Publications
83 results found
Hawryluk I, Mishra S, Flaxman S, et al., 2023, Application of referenced thermodynamic integration to Bayesian model selection, PLoS One, Vol: 18, Pages: 1-16, ISSN: 1932-6203
Evaluating normalising constants is important across a range of topics in statisticallearning, notably Bayesian model selection. However, in many realistic problems thisinvolves the integration of analytically intractable, high-dimensional distributions, andtherefore requires the use of stochastic methods such as thermodynamic integration(TI). In this paper we apply a simple but under-appreciated variation of the TI method,here referred to as referenced TI, which computes a single model’s normalising constantin an efficient way by using a judiciously chosen reference density. The advantages ofthe approach and theoretical considerations are set out, along with pedagogical 1 and2D examples. The approach is shown to be useful in practice when applied to a realproblem — to perform model selection for a semi-mechanistic hierarchical Bayesianmodel of COVID-19 transmission in South Korea involving the integration of a 200Ddensity.
Katsiferis A, Bhatt S, Mortensen LH, et al., 2023, Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals, PLOS ONE, Vol: 18, ISSN: 1932-6203
Katsiferis A, Mortensen LH, Khurana MP, et al., 2023, Predicting mortality risk after a fall in older adults using health care spending patterns: a population-based cohort study, AGE AND AGEING, Vol: 52, ISSN: 0002-0729
Pakkanen MS, Miscouridou X, Penn MJ, et al., 2023, Unifying incidence and prevalence under a time-varying general branching process, Journal of Mathematical Biology, Vol: 87, ISSN: 0303-6812
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump–Mode–Jagers branching process. This model accommodates a time-varying reproduction number and a time-varying distribution for the generation interval. We then derive renewal-like integral equations for incidence, cumulative incidence and prevalence under this model. We show that the equations for incidence and prevalence are consistent with the so-called back-calculation relationship. We analyse two particular cases of these integral equations, one that arises from a Bellman–Harris process and one that arises from an inhomogeneous Poisson process model of transmission.We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling. We present a numerical discretisation scheme to solve these equations, and use this scheme to estimate rates of transmission from serological prevalence of SARS-CoV-2 in the UK and historical incidence data on Influenza, Measles, SARS and Smallpox.
Penn MJJ, Laydon DJJ, Penn J, et al., 2023, Intrinsic randomness in epidemic modelling beyond statistical uncertainty, Communications Physics, Vol: 6, ISSN: 2399-3650
Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a time-varying general branching process. Our framework explicitly decomposes aleatoric variance into mechanistic components, quantifying the contribution to uncertainty produced by each factor in the epidemic process, and how these contributions vary over time. The aleatoric variance of an outbreak is itself a renewal equation where past variance affects future variance. We find that, superspreading is not necessary for substantial uncertainty, and profound variation in outbreak size can occur even without overdispersion in the offspring distribution (i.e. the distribution of the number of secondary infections an infected person produces). Aleatoric forecasting uncertainty grows dynamically and rapidly, and so forecasting using only epistemic uncertainty is a significant underestimate. Therefore, failure to account for aleatoric uncertainty will ensure that policymakers are misled about the substantially higher true extent of potential risk. We demonstrate our method, and the extent to which potential risk is underestimated, using two historical examples.
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, Vol: 8, Pages: e311-e317, 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.
Katsiferis A, Bhatt S, Mortensen LH, et al., 2023, Sex differences in health care expenditures and mortality after spousal bereavement: A register-based Danish cohort study, PLOS ONE, Vol: 18, ISSN: 1932-6203
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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
Kundu R, Datta J, Ray D, et al., 2023, Comparative impact assessment of COVID-19 policy interventions in five South Asian countries using reported and estimated unreported death counts during 2020-2021., PLOS Glob Public Health, Vol: 3
There has been raging discussion and debate around the quality of COVID death data in South Asia. According to WHO, of the 5.5 million reported COVID-19 deaths from 2020-2021, 0.57 million (10%) were contributed by five low and middle income countries (LMIC) countries in the Global South: India, Pakistan, Bangladesh, Sri Lanka and Nepal. However, a number of excess death estimates show that the actual death toll from COVID-19 is significantly higher than the reported number of deaths. For example, the IHME and WHO both project around 14.9 million total deaths, of which 4.5-5.5 million were attributed to these five countries in 2020-2021. We focus our gaze on the COVID-19 performance of these five countries where 23.5% of the world population lives in 2020 and 2021, via a counterfactual lens and ask, to what extent the mortality of one LMIC would have been affected if it adopted the pandemic policies of another, similar country? We use a Bayesian semi-mechanistic model developed by Mishra et al. (2021) to compare both the reported and estimated total death tolls by permuting the time-varying reproduction number (Rt) across these countries over a similar time period. Our analysis shows that, in the first half of 2021, mortality in India in terms of reported deaths could have been reduced to 96 and 102 deaths per million compared to actual 170 reported deaths per million had it adopted the policies of Nepal and Pakistan respectively. In terms of total deaths, India could have averted 481 and 466 deaths per million had it adopted the policies of Bangladesh and Pakistan. On the other hand, India had a lower number of reported COVID-19 deaths per million (48 deaths per million) and a lower estimated total deaths per million (80 deaths per million) in the second half of 2021, and LMICs other than Pakistan would have lower reported mortality had they followed India's strategy. The gap between the reported and estimated total deaths highlights the varying level and extent of
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
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>
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
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, 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.
