Impact of early-stage HIV transmission on treatment as prevention

Timely HIV treatment improves health (1) and reduces transmission (2). These individual- level benefits of HIV treatment for both clinical and preventive purposes are well established, but several questions remain about the population-level impact of HIV treatment as prevention (3). In PNAS, Eaton and Hallett (4) use a mathematical model to address one such question: Does the proportion of transmission during early HIV infection affect the impact of HIV treatment on HIV incidence

Analysis of timeliness of infectious disease reporting in the Netherlands

<p>Abstract</p> <p>Background</p> <p>Timely reporting of infectious disease cases to public health authorities is essential to effective public health response. To evaluate the timeliness of reporting to the Dutch Municipal Health Services (MHS), we used as quantitative measures the intervals between onset of symptoms and MHS notification, and between laboratory diagnosis and notification with regard to six notifiable diseases.</p> <p>Methods</p> <p>We retrieved reporting data from June 2003 to December 2008 from the Dutch national notification system for shigellosis, EHEC/STEC infection, typhoid fever, measles, meningococcal disease, and hepatitis A virus (HAV) infection. For each disease, median intervals between date of onset and MHS notification were calculated and compared with the median incubation period. The median interval between date of laboratory diagnosis and MHS notification was similarly analysed. For the year 2008, we also investigated whether timeliness is improved by MHS agreements with physicians and laboratories that allow direct laboratory reporting. Finally, we investigated whether reports made by post, fax, or e-mail were more timely.</p> <p>Results</p> <p>The percentage of infectious diseases reported within one incubation period varied widely, between 0.4% for shigellosis and 90.3% for HAV infection. Not reported within two incubation periods were 97.1% of shigellosis cases, 76.2% of cases of EHEC/STEC infection, 13.3% of meningococcosis cases, 15.7% of measles cases, and 29.7% of typhoid fever cases. A substantial percentage of infectious disease cases was reported more than three days after laboratory diagnosis, varying between 12% for meningococcosis and 42% for shigellosis. MHS which had agreements with physicians and laboratories showed a significantly shorter notification time compared to MHS without such agreements.</p> <p>Conclusions</p> <p>Over the study period, many cases of the six notifiable diseases were not reported within two incubation periods, and many were reported more than three days after laboratory diagnosis. An increase in direct laboratory reporting of diagnoses to MHS would improve timeliness, as would the use of fax rather than post or e-mail. Automated reporting systems have to be explored in the Netherlands. Development of standardised and improved measures for timeliness is needed.</p

Клинические, гемодинамические и биохимические эффекты рамиприла у больных сахарным диабетом 2-го типа и артериальной гипертензией

Изучены клинические, гемодинамические и биохимические эффекты рамиприла у больных сахарным диабетом 2−го типа и артериальной гипертензией.Вивчено клінічні, гемодинамічні та біохімічні ефекти раміприлу у хворих на цукровий діабет 2−го типу та артеріальну гіпертензію.Clinical, hemodynamic, and biochemical effects of Ramipril were investigated in patients with type 2 diabetes mellitus and arterial hypertension

The effect of competition between health opinions on epidemic dynamics

Past major epidemic events showed that when an infectious disease is perceived to cause severe health outcomes, individuals modify health behavior affecting epidemic dynamics. To investigate the effect of this feedback relationship on epidemic dynamics, we developed a compartmental model that couples a disease spread framework with competition of two mutually exclusive health opinions (health-positive and health-neutral) associated with different health behaviors. The model is based on the assumption that individuals switch health opinions as a result of exposure to opinions of others through interpersonal communications. To model opinion switch rates, we considered a family of functions and identified the ones that allow health opinions to coexist. Finally, the model includes assortative mixing by opinions. In the disease-free population, either the opinions cannot coexist and one of them is always dominating (mono-opinion equilibrium) or there is at least one stable coexistence of opinions equilibrium. In the latter case, there is multistability between the coexistence equilibrium and the two mono-opinion equilibria. When two opinions coexist, it depends on their distribution whether the infection can invade. If presence of the infection leads to increased switching to a health-positive opinion, the epidemic burden becomes smaller than indicated by the basic reproduction number. Additionally, a feedback between epidemic dynamics and health opinion dynamics may result in (sustained) oscillatory dynamics and a switch to a different stable opinion distribution. Our model captures feedback between spread of awareness through social interactions and infection dynamics and can serve as a basis for more elaborate individual-based models

