Stochastic model to assess bioeconomic impact of PRRS on pig farms in Costa Rica.

Despite the economic importance of PRRS and its high prevalence in Costa Rica, there are no studies on the bioeconomic impact of the disease in the country or, even, in Central America. Such studies are essential in finding cost-effective preventive measures tailored for different production circumstances. Therefore, the objective of this study was to evaluate economic and production parameters of a PRRSV-infection for a medium-sized farrow-to-finish pig farm system in Costa Rica with a farm-level stochastic Monte Carlo simulation model. The effect of PRRS was assessed by scenario analysis, in which a baseline PRRS-free situation was compared against three alternative scenarios that assumed low, medium and high PRRS effects. The PRRS effects were based on data from local farms, scientific literature and expert opinion. Sensitivity analyses were performed to assess the impact of key input parameters on output variables. Results show that at the animal level, changes between the baseline and the PRRS-high scenario were estimated as: + 25 d in age to slaughter, - 9.9 pigs to slaughter (per breeding sow/yr), + 6% annual replacement rate, - 255 d in sow productive lifetime, - 6.9 mo in age at culling of sows, and + 24 non- productive days. For a medium size local farm (n = 588 sows), a reduction of 5826 fat pigs to slaughter per farm/yr from baseline compared to PRRS-high scenario was observed. PRRS-induced loss per farm per year was estimated at -US $142,542, US $180,109 and -US $524,719 for PRRS-low, medium and high scenarios, respectively. Revenues/costs ratio changed from 1.12 in the baseline to 0.89 in the PRRS-high scenario. The production cost per kg carcass weight increased from US $2.63 for the baseline to US $3.35 in the PRRS-high scenario. PRRS-induced loss was estimated at US $77.1 per slaughtered pig/yr and US $892 per breeding sow/yr for the PRRS-high scenario. Results from the model indicate that pig farms with medium to high prevalence of PRRS will require optimal market conditions in order to have positive economic outcomes. These results can be helpful in the design of better control strategies for PRRS.

Analysis and comparison of wellhead protection areas delimitation methods applying a stochastic MODFLOW model as a reference.

Sustainable use of groundwater while maintaining economic and social development is a major challenge, and the implementation of wellhead protection areas (WHPA) for public supply wells has been applied as an instrument to overcome it. This study analyzes the WHPA delineation methods: calculated fixed radius (CFR) and two solutions of the WhAEM software (USEPA, 2018), one analytical and one semi-analytical. We compare their results with WHPAs generated by a stochastic three-dimensional MODFLOW-MODPATH model in two scenarios: eight pumping wells operating simultaneously and a single well pumping, both at the same public drinking water supply wellfield located on a coastal plain in Jaguaruna County, south Brazil. For the specific hydrogeological settings, all methods produced satisfactory results when delineating a 50-day time-of-travel (TOT) WHPA for a single well. However, as TOT increases, uncertainties are introduced, and the precision of the results is reduced. Multiple well pumping simultaneously presented similar issues regarding uncertainties caused by three-dimensional flow complexities resulting from well interferences. Despite being the simplest method applied in terms of hydrogeological data needs, the CFR method demonstrated reliability in its results. Additionally, we present an analysis comparing the dimensions of the capture zone with the 10- and 20-year TOT WHPAs, indicating that managing the entire capture zone is the best way to protect groundwater against conservative contaminants. Finally, we compare WHPA generated by a stochastic and a deterministic model to understand how uncertainties can affect model results.

Solar photo-Fenton oxidation of mature landfill leachate: empirical model and chemical inferences.

