Modeling and simulation of infectious diseases: Microscale transmission, decontamination and macroscale propagation

The COVID-19 pandemic that started in 2019-2020 has led to a gigantic increase in modeling and simulation of infectious diseases. There are numerous topics associated with this epoch-changing event, such as (a) disease propagation, (b) transmission, (c) decontamination, and (d) vaccines. This is an evolving field. The targeted objective of this book is to expose researchers to key topics in this area, in a very concise manner. The topics selected for discussion have evolved with the progression of the pandemic. Beyond the introductory chapter on basic mathematics, optimization, and machine learning, the book covers four themes in modeling and simulation infectious diseases, specifically: Part 1: Macroscale disease propagation, Part 2: Microscale disease transmission and ventilation system design, Part 3: Ultraviolet viral decontamination, and Part 4: Vaccine design and immune response. It is important to emphasize that the rapid speed at which the simulations operate makes the presented computational tools easily deployable as digital twins, i.e., digital replicas of complex systems that can be inexpensively and safely optimized in a virtual setting and then used in the physical world afterward, thus reducing the costs of experiments and also accelerating development of new technologies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

An engineering model of the COVID-19 trajectory to predict the success of isolation initiatives.

Much media and societal attention is today focused on how to best control the spread of coronavirus (COVID-19). Every day brings us new data, and policy makers are implementing different strategies in different countries to manage the impact of COVID-19. To respond to the first 'wave' of infection, several countries, including the UK, opted for isolation/lockdown initiatives, with different degrees of rigour. Data showed that these initiatives have yielded the expected results in terms of containing the rapid trajectory of the virus. When this article was first prepared (April 2020), the affected societies were wondering when the isolation/lockdown initiatives should be lifted. While detailed epidemiological, economic as well as social studies would be required to answer this question completely, here we employ a simple engineering model. Albeit simple, the model is capable of reproducing the main features of the data reported in the literature concerning the COVID-19 trajectory in different countries, including the increase in cases in countries following the initially successful isolation/lockdown initiatives. Keeping in mind the simplicity of the model, we attempt to draw some conclusions, which seem to suggest that a decrease in the number of infected individuals after the initiation of isolation/lockdown initiatives does not necessarily guarantee that the virus trajectory is under control. Within the limit of this model, it would seem that rigid isolation/lockdown initiatives for the medium term would lead to achieving the desired control over the spread of the virus. This observation seems consistent with the 2020 summer months, during which the COVID-19 trajectory seemed to be almost under control across most European countries. Consistent with the results from our simple model, winter 2020 data show that the virus trajectory was again on the rise. Because the optimal solution will achieve control over the spread of the virus while minimising negative societal impacts due to isolation/lockdown, which include but are not limited to economic and mental health aspects, the engineering model presented here is not sufficient to provide the desired answer. However, the model seems to suggest that to keep the COVID-19 trajectory under control, a series of short-to-medium term isolation measures should be put in place until one or more of the following scenarios is achieved: a cure has been developed and has become accessible to the population at large; a vaccine has been developed, tested and distributed to large portions of the population; a sufficiently large portion of the population has developed resistance to the COVID-19 virus; or the virus itself has become less aggressive. It is somewhat remarkable that an engineering model, despite all its approximations, provides suggestions consistent with advanced epidemiological models developed by several experts in the field. The model proposed here is however not expected to be able to capture the emergence of variants of the virus, which seem to be responsible for significant outbreaks, notably in India, in the spring of 2021, it cannot describe the effectiveness of vaccine strategies, as it does not differentiate among different age groups within the population, nor does it allow us to consider the duration of the immunity achieved after infection or vaccination.

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(n(2)).

Spatiotemporal transmission of infectious particles in environment: A case study of Covid-19.

