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The rapid development of vaccines against SARS-CoV-2 altered the course of the COVID-19 pandemic. In most countries, vaccinations were initially targeted at high-risk populations, including older individuals and healthcare workers. Now, despite substantial infection- and vaccine-induced immunity in host populations worldwide, waning immunity and the emergence of novel variants continue to cause significant waves of infection and disease. Policy makers must determine how to deploy booster vaccinations, particularly when constraints in vaccine supply, delivery and cost mean that booster vaccines cannot be administered to everyone. A key question is therefore whether older individuals should again be prioritised for vaccination, or whether alternative strategies (e.g. offering booster vaccines to the individuals who have most contacts with others and therefore drive infection) can instead offer indirect protection to older individuals. Here, we use mathematical modelling to address this question, considering SARS-CoV-2 transmission in a range of countries with different socio-economic backgrounds. We show that the population structures of different countries can have a pronounced effect on the impact of booster vaccination, even when identical booster vaccination targeting strategies are adopted. However, under the assumed transmission model, prioritising older individuals for booster vaccination consistently leads to the most favourable public health outcomes in every setting considered. This remains true for a range of assumptions about booster vaccine supply and timing, and for different assumed policy objectives of booster vaccination.
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Vacunas contra la COVID-19 , COVID-19 , Inmunización Secundaria , Salud Pública , SARS-CoV-2 , Humanos , COVID-19/prevención & control , COVID-19/epidemiología , Inmunización Secundaria/estadística & datos numéricos , Vacunas contra la COVID-19/administración & dosificación , Vacunas contra la COVID-19/inmunología , Anciano , SARS-CoV-2/inmunología , Factores Socioeconómicos , Persona de Mediana Edad , Vacunación/estadística & datos numéricos , Pandemias/prevención & controlRESUMEN
The global outbreak of mpox in 2022 and subsequent sporadic outbreaks in 2023 highlighted the importance of nonpharmaceutical interventions such as case isolation. Individual variations in viral shedding dynamics may lead to either premature ending of isolation for infectious individuals, or unnecessarily prolonged isolation for those who are no longer infectious. Here, we developed a modeling framework to characterize heterogeneous mpox infectiousness profiles - specifically, when infected individuals cease to be infectious - based on viral load data. We examined the potential effectiveness of three different isolation rules: a symptom-based rule (the current guideline in many countries) and rules permitting individuals to stop isolating after either a fixed duration or following tests that indicate that they are no longer likely to be infectious. Our analysis suggests that the duration of viral shedding ranges from 23 to 50 days between individuals. The risk of infected individuals ending isolation too early was estimated to be 8.8% (95% CI: 6.7-10.5) after symptom clearance and 5.4% (95% CI: 4.1-6.7) after 3 weeks of isolation. While these results suggest that the current standard practice for ending isolation is effective, we found that unnecessary isolation following the infectious period could be reduced by adopting a testing-based rule.
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Brotes de Enfermedades , Humanos , Brotes de Enfermedades/prevención & control , Carga Viral , COVID-19/epidemiología , COVID-19/virología , COVID-19/prevención & control , COVID-19/transmisión , Esparcimiento de Virus , SARS-CoV-2/aislamiento & purificación , Aislamiento de Pacientes/métodosRESUMEN
We demonstrate that heterogeneity in the perceived risks associated with infection within host populations amplifies chances of superspreading during the crucial early stages of epidemics. Under this behavioural model, individuals less concerned about dangers from infection are more likely to be infected and attend larger sized (riskier) events, where we assume event sizes remain unchanged. For directly transmitted diseases such as COVID-19, this leads to infections being introduced at rates above the population prevalence to those events most conducive to superspreading. We develop an interpretable, computational framework for evaluating within-event risks and derive a small-scale reproduction number measuring how the infections generated at an event depend on transmission heterogeneities and numbers of introductions. This generalizes previous frameworks and quantifies how event-scale patterns and population-level characteristics relate. As event duration and size grow, our reproduction number converges to the basic reproduction number. We illustrate that even moderate levels of heterogeneity in the perceived risks of infection substantially increase the likelihood of disproportionately large clusters of infections occurring at larger events, despite fixed overall disease prevalence. We show why collecting data linking host behaviour and event attendance is essential for accurately assessing the risks posed by invading pathogens in emerging stages of outbreaks.
