Publications by authors named "Lorenzo Pellis"

35 Publications

Insights gained from early modelling of COVID-19 to inform the management of outbreaks in UK prisons.

Int J Prison Health 2021 08;ahead-of-print(ahead-of-print)

Emergency Response Department, Public Health England, London, UK.

Purpose: In this work, the authors present some of the key results found during early efforts to model the COVID-19 outbreak inside a UK prison. In particular, this study describes outputs from an idealised disease model that simulates the dynamics of a COVID-19 outbreak in a prison setting when varying levels of social interventions are in place, and a Monte Carlo-based model that assesses the reduction in risk of case importation, resulting from a process that requires incoming prisoners to undergo a period of self-isolation prior to admission into the general prison population.

Design/methodology/approach: Prisons, typically containing large populations confined in a small space with high degrees of mixing, have long been known to be especially susceptible to disease outbreaks. In an attempt to meet rising pressures from the emerging COVID-19 situation in early 2020, modellers for Public Health England's Joint Modelling Cell were asked to produce some rapid response work that sought to inform the approaches that Her Majesty's Prison and Probation Service (HMPPS) might take to reduce the risk of case importation and sustained transmission in prison environments.

Findings: Key results show that deploying social interventions has the potential to considerably reduce the total number of infections, while such actions could also reduce the probability that an initial infection will propagate into a prison-wide outbreak. For example, modelling showed that a 50% reduction in the risk of transmission (compared to an unmitigated outbreak) could deliver a 98% decrease in total number of cases, while this reduction could also result in 86.8% of outbreaks subsiding before more than five persons have become infected. Furthermore, this study also found that requiring new arrivals to self-isolate for 10 and 14 days prior to admission could detect up to 98% and 99% of incoming infections, respectively.

Research Limitations/implications: In this paper we have presented models which allow for the studying of COVID-19 in a prison scenario, while also allowing for the assessment of proposed social interventions. By publishing these works, the authors hope these methods might aid in the management of prisoners across additional scenarios and even during subsequent disease outbreaks. Such methods as described may also be readily applied use in other closed community settings.

Originality/value: These works went towards informing HMPPS on the impacts that the described strategies might have during COVID-19 outbreaks inside UK prisons. The works described herein are readily amendable to the study of a range of addition outbreak scenarios. There is also room for these methods to be further developed and built upon which the timeliness of the original project did not permit.
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http://dx.doi.org/10.1108/IJPH-09-2020-0075DOI Listing
August 2021

Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning.

BMC Infect Dis 2021 Jul 22;21(1):700. Epub 2021 Jul 22.

Department of Social Statistics, School of Social Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

Background: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data.

Method: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy.

Results: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days.

Conclusions: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.
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http://dx.doi.org/10.1186/s12879-021-06371-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295642PMC
July 2021

SARS-CoV-2 antigen testing: weighing the false positives against the costs of failing to control transmission.

Lancet Respir Med 2021 07 14;9(7):685-687. Epub 2021 Jun 14.

Department of Clinical Sciences, and Department of International Public Health, Liverpool School of Tropical Medicine, Liverpool, UK; Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK; WHO Collaborating Centre in Tuberculosis and Social Medicine, Department of Global Public Health, Karolinska Institutet, Solna, Sweden.

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http://dx.doi.org/10.1016/S2213-2600(21)00234-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203180PMC
July 2021

Shut and re-open: the role of schools in the spread of COVID-19 in Europe.

Philos Trans R Soc Lond B Biol Sci 2021 07 31;376(1829):20200277. Epub 2021 May 31.

Emergency Response Department, Public Health England, London, UK.

We investigate the effect of school closure and subsequent reopening on the transmission of COVID-19, by considering Denmark, Norway, Sweden and German states as case studies. By comparing the growth rates in daily hospitalizations or confirmed cases under different interventions, we provide evidence that school closures contribute to a reduction in the growth rate approximately 7 days after implementation. Limited school attendance, such as older students sitting exams or the partial return of younger year groups, does not appear to significantly affect community transmission. In countries where community transmission is generally low, such as Denmark or Norway, a large-scale reopening of schools while controlling or suppressing the epidemic appears feasible. However, school reopening can contribute to statistically significant increases in the growth rate in countries like Germany, where community transmission is relatively high. In all regions, a combination of low classroom occupancy and robust test-and-trace measures were in place. Our findings underscore the need for a cautious evaluation of reopening strategies. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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http://dx.doi.org/10.1098/rstb.2020.0277DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165592PMC
July 2021

Challenges in control of COVID-19: short doubling time and long delay to effect of interventions.