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.
Brizzi A, Whittaker C, Servo LMS, et al., 2022, Author correction: spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals, Nature Medicine, Vol: 28, Pages: 1509-1509, ISSN: 1078-8956
Correction to: Nature Medicine https://doi.org/10.1038/s41591-022-01807-1, published online 10 May 2022.
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.
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.
Altman G, Ahuja J, Monrad JT, et al., 2022, A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe, SCIENTIFIC DATA, Vol: 9
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Dhar MS, Marwal R, Radhakrishnan VS, et al., 2021, Genomic characterization and epidemiology of an emerging SARS-CoV-2 variant in Delhi, India, SCIENCE, Vol: 374, Pages: 995-+, ISSN: 0036-8075
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Mlcochova P, Kemp SA, Dhar MS, et al., 2021, SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion, NATURE, Vol: 599, Pages: 114-+, ISSN: 0028-0836
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Brizzi A, Whittaker C, Servo LMS, et al., 2021, Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
The SARS‐CoV‐2 Gamma variant spread rapidly across Brazil, causing substantial infection and death wa ves. 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 pe riods. 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 d emand. Using a Bayesian fatality rate model, we find that the geo‐graphic and temporal fluctuations in Brazil’s COVID‐19 in‐hospital fatality rates are primarily associated with geo‐graphic inequities and shortages in healthcare c apacity. 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 trans‐missible and deadly pathogens such as SARS‐CoV‐2, especially in low‐ and middle‐income countries.
Bhatt S, 2021, Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe, Nature Communications, Vol: 12, Pages: 1-12, ISSN: 2041-1723
As European governments face resurging waves of COVID-19, non-pharmaceutical interventions (NPIs) continue to be the primary tool for infection control. However, updated estimates of their relative effectiveness have been absent for Europe’s second wave, largely due to a lack of collated data that considers the increased subnational variation and diversity of NPIs. We collect the largest dataset of NPI implementation dates in Europe, spanning 114 subnational areas in 7 countries, with a systematic categorisation of interventions tailored to the second wave. Using a hierarchical Bayesian transmission model, we estimate the effectiveness of 17 NPIs from local case and death data. We manually validate the data, address limitations in modelling from previous studies, and extensively test the robustness of our estimates. The combined effect of all NPIs was smaller relative to estimates from the first half of 2020, indicating the strong influence of safety measures and individual protective behaviours--such as distancing--that persisted after the first wave. Closing specific businesses was highly effective. Gathering restrictions were highly effective but only for the strictest limits. We find smaller effects for closing educational institutions compared to the first wave, suggesting that safer operation of schools was possible with a set of stringent safety measures including testing and tracing, preventing mixing, and smaller classes. These results underscore that effectiveness estimates from the early stage of an epidemic are measured relative to pre-pandemic behaviour. Updated estimates are required to inform policy in an ongoing pandemic.
Mishra S, Scott JA, Laydon DJ, et al., 2021, Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling, SCIENTIFIC REPORTS, Vol: 11, Pages: 1-9, ISSN: 2045-2322
The UK and Sweden have among the worst per-capita COVID-19 mortality in Europe. Sweden stands out for its greater reliance on voluntary, rather than mandatory, control measures. We explore how the timing and effectiveness of control measures in the UK, Sweden and Denmark shaped COVID-19 mortality in each country, using a counterfactual assessment: what would the impact have been, had each country adopted the others’ policies? Using a Bayesian semi-mechanistic model without prior assumptions on the mechanism or effectiveness of interventions, we estimate the time-varying reproduction number for the UK, Sweden and Denmark from daily mortality data. We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country’s first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic.