This study explores the dynamic transmission of infectious particles due to COVID-19 in the environment using a spatiotemporal epidemiological approach. We proposed a novel multi-agent model to simulate the spread of COVID-19 by considering several influencing factors. The model divides the population into susceptible and infected and analyzes the impact of different prevention and control measures, such as limiting the number of people and wearing masks on the spread of COVID-19. The findings suggest that reducing population density and wearing masks can significantly reduce the likelihood of virus transmission. Specifically, the research shows that if the population moves within a fixed range, almost everyone will eventually be infected within 1 h. When the population density is 50%, the infection rate is as high as 96%. If everyone does not wear a mask, nearly 72.33% of the people will be infected after 1 h. However, when people wear masks, the infection rate is consistently lower than when they do not wear masks. Even if only 25% of people wear masks, the infection rate with masks is 27.67% lower than without masks, which is strong evidence of the importance of wearing a mask. As people's daily activities are mostly carried out indoors, and many super-spreading events of the new crown epidemic also originated from indoor gatherings, the research on indoor epidemic prevention and control is essential. This study provides decision-making support for epidemic preventions and controls and the proposed methodology can be used in other regions and future epidemics.

Research on the propagation model of COVID-19 based on virus dynamics

At the end of 2019, a new kind of coronavirus spread in Wuhan city of Hubei province and other places, seriously endangering people's health. Scientific prediction of the spread trend of the novel coronavirus makes a big difference in epidemic prevention, treatment, and relevant health decisions. The COVID-19 transmission model based on virus dynamics was established. We propose a data-driven dynamic modeling method for infectious disease transmission, which is a sparse identification method for nonlinear dynamic models. Sparse regression and parameter identification are used to accurately find the control equation from the potential dynamic models to simulate the dynamic transmission process of the novel coronavirus in Wuhan at the end of 2019. Through the experiment, we get the results that the model can well describe the spread of COVID-19 in Wuhan and also prove that the model is practical and can be extended to the prediction of related epidemic situations. © 2023 SPIE.

Role of tissue culture as applied in seed potato multiplication to improve food security: a review

Rapid population growth, natural and man-made factors (COVID-19 and the lack of a social safety net) have led to an increase in the demand for food, which calls for significant improvements to the food system worldwide to supply food more efficiently with the same or fewer resources. Potatoes have great potential to contribute to food security and incomes for rural smallholder farmers, as well as provide nutritious, affordable food for urban consumers. The availability of disease-free and certified seed potatoes of better-performing varieties remains limited. The use of tissue culture to provide a disease-free seed potato is therefore crucial to ensuring food security. A key goal of this paper is to summarize the work done on various aspects of seed potato multiplication, and how it can improve the food security of smallholder farmers. The systematic review method was applied to summarize how tissue culture application can produce excess disease-free seed potatoes to improve food availability for marginal farmers. The most effective way for farmers in developing countries or areas prone to natural or man-made disasters to increase their incomes and improve nutrition is to use high-quality certified seeds. Tissue cultures are used worldwide to produce pre-basic, virus-free seed potatoes. Early Generation Seed (micro-tubers, cuttings and mini-tuber), multiplication of mother plants and production of apical rooted cutting for seed production for field planting are popular. The activities of diseases-free seed production start at the laboratory and end at the field with seed production for planting. In general, three major steps were used in seed potato multiplication: (1) Tissue culture (to produce disease-free tissue culture plantlets);(2) Production of cuttings (involves two important stages: (i) multiplication of mother plants and (ii) production of apical rooted cutting for planting) for further multiplication and (3) production of seeds for field planting.

Tourism Demand Prediction after COVID-19 with Deep Learning Hybrid CNN–LSTM—Case Study of Vietnam and Provinces

The tourism industry experienced a positive increase after COVID-19 and is the largest segment in the foreign exchange contribution in developing countries, especially in Vietnam, where China has begun reopening its borders and lifted the pandemic limitation on foreign travel. This research proposes a hybrid algorithm, combined convolution neural network (CNN) and long short-term memory (LSTM), to accurately predict the tourism demand in Vietnam and some provinces. The number of new COVID-19 cases worldwide and in Vietnam is considered a promising feature in predicting algorithms, which is novel in this research. The Pearson matrix, which evaluates the correlation between selected features and target variables, is computed to select the most appropriate input parameters. The architecture of the hybrid CNN–LSTM is optimized by utilizing hyperparameter fine-tuning, which improves the prediction accuracy and efficiency of the proposed algorithm. Moreover, the proposed CNN–LSTM outperformed other traditional approaches, including the backpropagation neural network (BPNN), CNN, recurrent neural network (RNN), gated recurrent unit (GRU), and LSTM algorithms, by deploying the K-fold cross-validation methodology. The developed algorithm could be utilized as the baseline strategy for resource planning, which could efficiently maximize and deeply utilize the available resource in Vietnam.

