Improving web-based respondent-driven sampling performance among men who have sex with men in the Netherlands.

Respondent-driven sampling (RDS) uses the social network of participants to sample people of populations that can be challenging to engage. While in this context RDS offers improvements on standard sampling methods, it does not always generate a sufficiently large sample. In this study we aimed to identify preferences of men who have sex with men (MSM) in the Netherlands regarding surveys and recruitment to studies with the subsequent goal of improving the performance of web-based RDS in MSM. A questionnaire about preferences with respect to various aspects of an web-based RDS study was circulated among participants of the Amsterdam Cohort Studies, a study among MSM. The duration of a survey and the type and amount of participation reward were explored. Participants were also asked about their preferences regarding invitation and recruitment methods. We used multi-level and rank-ordered logistic regression to analyze the data and identify the preferences. The majority of the 98 participants were older than 45 years (59.2%), were born in the Netherlands (84.7%), and had a university degree (77.6%). Participants did not have a preference regarding the type of participation reward, but they preferred to spend less time on a survey and to get a higher monetary reward. Sending a personal email was the preferred option to getting invited or inviting someone to a study, while using Facebook messenger was the least preferred option. There are differences between age groups: monetary rewards were less important to older participants (45+) and younger participants (18-34) more often preferred SMS/WhatsApp to recruit others. When designing a web-based RDS study for MSM, it is important to balance the duration of the survey and the monetary reward. If the study takes more of a participants time, it might be beneficial to provide a higher incentive. To optimize expected participation, the recruitment method should be selected based on the targeted population group.

The importance of sustained compliance with physical distancing during COVID-19 vaccination rollout.

BACKGROUND: Increasing vaccination coverage against SARS-CoV-2 enabled relaxation of lockdowns in many countries in Europe. As the vaccination rollouts progressed, the public health authorities were seeking recommendations on the continuation of physical distancing measures during ongoing vaccination rollouts. Compliance with these measures was declining while more transmissible virus variants have emerged. METHODS: We used a SARS-CoV-2 transmission model to investigate the feedback between compliance, infection incidence, and vaccination coverage. We quantified our findings in terms of cumulative number of new hospitalisations three and six months after the start of vaccination. RESULTS: Our results suggest that the combination of fast waning compliance in non-vaccinated individuals, low compliance in vaccinated individuals, low vaccine efficacy against infection and more transmissible virus variants may result in a higher cumulative number of new hospitalisations than in a situation without vaccination. These adverse effects can be alleviated by deploying behavioural interventions that should preferably target both vaccinated and non-vaccinated individuals. The choice of the most appropriate intervention depends on vaccination rate and vaccine efficacy against infection. CONCLUSIONS: Supplementary behavioural interventions aiming to boost compliance to physical distancing measures can improve the outcome of vaccination programmes, until vaccination coverage is sufficiently high. For optimal results, these interventions should be selected based on the vaccine efficacy against infection and expected vaccination rate. While we considered the dynamics of SARS-CoV-2, the qualitative effects of the interplay between infectious disease spread and behavior on the outcomes of a vaccination programme can be used as guidance in a future similar pandemic.

Health behavior homophily can mitigate the spread of infectious diseases in small-world networks.

Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.

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.

Impact of self-imposed prevention measures and short-term government-imposed social distancing on mitigating and delaying a COVID-19 epidemic: A modelling study.

