Pub Date : 2025-04-09DOI: 10.1016/j.idm.2025.03.002
Jiangmin Li , Zhen Jin , Ming Tang
For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures.
{"title":"Analysis of the SEIR mean-field model in dynamic networks under intervention","authors":"Jiangmin Li , Zhen Jin , Ming Tang","doi":"10.1016/j.idm.2025.03.002","DOIUrl":"10.1016/j.idm.2025.03.002","url":null,"abstract":"<div><div>For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 850-874"},"PeriodicalIF":8.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-07DOI: 10.1016/j.idm.2025.04.003
Golsa Sayyar , Ábel Garab , Gergely Röst
Predicting viral evolution presents a significant challenge and is a critical public health priority. In response to this challenge, we develop a novel model for viral evolution that considers a trade-off between immunity evasion and transmissibility. The model selects for a new strain with the highest invasion fitness, taking into account this trade-off. When the dominant strain of the pathogen is highly transmissible, evolution tends to favor immune evasion, whereas for less contagious strains the direction of evolution leads toward increasing transmissibility. Assuming a linear functional form of this trade-off, we can express the long-term evolutionary patterns following the emergence of subsequent strains by a non-linear difference equation. We provide sufficient criteria for when evolution converges, and successive strains exhibit similar transmissibility. We also identify scenarios characterized by a two-periodic pattern in upcoming strains, indicating a situation where a highly transmissible but not immune-evasive strain is replaced by a less transmissible but highly immune-evasive strain, and vice versa, creating a cyclic pattern. Finally, we show that under certain conditions, viral evolution becomes chaotic and thus future transmissibilites become unpredictable in the long run. Visualization via bifurcation diagrams elucidates our analytical findings, revealing complex dynamic behaviors that include the presence of multiple periodic solutions and extend to chaotic regimes. Our analysis provides valuable insights into the complexities of viral evolution in the light of the trade-off between immune evasion and transmissibility.
{"title":"Evolution into chaos – Implications of the trade-off between transmissibility and immune evasion","authors":"Golsa Sayyar , Ábel Garab , Gergely Röst","doi":"10.1016/j.idm.2025.04.003","DOIUrl":"10.1016/j.idm.2025.04.003","url":null,"abstract":"<div><div>Predicting viral evolution presents a significant challenge and is a critical public health priority. In response to this challenge, we develop a novel model for viral evolution that considers a trade-off between immunity evasion and transmissibility. The model selects for a new strain with the highest invasion fitness, taking into account this trade-off. When the dominant strain of the pathogen is highly transmissible, evolution tends to favor immune evasion, whereas for less contagious strains the direction of evolution leads toward increasing transmissibility. Assuming a linear functional form of this trade-off, we can express the long-term evolutionary patterns following the emergence of subsequent strains by a non-linear difference equation. We provide sufficient criteria for when evolution converges, and successive strains exhibit similar transmissibility. We also identify scenarios characterized by a two-periodic pattern in upcoming strains, indicating a situation where a highly transmissible but not immune-evasive strain is replaced by a less transmissible but highly immune-evasive strain, and vice versa, creating a cyclic pattern. Finally, we show that under certain conditions, viral evolution becomes chaotic and thus future transmissibilites become unpredictable in the long run. Visualization via bifurcation diagrams elucidates our analytical findings, revealing complex dynamic behaviors that include the presence of multiple periodic solutions and extend to chaotic regimes. Our analysis provides valuable insights into the complexities of viral evolution in the light of the trade-off between immune evasion and transmissibility.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 909-923"},"PeriodicalIF":8.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-07DOI: 10.1016/j.idm.2025.04.001
Alejandro Anderson , Matthew W. Kinahan , Alejandro H. Gonzalez , Klas Udekwu , Esteban A. Hernandez-Vargas
Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies.
Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic Pseudomonas aeruginosa infections.