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COVID-19 , SARS-CoV-2 , Humanos , COVID-19/transmisión , COVID-19/epidemiología , Número Básico de Reproducción , Modelos BiológicosRESUMEN
Following the apparent final case in an Ebola virus disease (EVD) outbreak, the decision to declare the outbreak over must balance societal benefits of relaxing interventions against the risk of resurgence. Estimates of the end-of-outbreak probability (the probability that no future cases will occur) provide quantitative evidence that can inform the timing of an end-of-outbreak declaration. An existing modeling approach for estimating the end-of-outbreak probability requires comprehensive contact tracing data describing who infected whom to be available, but such data are often unavailable or incomplete during outbreaks. Here, we develop a Markov chain Monte Carlo-based approach that extends the previous method and does not require contact tracing data. Considering data from two EVD outbreaks in the Democratic Republic of the Congo, we find that data describing who infected whom are not required to resolve uncertainty about when to declare an outbreak over.
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Brotes de Enfermedades , Fiebre Hemorrágica Ebola , Fiebre Hemorrágica Ebola/epidemiología , Fiebre Hemorrágica Ebola/prevención & control , República Democrática del Congo/epidemiología , Humanos , Ebolavirus , Cadenas de Markov , Método de MontecarloRESUMEN
During an infectious disease outbreak, public health policy makers are tasked with strategically implementing interventions whilst balancing competing objectives. To provide a quantitative framework that can be used to guide these decisions, it is helpful to devise a clear and specific objective function that can be evaluated to determine the optimal outbreak response. In this study, we have developed a mathematical modelling framework representing outbreaks of a novel emerging pathogen for which non-pharmaceutical interventions (NPIs) are imposed or removed based on thresholds for hospital occupancy. These thresholds are set at different levels to define four unique strategies for disease control. We illustrate that the optimal intervention strategy is contingent on the choice of objective function. Specifically, the optimal strategy depends on the extent to which policy makers prioritise reducing health costs due to infection over the costs associated with maintaining interventions. Motivated by the scenario early in the COVID-19 pandemic, we incorporate the development of a vaccine into our modelling framework and demonstrate that a policy maker's belief about when a vaccine will become available in future, and its eventual coverage (and/or effectiveness), affects the optimal strategy to adopt early in the outbreak. Furthermore, we show how uncertainty in these quantities can be accounted for when deciding which interventions to introduce. This research highlights the benefits of policy makers being explicit about the precise objectives of introducing interventions.
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COVID-19 , Análisis Costo-Beneficio , Humanos , COVID-19/prevención & control , COVID-19/epidemiología , Incertidumbre , SARS-CoV-2 , Vacunas contra la COVID-19/economía , Pandemias/prevención & control , Cuarentena , Biología Computacional , Brotes de Enfermedades/prevención & control , Modelos Teóricos , Epidemias/prevención & controlRESUMEN
Understanding changes in the transmission dynamics of mpox requires comparing recent estimates of key epidemiologic parameters with historical data. We derived historical estimates for the incubation period and serial interval for mpox and contrasted them with pooled estimates from the 2022 outbreak. Our findings show the pooled mean infection-to-onset incubation period was 8.1 days for the 2022 outbreak and 8.2 days historically, indicating the incubation periods remained relatively consistent over time, despite a shift in the major mode of transmission. However, we estimated the onset-to-onset serial interval at 8.7 days using 2022 data, compared with 14.2 days using historical data. Although the reason for this shortening of the serial interval is unclear, it may be because of increased public health interventions or a shift in the mode of transmission. Recognizing such temporal shifts is essential for informed response strategies, and public health measures remain crucial for controlling mpox and similar future outbreaks.