A Study on Information Disorders on Social Networks during the Chilean Social Outbreak and COVID-19 Pandemic

Information disorders on social media can have a significant impact on citizens' participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes.

Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression

Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions of individuals worldwide suffer from mental illnesses. It might be difficult to identify those who are experiencing mental health illnesses and to provide them with the early help that they need. Additionally, depression may be associated with thoughts of suicide. Currently, there are no clinically specific diagnostic biomarkers that can identify the severity and type of depression. In this research paper, the novel particle swarm-cuckoo search (PS-CS) optimization algorithm is proposed instead of the traditional backpropagation algorithm for training deep neural networks. The backpropagation algorithm is widely used for supervised learning in deep neural networks, but it has limitations in terms of convergence speed and the possibility of getting trapped in local optima. These problems were addressed by using a deep neural network architecture for depression detection tasks along with the PS-CS optimization technique. The PS-CS algorithm combines the strengths of both particle swarm optimization and cuckoo search algorithms, which allows for a more efficient and effective optimization of the network parameters. We also evaluated how well the suggested methods performed against the most widely used classification models, including (K-nearest neighbor) KNN, (support vector regression) SVR, and decision trees, as well as the most widely used deep learning models, including residual neural network (ResNet), visual geometry group (VGG), and simple neural network (LeNet). The findings show that the suggested method, PS-CS, in conjunction with the CNN model, outperformed all other models, achieving the maximum accuracy of 99.5%. Other models, such as the KNN, decision trees, and logistic regression, achieved lower accuracies ranging from 69% to 97%.

Control and Monitoring of Thermographic Chambers by Means of PLC and HMI

Covid-19 disease affects the individual's body in different ways. Most of the infected people present various symptoms of complexity. This article develops the design of a system of control and monitoring of people through the use of thermographic cameras, which includes an intelligent control system for the detection of people with symptoms of Covid-19, which at the same time allows estimating a reading of parameters obtained from the thermographic camera, the possible suspected cases of people entering the Continental University. The development of the proposed system will allow obtaining real-time data of each user entering the Continental University, these parameters obtained will be stored in a SQL database that is linked to an HMI screen where the temperature of each person is displayed, if in case they exceed the established temperature ranges, instant access to the facility is restricted. The results of the research showed that the system design contributes to the prevention and mass propagation of Covid-19. © 2022 IEEE.

Predicting the COVID-19 Outbreak

This paper focuses on the use of mathematical modelling of propagation dynamics of infectious diseases. We use the discrete logistic model to propose a simple method to determine the start of coronavirus outbreak. Further, we apply the proposed method on real data of confirmed coronavirus cases from the Kingdom of Saudi Arabia. Our results suggested that the proposed method can be used for raising an alarm of coronavirus outbreak. © 2022 IEEE.

Pharmaceutical plant machine availability prediction using Artificial Neural Network

During the 2(nd) phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

Diagnosis of Pulmonary Edema and Covid-19 from CT slices using Squirrel Search Algorithm, Support Vector Machine and Back Propagation Neural Network

A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE) and covid-19 from the chest Computed Tomography (CT) slices were developed and implemented in this work. The lung tissues have been segmented using Otsu's thresholding method. The Regions of Interest (ROI) considered in this work were edema lesions and covid-19 lesions. For each ROI, the edema lesions and covid-19 lesions were elucidated by an expert radiologist, followed by texture and shape extraction. The extracted features were stored as feature vectors. The feature vectors were split into train and test set in the ratio of 80 : 20. A wrapper based feature selection approach using Squirrel Search Algorithm (SSA) with the Support Vector Machine (SVM) classifier's accuracy as the fitness function was used to select the optimal features. The selected features were trained using the Back Propagation Neural Network (BPNN) classifier. This framework was tested on a real-time PE and covid-19 dataset. The BPNN classifier's accuracy with SSA yielded 88.02%, whereas, without SSA it yielded 83.80%. Statistical analysis, namely Wilcoxon's test, Kendall's Rank Correlation Coefficient test and Mann Whitney U test were performed, which indicates that the proposed method has a significant impact on the accuracy, sensitivity and specificity of the novel dataset considered. Comparative experimentations of the proposed system with existing benchmark ML classifiers, namely Cat Boost, Ada Boost, XGBoost, RBF SVM, Poly SVM, Sigmoid SVM and Linear SVM classifiers demonstrate that the proposed system outperforms the benchmark classifiers' results. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Deciphering the covid-19 epidemic and analysis of its controllability

This chapter discusses lessons from the Covid-19 crisis, based on the history of the disease in France and distribution throughout the world. The Covid-19 crisis raises many questions, in addition to those addressed in the deciphering of the epidemic. In addition to the pre-positioning of the epidemic control system, for which the best organization must be found, the tools for analyzing the emergence that have just been presented can be optimized through predictive modeling, propagation scenarios and the study of the consequences of anti-epidemic measures. While no one appears "especially guilty" of the occurrence of the Covid-19 crisis, it is highly unfortunate that real-time epidemic threat analysis systems, whose annual cost can be estimated at 1/10,000th the cost of the epidemic, were not used to contain severe acute respiratory syndrome coronavirus 2. © ISTE Ltd 2022.

Privacy-Preserving-Enabled Lightweight COVID-19 Simulation Model for Mobile Intelligent Application

In order to control the first wave of COVID-19 pandemic in 2020, many models have shown effectiveness in predicting the spread of new coronary pneumonia and the different interventions. However, few models can collect large amounts of high-quality real-time data faster under the premise of protecting privacy, considering the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant and the mass vaccination program as a new intervention. Therefore, we developed a mobile intelligent application that can collect a large amount of real-time data while protecting privacy and conducted a feasibility study by defining a new COVID-19 mathematical model SEMCVRD. By simulating different intervention measures, the prediction model of the mobile intelligent application used in this article simulates the epidemic situation in the U.K. as an example. The findings are as below: the optimal intervention strategy is to suppress the intervention at [Formula Omitted] (intervention intensity: the average number of contacts per person per day) before the end of March 2021, then gradually release the intervention intensity at a rate of [Formula Omitted], and finally release the intensity to [Formula Omitted] in June 2021. The COVID-19 pandemic will end at the end of June 2021, when the total number of deaths will reach 128772. This strategy will be able to balance the tradeoff between loss of life and economic loss. Compared with the official statistics released by the U.K. government on May 31, 2021, our model can accurately predict the relative error rate of the total number of cases is less than 6.9%, and the relative error rate of the total number of deaths is less than 1%. Furthermore, the model is also suitable for collecting data from countries/regions around the world.

Risk Propagation Model and Simulation of an Assembled Building Supply Chain Network

In recent years, the prefabricated building supply chain has received strong support from the government and has developed rapidly, but there are various risks in the operation process. In this paper, on the basis of considering asymptomatic infections and relapse, this paper establishes a risk transmission model that considers a recurrent Susceptible–Exposed–Asymptomatic–Infectious–Recovered (abbr. SEAIR) model, systematically analyses the risks in the supply chain, and calculates the risk balance point to conclude that the risks can exist in the supply chain for a long time. By drawing a causal circuit diagram, the relationship between the influencing factors in the process of risk transmission is found, establishing a stock flow map to explore the law of risk propagation. The simulation results using Vensim PLE software show that the five influencing factors of infection rate, transmission rate, government financial support, government policy supervision, and immunity loss ratio have an important impact on the number of risk-unknown enterprises, risk-latent enterprises, risk transmission enterprises, and infection rehabilitation enterprises in risk transmission, and relevant countermeasures to deal with risk transmission in the supply chain are proposed. Theoretically, this paper broadens the ideas for improving infectious disease models. From the management point of view, it reveals how the prefabricated building supply chain enables enterprises to improve their ability to deal with risks through the risk propagation model, providing reference and helping to manage the risks faced by the prefabricated building supply chain.

Supervised Machine Learning Strategy for detection of covid19 patients

The current severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) public health catastrophe, both human lives have been lost and the economy has disrupted severely the current scenario. In this paper, we develop a detection module using a series of steps that involves pre-processing, feature extraction and detection of covid-19 patients based on the images collected from the computerized tomography (CT) images. The images are initially pre-processed and then the features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then finally classified using back propagation neural network (BPNN). The simulation is conducted to test the efficacy of the model against various CT image datasets of numerous patients. The results of simulation shows that the proposed method achieves higher detection rate, and reduced mean average percentage error (MAPE) than other existing methodologies. © 2023 IEEE.

