SEIR-diffusion modeling and stability analysis of supply chain finance based on blockchain technology

Inadequate information sharing and difficult information diffusion are the main factors that cause upstream and downstream enterprises to default on supply chain finance. Blockchain technology, which exploits distributed storage and a consensus mechanism, can provide effective solutions to overcome these problems such as information sharing. When blockchain technology is adopted by the enterprises that comprise the supply chain finance business, this technology shows a diffusion trend. As a result, the decision pertaining to the application of novel technologies is affected. Therefore, to investigate the diffusion mechanism pertaining to the blockchain technology that is applied in supply chain finance, the study exploited the idea of a class of SEIR infectious disease models, and built a blockchain model that considers the supply chain financial system. Besides, the study verifies the stability of the model by constructing a Lyapunov function. The results indicate that the basic reproduction number determines the proliferation of the blockchain technology. When the basic reproduction number is less than 1, the proliferation of the blockchain technology that is applied in supply chain finance system would terminate. By contrast, when the basic reproduction number is greater than 1, during the average infection period, the number of non-adopting enterprises that accept the blockchain technology becomes greater than 1, which can maintain a continuous impact on supply chain finance system. Over time, the number of enterprises that accept blockchain technology tends to be stable. Through numerical simulations that consider the influencing parameters pertaining to the basic regeneration number, which has important effect on blockchain technology diffusion, we enlarge the diffusion efficiency and increase the transfer rate of potential on-chain enterprises or decrease the default exit rate. As a result, we facilitate the diffusion of blockchain technology in the system.

Incorporating heterogeneity in farmer disease control behaviour into a livestock disease transmission model

Human behaviour is critical to effective responses to livestock disease outbreaks, especially with respect to vaccination uptake. Traditionally, mathematical models used to inform this behaviour have not taken heterogeneity in farmer behaviour into account. We address this by exploring how heterogeneity in farmers vaccination behaviour can be incorporated to inform mathematical models. We developed and used a graphical user interface to elicit farmers (n = 60) vaccination decisions to an unfolding fast-spreading epidemic and linked this to their psychosocial and behavioural profiles. We identified, via cluster analysis, robust patterns of heterogeneity in vaccination behaviour. By incorporating these vaccination behavioural groupings into a mathematical model for a fast-spreading livestock infection, using computational simulation we explored how the inclusion of heterogeneity in farmer disease control behaviour may impact epidemiological and economic focused outcomes. When assuming homogeneity in farmer behaviour versus configurations informed by the psychosocial profile cluster estimates, the modelled scenarios revealed a disconnect in projected distributions and threshold statistics across outbreak size, outbreak duration and economic metrics.

Big data technology in infectious diseases modeling, simulation, and prediction after the COVID-19 outbreak

After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and artificial intelligence related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, nonpharmaceutical interventions (NPIs), ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review started with a research structure of research purpose, factor, data, model, and conclusion. Focusing on the post-COVID-19 infectious disease prediction simulation research, this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.

Assessing the impact of insecticide-treated nets in the face of insecticide resistance on malaria control

The mosquito-borne disease, malaria, continues to impose a devastating health and economic burden worldwide. In malaria-endemic areas, insecticide-treated nets (ITNs) have been useful in curtailing the burden of the disease. However, mosquito resistance to insecticides, decay in ITN efficacy, net attrition, etc., undermine the effectiveness of ITNs in combatting malaria. In this study, mathematical models that account for asymptomatic infectious humans (through a partially immune class or a separate asymptomatic infectious class), insecticide resistance, and decay in ITN efficacy are proposed and analyzed. Analytical and numerical results of the models when ITN efficacy is constant show that there are parameter regimes for which a backward bifurcation occurs. Local and global sensitivity analyses are performed to identify parameters (some of which are potential targets for disease control) with the most significant influence on the control reproduction (R_c) and disease prevalence. These influential parameters include the maximum biting rate of resistant mosquitoes, ITN coverage, initial ITN efficacy against sensitive mosquitoes, the probability that an infectious mosquito (human) infects a susceptible human (mosquito), and the rate at which adult mosquitoes develop (lose) resistance to insecticides. Simulations of the models show that accounting for asymptomatic infectious humans through a separate class, or not accounting for the decay in ITN efficacy leads to an underestimation of disease burden. In particular, if the initial efficacy of ITNs against sensitive and resistance mosquitoes is 96%, the minimum ITN coverage required to reduce R_c below one (and hence, contain malaria) is approximately 11% (27%) lower when ITN efficacy is averaged (constant) for a model with a separate asymptomatic class. For the model with a partially immune class and decaying ITN efficacy, reducing R_c below one is impossible even if the entire populace uses ITNs. The study shows that replacing ITNs before their prescribed lifespans, or designing ITNs with longer lifespans is important for malaria control. Furthermore, the study shows that piperonyl butoxide (PBO) ITNs (which inhibit or reverse insecticide resistance) outperform regular ITNs in malaria control. Hence, prospects for effectively controlling malaria are enhanced by widespread use of high quality ITNs (e.g. PBO ITNs), especially if the useful lifespans of the ITNs are long enough and the ITNs are replaced before the end of their useful lifespans.