In this study, a stochastic model was applied to investigate the degradation of landfill leachate by solar photo-Fenton processes. The coefficient of determination (R2) between experimental and predicted data ranged from 0.9958-0.9995. The optimal conditions for the initial phase (lasting 5-22 min) were high Fe2+ level, low pH level, and intermediate H2O2 level. For the second phase, optimal leachate degradation percentages were obtained by maintaining the pH, increasing H2O2, and decreasing Fe2+ to the lowest level. Determination of optimal reaction conditions (such as pH, Fe2+, and H2O2 values) for both degradation phases is of paramount importance for process scale-up. The major contribution of this study was the development of a tool that considers the effects of one or more reactions on organic carbon degradation. This was achieved by assessing the significance of the effects of experimental conditions on model parameters for the fast and slow steps of leachate degradation by advanced oxidation processes.

Mathematical modelling of the second wave of COVID-19 infections using deterministic and stochastic SIDR models.

Recently, various countries from across the globe have been facing the second wave of COVID-19 infections. In order to understand the dynamics of the spread of the disease, much effort has been made in terms of mathematical modeling. In this scenario, compartmental models are widely used to simulate epidemics under various conditions. In general, there are uncertainties associated with the reported data, which must be considered when estimating the parameters of the model. In this work, we propose an effective methodology for estimating parameters of compartmental models in multiple wave scenarios by means of a dynamic data segmentation approach. This robust technique allows the description of the dynamics of the disease without arbitrary choices for the end of the first wave and the start of the second. Furthermore, we adopt a time-dependent function to describe the probability of transmission by contact for each wave. We also assess the uncertainties of the parameters and their influence on the simulations using a stochastic strategy. In order to obtain realistic results in terms of the basic reproduction number, a constraint is incorporated into the problem. We adopt data from Germany and Italy, two of the first countries to experience the second wave of infections. Using the proposed methodology, the end of the first wave (and also the start of the second wave) occurred on 166 and 187 days from the beginning of the epidemic, for Germany and Italy, respectively. The estimated effective reproduction number for the first wave is close to that obtained by other approaches, for both countries. The results demonstrate that the proposed methodology is able to find good estimates for all parameters. In relation to uncertainties, we show that slight variations in the design variables can give rise to significant changes in the value of the effective reproduction number. The results provide information on the characteristics of the epidemic for each country, as well as elements for decision-making in the economic and governmental spheres.

Following the footprints of variability during filopodial growth.

Filopodia are actin-built finger-like dynamic structures that protrude from the cell cortex. These structures can sense the environment and play key roles in migration and cell-cell interactions. The growth-retraction cycle of filopodia is a complex process exquisitely regulated by intra- and extra-cellular cues, whose nature remains elusive. Filopodia present wide variation in length, lifetime and growth rate. Here, we investigate the features of filopodia patterns in fixed prostate tumor cells by confocal microscopy. Analysis of almost a thousand filopodia suggests the presence of two different populations: one characterized by a narrow distribution of lengths and the other with a much more variable pattern with very long filopodia. We explore a stochastic model of filopodial growth which takes into account diffusion and reactions involving actin and the regulatory proteins formin and capping, and retrograde flow. Interestingly, we found an inverse dependence between the filopodial length and the retrograde velocity. This result led us to propose that variations in the retrograde velocity could explain the experimental lengths observed for these tumor cells. In this sense, one population involves a wider range of retrograde velocities than the other population, and also includes low values of this velocity. It has been hypothesized that cells would be able to regulate retrograde flow as a mechanism to control filopodial length. Thus, we propound that the experimental filopodia pattern is the result of differential retrograde velocities originated from heterogeneous signaling due to cell-substrate interactions or prior cell-cell contacts.

Random perturbations in a mathematical model of bacterial resistance: Analysis and optimal control.

In this work, we study a mathematical model for the interaction of sensitive-resistant bacteria to antibiotics and analyse the effects of introducing random perturbations to this model. We compare the results of existence and stability of equilibrium solutions between the deterministic and stochastic formulations, and show that the conditions for the bacteria to die out are weaker in the stochastic model. Moreover, a corresponding optimal control problem is formulated for the unperturbed and the perturbed system, where the control variable is prophylaxis. The results of the optimal control problem reveal that, depending on the antibiotics, the costs of the prophylaxis, such as implementation, ordering and distribution, have to be much lower than the social costs, to achieve a bacterial resistance effective control.

