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He Z, Bauch CT. Effect of homophily on coupled behavior-disease dynamics near a tipping point. Math Biosci 2024; 376:109264. [PMID: 39097225 DOI: 10.1016/j.mbs.2024.109264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/18/2024] [Accepted: 07/26/2024] [Indexed: 08/05/2024]
Abstract
Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. Recent studies using coupled behavior-disease models assumed homogeneous populations. However, heterogeneity in population, such as different social groups, cannot be ignored. In this study, we divided the population into social media users and non-users, and investigated the impact of homophily (the tendency for individuals to associate with others similar to themselves) and online events on disease dynamics. Our results reveal that homophily hinders the adoption of vaccinating strategies, hastening the approach to a tipping point after which the population converges to an endemic equilibrium with no vaccine uptake. Furthermore, we find that online events can significantly influence disease dynamics, with early discussions on social media platforms serving as an early warning signal of potential disease outbreaks. Our model provides insights into the mechanisms underlying these phenomena and underscores the importance of considering homophily in disease modeling and public health strategies.
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Affiliation(s)
- Zitao He
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
| | - Chris T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
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2
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Chakraborty AK, Gao S, Miry R, Ramazi P, Greiner R, Lewis MA, Wang H. An early warning indicator trained on stochastic disease-spreading models with different noises. J R Soc Interface 2024; 21:20240199. [PMID: 39118548 PMCID: PMC11310706 DOI: 10.1098/rsif.2024.0199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 06/12/2024] [Accepted: 07/01/2024] [Indexed: 08/10/2024] Open
Abstract
The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.
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Affiliation(s)
- Amit K. Chakraborty
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Shan Gao
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Reza Miry
- Department of Mathematics and Statistics, Brock University, St. Catharines, Ontario, Canada
| | - Pouria Ramazi
- Department of Mathematics and Statistics, Brock University, St. Catharines, Ontario, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Mark A. Lewis
- Department of Mathematics and Statistics and Department of Biology, University of Victoria, Victoria, British Columbia, Canada
| | - Hao Wang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada
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3
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Delecroix C, van Nes EH, Scheffer M, van de Leemput IA. Monitoring resilience in bursts. Proc Natl Acad Sci U S A 2024; 121:e2407148121. [PMID: 39047042 PMCID: PMC11295040 DOI: 10.1073/pnas.2407148121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/17/2024] [Indexed: 07/27/2024] Open
Abstract
The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.
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Affiliation(s)
- Clara Delecroix
- Department of Environmental Sciences, Wageningen University and Research, Wageningen6700 AA, The Netherlands
| | - Egbert H. van Nes
- Department of Environmental Sciences, Wageningen University and Research, Wageningen6700 AA, The Netherlands
| | - Marten Scheffer
- Department of Environmental Sciences, Wageningen University and Research, Wageningen6700 AA, The Netherlands
| | - Ingrid A. van de Leemput
- Department of Environmental Sciences, Wageningen University and Research, Wageningen6700 AA, The Netherlands
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4
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Lehnertz K. Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems. CHAOS (WOODBURY, N.Y.) 2024; 34:072102. [PMID: 38985967 DOI: 10.1063/5.0214733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/21/2024] [Indexed: 07/12/2024]
Abstract
Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions, and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.
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5
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Zhang L, Li MY, Zhi C, Zhu M, Ma H. Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention. Curr Med Sci 2024; 44:273-280. [PMID: 38632143 DOI: 10.1007/s11596-024-2850-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/19/2024] [Indexed: 04/19/2024]
Abstract
The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging. Consequently, there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals. This paper examines the role of medical data in the early identification of infectious diseases, explores early warning technologies for infectious disease recognition, and assesses monitoring and early warning mechanisms for infectious diseases. We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems, in compliance with national strategies to integrate clinical treatment and disease prevention. Furthermore, hospitals should establish institution-specific, clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.
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Affiliation(s)
- Lei Zhang
- Sixth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China
| | - Min-Ye Li
- The Nursing Department, Chinese PLA General Hospital, Beijing, 100853, China
| | - Chen Zhi
- The Nursing Department, Chinese PLA General Hospital, Beijing, 100853, China
| | - Min Zhu
- Sixth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China
| | - Hui Ma
- The Nursing Department, Chinese PLA General Hospital, Beijing, 100853, China.