Bias and bias-correction for individual-level models of infectious disease

Accurate infectious disease models can help scientists understand how an ongoing disease epidemic spreads and forecast the course of epidemics more effectively. Considering various factors that affect the spread of a disease (e.g. geographical, social, domestic, and genetic), a class of individual-level models (ILMs) was developed to incorporate population heterogeneity. In these models, inferences are developed within a Bayesian Markov chain Monte Carlo (MCMC) framework, obtaining posterior estimates of model parameters. The issues of bias of parameter estimates, and methods for bias correction, have been widely studied with respect to many of the most established and commonly used statistical models and associated methods of parameter estimation. However, these methods are not directly applicable to infectious disease data. This paper investigates circumstances in which ILM parameter estimates may be biased in some simple disease system scenarios. Further, we aim to compare the performance of bias-corrected estimates of ILM parameters, using simulation, with the posterior estimates of the parameter. We also discuss the factors that affect the performance of these estimators.

SQEIR: An epidemic virus spread analysis and prediction model

In 2019, a new strain of coronavirus pneumonia spread quickly worldwide. Viral propagation may be simulated using the Susceptible Infectious Removed (SIR) model. However, the SIR model fails to consider that separation of patients in the COVID-19 incubation stage entails difficulty and that these patients have high transmission potential. The model also ignores the positive effect of quarantine measures on the spread of the epidemic. To address the two flaws in the SIR model, this study proposes a new infectious disease model referred to as the Susceptible Quarantined Exposed Infective Removed (SQEIR) model. The proposed model uses the weighted least squares for the optimal estimation of important parameters in the infectious disease model. Based on these parameters, new differential equations were developed to describe the spread of the epidemic. The experimental results show that this model exhibits an accuracy 6.7% higher than that of traditional infectious disease models.

Infectious disease model generalization based on diffuse perturbations under conditions of body's temperature reaction

The infectious disease mathematical model is generalized based on the influence of diffuse perturbations on the development of the disease under conditions of the body's temperature reaction. The singularly perturbed model problem was reduced with delay to a sequence of problems without delay, for which the corresponding asymptotic expansions of solutions are obtained. The presented results of computer modeling in various situational states illustrate the expected decrease in the growth rate of the number of viral particles as a result of the action of the body's protective temperature reaction. The results of numerical experiments demonstrate the influence of the diffuse effect of "scattering" of forcing factors on the dynamics of a viral disease under conditions of the body's temperature reaction are presented too. It is noted that the decrease of the model amount of antigens in the epicenter of infection to a non-critical level caused by diffuse "scattering" over a relatively short time period makes them further destroyed by immune agents presented in the body, or requires the introduction of an injection solution with a smaller amount of donor antibodies.

Modeling and analysis of a fractional anthroponotic cutaneous leishmania model with Atangana-Baleanu derivative

Very recently, Atangana and Baleanu defined a novel arbitrary order derivative having a kernel of non-locality and non-singularity, known as AB derivative. We analyze a non-integer order Anthroponotic Leshmania Cutaneous (ACL) problem exploiting this novel AB derivative. We derive equilibria of the model and compute its threshold quantity, i.e. the so-called reproduction number. Conditions for the local stability of the no-disease as well as the disease endemic states are derived in terms of the threshold quantity. The qualitative analysis for solution of the proposed problem have derived with the aid of the theory of fixed point. We use the predictor corrector numerical approach to solve the proposed fractional order model for approximate solution. We also provide, the numerical simulations for each of the compartment of considered model at different fractional orders along with comparison with integer order to elaborate the importance of modern derivative. The fractional investigation shows that the non-integer order derivative is more realistic about the inner dynamics of the Leishmania model lying between integer order.

Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection worldwide, resulting in approximately sixty thousand annual hospitalizations of< 5-year-olds in the United States alone and three million annual hospitalizations globally. The development of over 40 vaccines and immunoprophylactic interventions targeting RSV has the potential to significantly reduce the disease burden from RSV infection in the near future. In the context of RSV, a highly contagious pathogen, dynamic transmission models (DTMs) are valuable tools in the evaluation and comparison of the effectiveness of different interventions. This review, the first of its kind for RSV DTMs, provides a valuable foundation for future modelling efforts and highlights important gaps in our understanding of RSV epidemics. Specifically, we have searched the literature using Web of Science, Scopus, Embase, and PubMed to identify all published manuscripts reporting the development of DTMs focused on the population transmission of RSV. We reviewed the resulting studies and summarized the structure, parameterization, and results of the models developed therein. We anticipate that future RSV DTMs, combined with cost-effectiveness evaluations, will play a significant role in shaping decision making in the development and implementation of intervention programs.

Impact of asymptomatic COVID-19 carriers on pandemic policy outcomes

This paper provides a mathematical model that makes it clearly visible why the underestimation of r, the fraction of asymptomatic COVID-19 carriers in the general population, may lead to a catastrophic reliance on the standard policy intervention that attempts to isolate all confirmed infectious cases. The SE(A+O)R model with infectives separated into asymptomatic and ordinary carriers, supplemented by a model of the data generation process, is calibrated to standard early pandemic datasets for two countries. It is shown that certain fundamental parameters, critically r, are unidentifiable with this data. A general analytical framework is presented that projects the impact of different types of policy intervention. It is found that the lack of parameter identifiability implies that some, but not all, potential policy interventions can be correctly predicted. In an example representing Italy in March 2020, a hypothetical optimal policy of isolating confirmed cases that aims to reduce the basic reproduction number R₀ of the outbreak from 4.4 to 0.8 assuming r = 0, only achieves 3.8 if it turns out that r = 40%.

Modelling SARS-CoV-2 transmission in a UK university setting

Around 40% of school leavers in the UK attend university and individual universities generally host thousands of students each academic year. Bringing together these student communities during the COVID-19 pandemic may require strong interventions to control transmission. Prior modelling analyses of SARS-CoV-2 transmission within universities using compartmental modelling approaches suggest that outbreaks are almost inevitable. We constructed a network-based model to capture the interactions of a student population in different settings (housing, social and study). For a single academic term of a representative campus-based university, we ran a susceptible-latent-infectious-recovered type epidemic process, parameterised according to available estimates for SARS-CoV-2. We investigated the impact of: adherence to (or effectiveness of) isolation and test and trace measures; room isolation of symptomatic students; and supplementary mass testing. With all adhering to test, trace and isolation measures, we found that 22% (7%-41%) of the student population could be infected during the autumn term, compared to 69% (56%-76%) when assuming zero adherence to such measures. Irrespective of the adherence to isolation measures, on average a higher proportion of students resident on-campus became infected compared to students resident off-campus. Room isolation generated minimal benefits. Regular mass testing, together with high adherence to isolation and test and trace measures, could substantially reduce the proportion infected during the term compared to having no testing. Our findings suggest SARS-CoV-2 may readily transmit in a university setting if there is limited adherence to nonpharmaceutical interventions and/or there are delays in receiving test results. Following isolation guidance and effective contact tracing curbed transmission and reduced the expected time an adhering student would spend in isolation.

Zika virus outbreak in Brazil under current and future climate

INTRODUCTION

Zika virus (ZIKV) is primarily transmitted byAedes aegypti and Aedes albopictus mosquitoes between humans and non-human primates. Climate change may enhance virus reproduction in Aedes spp. mosquito populations, resulting in intensified ZIKV outbreaks. The study objective was to explore how an outbreak similar to the 2016 ZIKV outbreak in Brazil might unfold with projected climate change.