Modeling the Population Dynamics and Management of Italian Ryegrass under Two Climatic Scenarios in Brazil.

Italian ryegrass (Lolium multiflorum L.) is an annual grass widely distributed in cultivated crops around the world. This weed causes significant yield reduction in many crops and has developed herbicide resistance. The aim of this study was to develop a cohort-based stochastic population dynamics model that integrates both emergence (thermal time) and dynamic population models as a tool to simulate the population dynamics of susceptible and resistant populations of L. multiflorum under the effects of climate change. The current climate scenario and the increase in the average air temperature by 2.5 °C were considered. Chemical and cultural management strategies commonly used in the South Region of Brazil during the winter and summer seasons were incorporated into the model. In the absence of control and under the current climate conditions, the seed bank population grew until reaching an equilibrium density of 19,121 ± 371 seeds m-2 for the susceptible and 20463 ± 363 seeds m-2 for the resistant populations. Considering the second climate scenario, the seed bank reaches an equilibrium density of 24,182 ± 253 seeds m-2 (+26% in relation to the current scenario) for the susceptible population and 24,299 ± 254 seeds m-2 (+18% in relation to the current scenario) for the resistant one. The results showed that the effect of the rise in temperature implies an increase in population in all the management strategies in relation to the current climate scenario. In both climate scenarios, the strategies based on herbicides application controlling cohorts 1 and 2 were the most efficient, and cropping systems including winter oat-soybeans rotation had a smaller impact on the L. multiflorum seed bank than crop rotations including winter wheat or summer corn. Crop rotations including wheat and corn for L. multiflorum management as an adaptive strategy under the future climate change are suggested.

Estimation and prediction for a mechanistic model of measles transmission using particle filtering and maximum likelihood estimation.

Disease incidence reported directly within health systems frequently reflects a partial observation relative to the true incidence in the population. State-space models present a general framework for inferring both the dynamics of infectious disease processes and the unobserved burden of disease in the population. Here, we present a state-space model of measles transmission and vaccine-based interventions at the country-level and a particle filter-based estimation procedure. Our dynamic transmission model builds on previous work by incorporating population age-structure to allow explicit representation of age-targeted vaccine interventions. We illustrate the performance of estimators of model parameters and predictions of unobserved states on simulated data from two dynamic models: one on the annual time-scale of observations and one on the biweekly time-scale of the epidemiological dynamics. We show that our model results in approximately unbiased estimates of unobserved burden and the underreporting rate. We further illustrate the performance of the fitted model for prediction of future disease burden in the next one to 15 years.

Simulation modeling of influenza transmission through backyard pig trade networks in a wildlife/livestock interface area.

Influenza constitutes a challenge to animal and human health. It is a highly contagious disease with wildlife reservoirs and considered as endemic among swine populations. Pigs are crucial in the disease dynamics due to their capacity to generate new reassortant viruses. The risk of informal animal trade in the spread of zoonotic diseases is well recognized worldwide. Nevertheless, the contribution of the backyard pig trade network in the transmission of influenza in a wildlife/livestock interface area is unknown. This study provides the first simulation of influenza transmission based on backyard farm connections in Mexico. A susceptible-infectious-recovered (SIR) model was implemented using the Epimodel software package in R, and 260 backyard farms were considered as nodes. Three different scenarios of connectivity (low, medium, and high) mediated by trade were generated and compared. Our results suggest that half of the pig population were infected within 5 days in the high connectivity scenario and the number of infected farms was approximately 65-fold higher compared to the low connected one. The consequence of connectivity variations directly influenced both time and duration of influenza virus transmission. Therefore, high connectivity driven by informal trade constitutes a significant risk to animal health. Trade patterns of animal movements are complex. This approach emphasizes the importance of pig movements and spatial dynamics among backyard production, live animal markets, and wildlife.

