Disease Spread Model Research Articles

Simulating the Spread of Peste des Petits Ruminants in Kazakhstan Using the North American Animal Disease Spread Model

In this study, we simulated the potential spread of Peste des Petits Ruminants (PPR) between small ruminant (SR) farms in the Republic of Kazakhstan (RK) in case of the disease’s introduction into the country. The simulation was based on actual data on the location and population of SR farms in the RK using the North American Animal Disease Spread Model (NAADSM). The NAADSM employs the stochastic simulations of the between-farm disease spread predicated on the SIR compartmental epidemic model. The most important epidemiological indicators of PPR, demography of SR farms, and livestock management characteristics in the RK were used for model parameterization. This article considers several scenarios for the initial introduction of PPR into the territory of Kazakhstan, based on previously identified high-risk regions and varying sizes of initially infected farms. It is demonstrated that the duration and size of the outbreak do not depend on the size of initially infected farms but rather depend on the livestock concentration and number of farms in the affected area. This implies that the outbreak may affect the largest number of farms in the case of introduction of the disease into farms in southern Kazakhstan. However, even in the most unfavorable scenario, the total number of affected farms does not exceed 2.4% of all SR farms in the RK. The size of the affected area is, in most cases, no larger than an averaged 2-level administrative division’s size, which suggests the scale of a local epidemic. The chosen model provides ample opportunity to study the impact of different control and prevention measures on the spread of PPR as well as to assess the potential economic damage.

Simulation of Foot-and-Mouth Disease Spread and Effects of Mitigation Strategies to Support Veterinary Contingency Planning in Denmark.

To forge a path towards livestock disease emergency preparedness in Denmark, 15 different strategies to mitigate foot-and-mouth disease (FMD) were examined by modelling epidemics initiated in cattle, pig or small ruminant herds across various production systems located in four different Danish regions (Scenario 1), or in one specific livestock production system within each of the three species geographically distributed throughout Denmark (Scenario 2). When additional mitigation strategies were implemented on top of basic control strategies in the European foot-and-mouth disease spread model (EuFMDiS), no significant benefits were predicted in terms of the number of infected farms, the epidemic control duration, and the total economic cost. Further, the model results indicated that the choice of index herd, the resources for outbreak control, and the detection time of FMD significantly influenced the course of an epidemic. The present study results emphasise the importance of basic mitigation strategies, including an effective back-and-forward traceability system, adequate resources for outbreak response, and a high level of awareness among farmers and veterinarians concerning the detection and reporting of FMD at an early stage of an outbreak for FMD control in Denmark.

Simulation of COVID-19 Outbreak in Nanjing Lukou Airport Based on Complex Dynamical Networks

The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff.

Analysis and Optimal Control of the Tungro Virus Disease Spread Model in Rice Plants by Considering the Characteristics of the Virus, Roguing, and Pesticides

Farmers have an essential role in maintaining food security. One of the food crops that occupies a high position in Indonesia is rice. However, farmers often experience problems when cultivating rice plants, one of which is affected by the tungro virus disease in rice plants. The spread of the disease can be controlled by the roguing process and applying pesticides. In this study, an analysis of the model of the spread of tungro virus disease in rice plants took into account the characteristics of the rice tungro spherical virus (RTSV) and rice tungro bacilliform virus (RTBV), as well as control in the form of roguing processes and application of pesticides. The analysis carried out was in the form of dynamic analysis, sensitivity analysis, and optimal control. In addition, numerical simulations were also carried out to describe the results of the analysis. The results showed that the roguing process and the application of pesticides could control the spread of the tungro virus disease. The application is sufficient, at as much as 75%.

A methodological approach for modeling the spread of disease using geographical discrete-event spatial models

The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available.

