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Johnson R, Carnalla M, Basto-Abreu A, Haw D, Morgenstern C, Doohan P, Forchini G, Hauck KD, Barrientos-Gutiérrez T. Promoting healthy populations as a pandemic preparedness strategy: a simulation study from Mexico. LANCET REGIONAL HEALTH. AMERICAS 2024; 30:100682. [PMID: 38332937 PMCID: PMC10850772 DOI: 10.1016/j.lana.2024.100682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 02/10/2024]
Abstract
Background The underlying health status of populations was a major determinant of the impact of the COVID-19 pandemic, particularly obesity prevalence. Mexico was one of the most severely affected countries during the COVID-19 pandemic and its obesity prevalence is among the highest in the world. It is unknown by how much the COVID-19 burden could have been reduced if systemic actions had been implemented to reduce excess weight in Mexico before the onset of the pandemic. Methods Using a dynamic epidemic model based on nationwide data, we compare actual deaths with those under hypothetical scenarios assuming a lower body mass index in the Mexican population, as observed historically. We also model the number of deaths that would have been averted due to earlier implementation of front-of-pack warning labels or due to increases in taxes on sugar-sweetened beverages and non-essential high-energy foods in Mexico. Findings We estimate that 52.5% (95% prediction interval (PI) 43.2, 61.6%) of COVID-19 deaths were attributable to obesity for adults aged 20-64 and 23.8% (95% PI 18.7, 29.1%) for those aged 65 and over. Had the population BMI distribution remained as it was in 2000, 2006, or 2012, COVID-19 deaths would have been reduced by an expected 20.6% (95% PI 16.9, 24.6%), 9.9% (95% PI 7.3, 12.9%), or 6.9% (95% PI 4.5, 9.5%), respectively. If the food-labelling intervention introduced in 2020 had been introduced in 2018, an expected 6.2% (95% PI 5.2, 7.3%) of COVID-19 deaths would have been averted. If taxes on sugar-sweetened beverages and high-energy foods had been doubled, trebled, or quadrupled in 2018, COVID-19 deaths would have been reduced by an expected 4.1% (95% PI 2.5, 5.7%), 7.9% (95% PI 4.9, 11.0%), or 11.6% (95% PI 7.3, 15.8%), respectively. Interpretation Public health interventions targeting underlying population health, including non-communicable chronic diseases, is a promising line of action for pandemic preparedness that should be included in all pandemic plans. Funding This study received funding from Bloomberg Philanthropies, awarded to Juan A. Rivera from the National Institute of Public Health; Community Jameel, the UK Medical Research Council (MRC), Kenneth C Griffin, and the World Health Organization.
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Affiliation(s)
- Rob Johnson
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK
| | - Martha Carnalla
- Centro de Salud en Investigación Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, México
| | - Ana Basto-Abreu
- Centro de Salud en Investigación Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, México
| | - David Haw
- Department of Mathematical Sciences and Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, UK
| | - Christian Morgenstern
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK
| | - Patrick Doohan
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK
| | - Giovanni Forchini
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK
- USBE, Umeå Universitet, Umeå, Sweden
| | - Katharina D. Hauck
- MRC Centre for Global Infectious Disease Analysis & WHO Collaborating Centre for Infectious Disease Modelling, Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK
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Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review. J Math Biol 2022; 84:26. [PMID: 35218424 PMCID: PMC8882104 DOI: 10.1007/s00285-021-01706-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 09/10/2021] [Accepted: 12/01/2021] [Indexed: 11/17/2022]
Abstract
Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection worldwide, resulting in approximately sixty thousand annual hospitalizations of< 5-year-olds in the United States alone and three million annual hospitalizations globally. The development of over 40 vaccines and immunoprophylactic interventions targeting RSV has the potential to significantly reduce the disease burden from RSV infection in the near future. In the context of RSV, a highly contagious pathogen, dynamic transmission models (DTMs) are valuable tools in the evaluation and comparison of the effectiveness of different interventions. This review, the first of its kind for RSV DTMs, provides a valuable foundation for future modelling efforts and highlights important gaps in our understanding of RSV epidemics. Specifically, we have searched the literature using Web of Science, Scopus, Embase, and PubMed to identify all published manuscripts reporting the development of DTMs focused on the population transmission of RSV. We reviewed the resulting studies and summarized the structure, parameterization, and results of the models developed therein. We anticipate that future RSV DTMs, combined with cost-effectiveness evaluations, will play a significant role in shaping decision making in the development and implementation of intervention programs.