Philos Trans R Soc Lond B Biol Sci 2021 07 31;376(1829):20200264. Epub 2021 May 31.

Department of Mathematics, The University of Manchester, Manchester, UK.

Early assessments of the growth rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but as cases were recorded in multiple countries, more robust inferences could be made. Using multiple countries, data streams and methods, we estimated that, when unconstrained, European COVID-19 confirmed cases doubled on average every 3 days (range 2.2-4.3 days) and Italian hospital and intensive care unit admissions every 2-3 days; values that are significantly lower than the 5-7 days dominating the early published literature. Furthermore, we showed that the impact of physical distancing interventions was typically not seen until at least 9 days after implementation, during which time confirmed cases could grow eightfold. We argue that such temporal patterns are more critical than precise estimates of the time-insensitive basic reproduction number for initiating interventions, and that the combination of fast growth and long detection delays explains the struggle in countries' outbreak response better than large values of alone. One year on from first reporting these results, reproduction numbers continue to dominate the media and public discourse, but robust estimates of unconstrained growth remain essential for planning worst-case scenarios, and detection delays are still key in informing the relaxation and re-implementation of interventions. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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http://dx.doi.org/10.1098/rstb.2020.0264DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165602PMC
July 2021

Outbreaks in care homes may lead to substantial disease burden if not mitigated.

Philos Trans R Soc Lond B Biol Sci 2021 07 31;376(1829):20200269. Epub 2021 May 31.

Public Health England, Emergency Response, London, UK.

The number of COVID-19 outbreaks reported in UK care homes rose rapidly in early March of 2020. Owing to the increased co-morbidities and therefore worse COVID-19 outcomes for care home residents, it is important that we understand this increase and its future implications. We demonstrate the use of an SIS model where each nursing home is an infective unit capable of either being susceptible to an outbreak (S) or in an active outbreak (I). We use a generalized additive model to approximate the trend in growth rate of outbreaks in care homes and find the fit to be improved in a model where the growth rate is proportional to the number of current care home outbreaks compared with a model with a constant growth rate. Using parameters found from the outbreak-dependent growth rate, we predict a 73% prevalence of outbreaks in UK care homes without intervention as a reasonable worst-case planning assumption. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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http://dx.doi.org/10.1098/rstb.2020.0269DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165603PMC
July 2021

Using a household-structured branching process to analyse contact tracing in the SARS-CoV-2 pandemic.

Philos Trans R Soc Lond B Biol Sci 2021 07 31;376(1829):20200267. Epub 2021 May 31.

Department of Mathematics, University of Manchester, Manchester M13 9PY, UK.

We explore strategies of contact tracing, case isolation and quarantine of exposed contacts to control the SARS-CoV-2 epidemic using a branching process model with household structure. This structure reflects higher transmission risks among household members than among non-household members. We explore strategic implementation choices that make use of household structure, and investigate strategies including two-step tracing, backwards tracing, smartphone tracing and tracing upon symptom report rather than test results. The primary model outcome is the effect of contact tracing, in combination with different levels of physical distancing, on the growth rate of the epidemic. Furthermore, we investigate epidemic extinction times to indicate the time period over which interventions must be sustained. We consider effects of non-uptake of isolation/quarantine, non-adherence, and declining recall of contacts over time. Our results find that, compared to self-isolation of cases without contact tracing, a contact tracing strategy designed to take advantage of household structure allows for some relaxation of physical distancing measures but cannot completely control the epidemic absent of other measures. Even assuming no imported cases and sustainment of moderate physical distancing, testing and tracing efforts, the time to bring the epidemic to extinction could be in the order of months to years. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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http://dx.doi.org/10.1098/rstb.2020.0267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165594PMC
July 2021

Modelling that shaped the early COVID-19 pandemic response in the UK.

Philos Trans R Soc Lond B Biol Sci 2021 07 31;376(1829):20210001. Epub 2021 May 31.

Department of Mathematics, University of Manchester, Manchester M13 9PL, UK.

