Publications
218 results found
Rod NH, Bengtsson J, Elsenburg LK, et al., 2023, Cancer burden among adolescents and young adults in relation to childhood adversity: a nationwide life-course cohort study of 1.2 million individuals., Lancet Reg Health Eur, Vol: 27
BACKGROUND: Childhood adversity such as poverty, loss of a parent, and dysfunctional family dynamics may be associated with exposure to environmental and behavioral hazards, interfere with normal biological functions, and affect cancer care and outcomes. To explore this hypothesis, we assessed the cancer burden among young men and women exposed to adversity during childhood. METHODS: We undertook a population-based study using Danish nationwide register data on childhood adversity and cancer outcomes. Children who were alive and resident in Denmark until their 16th birthday were followed into young adulthood (16-38 years). Group-based multi-trajectory modelling was used to categorize individuals into five distinct groups: low adversity, early material deprivation, persistent material deprivation, loss/threat of loss, and high adversity. We assessed the association with overall cancer incidence, mortality, and five-year case fatality; and cancer specific outcomes for the four most common cancers in this age group in sex-stratified survival analyses. FINDINGS: 1,281,334 individuals born between Jan 1, 1980, and Dec 31, 2001, were followed up until Dec 31, 2018, capturing 8229 incident cancer cases and 662 cancer deaths. Compared to low adversity, women who experienced persistent material deprivation carried a slightly lower risk of overall cancer (hazard ratio (HR) 0.90; 95% CI 0.82; 0.99), particularly due to malignant melanoma and brain and central nervous system cancers, while women who experienced high adversity carried a higher risk of breast cancer (HR 1.71; 95% CI 1.09; 2.70) and cervical cancer incidence (HR 1.82; 95% CI 1.18; 2.83). While there was no clear association between childhood adversity and cancer incidence in men, those men who had experienced persistent material deprivation (HR 1.72; 95% CI 1.29; 2.31) or high adversity (HR 2.27; 95% CI 1.38; 3.72) carried a disproportionate burden of cancer mortality during adolescence or young adulthood compared
Whittaker C, Hamlet A, Sherrard-Smith E, et al., 2023, Seasonal dynamics of Anopheles stephensi and its implications for mosquito detection and emergent malaria control in the Horn of Africa, Proceedings of the National Academy of Sciences of USA, Vol: 120, Pages: 1-9, ISSN: 0027-8424
Invasion of the malaria vector Anopheles stephensi across the Horn of Africa threatens control efforts across the continent, particularly in urban settings where the vector is able to proliferate. Malaria transmission is primarily determined by the abundance of dominant vectors, which often varies seasonally with rainfall. However, it remains unclear how An. stephensi abundance changes throughout the year, despite this being a crucial input to surveillance and control activities. We collate longitudinal catch data from across its endemic range to better understand the vector's seasonal dynamics and explore the implications of this seasonality for malaria surveillance and control across the Horn of Africa. Our analyses reveal pronounced variation in seasonal dynamics, the timing and nature of which are poorly predicted by rainfall patterns. Instead, they are associated with temperature and patterns of land use; frequently differing between rural and urban settings. Our results show that timing entomological surveys to coincide with rainy periods is unlikely to improve the likelihood of detecting An. stephensi. Integrating these results into a malaria transmission model, we show that timing indoor residual spraying campaigns to coincide with peak rainfall offers little improvement in reducing disease burden compared to starting in a random month. Our results suggest that unlike other malaria vectors in Africa, rainfall may be a poor guide to predicting the timing of peaks in An. stephensi-driven malaria transmission. This highlights the urgent need for longitudinal entomological monitoring of the vector in its new environments given recent invasion and potential spread across the continent.
