Quantifying optimal resource allocation strategies for controlling epidemics.

Frequent emergence of communicable diseases is a major concern worldwide. Lack of sufficient resources to mitigate the disease burden makes the situation even more challenging for lower-income countries. Hence, strategy development for disease eradication and optimal management of the social and economic burden has garnered a lot of attention in recent years. In this context, we quantify the optimal fraction of resources that can be allocated to two major intervention measures, namely reduction of disease transmission and improvement of healthcare infrastructure. Our results demonstrate that the effectiveness of each of the interventions has a significant impact on the optimal resource allocation in both long-term disease dynamics and outbreak scenarios. The optimal allocation strategy for long-term dynamics exhibits non-monotonic behaviour with respect to the effectiveness of interventions, which differs from the more intuitive strategy recommended in the case of outbreaks. Further, our results indicate that the relationship between investment in interventions and the corresponding increase in patient recovery rate or decrease in disease transmission rate plays a decisive role in determining optimal strategies. Intervention programmes with decreasing returns promote the necessity for resource sharing. Our study provides fundamental insights into determining the best response strategy when controlling epidemics in resource-constrained situations.

Theory of Flowing: Going with the Flow of the Ups-and-Downs of Recovering from an Ordeal

This article outlines the theory of flowing. Flowing is an intervention strategy that ordinary people implement in order to go with the flow of the ups-and-downs of recovering from an ordeal. It ensures that they continue to progress in recovering from their ordeal. Ordinary people experience ups-and-downs when they are recovering from their ordeal in the following domains: functioning, symptoms, energy, support, connection and progress in recovery. These ups-and-downs lead the person to perpetually struggle with uncertainty and feel increasingly insecure and distressed. Recovering from an ordeal is a process of getting better where the distressing ups-and -downs are gradually stabilized where the person intervenes the downward trends of regression, rises up and maintains their upward trends of recovery;and the ordeal is progressively resolved. Flowing consists of the following intervention strategies: recognizing the ordeal and associated symptoms;alleviating symptoms;activating and nourishing;self-caring;staying open and aware of progress;seeking caring support and connections;becoming a caring support and connection;and staying grateful. This mid-range theory of flowing was discovered by conceptualizing data that were sourced from people who are experiencing the ups-and-downs of recovering from ordeals that are triggered by COVID-19 (Coronavirus disease). Thus, this data represents a slice of data from a broader population of ordinary people who are experiencing the ups-and-downs of recovering from their ordeal. This study has implications in how data could be used to discover theories, coaching of people to overcome their ordeals in life and how to manage life and health as we approach COVID-19 endemicity.

Crowd intervention strategies considering indoor respiratory exposure risks

In order to explore impacts of crowd intervention strategies on indoor respiratory exposure risks during major pandemics, a variety of crowd motion scenarios were established in general indoor conditions based on improved pedestrian dynamics model and respiratory infection probability model. Then, multi-agent simulation technology was utilized to simulate impacts of strategies, including protection optimization, pedestrian flow optimization and route optimization, on the exposure risks. The results show that indoor respiratory exposure risks are mainly determined by total pedestrian flow, individuals' stay length, movement route planning and duration of stay in contaminated areas. The carryover effect will be formed due to pedestrians' obedience behavior of social distancing, which will further increase exposure time to contaminated areas. The lower pathogen permeability of masks, and the greater space ventilation are, the lower infection probability the crowd will face. © 2022 China Safety Science Journal. All rights reserved.

Producing Generative Digital Data Objects: An Empirical Study on COVID-19 Data Flows in Online Communities

Digital data objects on viruses have played a pivotal role in the fight against COVID-19, leading to healthcare innovation such as new diagnostics, vaccines, and societal intervention strategies. To effectively achieve this, scientists access viral data from online communities (OCs). The social-interactionist view on generativity, however, has put little emphasis on data. We argue that generativity on data depends on the number of data instances, data timeliness, and completeness of data classes. We integrated and analyzed eight OCs containing SARS-CoV-2 nucleotide sequences to explore how community structures influence generativity, revealing considerable differences between OCs. By assessing provided data classes from user perspectives, we found that generativity was limited in two important ways: When required data classes were either insufficiently collected or not made available by OC providers. Our findings highlight that OC providers control generativity of data objects and provide guidance for scientists selecting OCs for their research. © 2022 IEEE Computer Society. All rights reserved.