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Mathematical modelling of respiratory syncytial virus (RSV) in low- and middle-income countries: A systematic review. Epidemics 2021; 35:100444. [PMID: 33662812 PMCID: PMC8262087 DOI: 10.1016/j.epidem.2021.100444] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 01/31/2021] [Accepted: 02/17/2021] [Indexed: 01/28/2023] Open
Abstract
Background: Due to high burden of respiratory syncytial virus (RSV) in low- and middle-income countries (LMIC), international funding organizations have prioritized the development of RSV vaccines. Mathematical models of RSV will play an important role in assessing the relative value of these interventions. Our objectives were to provide an overview of the existing RSV modelling literature in LMIC and summarize available results on population-level effectiveness and cost-effectiveness. Methods: We searched MEDLINE from 2000 to 2020 for English language publications that employed a mathematical model of RSV calibrated to LMIC. Qualitative data were extracted on study and model characteristics. Quantitative data were collected on key model input assumptions and base case effectiveness and cost-effectiveness estimates for various immunization strategies. Findings: Of the 283 articles reviewed, 15 met inclusion criteria. Ten studies used modelling techniques to explore RSV transmission and/or natural history, while eight studies evaluated RSV vaccines and/or monoclonal antibodies, three of which included cost-effectiveness analyses. Six studies employed deterministic compartmental models, five studies employed individual transmission models, and four studies used different types of cohort models. Nearly every model was calibrated to at least one middle-income country, while four were calibrated to low-income countries. Interpretation: The mathematical modelling literature in LMIC has demonstrated the potential effectiveness of RSV vaccines and monoclonal antibodies. This review has demonstrated the importance of accounting for seasonality, social contact rates, immunity from prior infection and maternal antibody transfer. Future models should consider incorporating individual-level risk factors, subtype-specific effects, long-term sequelae of RSV infections, and out-of-hospital mortality.
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Friston K, Costello A, Pillay D. 'Dark matter', second waves and epidemiological modelling. BMJ Glob Health 2020; 5:e003978. [PMID: 33328201 PMCID: PMC7745338 DOI: 10.1136/bmjgh-2020-003978] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/14/2020] [Accepted: 11/17/2020] [Indexed: 12/23/2022] Open
Abstract
Recent reports using conventional Susceptible, Exposed, Infected and Removed models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities exceeding the first wave. We used Bayesian model comparison to revisit these conclusions, allowing for heterogeneity of exposure, susceptibility and transmission. We used dynamic causal modelling to estimate the evidence for alternative models of daily cases and deaths from the USA, the UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany and Canada over the period 25 January 2020 to 15 June 2020. These data were used to estimate the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed and (iii) not infectious when susceptible to infection. Bayesian model comparison furnished overwhelming evidence for heterogeneity of exposure, susceptibility and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain large differences in mortality rates. The best model of UK data predicts a second surge of fatalities will be much less than the first peak. The size of the second wave depends sensitively on the loss of immunity and the efficacy of Find-Test-Trace-Isolate-Support programmes. In summary, accounting for heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes.
