A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance

The evolutionary responses of infectious pathogens often have ruinous consequences for the control of disease spread in the population. Drug resistance is a well-documented instance that is generally driven by the selective pressure of drugs on both the replication of the pathogen within hosts and its transmission between hosts. Management of drug resistance therefore requires the development of treatment strategies that can impede the emergence and spread of resistance in the population. This study evaluates various treatment strategies for influenza infection as a case study by comparing the long-term epidemiological outcomes predicted by deterministic and stochastic versions of a homogeneously mixing (mean-field) model and those predicted by a heterogeneous model that incorporates spatial pair-wise correlation. We discuss the importance of three major parameters in our evaluation: the basic reproduction number, the population level of treatment, and the degree of clustering as a key parameter determining the structure of heterogeneous interactions. The results show that, as a common feature in all models, high treatment levels during the early stages of disease outset can result in large resistant outbreaks, with the possibility of a second wave of infection appearing in the pair-approximation model. Our simulations demonstrate that, if the basic reproduction number exceeds a threshold value, the population-wide spread of the resistant pathogen emerges more rapidly in the pair-approximation model with significantly lower treatment levels than in the homogeneous models. We tested an antiviral strategy that delays the onset of aggressive treatment for a certain amount of time after the onset of the outbreak. The findings indicate that the overall disease incidence is reduced as the degree of clustering increases, and a longer delay should be considered for implementing the large-scale treatment.     

Inhibitory effect of Bunium persicum on histamine (H1) receptors of guinea pig tracheal chains.

The antihistaminic effects of aqueous and macerated extracts, essential oil, 20 nM chlorpheniramine, and saline were tested by performing the cumulative log concentration-response curves of histamine-induced contraction of isolated guinea pig tracheal chains incubated with three different conditions including: (1) 1.4 microM indomethacin, (2) indomethacin, 1 microM propranolol, and 10 nM atropine, and (3) indomethacin and propranolol (for each group n = 8). The results showed clear parallel rightward shifts in histamine-response curves obtained in the presence of macerated extract in group 2, aqueous extract in group 3, and essential oil in groups 2 and 3 experiments compared with the curves obtained in the presence of saline. The EC50 (effective concentration of histamine causing 50% of maximum response) obtained in the presence of essential oil, extracts, and chlorpheniramine in all three sets of experiments were significantly higher than that of saline (P<0.05 to p<0.001). The maximum response obtained in the presence of aqueous extract in group 3 compared to group I and that of macerated extract in group 2 compared to the other two sets of experiments were improved. These results indicated a competitive antagonistic effect of Bunium persicum at histamine H1 receptors.     

Contrasting responses of large carnivores to land use management across an Asian montane landscape in Iran

Land-use change has led to substantial range contractions for many species. Such contractions are particularly acute for wide-ranging large carnivores in Asia’s high altitude areas, which are marked by high spatiotemporal variability in resources. Current conservation planning for human-dominated landscapes often takes one of two main approaches: a “coexistence” (land sharing) approach or a “separation” (land sparing) approach. In this study, we evaluated the effects of land-use management on a guild of large carnivores in a montane ecosystem located in northeastern Iran. We used interview surveys to collect data on Persian leopard Panthera pardus saxicolor and grey wolf Canis lupus and modeled the areas occupied by these species in a Bayesian framework. After accounting for imperfect detection, we found that wolves had a higher probability of occupying the study area than leopards (82%; 95% CI 73–90% vs. 63%; 95% CI 53–73%). Importantly, each predator showed contrasting response to land-use management. National Parks (i.e. human-free areas) had a positive association with leopard occupancy (α_{National Park}?=?2.56, 95% CI 0.22–5.77), in contrast to wolves, which displayed a negative association with National Parks (α_{National Park} = ? 1.62, 95% CI ? 2.29 to 0.31). An opposite pattern was observed for human-dominated areas (i.e. Protected Areas and Communal Lands), where occupancy was higher for wolves but lower for leopards. Our study suggests that to protect these large carnivores, a combination of land sharing and land sparing approaches is desirable within Iran montane landscapes. Any recovery program for big cats in Iranian mountains, and likely similar mountainous landscapes in west Asia, should take into account other sympatric carnivores and how they can affect adjacent human communities. For example, conflict mitigation and compensation efforts are required to include the guild of large carnivores, instead of solely targeting the charismatic big cats.