{"title":"Invariant set theory for predicting potential failure of antibiotic cycling","authors":"Alejandro Anderson , Matthew W. Kinahan , Alejandro H. Gonzalez , Klas Udekwu , Esteban A. Hernandez-Vargas","doi":"10.1016/j.idm.2025.04.001","DOIUrl":"10.1016/j.idm.2025.04.001","url":null,"abstract":"<div><div>Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies.</div><div>Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic <em>Pseudomonas aeruginosa</em> infections.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 897-908"},"PeriodicalIF":8.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-05DOI: 10.1016/j.idm.2025.03.008
Tingting Chen , Guirong Liu , Zhen Jin
Due to the heterogeneity of contact structure, it is more reasonable to model on networks for epidemics. Because of the stochastic nature of events and the discrete number of individuals, the spread of epidemics is more appropriately viewed as a Markov chain. Therefore, we establish stochastic SIRS models with vaccination on networks to study the mean and variance of the number of susceptible and infected individuals for large-scale populations. Using van Kampen's system-size expansion, we derive a high-dimensional deterministic system which describes the mean behaviour and a Fokker-Planck equation which characterizes the variance around deterministic trajectories. Utilizing the qualitative analysis technique and Lyapunov function, we demonstrate that the disease-free equilibrium of the deterministic system is globally asymptotically stable if the basic reproduction number R0 < 1; and the endemic equilibrium is globally asymptotically stable if R0 > 1. Through the analysis of the Fokker-Planck equation, we obtain the asymptotic expression for the variance of the number of susceptible and infected individuals around the endemic equilibrium, which can be approximated by the elements of principal diagonal of the solution of the corresponding Lyapunov equation. Here, the solution of Lyapunov equation is expressed by vectorization operator of matrices and Kronecker product. Finally, numerical simulations illustrate that vaccination can reduce infections and increase fluctuations of the number of infected individuals and show that individuals with greater degree are more easily infected.
{"title":"Stochastic SIRS models on networks: mean and variance of infection","authors":"Tingting Chen , Guirong Liu , Zhen Jin","doi":"10.1016/j.idm.2025.03.008","DOIUrl":"10.1016/j.idm.2025.03.008","url":null,"abstract":"<div><div>Due to the heterogeneity of contact structure, it is more reasonable to model on networks for epidemics. Because of the stochastic nature of events and the discrete number of individuals, the spread of epidemics is more appropriately viewed as a Markov chain. Therefore, we establish stochastic SIRS models with vaccination on networks to study the mean and variance of the number of susceptible and infected individuals for large-scale populations. Using van Kampen's system-size expansion, we derive a high-dimensional deterministic system which describes the mean behaviour and a Fokker-Planck equation which characterizes the variance around deterministic trajectories. Utilizing the qualitative analysis technique and Lyapunov function, we demonstrate that the disease-free equilibrium of the deterministic system is globally asymptotically stable if the basic reproduction number <em>R</em><sub>0</sub> < 1; and the endemic equilibrium is globally asymptotically stable if <em>R</em><sub>0</sub> > 1. Through the analysis of the Fokker-Planck equation, we obtain the asymptotic expression for the variance of the number of susceptible and infected individuals around the endemic equilibrium, which can be approximated by the elements of principal diagonal of the solution of the corresponding Lyapunov equation. Here, the solution of Lyapunov equation is expressed by vectorization operator of matrices and Kronecker product. Finally, numerical simulations illustrate that vaccination can reduce infections and increase fluctuations of the number of infected individuals and show that individuals with greater degree are more easily infected.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 875-896"},"PeriodicalIF":8.8,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-01DOI: 10.1016/j.idm.2025.03.009
I. Ogi-Gittins , J. Polonsky , M. Keita , S. Ahuka-Mundeke , W.S. Hart , M.J. Plank , B. Lambert , E.M. Hill , R.N. Thompson
Professor Pierre Magal made important contributions to the field of mathematical biology before his death on February 20, 2024, including research in which epidemiological models were used to study the ends of infectious disease outbreaks. In related work, there has been interest in inferring (in real-time) when outbreaks have ended and control interventions can be relaxed. Here, we analyse data from the 2018 Ebola outbreak in Équateur Province, Democratic Republic of the Congo, during which an Ebola Response Team (ERT) was deployed to implement public health measures. We use a renewal equation transmission model to perform a quasi real-time investigation into when the ERT could be withdrawn safely at the tail end of the outbreak. Specifically, each week following the arrival of the ERT, we calculate the probability of future cases if the ERT is withdrawn. First, we show that similar estimates of the probability of future cases can be obtained from either daily or weekly case reports. This demonstrates that high temporal resolution case reporting may not always be necessary to determine when interventions can be relaxed. Second, we demonstrate how case under-reporting can be accounted for rigorously when estimating the probability of future cases. We find that, the lower the level of case reporting, the longer it is necessary to wait after the apparent final case before interventions can be removed safely (with only a small probability of additional cases). Finally, we show how uncertainty in the extent of case reporting can be included in estimates of the probability of future cases. Our research highlights the importance of accounting for under-reporting in deciding when to remove interventions at the tail ends of infectious disease outbreaks.