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Brotes de Enfermedades , Periodo de Incubación de Enfermedades Infecciosas , Mpox , Humanos , Mpox/epidemiología , Mpox/historia , Mpox/transmisión , Mpox/virología , Historia del Siglo XXI , Salud GlobalRESUMEN
Background and Aims: No recovery related surveillance system exists but given the evidence of effectiveness and growing supply, a house- and resident- level recovery house (RH) surveillance system could be beneficial for data collection on recovery support service (RSS) engagement, and retention; for improved standardization of RH programs and services; and for identification of outcomes associated with long-term recovery. Methods: This study aimed to explore current data collection practices at the resident- and house- level through qualitative focus interviews of RH representatives. The 13 RH interviews were scheduled with 16 RH representative respondents. Results: The most frequently collected resident data was at entry (92%) and departure (85%) and included demographics (n = 5), substance use history (n = 6), treatment and recovery history (n = 5), legal and correctional history (n = 6) and mental health information (n = 7). Recovery support data was collected by 85% of houses. Post-stay data was only collected by four RHs (31%). Conclusion: These results indicate that there is a lack of standardized systematic collection, analysis, and reporting of recovery related data in the RH field. A recovery related surveillance system has the potential to fill this gap and inform and improve standard of resident care to support long-term recovery from substance use disorder.
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Background: Individuals with substance use disorder (SUD) and recovery support services often face significant social stigma, especially in rural areas. One method of addressing stigma is through education and personal recovery stories. It is unclear if such messages will work similarly across rural and non-rural areas. Methods: We conduct an exploratory analysis of data from a national randomized controlled trial (N = 2,721) to determine if there are differences in the effectiveness of messages at reducing stigma across rurality. Specifically, we test four interventions to reduce stigma: education about the effectiveness of recovery housing and three versions of a personal recovery story that varied social distance and delivery medium (identified written story, anonymous written story, and video). Results: We find that messages may not have the same effect across rurality, with non-rural participants in the identified and anonymous written recovery story groups having lower stigma scores and only rural participants exposed to the anonymous written story having lower stigma scores compared to their counterparts in the control group. Further, non-rural participants exposed to both written story treatments had higher positive feelings towards those in recovery compared to the control group, but only rural participants in the anonymous written story group had higher positive feelings compared to the control group. Conclusion: Our results suggest that messages may have different effects on stigma across rurality and that rural participants' beliefs may be particularly hard to change. Future research should examine what types of stigma reduction interventions are most effective in rural areas.
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With over one-hundred thousand drug overdose deaths in 2021, substance use disorder (SUD) is a public health crisis in the United States. Medical stabilization has been the predominant focus of SUD interventions despite low levels of retention. Consequently, national quality measures for SUD care outside the clinical continuity of care are limited. The expansion of recovery support services addressing social drivers of health outside clinical settings is needed. The current SUD quality measures are not applicable nor feasible for recovery support service providers with limited resource capacities, like the estimated 17,900 recovery housing providers nationwide. Despite widespread support for recovery housing and its documented effectiveness, no universal set of measures has been developed for widespread adoption. In this brief, a matrix of quality measures are proposed to meet the needs of recovery housing providers with various capacities to support service evolution and improve equitable SUD treatment and recovery care.
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Vivienda , Trastornos Relacionados con Sustancias , Humanos , Estados Unidos , Trastornos Relacionados con Sustancias/terapia , Evaluación de Resultado en la Atención de SaludRESUMEN
The impact of adverse listening conditions on spoken language perception is well established, but the role of suboptimal viewing conditions on signed language processing is less clear. Viewing angle, i.e. the physical orientation of a perceiver relative to a signer, varies in many everyday deaf community settings for L1 signers and may impact comprehension. Further, processing from various viewing angles may be more difficult for late L2 learners of a signed language, with less variation in sign input while learning. Using a semantic decision task in a distance priming paradigm, we show that British Sign Language signers are slower and less accurate to comprehend signs shown from side viewing angles, with L2 learners in particular making disproportionately more errors when viewing signs from side angles. We also investigated how individual differences in mental rotation ability modulate processing signs from different angles. Speed and accuracy on the BSL task correlated with mental rotation ability, suggesting that signers may mentally represent signs from a frontal view, and use mental rotation to process signs from other viewing angles. Our results extend the literature on viewpoint specificity in visual recognition to linguistic stimuli. The data suggests that L2 signed language learners should maximise their exposure to diverse signed language input, both in terms of viewing angle and other difficult viewing conditions to maximise comprehension.