Infectious disease modelling has played an integral part of the scientific evidence used to guide the response to the COVID-19 pandemic. In the UK, modelling evidence used for policy is reported to the Scientific Advisory Group for Emergencies (SAGE) modelling subgroup, SPI-M-O (Scientific Pandemic Influenza Group on Modelling-Operational). This Special Issue contains 20 articles detailing evidence that underpinned advice to the UK government during the SARS-CoV-2 pandemic in the UK between January 2020 and July 2020. Here, we introduce the UK scientific advisory system and how it operates in practice, and discuss how infectious disease modelling can be useful in policy making. We examine the drawbacks of current publishing practices and academic credit and highlight the importance of transparency and reproducibility during an epidemic emergency. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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http://dx.doi.org/10.1098/rstb.2021.0001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165593PMC
July 2021

What Can Modeling Tell Us About Sustainable End Points for Neglected Tropical Diseases?

Clin Infect Dis 2021 06;72(Suppl 3):S129-S133

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.

As programs move closer toward the World Health Organization (WHO) goals of reduction in morbidity, elimination as a public health problem or elimination of transmission, countries will be faced with planning the next stages of surveillance and control in low prevalence settings. Mathematical models of neglected tropical diseases (NTDs) will need to go beyond predicting the effect of different treatment programs on these goals and on to predicting whether the gains can be sustained. One of the most important challenges will be identifying the policy goal and the right constraints on interventions and surveillance over the long term, as a single policy option will not achieve all aims-for example, minimizing morbidity and minimizing costs cannot both be achieved. As NTDs move toward 2030 and beyond, more nuanced intervention choices will be informed by quantitative analyses which are adapted to national context.
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http://dx.doi.org/10.1093/cid/ciab188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201563PMC
June 2021

A renewal equation model to assess roles and limitations of contact tracing for disease outbreak control.

R Soc Open Sci 2021 Apr 7;8(4):202091. Epub 2021 Apr 7.

LIAM-Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, Toronto, Ontario, Canada.

We propose a deterministic model capturing essential features of contact tracing as part of public health non-pharmaceutical interventions to mitigate an outbreak of an infectious disease. By incorporating a mechanistic formulation of the processes at the individual level, we obtain an integral equation (delayed in calendar time and advanced in time since infection) for the probability that an infected individual is detected and isolated at any point in time. This is then coupled with a renewal equation for the total incidence to form a closed system describing the transmission dynamics involving contact tracing. We define and calculate basic and effective reproduction numbers in terms of pathogen characteristics and contact tracing implementation constraints. When applied to the case of SARS-CoV-2, our results show that only combinations of diagnosis of symptomatic infections and contact tracing that are almost perfect in terms of speed and coverage can attain control, unless additional measures to reduce overall community transmission are in place. Under constraints on the testing or tracing capacity, a temporary interruption of contact tracing may, depending on the overall growth rate and prevalence of the infection, lead to an irreversible loss of control even when the epidemic was previously contained.
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http://dx.doi.org/10.1098/rsos.202091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025303PMC
April 2021

The effectiveness of social bubbles as part of a Covid-19 lockdown exit strategy, a modelling study.

Wellcome Open Res 2020 29;5:213. Epub 2021 Mar 29.

Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.

During the coronavirus disease 2019 (COVID-19) lockdown, contact clustering in social bubbles may allow extending contacts beyond the household at minimal additional risk and hence has been considered as part of modified lockdown policy or a gradual lockdown exit strategy. We estimated the impact of such strategies on epidemic and mortality risk using the UK as a case study. We used an individual based model for a synthetic population similar to the UK, stratified into transmission risks from the community, within the household and from other households in the same social bubble. The base case considers a situation where non-essential shops and schools are closed, the secondary household attack rate is 20% and the initial reproduction number is 0.8. We simulate social bubble strategies (where two households form an exclusive pair) for households including children, for single occupancy households, and for all households. We test the sensitivity of results to a range of alternative model assumptions and parameters.   Clustering contacts outside the household into exclusive bubbles is an effective strategy of increasing contacts while limiting the associated increase in epidemic risk. In the base case, social bubbles reduced fatalities by 42% compared to an unclustered increase of contacts. We find that if all households were to form social bubbles the reproduction number would likely increase to above the epidemic threshold of R=1. Strategies allowing households with young children or single occupancy households to form social bubbles increased the reproduction number by less than 11%. The corresponding increase in mortality is proportional to the increase in the epidemic risk but is focussed in older adults irrespective of inclusion in social bubbles. If managed appropriately, social bubbles can be an effective way of extending contacts beyond the household while limiting the increase in epidemic risk.
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http://dx.doi.org/10.12688/wellcomeopenres.16164.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7871360.2PMC
March 2021

Estimating hepatitis B virus cccDNA persistence in chronic infection.