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6
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Legault V, Pu Y, Weinans E, Cohen AA. Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1299162. [PMID: 38595863 PMCID: PMC11002238 DOI: 10.3389/fnetp.2024.1299162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/15/2024] [Indexed: 04/11/2024]
Abstract
Early warnings signs (EWSs) can anticipate abrupt changes in system state, known as "critical transitions," by detecting dynamic variations, including increases in variance, autocorrelation (AC), and cross-correlation. Numerous EWSs have been proposed; yet no consensus on which perform best exists. Here, we compared 15 multivariate EWSs in time series of 763 hemodialyzed patients, previously shown to present relevant critical transition dynamics. We calculated five EWSs based on AC, six on variance, one on cross-correlation, and three on AC and variance. We assessed their pairwise correlations, trends before death, and mortality predictive power, alone and in combination. Variance-based EWSs showed stronger correlations (r = 0.663 ± 0.222 vs. 0.170 ± 0.205 for AC-based indices) and a steeper increase before death. Two variance-based EWSs yielded HR95 > 9 (HR95 standing for a scale-invariant metric of hazard ratio), but combining them did not improve the area under the receiver-operating curve (AUC) much compared to using them alone (AUC = 0.798 vs. 0.796 and 0.791). Nevertheless, the AUC reached 0.825 when combining 13 indices. While some indicators did not perform overly well alone, their addition to the best performing EWSs increased the predictive power, suggesting that indices combination captures a broader range of dynamic changes occurring within the system. It is unclear whether this added benefit reflects measurement error of a unified phenomenon or heterogeneity in the nature of signals preceding critical transitions. Finally, the modest predictive performance and weak correlations among some indices call into question their validity, at least in this context.
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Affiliation(s)
- Véronique Legault
- Research Center of the Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Yi Pu
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Els Weinans
- Copernicus Institute of Sustainable Development, Environmental Science, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands
| | - Alan A. Cohen
- Research Center of the Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, QC, Canada
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7
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Masuda N, Aihara K, MacLaren NG. Anticipating regime shifts by mixing early warning signals from different nodes. Nat Commun 2024; 15:1086. [PMID: 38316802 PMCID: PMC10844243 DOI: 10.1038/s41467-024-45476-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/25/2024] [Indexed: 02/07/2024] Open
Abstract
Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.
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Affiliation(s)
- Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, 14260-2900, USA.
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, NY, 14260-5030, USA.
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Bunkyo City, Japan
| | - Neil G MacLaren
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, 14260-2900, USA
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8
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Drake KO, Boyd O, Franceschi VB, Colquhoun RM, Ellaby NAF, Volz EM. Phylogenomic early warning signals for SARS-CoV-2 epidemic waves. EBioMedicine 2024; 100:104939. [PMID: 38194742 PMCID: PMC10792554 DOI: 10.1016/j.ebiom.2023.104939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Epidemic waves of coronavirus disease 2019 (COVID-19) infections have often been associated with the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Rapid detection of growing genomic variants can therefore serve as a predictor of future waves, enabling timely implementation of countermeasures such as non-pharmaceutical interventions (social distancing), additional vaccination (booster campaigns), or healthcare capacity adjustments. The large amount of SARS-CoV-2 genomic sequence data produced during the pandemic has provided a unique opportunity to explore the utility of these data for generating early warning signals (EWS). METHODS We developed an analytical pipeline (Transmission Fitness Polymorphism Scanner - designated in an R package mrc-ide/tfpscanner) for systematically exploring all clades within a SARS-CoV-2 virus phylogeny to detect variants showing unusually high growth rates. We investigated the use of these cluster growth rates as the basis for a variety of statistical time series to use as leading indicators for the epidemic waves in the UK during the pandemic between August 2020 and March 2022. We also compared the performance of these phylogeny-derived leading indicators with a range of non-phylogeny-derived leading indicators. Our experiments simulated data generation and real-time analysis. FINDINGS Using phylogenomic analysis, we identified leading indicators that would have generated EWS ahead of significant increases in COVID-19 hospitalisations in the UK between August 2020 and March 2022. Our results also show that EWS lead time is sensitive to the threshold set for the number of false positive (FP) EWS. It is often possible to generate longer EWS lead times if more FP EWS are tolerated. On the basis of maximising lead time and minimising the number of FP EWS, the best performing leading indicators that we identified, amongst a set of 1.4 million, were the maximum logistic growth rate (LGR) amongst clusters of the dominant Pango lineage and the mean simple LGR across a broader set of clusters. In the case of the former, the time between the EWS and wave inflection points (a conservative measure of wave start dates) for the seven waves ranged between a 20-day lead time and a 7-day lag, with a mean lead time of 5.4 days. The maximum number of FP EWS generated prior to a true positive (TP) EWS was two and this only occurred for two of the seven waves in the period. The mean simple LGR amongst a broader set of clusters also performed well in terms of lead time but with slightly more FP EWS. INTERPRETATION As a result of the significant surveillance effort during the pandemic, early detection of SARS-CoV-2 variants of concern Alpha, Delta, and Omicron provided some of the first examples where timely detection and characterisation of pathogen variants has been used to tailor public health response. The success of our method in generating early warning signals based on phylogenomic analysis for SARS-CoV-2 in the UK may make it a worthwhile addition to existing surveillance strategies. In addition, the method may be translatable to other countries and/or regions, and to other pathogens with large-scale and rapid genomic surveillance. FUNDING This research was funded in whole, or in part, by the Wellcome Trust (220885_Z_20_Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. KOD, OB, VBF and EMV acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. RMC acknowledges funding from the Wellcome Trust Collaborators Award (206298/Z/17/Z).