Bager P, Nielsen J, Bhatt S, et al., 2023, Conflicting COVID-19 excess mortality estimates., Lancet, Vol: 401, Pages: 432-433
Laydon D, Cauchemez S, Hinsley W, et al., 2023, Proactive and reactive vaccination strategies for healthcare workers against MERS-CoV: a mathematical modelling study, The Lancet Global Health, ISSN: 2214-109X
Lison A, Banholzer N, Sharma M, et al., 2023, Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic, The Lancet Public Health, ISSN: 2468-2667
Numerous studies have assessed the effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and stay-at-home orders, during the COVID-19 pandemic. Such assessments can inform public health policy and contribute to evidence-based choices of NPIs during subsequentwaves or future epidemics. However, methodological issues and a lack of common standards have limited the practical value of the existing evidence. Based on our work and literature review, we discuss lessons learned from the COVID-19 pandemic and make recommendations for standardizing and improving assessment, data collection, and modeling. These recommendations can contribute to more reliable and policy-relevant assessments of NPI effectiveness during future epidemics.
Flaxman S, Whittaker C, Semenova E, et al., 2023, Assessment of COVID-19 as the underlying cause of death among children and young people aged 0 to 19 years in the US., Jama Network Open, Vol: 6, Pages: 1-9, ISSN: 2574-3805
IMPORTANCE: COVID-19 was the underlying cause of death for more than 940 000 individuals in the US, including at least 1289 children and young people (CYP) aged 0 to 19 years, with at least 821 CYP deaths occurring in the 1-year period from August 1, 2021, to July 31, 2022. Because deaths among US CYP are rare, the mortality burden of COVID-19 in CYP is best understood in the context of all other causes of CYP death. OBJECTIVE: To determine whether COVID-19 is a leading (top 10) cause of death in CYP in the US. DESIGN, SETTING, AND PARTICIPANTS: This national population-level cross-sectional epidemiological analysis for the years 2019 to 2022 used data from the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) database on underlying cause of death in the US to identify the ranking of COVID-19 relative to other causes of death among individuals aged 0 to 19 years. COVID-19 deaths were considered in 12-month periods between April 1, 2020, and August 31, 2022, compared with deaths from leading non-COVID-19 causes in 2019, 2020, and 2021. MAIN OUTCOMES AND MEASURES: Cause of death rankings by total number of deaths, crude rates per 100 000 population, and percentage of all causes of death, using the National Center for Health Statistics 113 Selected Causes of Death, for ages 0 to 19 and by age groupings (<1 year, 1-4 years, 5-9 years, 10-14 years, 15-19 years). RESULTS: There were 821 COVID-19 deaths among individuals aged 0 to 19 years during the study period, resulting in a crude death rate of 1.0 per 100 000 population overall; 4.3 per 100 000 for those younger than 1 year; 0.6 per 100 000 for those aged 1 to 4 years; 0.4 per 100 000 for those aged 5 to 9 years; 0.5 per 100 000 for those aged 10 to 14 years; and 1.8 per 100 000 for those aged 15 to 19 years. COVID-19 mortality in the time period of August 1, 2021, to July 31, 2022, was among the 10 leading causes of death in CYP aged 0 to 19 years in the US
Mehrjou A, Soleymani A, Abyaneh A, et al., 2023, Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases, PLOS COMPUTATIONAL BIOLOGY, Vol: 19, ISSN: 1553-734X
Mishra S, Scott JA, Laydon DJ, et al., 2022, A COVID-19 model for local authorities of the United Kingdom, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 185, Pages: S86-S95, ISSN: 0964-1998
Mishra S, Scott JA, Laydon DJ, et al., 2022, Authors' reply to the discussion of 'A COVID-19 Model for Local Authorities of the United Kingdom' by Mishra et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11 June 2021, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 185, Pages: S110-S111, ISSN: 0964-1998
Reiner RC, LBD Triple Burden Collaborators, Hay SI, 2022, The overlapping burden of the three leading causes of disability and death in sub-Saharan African children., Nat Commun, Vol: 13
Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival.
Mishra S, Flaxman S, Berah T, et al., 2022, pi VAE: a stochastic process prior for Bayesian deep learning with MCMC, STATISTICS AND COMPUTING, Vol: 32, ISSN: 0960-3174
Brito AF, Semonva E, Dudas G, et al., 2022, Global disparities in SARS-CoV-2 genomic surveillance, Nature Communications, Vol: 13, Pages: 1-13, ISSN: 2041-1723
Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times (TAT) on variant detection in 189 countries. In two years of pandemic, 78% of high income countries (HICs) sequenced >0.5% of their COVID-19 cases, while 42% of low (LICs) and middle income countries (MICs) reached that mark. Around 25% of the genomes from HICs were submitted within 21 days, a pattern observed in 5% of the genomes from LICs and MICs. We found that sequencing around 0.5% of the cases, with a TAT <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support LICs and MICs improve their local sequencing capacity.