     

Modelling the effect of a booster vaccination on disease epidemiology

Despite the effectiveness of vaccines in dramatically decreasing the number of new infectious cases and severity of illnesses, imperfect vaccines may not completely prevent infection. This is because the immunity afforded by these vaccines is not complete and may wane with time, leading to resurgence and epidemic outbreaks notwithstanding high levels of primary vaccination. To prevent an endemic spread of disease, and achieve eradication, several countries have introduced booster vaccination programs. The question of whether this strategy could eventually provide the conditions for global eradication is addressed here by developing a seasonally-forced mathematical model. The analysis of the model provides the threshold condition for disease control in terms of four major parameters: coverage of the primary vaccine; efficacy of the vaccine; waning rate; and the rate of booster administration. The results show that if the vaccine provides only temporary immunity, then the infection typically cannot be eradicated by a single vaccination episode. Furthermore, having a booster program does not necessarily guarantee the control of a disease, though the level of epidemicity may be reduced. In addition, these findings strongly suggest that the high coverage of primary vaccination remains crucial to the success of a booster strategy. Simulations using estimated parameters for measles illustrate model predictions. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). One of the authors (P.R.) acknowledges the support of the Ellison Medical Foundation.     

Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza

Background

In the face of an influenza pandemic, accurate estimates of epidemiologic parameters are required to help guide decision-making. We sought to estimate epidemiologic parameters for pandemic H1N1 influenza using data from initial reports of laboratory-confirmed cases.

Methods

We obtained data on laboratory-confirmed cases of pandemic H1N1 influenza reported in the province of Ontario, Canada, with dates of symptom onset between Apr. 13 and June 20, 2009. Incubation periods and duration of symptoms were estimated and fit to parametric distributions. We used competing-risk models to estimate risk of hospital admission and case-fatality rates. We used a Markov Chain Monte Carlo model to simulate disease transmission.

Results

The median incubation period was 4 days and the duration of symptoms was 7 days. Recovery was faster among patients less than 18 years old than among older patients (hazard ratio 1.23, 95% confidence interval 1.06–1.44). The risk of hospital admission was 4.5% (95% CI 3.8%–5.2%) and the case-fatality rate was 0.3% (95% CI 0.1%–0.5%). The risk of hospital admission was highest among patients less than 1 year old and those 65 years or older. Adults more than 50 years old comprised 7% of cases but accounted for 7 of 10 initial deaths (odds ratio 28.6, 95% confidence interval 7.3–111.2). From the simulation models, we estimated the following values (and 95% credible intervals): a mean basic reproductive number (R₀, the number of new cases created by a single primary case in a susceptible population) of 1.31 (1.25–1.38), a mean latent period of 2.62 (2.28–3.12) days and a mean duration of infectiousness of 3.38 (2.06–4.69) days. From these values we estimated a serial interval (the average time from onset of infectiousness in a case to the onset of infectiousness in a person infected by that case) of 4–5 days.