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Affiliation(s)
- Kieran O Drake
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
| | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Vinicius B Franceschi
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Rachel M Colquhoun
- Institute of Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Erik M Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
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9
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Cavallaro M, Dyson L, Tildesley MJ, Todkill D, Keeling MJ. Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis. J R Soc Interface 2023; 20:20230410. [PMID: 37963560 PMCID: PMC10645511 DOI: 10.1098/rsif.2023.0410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023] Open
Abstract
The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the imposition of control measures. While the primary role of testing is to identify infection, target treatment, and limit spread (through isolation and contact tracing), a secondary benefit is in terms of surveillance and the early detection of new variants. Here we study the spatial invasion and early spread of the Alpha, Delta and Omicron (BA.1 and BA.2) variants in England from September 2020 to February 2022 using the random neighbourhood covering (RaNCover) method. This is a statistical technique for the detection of aberrations in spatial point processes, which we tailored here to community PCR (polymerase-chain-reaction) test data where the TaqPath kit provides a proxy measure of the switch between variants. Retrospectively, RaNCover detected the earliest signals associated with the four novel variants that led to large infection waves in England. With suitable data our method therefore has the potential to rapidly detect outbreaks of future SARS-CoV-2 variants, thus helping to inform targeted public health interventions.
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Affiliation(s)
- Massimo Cavallaro
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Louise Dyson
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Michael J. Tildesley
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Dan Todkill
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Matt J. Keeling
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
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10
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Pateras K, Meletis E, Denwood M, Eusebi P, Kostoulas P. The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic. Infect Dis Model 2023; 8:484-490. [PMID: 37234097 PMCID: PMC10206801 DOI: 10.1016/j.idm.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/28/2023] [Accepted: 05/01/2023] [Indexed: 05/27/2023] Open
Abstract
This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.
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Affiliation(s)
- Konstantinos Pateras
- Department of Public and One Health, School of Medicine, University of Thessaly, Karditsa, Terma Mavromichali St., 43131, Greece
- Department of Data Science and Biostatistics, University of Utrecht, Postbus 85500, 3508, GA, Utrecht, the Netherlands
| | - Eleftherios Meletis
- Department of Public and One Health, School of Medicine, University of Thessaly, Karditsa, Terma Mavromichali St., 43131, Greece
| | - Matthew Denwood
- Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 8, 1870, Frederiksberg, Copenhagen, Denmark
| | - Paolo Eusebi
- Department of Medicine and Surgery, University of Perugia, Via Gambuli, 1, 06132, Perugia, Italy
| | - Polychronis Kostoulas
- Department of Public and One Health, School of Medicine, University of Thessaly, Karditsa, Terma Mavromichali St., 43131, Greece
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11
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Rhodes T, Lancaster K. Early warnings and slow deaths: A sociology of outbreak and overdose. THE INTERNATIONAL JOURNAL OF DRUG POLICY 2023; 117:104065. [PMID: 37229960 DOI: 10.1016/j.drugpo.2023.104065] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/27/2023]
Abstract
In this paper, we offer a sociological analysis of early warning and outbreak in the field of drug policy, focusing on opioid overdose. We trace how 'outbreak' is enacted as a rupturing event which enables rapid reflex responses of precautionary control, based largely on short-term and proximal early warning indicators. We make the case for an alternative view of early warning and outbreak. We argue that practices of detection and projection that help to materialise drug-related outbreaks are too focused on the proximal and short-term. Engaging with epidemiological and sociological work investigating epidemics of opioid overdose, we show how the short-termism and rapid reflex response of outbreak fails to appreciate the slow violent pasts of epidemics indicative of an ongoing need and care for structural and societal change. Accordingly, we gather together ideas of 'slow emergency' (Ben Anderson), 'slow death' (Lauren Berlant) and 'slow violence' (Rob Nixon), to re-assemble outbreaks in 'long view'. This locates opioid overdose in long-term attritional processes of deindustrialisation, pharmaceuticalisation, and other forms of structural violence, including the criminalisation and problematisation of people who use drugs. Outbreaks evolve in relation to their slow violent pasts. To ignore this can perpetuate harm. Attending to the social conditions that create the possibilities for outbreak invites early warning that goes 'beyond outbreak' and 'beyond epidemic' as generally configured.