METHODS

A compartmental infectious disease model that included compartments for humans and mosquitoes was developed to fit the 2016 ZIKV outbreak data from Brazil using least squares optimization. To explore the impact of climate change, published polynomial relationships between temperature and temperature-sensitive mosquito population and virus transmission parameters (mosquito mortality, development rate, and ZIKV extrinsic incubation period) were used. Projections for future outbreaks were obtained by simulating transmission with effects of projected average monthly temperatures on temperature-sensitive model parameters at each of three future time periods: 2011-2040, 2041-2070, and 2071-2100. The projected future climate was obtained from an ensemble of regional climate models (RCMs) obtained from the Co-Ordinated Regional Downscaling Experiment (CORDEX) that used Representative Concentration Pathways (RCP) with two radiative forcing values, RCP4.5 and RCP8.5. A sensitivity analysis was performed to explore the impact of temperature-dependent parameters on the model outcomes.

RESULTS

Climate change scenarios impacted the model outcomes, including the peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and the duration of the ZIKV outbreak. Comparing 2070-2100 to 2016, using RCP4.5, the peak incidence was 22,030 compared to 10,473; the time to epidemic peak was 12 compared to 9 weeks, and the outbreak duration was 52 compared to 41 weeks. Comparing 2070-2100 to 2016, using RCP8.5, the peak incidence was 21,786 compared to 10,473; the time to epidemic peak was 11 compared to 9 weeks, and the outbreak duration was 50 compared to 41weeks. The increases are due to optimal climate conditions for mosquitoes, with the mean temperature reaching 28 °C in the warmest months. Under a high emission scenario (RCP8.5), mean temperatures extend above optimal for mosquito survival in the warmest months.

CONCLUSION

Outbreaks of ZIKV in locations similar to Brazil are expected to be more intense with a warming climate. As climate change impacts are becoming increasingly apparent on human health, it is important to quantify the effect and use this knowledge to inform decisions on prevention and control strategies.

Comparison of static and dynamic models of maternal immunization to prevent infant pertussis in Brazil

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Dynamic transmission models of infectious disease capture the herd immunity effects of vaccination.

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We compared dynamic and static models of maternal acellular pertussis (aP) immunization built with Brazilian data.

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At infant vaccine coverage < 90–95%, both models estimate that maternal immunization is cost-effective.

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Only the dynamic model shows that maternal immunization is not cost-effective at infant coverage > 90–95%.

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The background effect of routine infant vaccination is critical to the cost-effectiveness of maternal aP immunization.

Background

This paper compares cost-effectiveness results from two models of maternal immunization to prevent pertussis in infants in Brazil, one static, one dynamic, to explore when static models are adequate for public health decisions and when the extra effort required by dynamic models is worthwhile.

Methods

We defined two scenarios to explore key differences between static and dynamic models, herd immunity and time horizon. Scenario 1 evaluates the incremental cost/DALY of maternal acellular pertussis (aP) immunization as routine infant vaccination coverage ranges from low/moderate up to, and above, the threshold at which herd immunity begins to eliminate pertussis. Scenario 2 compares cost-effectiveness estimates over the models’ different time horizons. Maternal vaccine prices of $9.55/dose (base case) and $1/dose were evaluated.

Results

The dynamic model shows that maternal immunization could be cost-saving as well as life-saving at low levels of infant vaccination coverage. When infant coverage reaches the threshold range (90–95%), it is expensive: the dynamic model estimates that maternal immunization costs $2 million/DALY at infant coverage > 95% and maternal vaccine price of $9.55/dose; at $1/dose, cost/DALY is $200,000. By contrast, the static model estimates costs/DALY only modestly higher at high than at low infant coverage. When the models’ estimates over their different time horizons are compared at infant coverage < 90–95%, their projections fall in the same range.

Conclusions

Static models may serve to explore an intervention’s cost-effectiveness against infectious disease: the direction and principal drivers of change were the same in both models. When, however, an intervention too small to have significant herd immunity effects itself, such as maternal aP immunization, takes place against a background of vaccination in the rest of the population, a dynamic model is crucial to accurate estimates of cost-effectiveness. This finding is particularly important in the context of widely varying routine infant vaccination rates globally.

Clinical Trial registry

Clinical Trial registry name and registration number: Not applicable.

Yellow fever virus outbreak in Brazil under current and future climate

Introduction

Yellow fever (YF) is primarily transmitted by Haemagogus species of mosquitoes. Under climate change, mosquitoes and the pathogens that they carry are expected to develop faster, potentially impacting the case count and duration of YF outbreaks. The aim of this study was to determine how YF virus outbreaks in Brazil may change under future climate, using ensemble simulations from regional climate models under RCP4.5 and RCP8.5 scenarios for three time periods: 2011–2040 (short-term), 2041–2070 (mid-term), and 2071–2100 (long-term).

Methods

A compartmental model was developed to fit the 2017/18 YF outbreak data in Brazil using least squares optimization. To explore the impact of climate change, temperature-sensitive mosquito parameters were set to change over projected time periods using polynomial equations fitted to their relationship with temperature according to the average temperature for years 2011–2040, 2041–2070, and 2071–2100 for climate change scenarios using RCP4.5 and RCP8.5, where RCP4.5/RCP8.5 corresponds to intermediate/high radiative forcing values and to moderate/higher warming trends. A sensitivity analysis was conducted to determine how the temperature-sensitive parameters impacted model results, and to determine how vaccination could play a role in reducing YF in Brazil.

Results

Yellow fever case projections for Brazil from the models varied when climate change scenarios were applied, including the peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and the outbreak duration. Overall, a decrease in YF cases and outbreak duration was observed. Comparing the observed incidence in 2017/18 to the projected incidence in 2070–2100, for RCP4.5, the cumulative case incidence decreased from 184 to 161, and the outbreak duration decreased from 21 to 20 weeks. For RCP8.5, the peak case incidence decreased from 184 to 147, and the outbreak duration decreased from 21 to 17 weeks. The observed decrease was primarily due to temperature increasing beyond that suitable for Haemagogus mosquito survival.

Conclusions

Climate change is anticipated to have an impact on mosquito-borne diseases. We found outbreaks of YF may reduce in intensity as temperatures increase in Brazil; however, temperature is not the only factor involved with disease transmission. Other factors must be explored to determine the attributable impact of climate change on mosquito-borne diseases.

COVID-19: Analytics of contagion on inhomogeneous random social networks

Motivated by the need for robust models of the Covid-19 epidemic that adequately reflect the extreme heterogeneity of humans and society, this paper presents a novel framework that treats a population of N individuals as an inhomogeneous random social network (IRSN). The nodes of the network represent individuals of different types and the edges represent significant social relationships. An epidemic is pictured as a contagion process that develops day by day, triggered by a seed infection introduced into the population on day 0. Individuals' social behaviour and health status are assumed to vary randomly within each type, with probability distributions that vary with their type. A formulation and analysis is given for a SEIR (susceptible-exposed-infective-removed) network contagion model, considered as an agent based model, which focusses on the number of people of each type in each compartment each day. The main result is an analytical formula valid in the large N limit for the stochastic state of the system on day t in terms of the initial conditions. The formula involves only one-dimensional integration. The model can be implemented numerically for any number of types by a deterministic algorithm that efficiently incorporates the discrete Fourier transform. While the paper focusses on fundamental properties rather than far ranging applications, a concluding discussion addresses a number of domains, notably public awareness, infectious disease research and public health policy, where the IRSN framework may provide unique insights.

Choices and trade-offs in inference with infectious disease models

Inference using mathematical models of infectious disease dynamics can be an invaluable tool for the interpretation and analysis of epidemiological data. However, researchers wishing to use this tool are faced with a choice of models and model types, simulation methods, inference methods and software packages. Given the multitude of options, it can be challenging to decide on the best approach. Here, we delineate the choices and trade-offs involved in deciding on an approach for inference, and discuss aspects that might inform this decision. We provide examples of inference with a dataset of influenza cases using the R packages pomp and rbi.

A framework for evaluating the effects of observational type and quality on vector-borne disease forecast

Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mechanistic model-inference approaches, a broad suite of epidemiological observations could be utilized, if these data were available in near real time. In cases where such data are limited, an in silica, synthetic framework for evaluating the potential benefits of observations on forecasting accuracy can allow researchers and public health officials to more optimally allocate resources for disease surveillance and monitoring. Here, we demonstrate the application of such a framework, using a model-inference system designed to predict dengue, and identify the type and quality of observations that would improve forecasting accuracy.