Virus Evol 2021 Jan 25;7(1):veaa063. Epub 2020 Aug 25.

Nuffield Department of Medicine, University of Oxford, Medawar Building, South Parks Road, Oxford OX1 3SY, UK.

Hepatitis B virus (HBV) infection is a major global health problem with over 240 million infected individuals at risk of developing progressive liver disease and hepatocellular carcinoma. HBV is an enveloped DNA virus that establishes its genome as an episomal, covalently closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Currently, available standard-of-care treatments for chronic hepatitis B (CHB) include nucleos(t)ide analogues (NAs) that suppress HBV replication but do not target the cccDNA and hence rarely cure infection. There is considerable interest in determining the lifespan of cccDNA molecules to design and evaluate new curative treatments. We took a novel approach to this problem by developing a new mathematical framework to model changes in evolutionary rates during infection which, combined with previously determined within-host evolutionary rates of HBV, we used to determine the lifespan of cccDNA. We estimate that during HBe-antigen positive (HBeAg) infection the cccDNA lifespan is 61 (36-236) days, whereas during the HBeAg phase of infection it is only 26 (16-81) days. We found that cccDNA replicative capacity declined by an order of magnitude between HBeAg and HBeAg phases of infection. Our estimated lifespan of cccDNA is too short to explain the long durations of chronic infection observed in patients on NA treatment, suggesting that either a sub-population of long-lived hepatocytes harbouring cccDNA molecules persists during therapy, or that NA therapy does not suppress all viral replication. These results provide a greater understanding of the biology of the cccDNA reservoir and can aid the development of new curative therapeutic strategies for treating CHB.
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http://dx.doi.org/10.1093/ve/veaa063DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947180PMC
January 2021

Superinfection and the evolution of an initial asymptomatic stage.

R Soc Open Sci 2021 Jan 27;8(1):202212. Epub 2021 Jan 27.

Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.

Pathogens have evolved a variety of life-history strategies. An important strategy consists of successful transmission by an infected host before the appearance of symptoms, that is, while the host is still partially or fully asymptomatic. During this initial stage of infection, it is possible for another pathogen to superinfect an already infected host and replace the previously infecting pathogen. Here, we study the effect of superinfection during the first stage of an infection on the evolutionary dynamics of the degree to which the host is asymptomatic (host latency) in that same stage. We find that superinfection can lead to major differences in evolutionary behaviour. Most strikingly, the duration of immunity following infection can significantly influence pathogen evolutionary dynamics, whereas without superinfection the outcomes are independent of host immunity. For example, changes in host immunity can drive evolutionary transitions from a fully symptomatic to a fully asymptomatic first infection stage. Additionally, if superinfection relative to susceptible infection is strong enough, evolution can lead to a unique strategy of latency that corresponds to a local fitness minimum, and is therefore invasible by nearby mutants. Thus, this strategy is a branching point, and can lead to coexistence of pathogens with different latencies. Furthermore, in this new framework with superinfection, we also find that there can exist two interior singular strategies. Overall, new evolutionary outcomes can cascade from superinfection.
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http://dx.doi.org/10.1098/rsos.202212DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890506PMC
January 2021

Key questions for modelling COVID-19 exit strategies.

Proc Biol Sci 2020 08 12;287(1932):20201405. Epub 2020 Aug 12.

School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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http://dx.doi.org/10.1098/rspb.2020.1405DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575516PMC
August 2020

Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example.

Infect Dis Model 2020 4;5:409-441. Epub 2020 Jul 4.

Department of Mathematics, University of Manchester, UK.

During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.
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http://dx.doi.org/10.1016/j.idm.2020.06.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334973PMC
July 2020

Canada needs to rapidly escalate public health interventions for its COVID-19 mitigation strategies.

Infect Dis Model 2020 31;5:316-322. Epub 2020 Mar 31.

Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, M3J 1P3, Canada.

Background: After the declaration of COVID-19 pandemic on March 11th 2020, local transmission chains starting in different countries including Canada are forcing governments to take decisions on public health interventions to mitigate the spread of the epidemic.

Methods: We conduct data-driven and model-free estimations for the growth rates of the COVID-19 epidemics in Italy and Canada, by fitting an exponential curve to the daily reported cases. We use these estimates to predict epidemic trends in Canada under different scenarios of public health interventions.

Results: In Italy, the initial growth rate (0.22) has reduced to 0.1 two weeks after the lockdown of the country on March 8th 2020. This corresponds to an increase of the doubling time from about 3.15 to almost 7 days. In comparison, the growth rate in Canada has increased from 0.13 between March 1st and 13th, to 0.25 between March 13th to 22nd. This current growth rate corresponds to a doubling time of 2.7 days, and therefore, unless further public health interventions are escalated in Canada, we project 15,000 cases by March 31st. However, the case number may be reduced to 4000 if escalated public health interventions could instantly reduce the growth rate to 0.1, the same level achieved in Italy.

Interpretation: Prompt and farsighted interventions are critical to counteract the very rapid initial growth of the COVID-19 epidemic in Canada. Mitigation plans must take into account the delayed effect of interventions by up to 2-weeks and the short doubling time of 3-4 days.
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http://dx.doi.org/10.1016/j.idm.2020.03.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270648PMC
March 2020

Detecting HLA-infectious disease associations for multi-strain pathogens.

Infect Genet Evol 2020 09 5;83:104344. Epub 2020 May 5.

Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, CV4 7AL, United Kingdom; School of Life Sciences, University of Warwick, CV4 7AL, United Kingdom. Electronic address:

Human Leukocyte Antigen (HLA) molecules play a vital role helping our immune system to detect the presence of pathogens. Previous work to try and ascertain which HLA alleles offer advantages against particular pathogens has generated inconsistent results. We have constructed an epidemiological model to understand why this may occur. The model captures the epidemiology of a multi strain pathogen for which the host's ability to generate immunological memory responses to particular strains depends on that host's HLA genotype. We find that an HLA allele's ability to protect against infection, as measured in a case control study, depends on the population frequency of that HLA allele. Furthermore, our capability to detect associations between HLA alleles and infection with a multi strain pathogen may be affected by the properties of the pathogen itself (i.e R and length of infectious period). Both host and pathogen genetics must be considered in order to identify true HLA associations. However, in the absence of detailed pathogen genetic information, a negative correlation between the frequency of an HLA type and its apparent protectiveness against disease caused by multi strain pathogen is a strong indication that the HLA type in question is well adapted to a subset of strains of that pathogen.
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http://dx.doi.org/10.1016/j.meegid.2020.104344DOI Listing
September 2020

Systematic selection between age and household structure for models aimed at emerging epidemic predictions.

Nat Commun 2020 02 14;11(1):906. Epub 2020 Feb 14.

Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Numerous epidemic models have been developed to capture aspects of human contact patterns, making model selection challenging when they fit (often-scarce) early epidemic data equally well but differ in predictions. Here we consider the invasion of a novel directly transmissible infection and perform an extensive, systematic and transparent comparison of models with explicit age and/or household structure, to determine the accuracy loss in predictions in the absence of interventions when ignoring either or both social components. We conclude that, with heterogeneous and assortative contact patterns relevant to respiratory infections, the model's age stratification is crucial for accurate predictions. Conversely, the household structure is only needed if transmission is highly concentrated in households, as suggested by an empirical but robust rule of thumb based on household secondary attack rate. This work serves as a template to guide the simplicity/accuracy trade-off in designing models aimed at initial, rapid assessment of potential epidemic severity.
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http://dx.doi.org/10.1038/s41467-019-14229-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7021781PMC
February 2020

Evaluating the Evidence for Lymphatic Filariasis Elimination.

Trends Parasitol 2019 11 7;35(11):860-869. Epub 2019 Sep 7.

Big Data Institute (BDI), Oxford University, Old Road Campus, Oxford OX3 7LF, UK.

In the global drive for elimination of lymphatic filariasis (LF), 15 countries have achieved validation of elimination as a public health problem (EPHP). Recent empirical evidence has demonstrated that EPHP does not always lead to elimination of transmission (EOT). Here we show how the probability of elimination explicitly depends on key biological parameters, many of which have been poorly characterized, leading to a poor evidence base for the elimination threshold. As more countries progress towards EPHP it is essential that this process is well-informed, as prematurely halting treatment and surveillance programs could pose a serious threat to global progress. We highlight that refinement of the weak empirical evidence base is vital to understand drivers of elimination and inform long-term policy.
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http://dx.doi.org/10.1016/j.pt.2019.08.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413036PMC
November 2019

The role of case proximity in transmission of visceral leishmaniasis in a highly endemic village in Bangladesh.

PLoS Negl Trop Dis 2018 10 8;12(10):e0006453. Epub 2018 Oct 8.

Zeeman Institute, University of Warwick, Coventry, UK.

Background: Visceral leishmaniasis (VL) is characterised by a high degree of spatial clustering at all scales, and this feature remains even with successful control measures. VL is targeted for elimination as a public health problem in the Indian subcontinent by 2020, and incidence has been falling rapidly since 2011. Current control is based on early diagnosis and treatment of clinical cases, and blanket indoor residual spraying of insecticide (IRS) in endemic villages to kill the sandfly vectors. Spatially targeting active case detection and/or IRS to higher risk areas would greatly reduce costs of control, but its effectiveness as a control strategy is unknown. The effectiveness depends on two key unknowns: how quickly transmission risk decreases with distance from a VL case and how much asymptomatically infected individuals contribute to transmission.

Methodology/principal Findings: To estimate these key parameters, a spatiotemporal transmission model for VL was developed and fitted to geo-located epidemiological data on 2494 individuals from a highly endemic village in Mymensingh, Bangladesh. A Bayesian inference framework that could account for the unknown infection times of the VL cases, and missing symptom onset and recovery times, was developed to perform the parameter estimation. The parameter estimates obtained suggest that, in a highly endemic setting, VL risk decreases relatively quickly with distance from a case-halving within 90m-and that VL cases contribute significantly more to transmission than asymptomatic individuals.

Conclusions/significance: These results suggest that spatially-targeted interventions may be effective for limiting transmission. However, the extent to which spatial transmission patterns and the asymptomatic contribution vary with VL endemicity and over time is uncertain. In any event, interventions would need to be performed promptly and in a large radius (≥300m) around a new case to reduce transmission risk.
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http://dx.doi.org/10.1371/journal.pntd.0006453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6175508PMC
October 2018

Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks.

PLoS Comput Biol 2016 12 20;12(12):e1005296. Epub 2016 Dec 20.

Zeeman Institute: SBIDER, University of Warwick, Coventry, United Kingdom.

Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection. As such, pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard (random-mixing) results. Recently, for SIR-type infections (that produce one epidemic in a closed population) on locally tree-like networks, these approximations have been shown to be exact. However, network models are ideally suited for Sexually Transmitted Infections (STIs) due to the greater level of detail available for sexual contact networks, and these diseases often possess SIS-type dynamics. Here, we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics, including SIS behaviour. We focus in particular on low degree networks, in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture. By examining how and when these approximation models converge to simulation results, we generate insights into the role of network structure in the infection dynamics of SIS-type infections.
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http://dx.doi.org/10.1371/journal.pcbi.1005296DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5283726PMC
December 2016

Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics.

PLoS Biol 2016 Oct 5;14(10):e1002567. Epub 2016 Oct 5.

Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, St. Mary's Campus, London, United Kingdom.

The viral population of HIV-1, like many pathogens that cause systemic infection, is structured and differentiated within the body. The dynamics of cellular immune trafficking through the blood and within compartments of the body has also received wide attention. Despite these advances, mathematical models, which are widely used to interpret and predict viral and immune dynamics in infection, typically treat the infected host as a well-mixed homogeneous environment. Here, we present mathematical, analytical, and computational results that demonstrate that consideration of the spatial structure of the viral population within the host radically alters predictions of previous models. We study the dynamics of virus replication and cytotoxic T lymphocytes (CTLs) within a metapopulation of spatially segregated patches, representing T cell areas connected by circulating blood and lymph. The dynamics of the system depend critically on the interaction between CTLs and infected cells at the within-patch level. We show that for a wide range of parameters, the system admits an unexpected outcome called the shifting-mosaic steady state. In this state, the whole body's viral population is stable over time, but the equilibrium results from an underlying, highly dynamic process of local infection and clearance within T-cell centers. Notably, and in contrast to previous models, this new model can explain the large differences in set-point viral load (SPVL) observed between patients and their distribution, as well as the relatively low proportion of cells infected at any one time, and alters the predicted determinants of viral load variation.
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http://dx.doi.org/10.1371/journal.pbio.1002567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051940PMC
October 2016

Reproduction numbers for epidemic models with households and other social structures II: Comparisons and implications for vaccination.