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Affiliation(s)
- Karl Friston
- Queen Square Institute of Neurology, University College London, London, UK
| | - Anthony Costello
- Institute of Global Health, University College London, London, UK
| | - Deenan Pillay
- University College London Faculty of Medical Sciences, London, UK
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Friston KJ, Parr T, Zeidman P, Razi A, Flandin G, Daunizeau J, Hulme OJ, Billig AJ, Litvak V, Price CJ, Moran RJ, Costello A, Pillay D, Lambert C. Effective immunity and second waves: a dynamic causal modelling study. Wellcome Open Res 2020; 5:204. [PMID: 33088924 PMCID: PMC7549178 DOI: 10.12688/wellcomeopenres.16253.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2020] [Indexed: 12/18/2022] Open
Abstract
This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
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Affiliation(s)
- Karl J. Friston
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Thomas Parr
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Peter Zeidman
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Adeel Razi
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, 3800, Australia
| | - Guillaume Flandin
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Jean Daunizeau
- Institut du Cerveau et de la Moelle épinière, INSERM UMRS 1127, Paris, France
| | - Oliver J. Hulme
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Hvidovre, Denmark
- London Mathematical Laboratory, Hammersmith, London, UK
| | | | - Vladimir Litvak
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Cathy J. Price
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Rosalyn J. Moran
- Centre for Neuroimaging Science, Department of Neuroimaging, IoPPN, King's College London, London, UK
| | - Anthony Costello
- UCL Institute for Global Health, Institute of Child Health, University College London, London, UK
| | - Deenan Pillay
- UCL Division of Infection and Immunity, University College London, London, UK
| | - Christian Lambert
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
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Friston KJ, Parr T, Zeidman P, Razi A, Flandin G, Daunizeau J, Hulme OJ, Billig AJ, Litvak V, Price CJ, Moran RJ, Costello A, Pillay D, Lambert C. Effective immunity and second waves: a dynamic causal modelling study. Wellcome Open Res 2020; 5:204. [PMID: 33088924 PMCID: PMC7549178 DOI: 10.12688/wellcomeopenres.16253.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2020] [Indexed: 08/15/2023] Open
Abstract
This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
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Affiliation(s)
- Karl J. Friston
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Thomas Parr
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Peter Zeidman
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Adeel Razi
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, 3800, Australia
| | - Guillaume Flandin
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Jean Daunizeau
- Institut du Cerveau et de la Moelle épinière, INSERM UMRS 1127, Paris, France
| | - Oliver J. Hulme
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Hvidovre, Denmark
- London Mathematical Laboratory, Hammersmith, London, UK
| | | | - Vladimir Litvak
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Cathy J. Price
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Rosalyn J. Moran
- Centre for Neuroimaging Science, Department of Neuroimaging, IoPPN, King's College London, London, UK
| | - Anthony Costello
- UCL Institute for Global Health, Institute of Child Health, University College London, London, UK
| | - Deenan Pillay
- UCL Division of Infection and Immunity, University College London, London, UK
| | - Christian Lambert
- The Wellcome Centre for Human Neuroimaging, University College London, London, UK
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Brooks-Pollock E, Danon L. Defining the population attributable fraction for infectious diseases. Int J Epidemiol 2018; 46:976-982. [PMID: 28472445 DOI: 10.1093/ije/dyx055] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2017] [Indexed: 01/11/2023] Open
Abstract
Background The population attributable fraction (PAF) is used to quantify the contribution of a risk group to disease burden. For infectious diseases, high-risk individuals may increase disease risk for the wider population in addition to themselves; therefore methods are required to estimate the PAF for infectious diseases. Methods A mathematical model of disease transmission in a population with a high-risk group was used to compare existing approaches for calculating the PAF. We quantify when existing methods are consistent and when estimates diverge. We introduce a new method, based on the basic reproduction number, for calculating the PAF, which bridges the gap between existing methods and addresses shortcomings. We illustrate the methods with two examples of the contribution of badgers to bovine tuberculosis in cattle and the role of commercial sex in an HIV epidemic. Results We demonstrate that current methods result in irreconcilable PAF estimates, depending on how chains of transmission are categorized. Using two novel simple formulae for emerging and endemic diseases, we demonstrate that the largest differences occur when transmission in the general population is not self-sustaining. Crucially, some existing methods are not able to discriminate between multiple risk groups. We show that compared with traditional estimates, assortative mixing leads to a decreased PAF, whereas disassortative mixing increases PAF. Conclusions Recent methods for calculating the population attributable fraction (PAF) are not consistent with traditional approaches. Policy makers and users of PAF statistics should be aware of these differences. Our approach offers a straightforward and parsimonious method for calculating the PAF for infectious diseases.
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Affiliation(s)
- Ellen Brooks-Pollock
- National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Evaluation of Interventions, School of Social and Community Medicine
| | - Leon Danon
- School of Social and Community Medicine, University of Bristol, Bristol, UK
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Friston KJ, Redish AD, Gordon JA. Computational Nosology and Precision Psychiatry. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2017; 1:2-23. [PMID: 29400354 PMCID: PMC5774181 DOI: 10.1162/cpsy_a_00001] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Accepted: 01/12/2017] [Indexed: 12/11/2022]
Abstract
This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present-and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations.
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Affiliation(s)
- Karl J. Friston
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, London WC1N 3BG, UK
| | - A. David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455
| | - Joshua A. Gordon
- Department of Psychiatry, Columbia University, New York, NY 10032
- Director: National Institute of Mental Health (NIMH), Bethesda MD 20814
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