Comparison of stochastic and deterministic frameworks in dengue modelling

We perform estimations of compartment models for dengue transmission in rural Cambodia with increasing complexity regarding both model structure and the account for stochasticity. On the one hand, we successively account for three embedded sources of stochasticity: observation noise, demographic variability and environmental hazard. On the other hand, complexity in the model structure is increased by introducing vector-borne transmission, explicit asymptomatic infections and interacting virus serotypes. Using two sources of case data from dengue epidemics in Kampong Cham (Cambodia), models are estimated in the bayesian framework, with Markov Chain Monte Carlo and Particle Markov Chain Monte Carlo. We highlight the advantages and drawbacks of the different formulations in a practical setting. Although in this case the deterministic models provide a good approximation of the mean trajectory for a low computational cost, the stochastic frameworks better reflect and account for parameter and simulation uncertainty.

Modeling the role of public health education in Ebola virus disease outbreaks in Sudan

Public involvement in Ebola Virus Disease (EVD) prevention efforts is key to reducing disease outbreaks. Targeted education through practical health information to particular groups and sub-populations is crucial to controlling the disease. In this paper, we study the dynamics of Ebola virus disease in the presence of public health education with the aim of assessing the role of behavior change induced by health education to the dynamics of an outbreak. The power of behavior change is evident in two outbreaks of EVD that took place in Sudan only 3 years apart. The first occurrence was the first documented outbreak of EVD and produced a significant number of infections. The second outbreak produced far fewer cases, presumably because the population in the region learned from the first outbreak. We derive a system of ordinary differential equations to model these two contrasting behaviors. Since the population in Sudan learned from the first outbreak of EVD and changed their behavior prior to the second outbreak, we use data from these two instances of EVD to estimate parameters relevant to two contrasting behaviors. We then simulate a future outbreak of EVD in Sudan using our model that contains two susceptible populations, one being more informed about EVD. Our finding show how a more educated population results in fewer cases of EVD and highlights the importance of ongoing public health education.

Microfluidics meets metabolomics to reveal the impact of Campylobacter jejuni infection on biochemical pathways

Microfluidic devices that are currently being used in pharmaceutical research also have a significant potential for utilization in investigating exposure to infectious agents. We have established a microfluidic device cultured with Caco-2 cells, and utilized metabolomics to investigate the biochemical responses to the bacterial pathogen Campylobacter jejuni. In the microfluidic devices, Caco-2 cells polarize at day 5, are uniform, have defined brush borders and tight junctions, and form a mucus layer. Metabolomics analysis of cell culture media collected from both Caco-2 cell culture systems demonstrated a more metabolic homogenous biochemical profile in the media collected from microfluidic devices, compared with media collected from transwells. GeneGo pathway mapping indicated that aminoacyl-tRNA biosynthesis was perturbed by fluid flow, suggesting that fluid dynamics and shear stress impacts the cells translational quality control. Both microfluidic device and transwell culturing systems were used to investigate the impact of Campylobacter jejuni infection on biochemical processes. Caco-2 cells cultured in either system were infected at day 5 with C. jejuni 81-176 for 48 h. Metabolomics analysis clearly differentiated C. jejuni 81-176 infected and non-infected medias collected from the microfluidic devices, and demonstrated that C. jejuni 81-176 infection in microfluidic devices impacts branched-chain amino acid metabolism, glycolysis, and gluconeogenesis. In contrast, no distinction was seen in the biochemical profiles of infected versus non-infected media collected from cells cultured in transwells. Microfluidic culturing conditions demonstrated a more metabolically homogenous cell population, and present the opportunity for studying host-pathogen interactions for extended periods of time.

Modelling the potential benefits of different strategies to control infection with Trypanosoma evansi in camels in Somaliland

Trypanosoma evansi (T. evansi), the protozoan parasitic cause of camel trypanosomosis (Surra), constitutes one of the major veterinary problems worldwide. An infectious disease model of camel trypanosomosis (Surra) was adopted from one developed for buffalo and applied to study the impact of T. evansi infection on camel production. The model contained deterministic and stochastic components and the seroprevalence based on a survey conducted in Somaliland in 2011 and 2012 to simulate and estimate the economic benefits of four different control options against T. evansi infection in camels (1, 2, 3 and 4 regimens). The mean benefit per animal of controlling surra was calculated at US$354 (the treatment of all camels biannually), US$426 (the monthly targeted treatment of clinically sick camels) and US$287 (biannual targeted treatment of seropositive camels), respectively, compared with US$137 for untreated camels. Consequently, the model predicted that the total net benefit loss to a camel herd or village that was not applying the recommended effective surra control strategy was US$115,605 (69.4 billion shilling per annum).