A model of behavioural response to risk accurately predicts the statistical distribution of COVID-19 infection and reproduction numbers

One clear aspect of behaviour in the COVID-19 pandemic has been people’s focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people’s behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This homeostatic response is active until approximate herd immunity is reached: in this domain the model predicts that the reproduction rate R will be centred around a median of 1, that proportional change in infection numbers will follow the standard Cauchy distribution with location and scale parameters 0 and 1, and that high infection numbers will follow a power-law frequency distribution with exponent 2. To test these predictions we used worldwide COVID-19 data from 1st February 2020 to 30th June 2022 to calculate 95% confidence interval estimates across countries for these R, location, scale and exponent parameters. The resulting median R estimate was 95%; CI=[0.99, 1.01] (predicted value 1) the proportional change location estimate was 95%; CI=[-0.01, 0.02] (predicted value 0), the proportional change scale estimate was 95%; CI=[0.99, 1.08] (predicted value 1), and the frequency distribution exponent estimate was 95%; CI=[1.97, 2.15] (predicted value 2); in each case the observed estimate agreed with model predictions.

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil.

Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. It safely provides access to health care services and expands assistance to remote, rural areas and underserved communities in situations of shortage of specialized health professionals. Qualified data are systematically collected by health care workers containing information on suspected cases and can be used as a proxy of disease spread for surveillance purposes. However, the use of this approach for syndromic surveillance has yet to be explored. Besides, the mathematical modeling of epidemics is a well-established field that has been successfully used for tracking the spread of SARS-CoV-2 infection, supporting the decision-making process on diverse aspects of public health response to the COVID-19 pandemic. The response of the current models depends on the quality of input data, particularly the transmission rate, initial conditions, and other parameters present in compartmental models. Telehealth systems may feed numerical models developed to model virus spread in a specific region. Herein, we evaluated whether a high-quality data set obtained from a state-based telehealth service could be used to forecast the geographical spread of new cases of COVID-19 and to feed computational models of disease spread. We analyzed structured data obtained from a statewide toll-free telehealth service during 4 months following the first notification of COVID-19 in the Bahia state, Brazil. Structured data were collected during teletriage by a health team of medical students supervised by physicians. Data were registered in a responsive web application for planning and surveillance purposes. The data set was designed to quickly identify users, city, residence neighborhood, date, sex, age, and COVID-19-like symptoms. We performed a temporal-spatial comparison of calls reporting COVID-19-like symptoms and notification of COVID-19 cases. The number of calls was used as a proxy of exposed individuals to feed a mathematical model called "susceptible, exposed, infected, recovered, deceased." For 181 (43%) out of 417 municipalities of Bahia, the first call to the telehealth service reporting COVID-19-like symptoms preceded the first notification of the disease. The calls preceded, on average, 30 days of the notification of COVID-19 in the municipalities of the state of Bahia, Brazil. Additionally, data obtained by the telehealth service were used to effectively reproduce the spread of COVID-19 in Salvador, the capital of the state, using the "susceptible, exposed, infected, recovered, deceased" model to simulate the spatiotemporal spread of the disease. Data from telehealth services confer high effectiveness in anticipating new waves of COVID-19 and may help understand the epidemic dynamics.

A Dynamical Model with Time Delay for Risk Contagion

The explanation of risk contagion among economic players—not only in financial crises—and how they spread across the world has fascinated scholars and scientists in the last few decades. Inspired by the literature dealing with the analogy between financial systems and ecosystems, we model risk contagion by revisiting the mathematical approach of epidemiological models for infectious disease spread in a new paradigm. We propose a time delay differential system describing risk diffusion among companies inside an economic sector by means of a SIR dynamics. Contagion is modelled in terms of credit and financial risks with low and high levels. A complete theoretical analysis of the problem is carried out: well-posedness and solution positivity are proven. The existence of a risk-free steady state together with an endemic equilibrium is verified. Global asymptotic stability is investigated for both equilibria by the classical Lyapunov functional theory. The model is tested on a case study of some companies operating in the food economic sector in a specific Italian region. The analysis allows for understanding the crucial role of both incubation time and financial immunity period in the asymptotic behaviour of any solution in terms of endemic permanence of risk rather than its disappearance.