Evaluation of the effectiveness of government intervention strategies to control and prevent COVID-19 in Malaysia by systems thinking

Purpose: This paper aims to identify the interaction of different intervention strategies implemented in Malaysia towards flattening the curve of COVID-19 cases. Since the outbreak of COVID-19, many approaches were adopted and implemented by the Malaysian government. Some strategies gained quick wins but with negative unintended consequences after execution, whereas other strategies were slow to take effect. Learning from the previous strategies is pivotal to avoid repeating mistakes. Design/methodology/approach: This paper presents the cause, effect of and connection among the implemented COVID-19 intervention strategies using systems thinking through the development of a causal loop diagram. It enables the visualisation of how each implemented strategy interacted with each other and collectively decreased or increased the spread of COVID-19. Findings: The results of this study suggested that it is not only essential to control the spread of COVID-19, but also to prevent the transmission of the virus. The Malaysian experience has demonstrated that both control and preventive strategies need to be in a state of equilibrium. Focusing only on one spectrum will throw off the balance, leaving COVID-19 infection to escalate rapidly. Originality/value: The developed feedback loops provided policy makers with the understanding of the merits, pitfalls and dynamics of prior implemented intervention strategies before devising other effective intervention strategies to defuse the spread of COVID-19 and prepare the nation for recovery. © 2023, Jack Kie Cheng, Fazeeda Mohamad, Puteri Fadzline M. Tamyez, Zetty Ain Kamaruzzaman, Maizura Mohd Zainudin and Faridah Zulkipli.

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. © 2021 TUP.

Insights on the current status of effective strategies for waste management in COVID-19 pandemic: challenges and opportunities

A large number of people got infected and many lost their lives due to COVID-19. The increased volume and source-shuffling of the waste generated during the pandemic have challenged the current waste management facilities. The major sources of infectious waste not only include hospitals but also houses and quarantine facilities that lack in source-management thereby increasing the spread of the virus. This article focuses on waste collection and disposal techniques as major aspects of COVID-19 waste management. Also, it discusses the various waste disinfection technologies along with waste management strategies formulated by different organisations. The non-pharmaceutical intervention strategies have also been identified. Alongside this, various challenges and opportunities in COVID-19 waste management are reviewed. Accordingly, recommendations to achieve efficient waste management are stated. Waste management in case of such a pandemic requires proper segregation, storage, collection and treatment. Usage of multiple processes like pyrolysis, chemical treatment, microwave and radio wave is needed to be found for treatment of infectious waste. Increased amount of mixed waste creates the need to have method that is flexible enough. Large amount of PPE waste needs to be taken care of. Development of materials that can provide hygiene and have recyclability is essential. © 2023 Indian Institute of Chemical Engineers.

Feature-Modified SEIR Model for Pandemic Simulation and Evaluation of Intervention Approaches

SEIR (susceptible-exposed-infected-recovered) model has been widely used to study infectious disease dynamics. For instance, there have been many applications of SEIR analyzing the spread of COVID to provide suggestions on pandemic/epidemic interventions. Nonetheless, existing models simplify the population, regardless of different demographic features and activities related to the spread of the disease. This paper provides a comprehensive SEIR model to enhance the prediction quality and effectiveness of intervention strategies. The new SEIR model estimates the exposed population via a new approach involving health conditions (sensitivity to disease) and social activity level (contact rate). To validate our model, we compare the estimated infection cases via our model with actual confirmed cases from CDC and the classic SEIR model. We also consider various protocols and strategies to utilize our modified SEIR model on many simulations and evaluate their effectiveness. © 2022 IEEE.

Optimal control analysis of a multigroup SEAIHRD model for COVID-19 epidemic.