Math Biosci 2016 Apr 2;274:108-39. Epub 2016 Feb 2.

Department of Mathematics, Stockholm University, Stockholm 106 91, Sweden.

In this paper we consider epidemic models of directly transmissible SIR (susceptible → infective → recovered) and SEIR (with an additional latent class) infections in fully-susceptible populations with a social structure, consisting either of households or of households and workplaces. We review most reproduction numbers defined in the literature for these models, including the basic reproduction number R0 introduced in the companion paper of this, for which we provide a simpler, more elegant derivation. Extending previous work, we provide a complete overview of the inequalities among these reproduction numbers and resolve some open questions. Special focus is put on the exponential-growth-associated reproduction number Rr, which is loosely defined as the estimate of R0 based on the observed exponential growth of an emerging epidemic obtained when the social structure is ignored. We show that for the vast majority of the models considered in the literature Rr ≥ R0 when R0 ≥ 1 and Rr ≤ R0 when R0 ≤ 1. We show that, in contrast to models without social structure, vaccination of a fraction 1-1/R0 of the population, chosen uniformly at random, with a perfect vaccine is usually insufficient to prevent large epidemics. In addition, we provide significantly sharper bounds than the existing ones for bracketing the critical vaccination coverage between two analytically tractable quantities, which we illustrate by means of extensive numerical examples.
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http://dx.doi.org/10.1016/j.mbs.2016.01.006DOI Listing
April 2016

Exact and approximate moment closures for non-Markovian network epidemics.

J Theor Biol 2015 Oct 12;382:160-77. Epub 2015 May 12.

Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK. Electronic address:

Moment-closure techniques are commonly used to generate low-dimensional deterministic models to approximate the average dynamics of stochastic systems on networks. The quality of such closures is usually difficult to asses and furthermore the relationship between model assumptions and closure accuracy are often difficult, if not impossible, to quantify. Here we carefully examine some commonly used moment closures, in particular a new one based on the concept of maximum entropy, for approximating the spread of epidemics on networks by reconstructing the probability distributions over triplets based on those over pairs. We consider various models (SI, SIR, SEIR and Reed-Frost-type) under Markovian and non-Markovian assumption characterising the latent and infectious periods. We initially study with care two special networks, namely the open triplet and closed triangle, for which we can obtain analytical results. We then explore numerically the exactness of moment closures for a wide range of larger motifs, thus gaining understanding of the factors that introduce errors in the approximations, in particular the presence of a random duration of the infectious period and the presence of overlapping triangles in a network. We also derive a simpler and more intuitive proof than previously available concerning the known result that pair-based moment closure is exact for the Markovian SIR model on tree-like networks under pure initial conditions. We also extend such a result to all infectious models, Markovian and non-Markovian, in which susceptibles escape infection independently from each infected neighbour and for which infectives cannot regain susceptible status, provided the network is tree-like and initial conditions are pure. This works represent a valuable step in enriching intuition and deepening understanding of the assumptions behind moment closure approximations and for putting them on a more rigorous mathematical footing.
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http://dx.doi.org/10.1016/j.jtbi.2015.04.039DOI Listing
October 2015

Real-time growth rate for general stochastic SIR epidemics on unclustered networks.

Math Biosci 2015 Jul 24;265:65-81. Epub 2015 Apr 24.

School of Mathematics, University of Manchester, Manchester, M13 9PL, UK; Warwick Infectious Disease Epidemiology Research Centre (WIDER) and Warwick Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.