Controlling the pandemic during the SARS-CoV-2 vaccination rollout

© The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point the control of COVID-19 would be achieved for each scenario.G.R., J.V., A.N., M.C.G. were supported by Fundação para a Ciência e a Tecnologia (FCT) project reference 131_596787873, awarded to G.R. M.V. was supported by the European Union H2020 ERA project (No. 667824 - EXCELLtoINNOV). The contribution of C.H.v.D. was under the auspices of the US Department of Energy (contract number 89233218CNA000001) and supported by the National Institutes of Health (grant number R01-OD011095). MK acknowledges support from the Netherlands Organization for Health Research and Development (ZonMw) Grant no. 10430022010001.info:eu-repo/semantics/publishedVersio

An evidence synthesis approach to estimating the incidence of seasonal influenza in the Netherlands.

OBJECTIVES: To estimate, using Bayesian evidence synthesis, the age-group-specific annual incidence of symptomatic infection with seasonal influenza in the Netherlands over the period 2005-2007. METHODS: The Netherlands population and age group distribution for 2006 defined the base population. The number of influenza-like illness (ILI) cases was estimated from sentinel surveillance data and adjusted for underascertainment using the estimated proportion of ILI cases that do not consult a general practitioner. The estimated number of symptomatic influenza (SI) cases was based on indirect evidence from the surveillance of ILI cases and the proportions of laboratory-confirmed influenza cases in the 2004/5, 2005/6 and 2006/7 respiratory years. In scenario analysis, the number of SI cases prevented by increasing vaccination uptake within the 65 +  age group was estimated. RESULTS: The overall symptomatic infection attack rate (SIAR) over the period 2005-2007 was estimated at 2·5% (95% credible interval [CI]: 2·1-3·2%); 410 200 SI cases (95% CI: 338 500-518 600) were estimated to occur annually. Age-group-specific SIARs were estimated for <5 years at 4·9% (2·1-13·7%), for 5-14 years at 3·0% (2·0-4·7%), for 15-44 years at 2·6% (2·1-3·2%), for 45-64 years at 1·9% (1·4-2·5%) and for 65 +  years at 1·7% (1·0-3·0%). Under assumed vaccination uptake increases of 5% and 15%, 1970 and 5310 SI cases would be averted. CONCLUSIONS: By synthesising the available information on seasonal influenza and ILI from diverse sources, the annual extent of symptomatic infection can be derived. These estimates are useful for assessing the burden of seasonal influenza and for guiding vaccination policy

Влияние ледового сжатия на составляющие скорости движения жидкости под ледяным покровом в бегущей периодической изгибно-гравитационной волне конечной амплитуды

Методом многих масштабов с точностью до величин третьего порядка малости получены асимптотические разложения, определяющие составляющие скорости движения жидкости под плавающим ледяным покровом при распространении периодической поверхностной изгибногравитационной волны конечной амплитуды в условиях ледового сжатия. Рассмотрена зависимость распределений составляющих скорости вдоль профиля волны от величины сжимающего усилия и характеристик начальной гармоники. Показано, что с увеличением сжимающего усилия происходит уменьшение амплитудных значений составляющих скорости и отставание фазы колебаний.Методом багатьох масштабів з точністю до величин третього порядку малості отримані асимптотичні розкладання, які визначають складові швидкості руху рідини під плаваючим льодяним покривом при розповсюдженні періодичної поверхневої згинально-гравітаційної хвилі кінцевої амплітуди в умовах льодяного стиснення. Розглянуто залежність розподілів складових швидкості вздовж профілю хвилі від величини стискаючого зусилля та характеристик початкової гармоніки. Показано, що із збільшенням стискаючого зусилля відбувається зменшення амплітудних значень складових швидкості та відставання фази коливань.Using the method of multiple scales, the asymptotic expansions are obtained up to the values of the third order. The expansions condition the components of fluid movement velocity under floating ice cover at propagation of periodic surface flexural-gravity wave of finite amplitude in the condition of ice compression. Dependence of distribution of velocity components along the wave profile upon the compressive force value and the initial harmonic characteristics is considered. It is shown that rise of compressive force is accompanied by decrease of amplitude values of velocity components and lag of oscillations’ phase