The COVID-19 pandemic has threatened public health and caused substantial economic loss to most countries worldwide. A multigroup susceptible-exposed-asymptomatic-infectious-hospitalized-recovered-dead (SEAIHRD) compartment model is first constructed to model the spread of the disease by dividing the population into three age groups: young (aged 0-19), prime (aged 20-64), and elderly (aged 65 and over). Then, we develop a free terminal time, partially fixed terminal state optimal control problem to minimize deaths and costs associated with hospitalization and the implementation of different control strategies. And the optimal strategies are derived under different assumptions about medical resources and vaccination. Specifically, we explore optimal control strategies for reaching herd immunity in the COVID-19 outbreak in a free terminal time situation to evaluate the effect of nonpharmaceutical interventions (NPIs) and vaccination as control measures. The transmission rate of SARS-CoV-2 is calibrated by using real data in the United States at the early stage of the epidemic. Through numerical simulation, we conclude that the outbreak of COVID-19 can be contained by implementing appropriate control of the prime age population and relatively strict control measures for young and elderly populations. Within a specific period, strict control measures should be implemented before the vaccine is marketed.

Multicriteria group decision making via generalized trapezoidal intuitionistic fuzzy number-based novel similarity measure and its application to diverse COVID-19 scenarios.

Havoc, brutality, economic breakdown, and vulnerability are the terms that can be rightly associated with COVID-19, for the kind of impact it is having on the whole world for the last two years. COVID-19 came as a nightmare and it is still not over yet, changing its form factor with each mutation. Moreover, each unpredictable mutation causes more severeness. In the present article, we outline a decision support algorithm using Generalized Trapezoidal Intuitionistic Fuzzy Numbers (GTrIFNs) to deal with various facets of COVID-19 problems. Intuitionistic fuzzy sets (IFSs) and their continuous counterparts, viz., the intuitionistic fuzzy numbers (IFNs), have the flexibility and effectiveness to handle the uncertainty and fuzziness associated with real-world problems. Although a meticulous amount of research works can be found in the literature, a wide majority of them are based mainly on normalized IFNs rather than the more generalized approach, and most of them had several limitations. Therefore, we have made a sincere attempt to devise a novel Similarity Measure (SM) which considers the evaluation of two prominent features of GTrIFNs, which are their expected values and variances. Then, to establish the superiority of our approach we present a comparative analysis of our method with several other established similarity methods considering ten different profiles of GTrIFNs. The proposed SM is then validated for feasibility and applicability, by elaborating a Fuzzy Multicriteria Group Decision Making (FMCGDM) algorithm and it is supportedby a suitable illustrative example. Finally, the proposed SM approach is applied to tackle some significant concerns due to COVID-19. For instance, problems like the selection of best medicine for COVID-19 infected patients; proper healthcare waste disposal technique; and topmost government intervention measures to prevent the COVID-19 spread, are some of the burning issues which are handled with our newly proposed SM approach.

The Effects of Intervention Strategies for COVID-19 Transmission Control on Campus Activity

University is one of the most likely environments for the cluster infection due to the long-time close contact in house and frequent communication. It is critical to understand the transmission risk of COVID-19 under various scenario, especially during public health emergency. Taking the Tsinghua university's anniversary as a representative case, a set of prevention and control strategies are established and investigated. In the case study, an alumni group coming from out of campus is investigated whose activities and routes are designed based on the previous anniversary schedule. The social closeness indicator is introduced into the Wells-Riley model to consider the factor of contact frequency. Based on the anniversary scenario, this study predicts the number of the infected people in each exposure indoor location (including classroom, dining hall, meeting room and so on) and evaluates the effects of different intervention measures on reducing infection risk using the modified Wells-Riley model, such as ventilation, social distancing and wearing mask. The results demonstrate that when applying the intervention measure individually, increasing ventilation rate is found to be the most effective, whereas the efficiency of increased ventilation on reducing infection cases decreases with the increase of the ventilation rate. To better prevent COVID-19 transmission, the combined intervention measures are necessary to be taken, which show the similar effectiveness on the reduction of infected cases under different initial infector proportion. The results provide the insights into the infection risk on university campus when dealing with public health emergency and can guide university to formulate effective operational strategies to control the spread of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Microscopic modeling of spatiotemporal epidemic dynamics