{"title":"Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting","authors":"I. Ogi-Gittins , J. Polonsky , M. Keita , S. Ahuka-Mundeke , W.S. Hart , M.J. Plank , B. Lambert , E.M. Hill , R.N. Thompson","doi":"10.1016/j.idm.2025.03.009","DOIUrl":"10.1016/j.idm.2025.03.009","url":null,"abstract":"<div><div>Professor Pierre Magal made important contributions to the field of mathematical biology before his death on February 20, 2024, including research in which epidemiological models were used to study the ends of infectious disease outbreaks. In related work, there has been interest in inferring (in real-time) when outbreaks have ended and control interventions can be relaxed. Here, we analyse data from the 2018 Ebola outbreak in Équateur Province, Democratic Republic of the Congo, during which an Ebola Response Team (ERT) was deployed to implement public health measures. We use a renewal equation transmission model to perform a <em>quasi</em> real-time investigation into when the ERT could be withdrawn safely at the tail end of the outbreak. Specifically, each week following the arrival of the ERT, we calculate the probability of future cases if the ERT is withdrawn. First, we show that similar estimates of the probability of future cases can be obtained from either daily or weekly case reports. This demonstrates that high temporal resolution case reporting may not always be necessary to determine when interventions can be relaxed. Second, we demonstrate how case under-reporting can be accounted for rigorously when estimating the probability of future cases. We find that, the lower the level of case reporting, the longer it is necessary to wait after the apparent final case before interventions can be removed safely (with only a small probability of additional cases). Finally, we show how uncertainty in the extent of case reporting can be included in estimates of the probability of future cases. Our research highlights the importance of accounting for under-reporting in deciding when to remove interventions at the tail ends of infectious disease outbreaks.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 935-945"},"PeriodicalIF":8.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1016/j.idm.2025.03.007
David V. Kalbaugh
Assuming a homogeneous population, we employ a deterministic model based on first principles of probability to explore dynamics of an epidemic controlled by isolation alone, quarantine alone, and the two together. We develop explicit closed-form equations for key metrics of control performance: cumulative fraction of population infected over the course of the epidemic (final size), maximum fraction infected at any one time, and epidemic duration. We derive an analytical solution for final size of an epidemic controlled by isolation, when final size is small, and develop empirical relations for the other cases. We frame equations in terms of reproduction numbers, measures of intervention effort and initial conditions. We model both strength and speed of interventions, assume second order gamma distributions for intervention waiting times and employ non-time-invariant equations for quarantine. We also account for quarantine of unexposed, susceptible individuals and for imperfect intervention.
{"title":"Dynamics of an epidemic controlled by isolation and quarantine: A probability-based deterministic model","authors":"David V. Kalbaugh","doi":"10.1016/j.idm.2025.03.007","DOIUrl":"10.1016/j.idm.2025.03.007","url":null,"abstract":"<div><div>Assuming a homogeneous population, we employ a deterministic model based on first principles of probability to explore dynamics of an epidemic controlled by isolation alone, quarantine alone, and the two together. We develop explicit closed-form equations for key metrics of control performance: cumulative fraction of population infected over the course of the epidemic (final size), maximum fraction infected at any one time, and epidemic duration. We derive an analytical solution for final size of an epidemic controlled by isolation, when final size is small, and develop empirical relations for the other cases. We frame equations in terms of reproduction numbers, measures of intervention effort and initial conditions. We model both strength and speed of interventions, assume second order gamma distributions for intervention waiting times and employ non-time-invariant equations for quarantine. We also account for quarantine of unexposed, susceptible individuals and for imperfect intervention.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 813-839"},"PeriodicalIF":8.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1016/j.idm.2025.03.006
Cristiano Trevisin , Lorenzo Mari , Marino Gatto , Vittoria Colizza , Andrea Rinaldo
Epidemiological indicators (e.g. reproduction numbers and epidemicity indices) describe long- and short-term behaviour of ongoing epidemics. Their evolving values provide context for designing control measures because maintaining both indices below suitable thresholds warrants waning infection numbers. However, current models for the computation of epidemiological metrics do not consider the stratification of the pathogen into variants endowed with different infectivity and epidemiological severity. This is the case, in particular, with SARS-CoV-2 infections. Failing to account for the variety of epidemiological features of emerging variants prevents epidemiological indices from spotting the possible onset of uncontrolled growth of specific variants, thus significantly limiting the prognostic value of the indicators. Here, we expand an existing framework for the computation of spatially explicit reproduction numbers and epidemicity indices to account for arising variants. By analysing the data of the COVID-19 pandemic in Italy, we show that embedding additional layers of complexity in the mathematical descriptions of unfolding epidemics reveals new angles. In particular, we find epidemiological metrics significantly exceeding their thresholds at the emergence of new variants. Such values foresee a recrudescence in new infections that only becomes evident after emerging new variants have effectively replaced the previous active strains. The demography of the variant composition flags the presence of specific strains growing more rapidly than the total number of infections generated by all variants combined. Variant-aware epidemiological indicators thus allow to engineer better control measures tailored to the shifting patterns of severity and evolving features of infectious disease epidemics.