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Aprendizaje , Lengua de Signos , Humanos , Individualidad , Lingüística , Examen FísicoRESUMEN
Mathematical modellers model infectious disease dynamics at different scales. Within-host models represent the spread of pathogens inside an individual, whilst between-host models track transmission between individuals. However, pathogen dynamics at one scale affect those at another. This has led to the development of multiscale models that connect within-host and between-host dynamics. In this article, we systematically review the literature on multiscale infectious disease modelling according to PRISMA guidelines, dividing previously published models into five categories governing their methodological approaches (Garira (2017)), explaining their benefits and limitations. We provide a primer on developing multiscale models of infectious diseases.
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Enfermedades Transmisibles , Humanos , Enfermedades Transmisibles/epidemiología , Modelos TeóricosRESUMEN
During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.
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COVID-19 , SARS-CoV-2 , Humanos , SARS-CoV-2/genética , COVID-19/epidemiología , Pandemias , Carga ViralRESUMEN
In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses.
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COVID-19 , SARS-CoV-2 , Humanos , COVID-19/epidemiología , Brotes de Enfermedades/prevención & control , ProbabilidadRESUMEN
Single State Agencies (SSAs) are responsible for managing the publicly funded alcohol and other drug prevention, treatment, and recovery service system. Recovery housing (RH) is an important recovery support service (RSS) for individuals experiencing substance use disorder (SUD). Despite its effectiveness, information on state utilization and support is limited. To assess state-level support for RH and its incorporation within the SSA-managed SUD service systems, we administered a survey with SSAs in the 50 United States and the District of Columbia. In total, 48 out of the 51 SSAs responded, yielding a 94% response rate. Findings indicate strong state-level support for RH in terms of it being an integral RSS (98%), part of state-level strategic plans (73%) and prioritized for funding (87.5%). States are making progress to formalize RH with 68% reporting RH had been defined formally or within their agency. However, activities around understanding the capacity and need for RH are limited, with 44% indicating a needs assessment had not been conducted. At the same time, states perceive RH as a priority RSS, with growing recognition of its positive impact on long-term SUD recovery. This research identifies the opportunities for stakeholders to further evolve and expand RH at the federal, state, and local levels.
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Viruses evolve in infected host populations, and host population dynamics affect viral evolution. RNA viruses with a short duration of infection and a high peak viral load, such as SARS-CoV-2, are maintained in human populations. By contrast, RNA viruses characterized by a long infection duration and a low peak viral load (e.g., borna disease virus) can be maintained in nonhuman populations, and the process of the evolution of persistent viruses has rarely been explored. Here, using a multi-level modeling approach including both individual-level virus infection dynamics and population-scale transmission, we consider virus evolution based on the host environment, specifically, the effect of the contact history of infected hosts. We found that, with a highly dense contact history, viruses with a high virus production rate but low accuracy are likely to be optimal, resulting in a short infectious period with a high peak viral load. In contrast, with a low-density contact history, viral evolution is toward low virus production but high accuracy, resulting in long infection durations with low peak viral load. Our study sheds light on the origin of persistent viruses and why acute viral infections but not persistent virus infection tends to prevail in human society.