Conventional techniques of epidemic modeling are based on compartmental models, where population groups are transitioning from one compartment to another - for example, S, I, or R, (Susceptible, Infectious, or Recovered). Then, they focus on learning macroscopic properties of disease spreading, such as the transition rates between compartments. Although these models are useful in studying epidemic dynamics, they lack the granularity needed for analyzing individual behaviors during an epidemic and understanding the relationship between individual decisions and the spread of the disease. In this paper, we develop microscopic models of spatiotemporal epidemic dynamics informed by mobility patterns of individuals and their interactions. In contrast to macroscopic models, microscopic epidemic models focus on individuals and their properties, such as their activity level, mobility behaviors, and impact of mobility behavior changes. Our microscopic spatiotemporal epidemic model allows to: (i) assess the risk of infection of an individual based on mobility patterns;(ii) assess the risk of infection associated with specific geographic areas and points-of-interest (POIs);(iii) assess the risk of infection of a trip in an urban environment;(iv) provide trip recommendation for mitigating the risk of infection;and (v) assess targeted intervention strategies that aim to control the epidemic spreading. Our work provides an evidence-based data-driven model to inform individuals about the infection risks associated with their mobility behavior during a pandemic, providing at the same time safer alternatives. It can also inform public policy about the effectiveness of targeted intervention strategies that aim to contain or mitigate the epidemic spread compared to horizontal measures. © 2022 ACM.

Epidemic Spread Optimization for Disease Containment with NPIs and Vaccination

The potential impact of epidemics, e.g., COVID-19, H1N1, and SARS, is severe on public health, the economy, education, and society. Before effective treatments are available and vaccines are fully deployed, combining Non-Pharmaceutical Interventions (NPIs) and vaccination strategies is the main approaches to contain the epidemic or live with the virus. Therefore, research for deciding the best containment operations to contain the epidemic based on various objectives and concerns is much needed. In this paper, we formulate the problem of Containment Operation Optimization Design (COOD) that optimizes the epidemic containment by carefully analyzing contacts between individuals. We prove the hardness of COOD and propose an approximation algorithm, named Multi-Type Action Scheduling (MTAS), with the ideas of Infected Ratio, Contact Risk, and Severity Score to select and schedule appropriate actions that implement NPIs and allocate vaccines for different groups of people. We evaluate MTAS on real epidemic data of a population with real contacts and compare it against existing approaches in epidemic and misinformation containment. Experimental results demonstrate that MTAS improves at least 200% over the baselines in the test case of sustaining public health and the economy. Moreover, the applicability of MTAS to various epidemics of different dynamics is demonstrated, i.e., MTAS can effectively slow down the peak and reduce the number of infected individuals at the peak. © 2022 IEEE.

Segmentized quarantine policy for managing a tradeoff between containment of infectious disease and social cost of quarantine

By the end of 2021, COVID-19 had spread to over 230 countries, with more than 5.4 million deaths. To contain the disease spread, many countries have deployed non-pharmaceutical intervention strategies, most notably contact tracing and self-quarantine policy. We have observed that containment of disease spread by such social distancing policy come at a large social cost, and prolonged pandemic raised the necessity of more sustainable policy with the least disruption to economic and societal activities. This research aims to investigate a segmentized quarantine policy where we apply different quarantine policies for different population segments with a goal of better managing the tradeoff between the benefit and cost of a quarantine strategy. Motivation for a segmentized policy is that different population groups, e.g., school students vs adults with jobs, exhibit different patterns of societal activities, thereby imposing different risks to disease spread. We define a segmentized quarantine policy in two dimensions - range of contact tracing and quarantine period, and determine the two parameters for each population segment to achieve two objectives: to minimize the total number of infected cases and to minimize the total days of self-quarantine. We use Agent-based Epidemics Simulation to evaluate the quarantine policies, and Evolutionary Algorithm is used to obtain the Pareto front of our problem. Our results demonstrate the effectiveness of the segmentized quarantine policies, and we identify the conditions where they outperform the uniform policy. We also find in the Pareto optimal solutions that only some population segments are recommended special policy features while other segments are subject to the conventional policy. The results suggest that segmentized quarantine policy is valid in terms of efficiency and sustainability, and the suggestions and framework presented are expected to be of great help in establishing public health decisions to prepare for an upcoming pandemic like COVID-19. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Intervention-Aware Epidemic Prediction by Enhanced Whale Optimization