BACKGROUND: The coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to nearly every country in the world since it first emerged in China in December 2019. Many countries have implemented social distancing as a measure to "flatten the curve" of the ongoing epidemics. Evaluation of the impact of government-imposed social distancing and of other measures to control further spread of COVID-19 is urgent, especially because of the large societal and economic impact of the former. The aim of this study was to compare the individual and combined effectiveness of self-imposed prevention measures and of short-term government-imposed social distancing in mitigating, delaying, or preventing a COVID-19 epidemic. METHODS AND FINDINGS: We developed a deterministic compartmental transmission model of SARS-CoV-2 in a population stratified by disease status (susceptible, exposed, infectious with mild or severe disease, diagnosed, and recovered) and disease awareness status (aware and unaware) due to the spread of COVID-19. Self-imposed measures were assumed to be taken by disease-aware individuals and included handwashing, mask-wearing, and social distancing. Government-imposed social distancing reduced the contact rate of individuals irrespective of their disease or awareness status. The model was parameterized using current best estimates of key epidemiological parameters from COVID-19 clinical studies. The model outcomes included the peak number of diagnoses, attack rate, and time until the peak number of diagnoses. For fast awareness spread in the population, self-imposed measures can significantly reduce the attack rate and diminish and postpone the peak number of diagnoses. We estimate that a large epidemic can be prevented if the efficacy of these measures exceeds 50%. For slow awareness spread, self-imposed measures reduce the peak number of diagnoses and attack rate but do not affect the timing of the peak. Early implementation of short-term government-imposed social distancing alone is estimated to delay (by at most 7 months for a 3-month intervention) but not to reduce the peak. The delay can be even longer and the height of the peak can be additionally reduced if this intervention is combined with self-imposed measures that are continued after government-imposed social distancing has been lifted. Our analyses are limited in that they do not account for stochasticity, demographics, heterogeneities in contact patterns or mixing, spatial effects, imperfect isolation of individuals with severe disease, and reinfection with COVID-19. CONCLUSIONS: Our results suggest that information dissemination about COVID-19, which causes individual adoption of handwashing, mask-wearing, and social distancing, can be an effective strategy to mitigate and delay the epidemic. Early initiated short-term government-imposed social distancing can buy time for healthcare systems to prepare for an increasing COVID-19 burden. We stress the importance of disease awareness in controlling the ongoing epidemic and recommend that, in addition to policies on social distancing, governments and public health institutions mobilize people to adopt self-imposed measures with proven efficacy in order to successfully tackle COVID-19.

A threshold delay model of HIV infection of newborn infants through breastfeeding.

The breast milk of HIV infected women contains HIV virus particles, therefore children can become infected through breastfeeding. We develop a mathematical epidemiological model of HIV infection in infants, infected children and infected women that represents infection of an infant/child as a series of exposures, by incorporating within-host virus dynamics in the individuals exposed to the virus through breastfeeding. We show that repeated exposures of the infant/child via breastfeeding can cause bi-stability dynamics and, subsequently, infection persistence even when the epidemiological basic reproduction number R 0 is less than unity. This feature of the model, due to a backward bifurcation, gives new insight into the control mechanisms of HIV disease through breastfeeding.

Cost-effectiveness of measles control during elimination in Ontario, Canada, 2015.

BackgroundGiven that measles is eliminated in Canada and measles immunisation coverage in Ontario is high, it has been questioned whether Ontario's measles outbreak response is worthwhile.AimOur objective was to determine cost-effectiveness of measles containment protocols in Ontario from the healthcare payer perspective.MethodsWe developed a decision-analysis model comparing Ontario's measles containment strategy (based on actual 2015 outbreak data) with a hypothetical 'modified response'. The modified scenario assumed 10% response costs with reduced case and contact tracing and no outbreak-associated vaccinations; it was based on local and provincial administrative and laboratory data and parameters from peer-reviewed literature. Short- and long-term health outcomes, quality-adjusted life years (QALYs) and costs discounted at 1.5%, were estimated. We conducted one- and two-way sensitivity analyses.ResultsThe 2015 outbreak in Ontario comprised 16 measles cases and an estimated 3,369 contacts. Predictive modelling suggested that the outbreak response prevented 16 outbreak-associated cases at a cost of CAD 1,213,491 (EUR 861,579). The incremental cost-effectiveness ratio was CAD 739,063 (EUR 524,735) per QALY gained for the outbreak response vs modified response. To meet the commonly accepted cost-effectiveness threshold of CAD 50,000 (EUR 35,500) per QALY gained, the outbreak response would have to prevent 94 measles cases. In sensitivity analyses, the findings were robust.ConclusionsOntario's measles outbreak response exceeds generally accepted cost-effectiveness thresholds and may not be the most efficient use of public health resources from a healthcare payer perspective. These findings should be balanced against benefits of increased vaccine coverage and maintaining elimination status.

An adaptive stepsize method for the chemical Langevin equation.

Mathematical and computational modeling are key tools in analyzing important biological processes in cells and living organisms. In particular, stochastic models are essential to accurately describe the cellular dynamics, when the assumption of the thermodynamic limit can no longer be applied. However, stochastic models are computationally much more challenging than the traditional deterministic models. Moreover, many biochemical systems arising in applications have multiple time-scales, which lead to mathematical stiffness. In this paper we investigate the numerical solution of a stochastic continuous model of well-stirred biochemical systems, the chemical Langevin equation. The chemical Langevin equation is a stochastic differential equation with multiplicative, non-commutative noise. We propose an adaptive stepsize algorithm for approximating the solution of models of biochemical systems in the Langevin regime, with small noise, based on estimates of the local error. The underlying numerical method is the Milstein scheme. The proposed adaptive method is tested on several examples arising in applications and it is shown to have improved efficiency and accuracy compared to the existing fixed stepsize schemes.