Clustering of chronic hepatitis B screening intentions in social networks of Moroccan immigrants in the Netherlands

Background Early detection, identification, and treatment of chronic hepatitis B through screening is vital for those at increased risk, e.g. born in hepatitis B endemic countries. In the Netherlands, Moroccan immigrants show low participation rates in health-related screening programmes. Since social networks influence health behaviour, we investigated whether similar screening intentions for chronic hepatitis B cluster within social networks of Moroccan immigrants. Methods We used respondent-driven sampling (RDS) where each participant ("recruiter") was asked to complete a questionnaire and to recruit three Moroccans ("recruitees") from their social network. Logistic regression analyses were used to analyse whether the recruiters' intention to request a screening test was similar to the intention of their recruitees. Results We sampled 354 recruiter-recruitee pairs: for 154 pairs both participants had a positive screening intention, for 68 pairs both had a negative screening intention, and the remaining 132 pairs had a discordant intention to request a screening test. A tie between a recruiter and recruitee was associated with having the same screening intention, after correction for sociodemographic variables (OR 1.70 [1.15-2.51]). Conclusions The findings of our pilot study show clustering of screening intention among individuals in the same network. This provides opportunities for social network interventions to encourage participation in hepatitis B screening initiatives

Challenges for modelling interventions for future pandemics

Funding: This work was supported by the Isaac Newton Institute (EPSRC grant no. EP/R014604/1). MEK was supported by grants from The Netherlands Organisation for Health Research and Development (ZonMw), grant number 10430022010001, and grant number 91216062, and by the H2020 Project 101003480 (CORESMA). RNT was supported by the UKRI, grant number EP/V053507/1. GR was supported by Fundação para a Ciência e a Tecnologia (FCT) project reference 131_596787873. and by the VERDI project 101045989 funded by the European Union. LP and CO are funded by the Wellcome Trust and the Royal Society (grant 202562/Z/16/Z). LP is also supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1) and by The Alan Turing Institute for Data Science and Artificial Intelligence. HBS is funded by the Wellcome Trust and Royal Society (202562/Z/16/Z), and the Alexander von Humboldt Foundation. DV had support from the National Council for Scientific and Technological Development of Brazil (CNPq - Refs. 441057/2020-9, 424141/2018-3, 309569/2019-2). FS is supported by the UKRI through the JUNIPER modelling consortium (grant number MR/V038613/1). EF is supported by UKRI (Medical Research Council)/Department of Health and Social Care (National Insitute of Health Research) MR/V028618/1. JPG's work was supported by funding from the UK Health Security Agency and the UK Department of Health and Social Care.Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.Publisher PDFPeer reviewe

Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study

Background In countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful. Methods We evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy (RCTS) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts. Findings For the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7–0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% (RCTS 0·8, 95% CI 0·7–1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep RCTS below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach RCTS values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay. Interpretation In our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, further enhanced contact tracing effectiveness, with the potential to prevent up to 80% of all transmissions. Access to testing should therefore be optimised, and mobile app technology might reduce delays in the contact tracing process and optimise contact tracing coverage. Funding ZonMw, Fundação para a Ciência e a Tecnologia, and EU Horizon 2020 RECOVER