 individuals 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.Conclusions:Information collected in the early phases of an out
Wardle J, Bhatia S, Kraemer MUG, et al., 2022, Gaps in mobility data and implications for modelling epidemic spread: a scoping review and simulation study
<jats:p>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.</jats:p>
Imai N, Gaythorpe KAM, Bhatia S, et al., 2022, COVID-19 in Japan: insights from the first three months of the epidemic, Publisher: Cold Spring Harbor Laboratory
BackgroundUnderstanding 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. MethodsWe conducted descriptive analyses based on information systematically extracted from individual 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, Rt, 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. ResultsThe 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 Rt peaking at 2.4 (95%CrI:1.6, 3.3) nationally. In the final week of the trusted period, Rt accounting for importations diverged from overall Rt 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 <20 years old developing pneumonia or severe respiratory symptoms.ConclusionsInformation 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 transmi
Polonsky JA, Bhatia S, Fraser K, et al., 2022, Feasibility, acceptability, and effectiveness of non-pharmaceutical interventions against infectious diseases among crisis-affected populations: a scoping review, Infectious Diseases of Poverty, Vol: 11, Pages: 1-19, ISSN: 2049-9957
BackgroundNon-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.MethodsWe 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.ResultsOur 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. Con
Bhatia S, Imai N, Cuomo-Dannenburg G, et al., 2021, Estimating the number of undetected COVID-19 cases among travellers from mainland China, Wellcome Open Research, Vol: 5, Pages: 143-143
<ns4:p><ns4:bold>Background:</ns4:bold> 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.</ns4:p><ns4:p> <ns4:bold>Methods: </ns4:bold>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.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>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.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>Our analysis shows that a large number of COVID-19 cases remain undetected across the world.<ns4:bold> </ns4:bold>These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.</ns4:p>
Bhatia S, Wardle J, Nash RK, et al., 2021, A generic method and software to estimate the transmission advantage of pathogen variants in real-time : SARS-CoV-2 as a case-study
<jats:title>Abstract</jats:title><jats:p>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.</jats:p><jats:p>We extend the EpiEstim R package, designed to estimate the time-varying reproduction number (<jats:italic>R</jats:italic><jats:sub><jats:italic>t</jats:italic></jats:sub>), 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.</jats:p><jats:p>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.</jats:p><jats:p>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.</jats:p><jats:sec><jats:title>Significance Statement</jats:title><jats:p>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
Bhatia S, Wardle J, Nash R, et al., 2021, Report 47: A generic method and software to estimate the transmission advantage of pathogen variants in real-time : SARS-CoV-2 as a case-study
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 ofcontrol strategies.We extend the EpiEstim R package, designed to estimate the time-varying reproduction number (Rt),to estimate in real-time the e ective transmission advantage of a new variant compared to a referencevariant. Our method can combine information across multiple locations and over time and was validatedusing 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 Francerespectively. We further estimate that Beta and Gamma combined are 1.25 (95% CrI 1.24-1.27) timesmore transmissible than the wildtype (France data). All results are in line with previous estimates fromliterature, but could have been obtained earlier and more easily with our o -the-shelf open-source tool.Our tool can be used as an important rst step towards quantifying the threat of new variants inreal-time. Given the popularity of EpiEstim, this extension will likely be used widely to monitor theco-circulation and/or emergence of multiple variants of infectious pathogens.
Bracher J, Wolffram D, Deuschel J, et al., 2021, National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Multi-model approaches can help to improve the performance of epidemiological forecasts. Howeve
Desai A, Nouvellet P, Bhatia S, et al., 2021, Data journalism and the COVID-19 pandemic: opportunities and challenges, The Lancet Digital Health, Vol: 3, Pages: e619-e621, ISSN: 2589-7500
Bracher J, Wolffram D, Deuschel J, et al., 2021, A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave, NATURE COMMUNICATIONS, Vol: 12
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Bhatia S, Parag K, Wardle J, et al., 2021, Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment
<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>
Bhatia S, Parag KV, Wardle J, et al., 2021, Global predictions of short- to medium-term COVID-19 transmission trends : a retrospective assessment
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods and Findings</jats:title><jats:p>From 8<jats:sup>th</jats:sup> March to 29<jats:sup>th</jats:sup> 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.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>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 fo
Knock ES, Whittles LK, Lees JA, et al., 2021, Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England, Science Translational Medicine, Vol: 13, Pages: 1-12, ISSN: 1946-6234
We fitted a model of SARS-CoV-2 transmission in care homes and the community to regional surveillance data for England. Compared with other approaches, our model provides a synthesis of multiple surveillance data streams into a single coherent modelling framework allowing transmission and severity to be disentangled from features of the surveillance system. Of the control measures implemented, only national lockdown brought the reproduction number (Rteff ) below 1 consistently; if introduced one week earlier it could have reduced deaths in the first wave from an estimated 48,600 to 25,600 (95% credible interval [95%CrI]: 15,900-38,400). The infection fatality ratio decreased from 1.00% (95%CrI: 0.85%-1.21%) to 0.79% (95%CrI: 0.63%-0.99%), suggesting improved clinical care. The infection fatality ratio was higher in the elderly residing in care homes (23.3%, 95%CrI: 14.7%-35.2%) than those residing in the community (7.9%, 95%CrI: 5.9%-10.3%). On 2nd December 2020 England was still far from herd immunity, with regional cumulative infection incidence between 7.6% (95%CrI: 5.4%-10.2%) and 22.3% (95%CrI: 19.4%-25.4%) of the population. Therefore, any vaccination campaign will need to achieve high coverage and a high degree of protection in vaccinated individuals to allow non-pharmaceutical interventions to be lifted without a resurgence of transmission.
Gaythorpe K, Bhatia S, Mangal T, et al., 2021, Children’s role in the COVID-19 pandemic: a systematic review of early surveillance data on susceptibility, severity, and transmissibility, Scientific Reports, Vol: 11, Pages: 1-14, ISSN: 2045-2322
Background: 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 early phases of the COVID-19 pandemic. Methods and findings: 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 2,775 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.Conclusions: 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 e
Djaafara A, Whittaker C, Watson OJ, et al., 2021, Using syndromic measures of mortality to capture the dynamics of COVID-19 in Java, Indonesia in the context of vaccination roll-out, BMC Medicine, Vol: 19, ISSN: 1741-7015
Background: 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 campaign launches, cases and deaths are rising across the island. Methods: 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. Results: 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 campaign. Conclusions: 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.
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