Morgenstern C, Laydon D, Whittaker C, et al., 2022, The interaction of transmission intensity, mortality, and the economy: a retrospective analysis of the COVID-19 pandemic
<jats:title>Abstract</jats:title> <jats:p>The COVID-19 pandemic has caused over 6.4 million registered deaths to date, and has had a profound impact on economic activity. Here, we study the interaction of transmission, mortality, and the economy during the SARS-CoV-2 pandemic from January 2020 to December 2022 across 25 European countries. We adopt a Bayesian vector autoregressive model with both fixed and random effects. We find that increases in disease transmission intensity decreases Gross domestic product (GDP) and increases daily excess deaths, with a longer lasting impact on excess deaths in comparison to GDP, which recovers more rapidly. Broadly, our results reinforce the intuitive phenomenon that significant economic activity arises from diverse person-to-person interactions. We report on the effectiveness of non-pharmaceutical interventions (NPIs) on transmission intensity, excess deaths and changes in GDP, and resulting implications for policy makers. Our results highlight a complex cost-benefit trade off from individual NPIs. For example, banning international travel increases GDP however reduces excess deaths. We consider country random effects and their associations with excess changes in GDP and excess deaths. For example, more developed countries in Europe typically had more cautious approaches to the COVID-19 pandemic, prioritising healthcare and excess deaths over economic performance. Long term economic impairments are not fully captured by our model, as well as long term disease effects (Long Covid). Our results highlight that the impact of disease on a country is complex and multifaceted, and simple heuristic conclusions to extract the best outcome from the economy and disease burden are challenging.</jats:p>
Miscouridou X, Bhatt S, Mohler G, et al., 2022, Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes
Hawkes processes are point process models that have been used to captureself-excitatory behavior in social interactions, neural activity, earthquakesand viral epidemics. They can model the occurrence of the times and locationsof events. Here we develop a new class of spatiotemporal Hawkes processes thatcan capture both triggering and clustering behavior and we provide an efficientmethod for performing inference. We use a log-Gaussian Cox process (LGCP) asprior for the background rate of the Hawkes process which gives arbitraryflexibility to capture a wide range of underlying background effects (forinfectious diseases these are called endemic effects). The Hawkes process andLGCP are computationally expensive due to the former having a likelihood withquadratic complexity in the number of observations and the latter involvinginversion of the precision matrix which is cubic in observations. Here wepropose a novel approach to perform MCMC sampling for our Hawkes process withLGCP background, using pre-trained Gaussian Process generators which providedirect and cheap access to samples during inference. We show the efficacy andflexibility of our approach in experiments on simulated data and use ourmethods to uncover the trends in a dataset of reported crimes in the US.
Mishra S, Flaxman S, Berah T, et al., 2022, πVAE: a stochastic process prior for Bayesian deep learning with MCMC, Statistics and Computing, Vol: 32, ISSN: 0960-3174
Stochastic processes provide a mathematically elegant way to model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. However, in practice efficient inference by optimisation or marginalisation is difficult, a problem further exacerbated with big data and high dimensional input spaces. We propose a novel variational autoencoder (VAE) called the prior encoding variational autoencoder (πVAE). πVAE is a new continuous stochastic process. We use πVAE to learn low dimensional embeddings of function classes by combining a trainable feature mapping with generative model using a VAE. We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions such as their integrals. For popular tasks, such as spatial interpolation, πVAE achieves state-of-the-art performance both in terms of accuracy and computational efficiency. Perhaps most usefully, we demonstrate an elegant and scalable means of performing fully Bayesian inference for stochastic processes within probabilistic programming languages such as Stan.