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Affiliation(s)
- Tim Rhodes
- London School of Hygiene and Tropical Medicine, London, UK; University of New South Wales, Sydney, Australia.
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12
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Descary MH, Froda S. Estimating the basic reproduction number from noisy daily data. J Theor Biol 2022; 549:111210. [PMID: 35788342 PMCID: PMC9250830 DOI: 10.1016/j.jtbi.2022.111210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/20/2022] [Accepted: 06/27/2022] [Indexed: 11/18/2022]
Abstract
In this paper, we propose an easy to implement generalized linear models (GLM) methodology for estimating the basic reproduction number, R0, a major epidemic parameter for assessing the transmissibility of an infection. Our approach rests on well known qualitative properties of the classical SIR and SEIR systems for large populations. Moreover, we assume that information at the individual network level is not available. In inference we consider non homogeneous Poisson observation processes and mainly concentrate on epidemics that spread through a completely susceptible population. Further, we examine the performance of the estimator under various scenarios of relevance in practice, like partially observed data. We perform a detailed simulation study and illustrate our approach on Covid-19 Canadian data sets. Finally, we present extensions of our methodology and discuss its merits and practical limitations, in particular the challenges in estimating R0 when mitigation measures are applied.
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Affiliation(s)
- Marie-Hélène Descary
- Université du Québec à Montréal, Département de mathématiques, Montréal H2X 3Y7, Québec, Canada.
| | - Sorana Froda
- Université du Québec à Montréal, Département de mathématiques, Montréal H2X 3Y7, Québec, Canada
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13
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Feld Y, Hartmann AK. Large deviations of a susceptible-infected-recovered model around the epidemic threshold. Phys Rev E 2022; 105:034313. [PMID: 35428162 DOI: 10.1103/physreve.105.034313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
We numerically study the dynamics of the SIR disease model on small-world networks by using a large-deviation approach. This allows us to obtain the probability density function of the total fraction of infected nodes and of the maximum fraction of simultaneously infected nodes down to very small probability densities like 10^{-2500}. We analyze the structure of the disease dynamics and observed three regimes in all probability density functions, which correspond to quick mild, quick extremely severe, and sustained severe dynamical evolutions, respectively. Furthermore, the mathematical rate functions of the densities are investigated. The results indicate that the so-called large-deviation property holds for the SIR model. Finally, we measured correlations with other quantities like the duration of an outbreak or the peak position of the fraction of infections, also in the rare regions which are not accessible by standard simulation techniques.
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Affiliation(s)
- Yannick Feld
- Institut für Physik, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany
| | - Alexander K Hartmann
- Institut für Physik, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany
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14
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Proverbio D, Kemp F, Magni S, Gonçalves J. Performance of early warning signals for disease re-emergence: A case study on COVID-19 data. PLoS Comput Biol 2022; 18:e1009958. [PMID: 35353809 PMCID: PMC9000113 DOI: 10.1371/journal.pcbi.1009958] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 04/11/2022] [Accepted: 02/23/2022] [Indexed: 01/12/2023] Open
Abstract
Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.
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Affiliation(s)
- Daniele Proverbio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Françoise Kemp
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Stefano Magni
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
- Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom
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Roberts M, Dobson A, Restif O, Wells K. Challenges in modelling the dynamics of infectious diseases at the wildlife-human interface. Epidemics 2021; 37:100523. [PMID: 34856500 PMCID: PMC8603269 DOI: 10.1016/j.epidem.2021.100523] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 02/01/2023] Open
Abstract
The Covid-19 pandemic is of zoonotic origin, and many other emerging infections of humans have their origin in an animal host population. We review the challenges involved in modelling the dynamics of wildlife–human interfaces governing infectious disease emergence and spread. We argue that we need a better understanding of the dynamic nature of such interfaces, the underpinning diversity of pathogens and host–pathogen association networks, and the scales and frequencies at which environmental conditions enable spillover and host shifting from animals to humans to occur. The major drivers of the emergence of zoonoses are anthropogenic, including the global change in climate and land use. These, and other ecological processes pose challenges that must be overcome to counterbalance pandemic risk. The development of more detailed and nuanced models will provide better tools for analysing and understanding infectious disease emergence and spread.
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Affiliation(s)
- Mick Roberts
- School of Natural & Computational Sciences, New Zealand Institute for Advanced Study and the Infectious Disease Research Centre, Massey University, Private Bag 102 904, North Shore Mail Centre, Auckland, New Zealand.
| | - Andrew Dobson
- EEB, Eno Hall, Princeton University, Princeton, NJ 08544, USA; Santa Fe Institute, Hyde Park Rd., Santa Fe, NM, USA
| | - Olivier Restif
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK
| | - Konstans Wells
- Department of Biosciences, Swansea University, Swansea SA2 8PP, UK
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