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COVID-19 , Virosis , Virus , Animales , Humanos , SARS-CoV-2/genética , Virus/genéticaRESUMEN
Non-pharmaceutical interventions (NPIs), such as social distancing and contact tracing, are important public health measures that can reduce pathogen transmission. In addition to playing a crucial role in suppressing transmission, NPIs influence pathogen evolution by mediating mutation supply, restricting the availability of susceptible hosts, and altering the strength of selection for novel variants. Yet it is unclear how NPIs might affect the emergence of novel variants that are able to escape pre-existing immunity (partially or fully), are more transmissible or cause greater mortality. We analyse a stochastic two-strain epidemiological model to determine how the strength and timing of NPIs affect the emergence of variants with similar or contrasting life-history characteristics to the wild type. We show that, while stronger and timelier NPIs generally reduce the likelihood of variant emergence, it is possible for more transmissible variants with high cross-immunity to have a greater probability of emerging at intermediate levels of NPIs. This is because intermediate levels of NPIs allow an epidemic of the wild type that is neither too small (facilitating high mutation supply), nor too large (leaving a large pool of susceptible hosts), to prevent a novel variant from becoming established in the host population. However, since one cannot predict the characteristics of a variant, the best strategy to prevent emergence is likely to be an implementation of strong, timely NPIs.
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We consider a hierarchy of ordinary differential equation models that describe the within-host viral kinetics of influenza infections: the IR model explicitly accounts for an immune response to the virus, while the simpler, target-cell limited TEIV and TV models do not. We show that when the IR model is fitted to pooled experimental murine data of the viral load, fraction of dead cells, and immune response levels, its parameters values can be determined. However, if, as is common, only viral load data are available, we can estimate parameters of the TEIV and TV models but not the IR model. These results are substantiated by a structural and practical identifiability analysis. We then use the IR model to generate synthetic data representing infections in hosts whose immune responses differ. We fit the TV model to these synthetic datasets and show that it can reproduce the characteristic exponential increase and decay of viral load generated by the IR model. Furthermore, the values of the fitted parameters of the TV model can be mapped from the immune response parameters in the IR model. We conclude that, if only viral load data are available, a simple target-cell limited model can reproduce influenza infection dynamics and distinguish between hosts with differing immune responses.
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Gripe Humana , Animales , Ratones , Humanos , Inmunidad InnataRESUMEN
Background: Recovery Housing (RH), an important resource for substance use disorder (SUD) recovery, centers on shared lived experience. Program evaluation considers the contribution of environmental factors to outcomes, yet most research on outcomes has focused on patient factors and fidelity to protocols. Investigations of process measures reflecting the dynamic interplay between patient factors and the treatment program are limited. Alliance, one's perceived connection with others, is a process measure associated with mental health outcomes and includes domains "tasks," "goals," and "bonds." We posit that alliance serves as a proxy construct to measure the impact of shared experience in RH.Objectives: Develop and assess the psychometric properties of the Fletcher Recovery Housing Alliance Measure (FRHAM-12) for RH.Methods: A cross-sectional survey with the 12-item FRHAM-12 was administered to 271 individuals (60% men, 39% women, 1% other) within six RH centers in Kentucky. Item-total correlations, internal consistency reliability, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) were conducted.Results: The FRHAM-12 was found to have a strong internal consistency (0.924 alpha coefficient) and the EFA yielded a single component (56.38% of cumulative scale variance). CFA indicated acceptable levels of absolute and relative fit of a unidimensional scale with values of 0.67 and 0.976 for the standardized root mean square residual and relative fit index.Conclusion: This study aimed to construct and validate an initial measure for RH alliance resulted in the brief, FRHAM-12; a tool with strong internal and factor validity. Future research should examine the measure's predictive and concurrent validity.
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Vivienda , Masculino , Humanos , Femenino , Psicometría , Estudios Transversales , Reproducibilidad de los Resultados , Encuestas y Cuestionarios , Análisis FactorialRESUMEN
Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens, which requires effective surveillance through the implementation of appropriate monitoring programmes. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. 'Sentinel' plants-alternative susceptible host species that display visible symptoms of infection more rapidly-could be introduced to at-risk populations and included in monitoring programmes to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.
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Olea , Especies Centinela , Teorema de Bayes , Enfermedades de las Plantas , PlantasRESUMEN
In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".