Analysis of SVEIL Model of Tuberculosis Disease Spread with Imperfect Vaccination

This study proposes a SVEIL model of tuberculosis disease spread with imperfect vaccination. Susceptible individuals can receive imperfect vaccination, but over the time the vaccine efficacy will decrease. Vaccinated individuals are in vulnerable class since they still have probability to get reinfected. The proposed model includes treatment for both high-risk latent and active TB patients. In fact, after getting appropriate treatment (get recovered) the individuals still have bacteria in their body and it is classified to low-risk laten class. Dynamical behaviour of the model is analyzed to understand the local stability equilibrium. The Routh-Hurwitz criterion is used to analyze the local stability equilibrium in disease free equilibrium (DFE) point and Center Manifold theorem is used to prove the local stability of the endemic equilibrium (EE) point. The local stability equilibrium state totally depends on the effective reproduction number R_v. If R_v<1 , then the DFE point is locally asymtotically stable, while if R_v>1 the EE point is locally asymptotically stable . The parameter used in this paper is based on the previous researches related to TB and the initial subpopulations are assumed. Numerical simulations show that the disease transmission rate affect the effective reproduction number, therefore it influences the stability of equilibrium points..

Mathematical Modeling of Foot and Mouth Disease Spread on Livestock using Saturated Incidence Rate

Foot and Mouth Disease (FMD) is an acute infectious disease that attacks livestock, thus threatening the availability of food and the husbandry industry. This paper discusses the formulation of a mathematical model for the spread of FMD in livestock with a saturated incidence rate. The research method used is quantitative mathematical modeling with simulation, with stages including problem identification, determining assumptions, model formulation, analysis and model simulation. The discussion results obtained two equilibrium points, namely the non-endemic equilibrium point and the endemic equilibrium point, and then analyzed for stability. Numerical simulation is presented using Runge-Kutta approximation with MATLAB. Furthermore, after a sensitivity analysis, the parameters that greatly influenced the spread of FMD were direct or indirect contact (which led to the entry of the FMD virus) and the supporting capacity of livestock. Then the most influential parameter in reducing the spread of FMD is the application of culling on exposed animals and infected animals. The FMD modeling is a form of mathematical application to simulate the spread of disease on livestock.

Large-scale measurement of aggregate human colocation patterns for epidemiological modeling

To understand and model public health emergencies, epidemiologists need data that describes how humans are moving and interacting across physical space. Such data has traditionally been difficult for researchers to obtain with the temporal resolution and geographic breadth that is needed to study, for example, a global pandemic. This paper describes Colocation Maps, which are spatial network datasets that have been developed within Meta’s Data For Good program. These Maps estimate how often people from different regions are colocated: in particular, for a pair of geographic regions x and y, these Maps estimate the rate at which a randomly chosen person from x and a randomly chosen person from y are simultaneously located in the same place during a randomly chosen minute in a given week. These datasets are well suited to parametrize metapopulation models of disease spread or to measure temporal changes in interactions between people from different regions; indeed, they have already been used for both of these purposes during the COVID-19 pandemic. In this paper, we show how Colocation Maps differ from existing data sources, describe how the datasets are built, provide examples of their use in compartmental modeling, and summarize ideas for further development of these and related datasets. Among the findings of this study, we observe that a pair of regions can exhibit high colocation despite few people moving between those regions. Additionally, for the purposes of clarifying how to interpret and utilize Colocation Maps, we scrutinize the Maps’ built-in assumptions about representativeness and contact heterogeneity.

Unveiling Viral Dynamics: Machine Learning and Network Analysis for Predictive Modeling of Disease Spread

Unveiling Viral Dynamics: Machine Learning and Network Analysis for Predictive Modeling of Disease Spread

Dynamics of foot-and-mouth disease spread model for cattle with carrier, vaccination, and environmental transmission

Dynamics of foot-and-mouth disease spread model for cattle with carrier, vaccination, and environmental transmission

Tracking riots with models of disease spread

To accurately explain the evolution of civil unrest in October 2019 in.Santiago, Chile, models need to account for population displacement.

Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of COVID-19

High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ordinary differential equation based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We also evaluate the metrics’ utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and an 87% F1-score.

Gaussian process forecasts Pseudogymnoascus destructans will cover coterminous United States by 2030.

White-nose syndrome has been decimating populations of several bat species since its first occurrence in the Northeastern United States in the winter 2006-2007. The spread of the disease has been monitored across the continent through the collaboration of many organizations. Inferring the rate of spread of the disease and predicting its arrival at new locations is critical when assessing the current and predicting the future status and trends of bat species. We developed a model of disease spread that simultaneously achieves high-predictive performance, computational efficiency, and interpretability. We modeled white-nose syndrome spread using Gaussian process variations to infer the spread rate of the disease front, identify areas of anomalous time of arrival, and provide future forecasts of the expected time of arrival throughout North America. Cross-validation of model predictive performance identified a stationary Gaussian process without an additional residual error process as the best-supported model. Results indicated that white-nose syndrome is likely to spread throughout the entire continental United States by 2030. These annually updatable model predictions will be useful in determining the horizon over which disease management actions must take place as well as in status and trend assessments of disease-affected bats.

Participant recruitment and retention from vulnerable populations in clinical trials is a matter of trust

Clinical research sites can struggle with recruiting and retaining vulnerable populations. Vulnerable research participants often have significant trauma histories making traditional approaches to recruitment and retention tenuous. Due to these difficulties, vulnerable populations are often intentionally excluded from clinical research due to the additional time and work involved. While it is important to provide protections for any participant that has decreased autonomy or increased susceptibility to coercion, it is equally important to assure that individuals in vulnerable populations have access to any clinical research that might pertain to them. In addition, the new trends in the drug development industry including early-stage development, risk-identification, preventative care, and disease spread modeling are likely to include health disparate patient populations that have increased probability of vulnerability. In this article we discuss the roots of many vulnerabilities and how to foster trust for more effective recruitment and retention of vulnerable populations.

Assessing strategic, tactical, and operational decision-making and risk in a livestock production chain through experimental simulation platforms.

This paper provides a research summary of a series of serious games and simulations that form the basis of an experimental platform for the study of human decision-making and behavior associated with biosecurity across complex livestock production chains. This platform is the first of its kind to address the challenges associated with scaling micro-behavior of biosecurity decision-making to macro-patterns of disease spread across strategic, tactical and operational levels, capturing the roles that facility managers and front-line workers play in making biosecurity decisions under risk and uncertainty. Informational and incentive treatments are tested within each game and simulation. Behavioral theories are used to explain these findings. Results from serious games in the form of behavioral probability distributions are then used to simulate disease incidence and spread across a complex production chain, demonstrating how micro-level behaviors contribute to larger macro-level patterns. In the case of this study, the propensity to adopt micro-level biosecurity practices are applied to a network percolation disease spread model. By presenting the suite of companion models of behavior and disease spread we are able to capture scaling dynamics of complex systems, and in the process, better understand how individual behaviors impact whole systems.

Pandemic effects in the Solow growth model.

We show how diseases can affect economic growth in a Solow growth model, with population growth and no technical progress, but modified to include a saving rate that depends on the individual health status. We successively insert this model into the SIS (susceptible-infected-susceptible) and SIR (susceptible-infected-recovered) models of disease spreading. In these two models, the spread of the infection proceeds according to the so-called basic reproductive number. This number determines in which of the two possible equilibria, the disease-free or the pandemic equilibrium, the economy ends. We show that output per capita is always lower in the pandemic steady state, which implies a contraction in the economy's production possibilitiesfrontier.

The Importance of Livestock Demography and Infrastructure in Driving Foot and Mouth Disease Dynamics.

Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions.