Disease Spread Model Research Articles

The impact of early large-scale screening on the evolution of pandemics

We study the problem of large-scale screening in the early stages of a pandemic. In this setting, resources such as testing kits, budget, and hospital beds are scarce, and testing in early stages has the potential to control the dynamics of disease spread. We thus devise a screening framework in which subjects are randomly sampled from the population for screening. We investigate two screening strategies - individual and group testing - and identify settings in which each is more effective. To model disease spread dynamics, we utilize a compartmental susceptible-infected-quarantined-removed-deceased model in which we embed sampling and screening decisions that account for testing misclassification errors. We perform a sensitivity analysis over the parameters that govern the disease dynamics and show the efficacy of our proposed screening solutions. We then calibrate our model using data on the COVID-19 pandemic in the United States and demonstrate the effectiveness of our model in reducing total number of infections while meeting healthcare system capacity.

An Individual-Based Spatial Epidemiological Model for the Spread of Plant Diseases

AbstractIn the study of plant disease epidemics, the state of each individual in the population and their spatial location should be considered when modeling disease spread. We present a model to describe the spread of plant diseases, where the infection of a susceptible individual depends on the transmission rate of infected individuals and the spatial correlation. This latter is introduced through the Matérn correlation function, accounting for spatial dependence based on distance. Almond leaf scorch disease, caused by the bacterium Xylella fastidiosa, was used as a case study to test the behavior of the model parameters and the variability due to the characteristics and location of initial disease introduction using a proposed simulation algorithm. The greatest variability in the results depended on the range parameter of the Matérn correlation, i.e., the distance at which two observations can be considered spatially uncorrelated, and the initial introduction. The spatial distribution of individuals also had a strong influence on disease spread, highlighting that areas without trees acted as barriers when their extent was greater than the range parameter. It should be stressed that this individual-based model can be applied to other plant diseases, adapting the parameter values to their particular epidemiological characteristics.

A SIR Epidemic Model Allowing Recovery

The deterministic SIR model for disease spread in a closed population is extended to allow infected individuals to recover to the susceptible state. This extension preserves the second constant of motion, i.e., a functional relationship of susceptible and removed numbers, S(t) and R(t), respectively. This feature allows a substantially complete elucidation of qualitative properties. The model exhibits three modes of behaviour classified in terms of the sign of −S′(0), the initial value of the epidemic curve. Model behaviour is similar to that of the SIS model if S′(0)>0 and to the SIR model if S′(0)<0. The separating case is completely soluble and S(t) is constant-valued. Long-term outcomes are determined for all cases, together with determination of the rate of convergence. Determining the shape of the epidemic curve motivates an investigation of curvature properties of all three state functions and quite complete results are obtained that are new, even for the SIR model. Finally, the second threshold theorem for the SIR model is extended in refined and generalised forms.

Epidemiology and Scenario Simulations of the Middle East Respiratory Syndrome Corona Virus (MERS-CoV) Disease Spread and Control for Dromedary Camels in United Arab Emirates (UAE)

Middle East Respiratory Syndrome (MERS-CoV) is a coronavirus-caused viral respiratory infection initially detected in Saudi Arabia in 2012. In UAE, high seroprevalence (97.1) of MERS-CoV in camels was reported in several Emirate of Abu Dhabi studies, including camels in zoos, public escorts, and slaughterhouses. The objectives of this research include simulation of MERS-CoV spread using a customized animal disease spread model (i.e., customized stochastic model for the UAE; analyzing the MERS-CoV spread and prevalence based on camels age groups and identifying the optimum control MERS-CoV strategy. This study found that controlling animal mobility is the best management technique for minimizing epidemic length and the number of affected farms. This study also found that disease dissemination differs amongst camels of three ages: camel kids under the age of one, young camels aged one to four, and adult camels aged four and up; because of their immunological state, kids, as well as adults, had greater infection rates. To save immunization costs, it is advised that certain age groups be targeted and that intense ad hoc unexpected vaccinations be avoided. According to the study, choosing the best technique must consider both efficacy and cost.

A HOLISTIC PERSPECTIVE ON THE IMPACT OF COVID-19 ON THE GLOBAL POPULATION IN TERMS OF MORBIDITY AND MORTALITY RATES: IS HUMANITY READY TO FACE FUTURE PANDEMICS?

For now, the spotlight is turning away from the recent Corona epidemic, but this should not pass without some important lessons being learned. Knowing that the epidemic is continuing and spreading, with 662 million new cases recorded in the year 2023. The arms race between countries has led to increased investment in biological weapons, highlighting the military human participation in spreading epidemics. Despite scientific and technical progress, humanity is not equipped to deal with microbial epidemics, and therefore the safeguards taken by public health officials in combating epidemics must be re-evaluated. The current mortality rate for SARS-CoV-2 is low, but some other viral diseases have high mortality rates, so if a pandemic occurs due to it worldwide, as happened with COVID-19, it will be a disaster. The WHO database provides a comprehensive view of COVID-19 cases and deaths, enabling public health measures to be implemented globally according to a recent revision until the end of 2023. Major affected countries in terms of total morbidity and mortality include the USA, India, France, Germany, Brazil, Japan, Russia, Turkey, Spain, Viet Nam, Australia, China, Argentina, Mexico, Indonesia, Poland, Colombia, Austria, Portugal, Greece, Ukraine, Chile, and Malaysia. The Coronavirus appears to affect developed countries more than developing countries, but the efficiency in collecting cases affects the reliability of interpreting data in this way in general. Despite the progress made, much effort remains to be made to combat the disease in its mild form at this time. Modeling disease spread helps understand community characteristics and assists public health authorities with management and control measures. Collaboration and quantitative risk index projections are crucial for pandemic prevention and control. Peer Review History: Received: 24 October 2023; Revised: 13 November; Accepted: 22 December, Available online: 15 January 2024 Academic Editor: Dr. Sally A. El-Zahaby, Pharos University in Alexandria, Egypt, sally.elzahaby@yahoo.com Received file: Reviewer's Comments: Average Peer review marks at initial stage: 6.5/10 Average Peer review marks at publication stage: 7.0/10 Reviewers: Prof. Hassan A.H. Al-Shamahy, Sana'a University, Yemen, shmahe@yemen.net.ye Dr. Ogbonna B. Onyebuchi, Nnamdi Azikiwe University, Awka, Nigeria, summitpharm@yahoo.com

Thermal preference of Culicoides biting midges in laboratory and semi-field settings

Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are hematophagous insects, and some species can transmit a plethora of pathogens, e.g., bluetongue virus and African horse sickness virus, that mainly affect animals. The transmission of vector-borne pathogens is strongly temperature dependent, and recent studies pointed to the importance of including microclimatic data when modelling disease spread. However, little is known about the preferred temperature of biting midges. The present study addressed the thermal selection of field-caught Culicoides with two experiments. In a laboratory setup, sugar-fed or blood-fed midges were video tracked for 15 min while moving inside a 60 × 30 × 4 cm setup with a 15–25 °C temperature gradient. Culicoides spent over double the time in the coldest zone of the setup compared to the warmest one. This cold selection was significantly stronger for sugar-fed individuals. Calculated preferred temperatures were 18.3 °C and 18.9 °C for sugar-fed and blood-fed Culicoides, respectively. The effect of temperature on walking speed was significant but weak, indicating that their skewed distribution results from preference and not cold trapping. A second experiment consisted of a two-way-choice-setup, performed in a 90 × 45 × 45 cm net cage, placed outdoors in a sheltered environment. Two UV LED CDC traps were placed inside the setup, and a mean temperature difference of 2.2 °C was created between the two traps. Hundred-fifty Culicoides were released per experiment. Recapture rates were negatively correlated with ambient temperature and were on average three times higher in the cooled trap. The higher prevalence of biting midges in cooler environments influences fitness and ability to transmit pathogens and should be considered in models that predict Culicoides disease transmission.

A mathematical model of meningitis with antibiotic effects

The mathematical model in this study is a SCIR-type meningitis disease spread model, namely susceptible (S), carrier (C), infected (I), and recovery (R). In the model used, there are two equilibrium points, namely the disease-free equilibrium point and the endemic equilibrium point. The conditions and stability of the equilibrium point are determined by the basic reproduction number, which is the value that determines whether or not the spread of meningitis infection in a population. The results of this study show that the stability of the disease-free equilibrium point and the endemic equilibrium point are locally asymptotically stable and by using the Lyapunov Function method it is found that the disease-free equilibrium point will be globally stable when, while the endemic equilibrium point will be globally stable when numerical simulations perform to support the theoretical results.

A mixed-method approach to determining contact matrices in the Cox's Bazar refugee settlement.

Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between subgroups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist for refugee settlements-locations under-served by epidemic models in general. In this paper, we present a novel, mixed-method approach for deriving contact matrices in populations, which combines a lightweight, rapidly deployable survey with an agent-based model of the population informed by census and behavioural data. We use this method to derive the first set of contact matrices for the Cox's Bazar refugee settlement in Bangladesh. To validate our approach, we apply it to the UK population and compare our derived matrices with well-known contact matrices collected using traditional methods. Our findings demonstrate that our mixed-method approach successfully addresses some of the challenges faced by traditional and agent-based approaches to deriving contact matrices. It also shows potential for implementation in resource-constrained environments. This work therefore contributes to a broader aim of developing new methods and mechanisms of data collection for modelling disease spread in refugee and internally displaced person (IDP) settlements and better serving these vulnerable communities.

Comparing intervention measures in a model of a disease outbreak on a university campus.

A number of theoretical models have been developed in recent years modelling epidemic spread in educational settings such as universities, often as part of efforts to inform re-opening strategies during the COVID-19 pandemic. However, these studies have had differing conclusions as to the most effective non-pharmaceutical interventions. They also largely assumed permanent acquired immunity, meaning we have less understanding of how disease dynamics will play out when immunity wanes. Here, we complement these studies by developing and analysing a general stochastic simulation model of disease spread on a university campus where we allow immunity to wane, exploring the effectiveness of different interventions. We find that the two most effective interventions to limit the severity of a disease outbreak are reducing extra-household mixing and surveillance testing backed-up by a moderate isolation period. We find that contact tracing only has a limited effect, while reducing class sizes only has much effect if extra-household mixing is already low. We identify a range of measures that can not only limit an outbreak but prevent it entirely, and also comment on the variation in measures of severity that emerge from our stochastic simulations. We hope that our model may help in designing effective strategies for universities in future disease outbreaks.

Using physical simulations to motivate the use of differential equations in models of disease spread

The SIR model is a differential equations based model of the spread of an infectious disease that compartmentalises individuals in a population into one of three states: those who are susceptible to a disease (S), those who are infected and can transmit the disease to others (I), and those who have recovered from the disease and are now immune (R). This Classroom Note describes how to initiate teaching the SIR model with two concrete physical simulations to provide students with first-hand experience with some of the nuanced behaviour of how an infectious disease spreads through a closed population. One simulation physically models disease spread by the exchange of fluids, using pH to simulate infection. A second simulation incorporates randomness through the use of a probability game to keep track of the state of each individual at each time step. Both simulations invite students to ask questions about what factors influence disease spread. The concrete experience from the physical simulations enables students to make connections to the abstract mathematical representation of the SIR model and discuss the sources of stochasticity present in the spread of an infectious disease.

Agent-Based Modeling to Simulate Aerosolized Transmission of SARS-CoV-2 inside Small Ventilated Spaces

Deterministic and stochastic models for aerosol virus spread have become aplenty in the last several years. We believe it is important to explore all avenues of models and look to expand the current repertoire of models in this domain using a simple stochastic agent-based method. The goal is to understand if this type of agent model is applicable to real-life scenarios and to discuss possible policy implications of our findings on disease spread through aerosols in small spaces with ventilation using our developed model. We apply our agent model to see how different spatial organizations of an infected individual impact infections and their distributions. We also perform some sensitivity analysis with regard to both how different vectors of infection change overall infectivity rates but also how different levels of ventilation and filtration can impact infectivity as well. Our findings show that the simple stochastic movement of particles should be explored further with regard to agent-based disease spread models, and that filtration plays a large role in determining the overall infection rate of people in small spaces with an infector. We also found that placement of the index infector with regard to other susceptible people and ventilation play an impactful role in how a disease may spread in a short time frame within small confines.

Cooperative Attention-Based Learning between Diverse Data Sources

Cooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spread variable, which determines the disease spread rate decision score. This work proposes a determination spread variable using a disease spread model and cooperative learning. It is a four-stage model that determines answers by identifying semantic cooperation using the spread model to identify events, infection factors, location spread, and change in spread rate. The proposed model analyses the spread of COVID-19 throughout the United States using a new approach by defining data cooperation using the dynamic variable of the spread rate and the optimal cooperative strategy. Game theory is used to define cooperative strategy and to analyze the dynamic variable determined with the help of a control algorithm. Our analysis successfully identifies the spread rate of disease from social data with an accuracy of 67% and can dynamically optimize the decision model using a control algorithm with a complexity of order O(n2).