Economic appraisal of vaccination against Streptoccocus agalactiae in Nile tilapia farms in Brazil.

Infection with Streptococcus agalactiae causes mortality and major economic losses in Nile tilapia (Oreochromis niloticus) farming worldwide. In Brazil, serotype strains Ia, Ib and III have been isolated in streptococcosis outbreaks, but serotype Ib is the most prevalent. Vaccination is considered an effective method to prevent economically-important diseases in aquaculture and has been associated with decreased use of antibiotics and improvements in fish survival. We developed a flexible partial-budget model to undertake an economic appraisal of vaccination against Streptococcus agalactiae in Nile tilapia farmed in net cages in large reservoirs. The model considers the benefits and costs that are likely to be associated with vaccination at the farm-level, in one production cycle. We built three epidemiological scenarios of cumulative mortality attributable to S. agalactiae (5%, 10%, and 20%, per production cycle) in a non-vaccinated farm. For each scenario, we applied a stochastic model to simulate the net return of vaccination, given a combination of values of "vaccine efficacy", "gain in feed conversion ratio", "feed price", "fish market price ", and "cost of vaccine dose". In the 20% cumulative mortality scenario, the net return would break-even (benefits ≥ costs) in at least 97.9% of interactions. Should cumulative mortality be lower than 10%, the profitability of vaccination would be more dependent on better feed conversion ratio. The inputs "feed price" and "cost of vaccine" had minor effects on the output, in all pre-vaccination mortality scenarios. Although our simulations are based on conservative values and consider uncertainty about the modeled parameters, we conclude that vaccination against S. agalactiae is likely to be profitable in Nile tilapia farms, under similar production conditions.

A Stochastic Model for Reproductive Isolation Under Asymmetrical Mating Preferences.

More and more evidence shows that mating preference is a mechanism that may lead to a reproductive isolation event. In this paper, a haploid population living on two patches linked by migration is considered. Individuals are ecologically and demographically neutral on the space and differ only on a trait, a or A, affecting both mating success and migration rate. The special feature of this paper is to assume that the strengths of the mating preference and the migration depend on the trait carried. Indeed, patterns of mating preferences are generally asymmetrical between the subspecies of a population. I prove that mating preference interacting with frequency-dependent migration behavior can lead to a reproductive isolation. Then, I describe the time before reproductive isolation occurs. To reach this result, I use an original method to study the limiting dynamical system, analyzing first the system without migration and adding migration with a perturbation method. Finally, I study how the time before reproductive isolation is influenced by the parameters of migration and of mating preferences, highlighting that large migration rates tend to favor types with weak mating preferences.

A process of rumour scotching on finite populations.

Rumour spreading is a ubiquitous phenomenon in social and technological networks. Traditional models consider that the rumour is propagated by pairwise interactions between spreaders and ignorants. Only spreaders are active and may become stiflers after contacting spreaders or stiflers. Here we propose a competition-like model in which spreaders try to transmit an information, while stiflers are also active and try to scotch it. We study the influence of transmission/scotching rates and initial conditions on the qualitative behaviour of the process. An analytical treatment based on the theory of convergence of density-dependent Markov chains is developed to analyse how the final proportion of ignorants behaves asymptotically in a finite homogeneously mixing population. We perform Monte Carlo simulations in random graphs and scale-free networks and verify that the results obtained for homogeneously mixing populations can be approximated for random graphs, but are not suitable for scale-free networks. Furthermore, regarding the process on a heterogeneous mixing population, we obtain a set of differential equations that describes the time evolution of the probability that an individual is in each state. Our model can also be applied for studying systems in which informed agents try to stop the rumour propagation, or for describing related susceptible-infected-recovered systems. In addition, our results can be considered to develop optimal information dissemination strategies and approaches to control rumour propagation.