Computational study of 2D photonic crystal based biosensor for SARS-COV-2 detection

A computational study to design a 2D-photonic crystal (PC) structure with a fluorescence-based biosensor has been demonstrated for the detection of the severe acute respiratory syndrome corona virus 2 (SARS-COV-2) virus in the lungs. The proposed sensor can detect the different concentrations of the virus without any pretreatment of the sample. The virus detection is performed by measuring the mid-gap wavelength from the dispersion diagram and a redshift in the mid-gap wavelength has been observed as the concentration of virus increases in the lung tissue. The plane wave expansion method is used to determine the dispersion diagram of the proposed PC. The interaction of incident light with the proposed PC-based biosensor has been analyzed to evaluate the shift in the mid-gap wavelength. A maximum sensitivity of about 1459.3 nm/RIU is obtained for r/a = 0.45 with a mid-gap wavelength shift of 145.93 nm at n net = 1.49 concentration of SARS-COV-2. Moreover, a very small detection time has been observed with the proposed device as compared to conventional methods. This study provides a simple process to detect the presence of a virus within a short period and could be helpful in the development of a direct and easy-to-use portable detection kit in the future. © 2023 IOP Publishing Ltd.

Micromodel and Analysis of COVID-19 Based on Hypergraph

In view of the fact that the existing propagation models ignore the influence of different fields and different virus variants on individual infection, and the classical propagation models only describe the macroscopic situation of virus transmission, which cannot be specific to individual cases, this paper proposes 67ya microscopic virus propagation model based on hypergraph (HC-SIRS). Firstly, the concept of hypergraph is used to divide different fields of individuals into corresponding hyperedges. Based on different contact probabilities of each hyperedge, the contact probability matrix is formed to relate the contact between individuals. The individual infection probability of micro-virus propagation model based on hypergraph is deduced, and the corresponding differential equation is established. Secondly, the basic regeneration number and its characteristics of the model are derived. The upper bound of the basic regeneration number of the model is less than or equal to that of the classical SIRS model, indicating that the virus is more difficult to spread in this model. In fact, the different fields people live in and the different personal constitutions have a certain impact on the spread of the virus. The model is more comprehensive, so it is more suitable for simulating the spread of the virus in theory. Finally, the COVID-19 data of Diamond Princess and two cities in China are used for simulation experiments, and the mean absolute error(MAE) is used as the evaluation standard. The results showed that HC-SIRS could well simulate the spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Enhancing Pharmaceutical Supply Chain Resilience: A Study of Pharmaceutical Companies in Multiple Geographies

Background: Pharmaceutical businesses had enormous difficulties in product distribution during COVID-19, and the solution to this perpetual issue is a resilient supply chain. Aim(s): The study aims to understand the vulnerabilities to which it subjected the pharmaceutical product distribution supply chains during the COVID-19 pandemic and further develop an adaptive model through which the pharmaceutical product supply chain can enhance its resilience capabilities. Material(s) and Method(s): The conceptual model is developed for the supply chain of pharmaceutical companies based on the literature survey, and then the conceptual model is explored through factor analysis. Researchers have developed a validated model after a statistical analysis using Cronbach's alpha. Subjective analysis has concluded that the pharmaceutical supply chain's resilience is driven by factors such as "trade cost," which comprises transport cost, business practices, and raw material sourcing cost;"shock propagation," which comprises country-specific shocks, production shocks, and policy changes;and "technological infrastructure bottleneck," which relates to the availability of cold chain storage warehouses and refrigerated transport vehicle facilities. Result(s): An empirical model pertaining to supply chain resilience may be further studied with different geographies, like Pune, Hyderabad, and Delhi NCR, for the purpose of generalizing the study. Conclusion(s): The identified major factors were trade cost, shock propagation, and technological infrastructure bottlenecks. The sensitivity of the issue under investigation required a personal touch to the survey, as the COVID-19 pandemic had left these respondents emotionally vulnerable. As COVID-19 is the recent catastrophe that has hit humanity, it has made the pharmaceutical product distribution channel vulnerable during the pandemic. This difficult time of pandemic has really tested the pharmaceutical products' supply chain capabilities as well.Copyright © 2023, Association of Pharmaceutical Teachers of India. All rights reserved.