{"title":"Epidemiological indices with multiple circulating pathogen strains","authors":"Cristiano Trevisin , Lorenzo Mari , Marino Gatto , Vittoria Colizza , Andrea Rinaldo","doi":"10.1016/j.idm.2025.03.006","DOIUrl":"10.1016/j.idm.2025.03.006","url":null,"abstract":"<div><div>Epidemiological indicators (e.g. reproduction numbers and epidemicity indices) describe long- and short-term behaviour of ongoing epidemics. Their evolving values provide context for designing control measures because maintaining both indices below suitable thresholds warrants waning infection numbers. However, current models for the computation of epidemiological metrics do not consider the stratification of the pathogen into variants endowed with different infectivity and epidemiological severity. This is the case, in particular, with SARS-CoV-2 infections. Failing to account for the variety of epidemiological features of emerging variants prevents epidemiological indices from spotting the possible onset of uncontrolled growth of specific variants, thus significantly limiting the prognostic value of the indicators. Here, we expand an existing framework for the computation of spatially explicit reproduction numbers and epidemicity indices to account for arising variants. By analysing the data of the COVID-19 pandemic in Italy, we show that embedding additional layers of complexity in the mathematical descriptions of unfolding epidemics reveals new angles. In particular, we find epidemiological metrics significantly exceeding their thresholds at the emergence of new variants. Such values foresee a recrudescence in new infections that only becomes evident after emerging new variants have effectively replaced the previous active strains. The demography of the variant composition flags the presence of specific strains growing more rapidly than the total number of infections generated by all variants combined. Variant-aware epidemiological indicators thus allow to engineer better control measures tailored to the shifting patterns of severity and evolving features of infectious disease epidemics.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 802-812"},"PeriodicalIF":8.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.idm.2025.03.005
Yunyi Cai , Weiyi Wang , Lanlan Yu , Ruixiao Wang , Gui-Quan Sun , Allisandra G. Kummer , Paulo C. Ventura , Jiancheng Lv , Marco Ajelli , Quan-Hui Liu
In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals. In this study, we propose a multi-layer temporal contact network to model transmission dynamics and assess the impact of different TTI implementations, using SARS-CoV-2 as a case study. The model was used to evaluate TTI effectiveness both in containing an outbreak and mitigating the impact of an epidemic. We estimated that a TTI strategy based on home isolation and testing of both primary and secondary contacts can contain outbreaks only when the reproduction number is up to 1.3, at which the epidemic prevention potential is 88.2% (95% CI: 87.9%–88.5%). On the other hand, for higher value of the reproduction number, TTI is estimated to noticeably mitigate disease burden but at high social costs (e.g., over a month in isolation/quarantine per person for reproduction numbers of 1.7 or higher). We estimated that strategies considering quarantine of contacts have a larger epidemic prevention potential than strategies that either avoid tracing contacts or require contacts to be tested before isolation. Combining TTI with other social distancing measures can improve the likelihood of successfully containing an outbreak but the estimated epidemic prevention potential remains lower than 50% for reproduction numbers higher than 2.1. In conclusion, our model-based evaluation highlights the challenges of relying on TTIs to contain an outbreak of a novel pathogen with characteristics similar to SARS-CoV-2, and that the estimated effectiveness of TTI depends on the way contact patterns are modeled, supporting the relevance of obtaining comprehensive data on human social interactions to improve preparedness.
{"title":"Assessing the effectiveness of test-trace-isolate interventions using a multi-layered temporal network","authors":"Yunyi Cai , Weiyi Wang , Lanlan Yu , Ruixiao Wang , Gui-Quan Sun , Allisandra G. Kummer , Paulo C. Ventura , Jiancheng Lv , Marco Ajelli , Quan-Hui Liu","doi":"10.1016/j.idm.2025.03.005","DOIUrl":"10.1016/j.idm.2025.03.005","url":null,"abstract":"<div><div>In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contacts of infected individuals. In this study, we propose a multi-layer temporal contact network to model transmission dynamics and assess the impact of different TTI implementations, using SARS-CoV-2 as a case study. The model was used to evaluate TTI effectiveness both in containing an outbreak and mitigating the impact of an epidemic. We estimated that a TTI strategy based on home isolation and testing of both primary and secondary contacts can contain outbreaks only when the reproduction number is up to 1.3, at which the epidemic prevention potential is 88.2% (95% CI: 87.9%–88.5%). On the other hand, for higher value of the reproduction number, TTI is estimated to noticeably mitigate disease burden but at high social costs (e.g., over a month in isolation/quarantine per person for reproduction numbers of 1.7 or higher). We estimated that strategies considering quarantine of contacts have a larger epidemic prevention potential than strategies that either avoid tracing contacts or require contacts to be tested before isolation. Combining TTI with other social distancing measures can improve the likelihood of successfully containing an outbreak but the estimated epidemic prevention potential remains lower than 50% for reproduction numbers higher than 2.1. In conclusion, our model-based evaluation highlights the challenges of relying on TTIs to contain an outbreak of a novel pathogen with characteristics similar to SARS-CoV-2, and that the estimated effectiveness of TTI depends on the way contact patterns are modeled, supporting the relevance of obtaining comprehensive data on human social interactions to improve preparedness.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 775-786"},"PeriodicalIF":8.8,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a novel multi-scale modeling framework for infectious disease spreading, borrowing ideas and modeling tools from the so-called Refractory Density (RD) approach. We introduce a microscopic model that describes the probability of infection for a single individual and the evolution of the disease within their body. From the individual-level description, we then present the corresponding population-level model of epidemic spreading on the mesoscopic and macroscopic scale. We conclude with numerical illustrations, taking into account either a white Gaussian noise or an escape noise to showcase the potential of our approach in producing both transient and asymptotic complex dynamics as well as finite-size fluctuations consistently across multiple scales. A comparison with the epidemiology of coronaviruses is also given to corroborate the qualitative relevance of our new approach.
{"title":"A refractory density approach to a multi-scale SEIRS epidemic model","authors":"Anton Chizhov , Laurent Pujo-Menjouet , Tilo Schwalger , Mattia Sensi","doi":"10.1016/j.idm.2025.03.004","DOIUrl":"10.1016/j.idm.2025.03.004","url":null,"abstract":"<div><div>We propose a novel multi-scale modeling framework for infectious disease spreading, borrowing ideas and modeling tools from the so-called Refractory Density (RD) approach. We introduce a microscopic model that describes the probability of infection for a single individual and the evolution of the disease within their body. From the individual-level description, we then present the corresponding population-level model of epidemic spreading on the mesoscopic and macroscopic scale. We conclude with numerical illustrations, taking into account either a white Gaussian noise or an escape noise to showcase the potential of our approach in producing both transient and asymptotic complex dynamics as well as finite-size fluctuations consistently across multiple scales. A comparison with the epidemiology of coronaviruses is also given to corroborate the qualitative relevance of our new approach.</div></div>","PeriodicalId":36831,"journal":{"name":"Infectious Disease Modelling","volume":"10 3","pages":"Pages 787-801"},"PeriodicalIF":8.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.idm.2025.03.003
Hao Yin , Zhu Liu , Daniel M. Kammen
COVID-19 has triggered an unprecedented public health crisis and a global economic shock. As countries and cities have transitioned away from strict pandemic restrictions, the most effective reopening strategies may vary significantly based on their demographic characteristics and social contact patterns. In this study, we employed an extended age-specific compartment model that incorporates population mobility to investigate the interaction between population age structure and various containment interventions in New York, Los Angeles, Daegu, and Nairobi – four cities with distinct age distributions that served as local epicenters of the epidemic from January 2020 to March 2021. Our results demonstrated that individual social distancing or quarantine strategies alone cannot effectively curb the spread of infection over a one-year period. However, a combined strategy, including school closure, 50 % working from home, 50 % reduction in other mobility, 10 % quarantine rate, and city lockdown interventions, can effectively suppress the infection. Furthermore, our findings revealed that social-distancing policies exhibit strong age-specific effects, and age-targeted interventions can yield significant spillover benefits. Specifically, reducing contact rates among the population under 20 can prevent 14 %, 18 %, 56 %, and 99 % of infections across all age groups in New York, Los Angeles, Daegu, and Nairobi, respectively, surpassing the effectiveness of policies exclusively targeting adults over 60 years old. In particular, to protect the elderly, it is essential to reduce contacts between the younger population and people of all age groups, especially those over 60 years old. While an older population structure may escalate fatality risk, it might also decrease infection risk. Moreover, a higher basic reproduction number amplifies the impact of an older population structure on the fatality risk of the elderly. The considerable variations in susceptibility, severity, and mobility across age groups underscore the need for targeted interventions to effectively control the spread of COVID-19 and mitigate risks in future pandemics.
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