Interpretation

The low estimates for R₀ indicate that effective mitigation strategies may reduce the final epidemic impact of pandemic H1N1 influenza.The emergence and global spread of pandemic H1N1 influenza led the World Health Organization to declare a pandemic on June 11, 2009. As the pandemic spreads, countries will need to make decisions about strategies to mitigate and control disease in the face of uncertainty.For novel infectious diseases, accurate estimates of epidemiologic parameters can help guide decision-making. A key parameter for any new disease is the basic reproductive number (R₀), defined as the average number of new cases created by a single primary case in a susceptible population. R₀ affects the growth rate of an epidemic and the final number of infected people. It also informs the optimal choice of control strategies. Other key parameters that affect use of resources, disease burden and societal costs during a pandemic are duration of illness, rate of hospital admission and case-fatality rate. Early in an epidemic, the case-fatality rate may be underestimated because of the temporal lag between onset of infection and death; the delay between initial identification of a new case and death may lead to an apparent increase in deaths several weeks into an epidemic that is an artifact of the natural history of the disease.We used data from initial reports of laboratory-confirmed pandemic H1N1 influenza to estimate epidemiologic parameters for pandemic H1N1 influenza. The parameters included R₀, incubation period and duration of illness. We also estimated risk of hospital admission and case-fatality rates, which can be used to estimate the burden of illness likely to be associated with this disease.     

Management of drug resistance in the population: influenza as a case study

The rise of drug resistance remains a major impediment to the treatment of some diseases caused by fast-evolving pathogens that undergo genetic mutations. Models describing the within-host infectious dynamics suggest that the resistance is unlikely to emerge if the pathogen-specific immune responses are maintained above a certain threshold during therapy. However, emergence of resistance in the population involves both within-host and between-host infection mechanisms. Here, we employ a mathematical model to identify an effective treatment strategy for the management of drug resistance in the population. We show that, in the absence of pre-existing immunity, the population-wide spread of drug-resistant pathogen strains can be averted if a sizable portion of susceptible hosts is depleted before drugs are used on a large scale. The findings, based on simulations for influenza infection as a case study, suggest that the initial prevalence of the drug-sensitive strain under low pressure of drugs, followed by a timely implementation of intensive treatment, can minimize the total number of infections while preventing outbreaks of drug-resistant infections.     

Friction in metal-on-metal total disc arthroplasty: effect of ball radius

Total disc arthroplasty (TDA) can be used to replace a degenerated intervertebral disc in the spine. There are different designs of prosthetic discs, but one of the most common is a ball-and-socket combination. Contact between the bearing surfaces can result in high frictional torque, which can then result in wear and implant loosening. This study was designed to determine the effects of ball radius on friction. Generic models of metal-on-metal TDA were manufactured with ball radii of 10, 12, 14 and 16 mm, with a radial clearance of 0.015 mm. A simulator was used to test each sample in flexion-extension, lateral bending and axial rotation at frequencies of 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75 and 2 Hz under loads of 50, 600, 1200 and 2000 N, in new born calf serum. Frictional torque was measured and Stribeck curves were plotted to illustrate the lubrication regime in each case. It was observed that implants with a smaller ball radius showed lower friction and showed boundary and mixed lubrication regimes, whereas implants with larger ball radius showed boundary lubrication only. This study suggests designing metal-on-metal TDAs with ball radius of 10 or 12 mm, in order to reduce wear and implant loosening.     

A Delay Differential Model for Pandemic Influenza with Antiviral Treatment

The use of antiviral drugs has been recognized as the primary public health strategy for mitigating the severity of a new influenza pandemic strain. However, the success of this strategy requires the prompt onset of therapy within 48 hours of the appearance of clinical symptoms. This requirement may be captured by a compartmental model that monitors the density of infected individuals in terms of the time elapsed since the onset of symptoms. We show that such a model can be expressed by a system of delay differential equations with both discrete and distributed delays. The model is analyzed to derive the criterion for disease control based on two critical factors: (i) the profile of treatment rate; and (ii) the level of treatment as a function of time lag in commencing therapy. Numerical results are also obtained to illustrate the feasible region of disease control. Our findings show that due to uncertainty in the attack rate of a pandemic strain, initiating therapy immediately upon diagnosis can significantly increase the likelihood of disease control and substantially reduce the required community-level of treatment. This suggests that reliable diagnostic methods for influenza cases should be rapidly implemented within an antiviral treatment strategy.