Charles G, Wolock TM, Winskill P, et al., 2022, Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
Epidemic models are powerful tools in understanding infectious disease.However, as they increase in size and complexity, they can quickly becomecomputationally intractable. Recent progress in modelling methodology has shownthat surrogate models can be used to emulate complex epidemic models with ahigh-dimensional parameter space. We show that deep sequence-to-sequence(seq2seq) models can serve as accurate surrogates for complex epidemic modelswith sequence based model parameters, effectively replicating seasonal andlong-term transmission dynamics. Once trained, our surrogate can predictscenarios a several thousand times faster than the original model, making themideal for policy exploration. We demonstrate that replacing a traditionalepidemic model with a learned simulator facilitates robust Bayesian inference.
Nyberg T, Ferguson NM, Blake J, et al., 2022, Misclassification bias in estimating clinical severity of SARS-CoV-2 variants - Authors' reply., Lancet, Vol: 400, Pages: 809-810
Monod M, Blenkinsop A, Brizzi A, et al., 2022, Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths, Bayesian Analysis, ISSN: 1931-6690
The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach over time and age, a low-rank Gaussian Process (GP) projected by regularised B-splines. We show that this projection defines a new GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmark results show that the B-splines projected GP may perform better than standard B-splines and Bayesian P-splines, and equivalently well as a standard GP at considerably lowerruntimes. We apply the model to weekly, age-stratified COVID-19 attributabledeaths reported by the US Centers for Disease Control, which are subject to censoring and reporting biases. Using the B-splines projected GP, we can estimate longitudinal trends in COVID-19 associated deaths across the US by 1-year age bands. These estimates are instrumental to calculate age-specific mortality rates, describe variation in age-specific deaths across the US, and for fitting epidemic models. Here, we couple the model with age-specific vaccination rates to show that vaccination rates were significantly associated with the magnitude of resurgences in COVID-19 deaths during the summer 2021. With counterfactual analyses, we quantify the avoided COVID-19 deaths under lower vaccination rates and avoidable COVID-19 deaths under higher vaccination rates. The B-splines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesianregularising priors.
Brizzi A, Whittaker C, Servo LMS, et al., 2022, Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals., Publisher: MedrXiv
The SARS-CoV-2 Gamma variant spread rapidly across Brazil, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 to document the extensive shocks in hospital fatality rates that followed Gamma's spread across 14 state capitals, and in which more than half of hospitalised patients died over sustained time periods. We show that extensive fluctuations in COVID-19 in-hospital fatality rates also existed prior to Gamma's detection, and were largely transient after Gamma's detection, subsiding with hospital demand. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates are primarily associated with geographic inequities and shortages in healthcare capacity. We project that approximately half of Brazil's COVID-19 deaths in hospitals could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization, and pandemic preparedness are critical to minimize population wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries. NOTE: The following manuscript has appeared as 'Report 46 - Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals' at https://spiral.imperial.ac.uk:8443/handle/10044/1/91875 . ONE SENTENCE SUMMARY: COVID-19 in-hospital fatality rates fluctuate dramatically in Brazil, and these fluctuations are primarily associated with geographic inequities and shortages in healthcare capacity.
McCrone JT, Hill V, Bajaj S, et al., 2022, Context-specific emergence and growth of the SARS-CoV-2 Delta variant, NATURE, Vol: 610, Pages: 154-+, ISSN: 0028-0836
- Author Web Link
- Cite
- Citations: 4
Gavenciak T, Monrad JT, Leech G, et al., 2022, Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions, PLOS COMPUTATIONAL BIOLOGY, Vol: 18, ISSN: 1553-734X
- Author Web Link
- Cite
- Citations: 1
Brizzi A, Whittaker C, Servo LMS, et al., 2022, Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals (May, 10.1038/s41591-022-01807-1, 2022), NATURE MEDICINE, Vol: 28, Pages: 1509-1509, ISSN: 1078-8956
Bager P, Wohlfahrt J, Bhatt S, et al., 2022, Risk of hospitalisation associated with infection with SARS-CoV-2 omicron variant versus delta variant in Denmark: an observational cohort study, LANCET INFECTIOUS DISEASES, Vol: 22, Pages: 967-976, ISSN: 1473-3099
- Author Web Link
- Cite
- Citations: 36
Saul J, Cooney C, Hosseini PR, et al., 2022, Modeling DREAMS impact: trends in new HIV diagnoses among women attending antenatal care clinics in DREAMS countries, AIDS, Vol: 36, Pages: S51-S59, ISSN: 0269-9370
Semenova E, Xu Y, Howes A, et al., 2022, PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation., Journal of the Royal Society Interface, Vol: 19, Pages: 1-11, ISSN: 1742-5662
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.