Assessment of control measures against livestock-associated methicillin-resistant Staphylococcus aureus in a farrow-to-finish pig herd using infectious disease modelling

Pigs are considered to be the main reservoir for livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA), which is a zoonotic opportunistic pathogen. As LA-MRSA is an occupational hazard, there is an incentive to control its spread in pig herds. Currently, knowledge about effective control measures which do not require culling the whole herd are limited, and the control strategies against LA-MRSA vary between countries. This study uses a stochastic compartment model to simulate possible control measures for LA-MRSA in a farrow-to-finish pig herd. The aims of the study were to (1) extend a previously published disease spread model with additional management and control measures; (2) use the extended model to study the effect of the individual LA-MRSA control measures on the within-herd LA-MRSA prevalence; (3) evaluate the effect of control measures when they are implemented in combinations. From the individual control measures tested in the study, thorough cleaning was found to be most effective in reducing the LA-MRSA prevalence in the herd. When the different control measures were combined, cleaning together with disease surveillance had the largest impact on reducing the LA-MRSA and a higher chance of causing disease elimination. The results of the study showed that achieving disease elimination once LA-MRSA had been introduced in the herd was challenging but was more likely when control measures were introduced early during the outbreak. This emphasises the importance of early detection of the pathogen and subsequent rapid implementation of LA-MRSA control measures.

Predicting and Controlling Spillover in Multispecies Disease Transmission Networks: Steady-State Analysis.

AbstractIn multispecies disease systems, pathogen spillover from a "reservoir community" can maintain disease in a "sink community" where it would otherwise die out. We develop and analyze models for spillover and disease spread in sink communities, focusing on questions of control: which species or transmission links are the most important to target to reduce the disease impact on a species of concern? Our analysis focuses on steady-state disease prevalence, assuming that the timescale of interest is long compared with that of disease introduction and establishment in the sink community. We identify three regimes as the sink community R0 scales from 0 to 1. Up to , overall infection patterns are dominated by direct exogenous infections and one-step subsequent transmission. For , infection patterns are characterized by dominant eigenvectors of a force-of-infection matrix. In between, additional network details can be important; we derive and apply general sensitivity formulas that identify particularly important links and species.

DYNAMIC ANALYSIS OF THE MATHEMATICAL MODEL FOR THE CHOLERA DISEASE SPREAD INVOLVING MEDICATION AND ENVIROMENTAL SANITATION

This study aims to analyze the mathematical model of the cholera disease spread involving medicationnd environmental sanitation. The model was analyzed by determining the equilibrium point and the basic reproduction number. The next step was to analyze the equilibrium point, sensitivity, and simulate numerically. Analysis of the stability of the disease-free and endemic equilibrium points usedhe Routh-Hurwitz criteria and the Castillo-Chaves and Song Theorem. The Analysis resultf the model produced two equilibrium points; namely the disease-freequilibrium point for local asymptotic stability and the endemic equilibrium point for local asymptotic stability if . Furthermore, the sensitivity analysis indicated the most sensitive parameters for basic reproductive number changes in succession are the parameters for natural birth rates , the transmission rate of bacteria from the environment to humans , the saturated concentration of bacteria in water , an increase in the bacterial population caused by environmental pollution rate by humans . Numerical simulations suggest an increase to give vaccine can contribute to slowing the transmission of cholera where as the reduction of a vaccine able to promote the transmission of cholera diseases.

The contagion number: How fast can a disease spread?

The burning number of a graph models the rate at which a disease, information, or other externality can propagate across a network. The burning number is known to be NP-hard even for a tree. Herein, we define a relative of the burning number that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the burning number as it only counts first time infections (i.e., constrains a node from getting the same disease/same variant/same alarm more than once). This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a method to solve for the contagion number of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis) and then employs simulation to determine the average contagion number (ACN) (i.e., a most-likely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are used to model human social networks generated through a preferential attachment mechanism. With CN differing across network structures and almost identical to ACN, our findings advance disease spread understanding and reveal the importance of network structure. In a borderless world without replete resources, understanding disease spread can do much to inform public policy and managerial decision makers’ allocation decisions. Furthermore, our direct interactions with supply chain executives at two COVID-19 vaccine developers provided practical grounding on what the results suggest for achieving social welfare objectives.

Simulasi Perhitungan Premi Asuransi Kesehatan dan Jiwa pada Penderita Covid-19 yang Dipengaruhi Model Penyebaran Penyakit Menular SIDRS

Determination of health and death insurance benefits according to the needs of policyholders is very important to determine from the beginning of making an insurance policy, especially for insurance that takes over the risk of being infected with the COVID-19 virus. Several factors that must be taken into account in determining the amount of benefits and premiums due to COVID-19 are the human population factor that is susceptible, infected and death in the SIDRS infectious disease spread model. In this study, the influence of these three factors on actuarial calculations is examined in more depth to produce an appropriate premium determination formula by taking into account two payment schemes in lump sum and annuity. From the simulation results by applying data on COVID-19 cases in Indonesia to determine the parameters of the SIDRS model, it is concluded that the premium with an annuity benefit payment scheme is smaller than the premium with a lump sum benefit scheme. Furthermore, it is also concluded that if the population of policyholders increases, the premium price will also be lower.

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.