Networks have become an important tool for infectious disease epidemiology. Most previous theoretical studies of transmission network models have either considered simple Markovian dynamics at the individual level, or have focused on the invasion threshold and final outcome of the epidemic. Here, we provide a general theory for early real-time behaviour of epidemics on large configuration model networks (i.e. static and locally unclustered), in particular focusing on the computation of the Malthusian parameter that describes the early exponential epidemic growth. Analytical, numerical and Monte-Carlo methods under a wide variety of Markovian and non-Markovian assumptions about the infectivity profile are presented. Numerous examples provide explicit quantification of the impact of the network structure on the temporal dynamics of the spread of infection and provide a benchmark for validating results of large scale simulations.
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http://dx.doi.org/10.1016/j.mbs.2015.04.006DOI Listing
July 2015

Seven challenges for metapopulation models of epidemics, including households models.

Epidemics 2015 Mar 17;10:63-7. Epub 2014 Aug 17.

Department of Mathematics, University of Rome Tor Vergata, Rome 00133, Italy.

This paper considers metapopulation models in the general sense, i.e. where the population is partitioned into sub-populations (groups, patches,...), irrespective of the biological interpretation they have, e.g. spatially segregated large sub-populations, small households or hosts themselves modelled as populations of pathogens. This framework has traditionally provided an attractive approach to incorporating more realistic contact structure into epidemic models, since it often preserves analytic tractability (in stochastic as well as deterministic models) but also captures the most salient structural inhomogeneity in contact patterns in many applied contexts. Despite the progress that has been made in both the theory and application of such metapopulation models, we present here several major challenges that remain for future work, focusing on models that, in contrast to agent-based ones, are amenable to mathematical analysis. The challenges range from clarifying the usefulness of systems of weakly-coupled large sub-populations in modelling the spread of specific diseases to developing a theory for endemic models with household structure. They include also developing inferential methods for data on the emerging phase of epidemics, extending metapopulation models to more complex forms of human social structure, developing metapopulation models to reflect spatial population structure, developing computationally efficient methods for calculating key epidemiological model quantities, and integrating within- and between-host dynamics in models.
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http://dx.doi.org/10.1016/j.epidem.2014.08.001DOI Listing
March 2015

Eight challenges for network epidemic models.

Epidemics 2015 Mar 4;10:58-62. Epub 2014 Aug 4.

Department of Mathematics, Stockholm University, Stockholm 106 91, Sweden.

Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host-pathogen biology (e.g. waning immunity) have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.
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http://dx.doi.org/10.1016/j.epidem.2014.07.003DOI Listing
March 2015

Nine challenges for deterministic epidemic models.

Epidemics 2015 Mar 27;10:49-53. Epub 2014 Sep 27.

Warwick Infectious Disease Epidemiology Research Centre (WIDER) and Warwick Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.

Deterministic models have a long history of being applied to the study of infectious disease epidemiology. We highlight and discuss nine challenges in this area. The first two concern the endemic equilibrium and its stability. We indicate the need for models that describe multi-strain infections, infections with time-varying infectivity, and those where superinfection is possible. We then consider the need for advances in spatial epidemic models, and draw attention to the lack of models that explore the relationship between communicable and non-communicable diseases. The final two challenges concern the uses and limitations of deterministic models as approximations to stochastic systems.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996659PMC
http://dx.doi.org/10.1016/j.epidem.2014.09.006DOI Listing
March 2015

Seven challenges in modeling pathogen dynamics within-host and across scales.

Epidemics 2015 Mar 30;10:45-8. Epub 2014 Sep 30.

Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA.

The population dynamics of infectious disease is a mature field in terms of theory and to some extent, application. However for microparasites, the theory and application of models of the dynamics within a single infected host is still an open field. Further, connecting across the scales--from cellular to host level, to population level--has potential to vastly improve our understanding of pathogen dynamics and evolution. Here, we highlight seven challenges in the following areas: transmission bottlenecks, heterogeneity within host, dynamic fitness landscapes within hosts, making use of next-generation sequencing data, capturing superinfection and when and how to model more than two scales.
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http://dx.doi.org/10.1016/j.epidem.2014.09.009DOI Listing
March 2015

Modeling infectious disease dynamics in the complex landscape of global health.

Science 2015 Mar;347(6227):aaa4339

Division of International Epidemiology and Population Studies, Fogarty International Center, NIH, Bethesda, MD, USA.

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.
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http://dx.doi.org/10.1126/science.aaa4339DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4445966PMC
March 2015
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