Mishra S, Mindermann S, Sharma M, et al., 2021, Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England, EClinicalMedicine, Vol: 39, Pages: 1-8, ISSN: 2589-5370
BackgroundSince its emergence in Autumn 2020, the SARS-CoV-2 Variant of Concern (VOC) B.1.1.7 (WHO label Alpha) rapidly became the dominant lineage across much of Europe. Simultaneously, several other VOCs were identified globally. Unlike B.1.1.7, some of these VOCs possess mutations thought to confer partial immune escape. Understanding when and how these additional VOCs pose a threat in settings where B.1.1.7 is currently dominant is vital.MethodsWe examine trends in the prevalence of non-B.1.1.7 lineages in London and other English regions using passive-case detection PCR data, cross-sectional community infection surveys, genomic surveillance, and wastewater monitoring. The study period spans from 31st January 2021 to 15th May 2021.FindingsAcross data sources, the percentage of non-B.1.1.7 variants has been increasing since late March 2021. This increase was initially driven by a variety of lineages with immune escape. From mid-April, B.1.617.2 (WHO label Delta) spread rapidly, becoming the dominant variant in England by late May.InterpretationThe outcome of competition between variants depends on a wide range of factors such as intrinsic transmissibility, evasion of prior immunity, demographic specificities and interactions with non-pharmaceutical interventions. The presence and rise of non-B.1.1.7 variants in March likely was driven by importations and some community transmission. There was competition between non-B.1.17 variants which resulted in B.1.617.2 becoming dominant in April and May with considerable community transmission. Our results underscore that early detection of new variants requires a diverse array of data sources in community surveillance. Continued real-time information on the highly dynamic composition and trajectory of different SARS-CoV-2 lineages is essential to future control effortsFundingNational Institute for Health Research, Medicines and Healthcare products Regulatory Agency, DeepMind, EPSRC, EA Funds programme, Open Philanthropy
Hawryluk I, Hoeltgebaum H, Mishra S, et al., 2021, Gaussian process nowcasting: application to COVID-19 mortality reporting, 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, Publisher: PMLR, Pages: 1258-1268
Updating observations of a signal due to the delays in the measurementprocess is a common problem in signal processing, with prominent examples in awide range of fields. An important example of this problem is the nowcasting ofCOVID-19 mortality: given a stream of reported counts of daily deaths, can wecorrect for the delays in reporting to paint an accurate picture of thepresent, with uncertainty? Without this correction, raw data will often misleadby suggesting an improving situation. We present a flexible approach using alatent Gaussian process that is capable of describing the changingauto-correlation structure present in the reporting time-delay surface. Thisapproach also yields robust estimates of uncertainty for the estimatednowcasted numbers of deaths. We test assumptions in model specification such asthe choice of kernel or hyper priors, and evaluate model performance on achallenging real dataset from Brazil. Our experiments show that Gaussianprocess nowcasting performs favourably against both comparable methods, andagainst a small sample of expert human predictions. Our approach hassubstantial practical utility in disease modelling -- by applying our approachto COVID-19 mortality data from Brazil, where reporting delays are large, wecan make informative predictions on important epidemiological quantities suchas the current effective reproduction number.
Meyerowitz-Katz G, Bhatt S, Ratmann O, et al., 2021, Is the cure really worse than the disease? The health impacts of lockdowns during COVID-19, BMJ Global Health, Vol: 6, Pages: 1-6, ISSN: 2059-7908
Krawczyk K, Chelkowski T, Laydon DJ, et al., 2021, Correction: Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource., J Med Internet Res, Vol: 23
[This corrects the article DOI: 10.2196/28253.].
Salvatore M, Bhattacharyya R, Purkayastha S, et al., 2021, Resurgence of SARS-CoV-2 in India: Potential role of the B.1.617.2 (Delta) variant and delayed interventions
<jats:title>Abstract</jats:title><jats:p>India has seen a surge of SARS-CoV-2 infections and deaths in early part of 2021, despite having controlled the epidemic during 2020. Building on a two-strain, semi-mechanistic model that synthesizes mortality and genomic data, we find evidence that altered epidemiological properties of B.1.617.2 (Delta) variant play an important role in this resurgence in India. Under all scenarios of immune evasion, we find an increased transmissibility advantage for B.1617.2 against all previously circulating strains. Using an extended SIR model accounting for reinfections and wanning immunity, we produce evidence in support of how early public interventions in March 2021 would have helped to control transmission in the country. We argue that enhanced genomic surveillance along with constant assessment of risk associated with increased transmission is critical for pandemic responsiveness.</jats:p><jats:sec><jats:title>One Sentence Summary</jats:title><jats:p>Altered epidemiological characteristics of B.1.617.2 and delayed public health interventions contributed to the resurgence of SARS-CoV-2 in India from February to May 2021.</jats:p></jats:sec>
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