Leech G, Rogers-Smith C, Monrad JT, et al., 2022, Mask wearing in community settings reduces SARS-CoV-2 transmission., Proc Natl Acad Sci U S A, Vol: 119
The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior (n= 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing.
Flaxman S, Whittaker C, Semenova E, et al., 2022, Covid-19 is a leading cause of death in children and young people ages 0-19 years in the United States
<jats:title>Abstract</jats:title><jats:p>Covid-19 has caused more than 1 million deaths in the US, including at least 1,204 deaths among children and young people (CYP) aged 0-19 years, with 796 occurring in the one year period April 1, 2021 - March 31, 2022. Deaths among US CYP are rare in general, and so we argue here that the mortality burden of Covid-19 in CYP is best understood in the context of all other causes of CYP death. Using publicly available data from CDC WONDER on NCHS’s 113 Selected Causes of Death, and comparing to mortality in 2019, the immediate pre-pandemic period, we find that Covid-19 mortality is among the 10 leading causes of death in CYP aged 0-19 years in the US, ranking 8th among all causes of deaths, 5th in disease-related causes of deaths (excluding accidents, assault and suicide), and 1st in deaths caused by infectious or respiratory diseases. Covid-19 deaths constitute 2.3% of the 10 leading causes of death in this age group. Covid-19 caused substantially more deaths in CYP than major vaccine-preventable diseases did historically in the period before vaccines became available. Various factors including underreporting and Covid-19’s role as a contributing cause of death from other diseases mean that our estimates may understate the true mortality burden of Covid-19. Our findings underscore the public health relevance of Covid-19 to CYP. In the likely future context of sustained SARS-CoV-2 circulation, pharmaceutical and non-pharmaceutical interventions will continue to play an important role in limiting transmission of the virus in CYP and mitigating severe disease.</jats:p>
Okell L, Brazeau NF, Verity R, et al., 2022, Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling, Communications Medicine, Vol: 2, Pages: 1-13, ISSN: 2730-664X
Background: The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion. Methods: We built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries. Results: We demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49 -2.53%.Conclusion: We developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics.
Brizzi A, Whittaker C, Servo LMS, et al., 2022, Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals, Nature Medicine, Vol: 28, ISSN: 1078-8956
The SARS-CoV-2 Gamma variant of concern spread rapidly across Brazil since late 2020, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 between 20 January 2020 and 26 July 2021 to document temporary, sweeping shocks in hospital fatality rates that followed Gamma’s spread across 14 state capitals, during which typically more than half of hospitalised patients aged 70 and over died. We show that such extensive shocks in COVID-19 in-hospital fatality rates also existed prior to detection of Gamma. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil’s COVID-19 in-hospital fatality rates were primarily associated with geographic inequities and shortages in healthcare capacity. We estimate that approximately half of the COVID-19 deaths in hospitals in the 14 cities could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization, and pandemic preparedness are critical to minimize population wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries.
Whittaker C, Winskill P, Sinka M, et al., 2022, A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations, Proceedings of the Royal Society B: Biological Sciences, Vol: 289, Pages: 1-10, ISSN: 0962-8452
Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species—questions rarely answerable from individual entomological studies (that typically focus on a single location or species). We develop a novel statistical framework enabling identification and classification of time series with similar temporal properties, and use this framework to systematically explore variation in population dynamics and seasonality in anopheline mosquito time series catch data spanning seven species, 40 years and 117 locations across mainland India. Our analyses reveal pronounced variation in dynamics across locations and between species in the extent of seasonality and timing of seasonal peaks. However, we show that these diverse dynamics can be clustered into four ‘dynamical archetypes’, each characterized by distinct temporal properties and associated with a largely unique set of environmental factors. Our results highlight that a range of environmental factors including rainfall, temperature, proximity to static water bodies and patterns of land use (particularly urbanicity) shape the dynamics and seasonality of mosquito populations, and provide a generically applicable framework to better identify and understand patterns of seasonal variation in vectors relevant to public health.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.