In recent decades, new epidemics have seriously endangered people’s lives and are now the leading cause of death in the world. The prevention of pandemic diseases has therefore become a top priority today. However, effective prevention remains a difficult challenge due to factors such as transmission mechanisms, lack of documentation of clinical outcomes, and population control. To this end, this paper proposes a susceptible-exposed-infected-quarantined (hospital or home)-recovered (SEIQHR) model based on human intervention strategies to simulate and predict recent outbreak transmission trends and peaks in Changchun, China. In this study, we introduce Levy operator and random mutation mechanism to reduce the possibility of the algorithm falling into a local optimum. The algorithm is then used to identify the parameters of the model optimally. The validity and adaptability of the proposed model are verified by fitting experiments to the number of infections in cities in China that had COVID-19 outbreaks in previous periods (Nanjing, Wuhan, and Xi’an), where the peaks and trends obtained from the experiments largely match the actual situation. Finally, the model is used to predict the direction of the disease in Changchun, China, for the coming period. The results indicated that the number of COVID-19 infections in Changchun would peak around April 3 and continue to decrease until the end of the outbreak. These predictions can help the government plan countermeasures to reduce the expansion of the epidemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

District-Coupled Epidemic Control via Deep Reinforcement Learning

The rapid spread of the Coronavirus (COVID-19) poses an unprecedented threat to the public health system and social economy, with approximately 500 million confirmed cases worldwide. Policymakers confront with high-stakes to make a decision on interventions to prevent the pandemic from further spreading, which is a dilemma between public health and a steady economy. However, the epidemic control problem has vast solution space and its internal dynamic is driven by population mobility, which makes it difficult for policymakers to find the optimal intervention strategy based on rules-of-thumb. In this paper, we propose a Deep Reinforcement Learning enabled Epidemic Control framework (DRL-EC) to make a decision on intervention to effectively alleviate the impacts of the epidemic outbreaks. Specifically, it is driven by reinforcement learning to learn the intervention policy autonomously for the policymaker, which can be adaptive to the various epidemic situation. Furthermore, District-Coupled Susceptible-Exposed-Infected-Recovered (DC-SEIR) model is hired to simulate the pandemic transmission between inter-district, which characterize the spatial and temporal nature of infectious disease transmission simultaneously. Extensive experimental results on a real-world dataset, the Omicron local outbreaks in China, demonstrate the superiority of the DRL-EC compared with the strategy based on rules-of-thumb. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

EpidRLearn: Learning Intervention Strategies for Epidemics with Reinforcement Learning

Epidemics of infectious diseases can pose a serious threat to public health and the global economy. Despite scientific advances, containment and mitigation of infectious diseases remain a challenging task. In this paper, we investigate the potential of reinforcement learning as a decision making tool for epidemic control by constructing a deep Reinforcement Learning simulator, called EpidRLearn, composed of a contact-based, age-structured extension of the SEIR compartmental model, referred to as C-SEIR. We evaluate EpidRLearn by comparing the learned policies to two deterministic policy baselines. We further assess our reward function by integrating an alternative reward into our deep RL model. The experimental evaluation indicates that deep reinforcement learning has the potential of learning useful policies under complex epidemiological models and large state spaces for the mitigation of infectious diseases, with a focus on COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Decision Making Aid for Organizations during Epidemic Situations

An epidemic is a widespread infection within a population at a particular period. COVID-19 is one such epidemic which is turned into a pandemic by mid-2020. COVID-19 had an enormous impact on people's livelihood, health, economy, and social life. In Sri Lanka, we have faced the dreadful side of the COVID-19 during its second, third, and fourth waves. Some of these waves were propagated by the behavior of individuals in organizations. During this period several intervention strategies have been introduced in order to stop the disease spread globally and as well as locally using. Many different epidemic models built using techniques ranging from statistical prediction to simulation. For this research we used Agent-Based modeling to simulate the spread of a contagious disease in different organizations. Several parameters have been introduced in the development process of these models considering some important aspects of contagious disease spreads. Two common interventions practiced in countries were implemented to evaluate their effectiveness, namely social distancing and face mask. Agent-based simulation models were generated from these computational models and evaluated using parameter sweeping. The effectiveness of the two interventions in mitigation of the spread of the disease were compared. Flattening the curves of the graphs of infection spread can be achieved by timing the interventions early. The simulation clearly shows the impact of parameters these and their importance in the control of disease spreads. © 2021 IEEE.

Exploring Intervention Strategies for Distracted Students in VR Classrooms

As one of the manifestations of virtual reality (VR) in education, virtual classroom allows students to enjoy a near-real classroom experience. VR class creates much better engagement and helps to stimulate interest and motivation in learning, making it an ideal solution to online teaching and learning activities, especially during the COVID-19 pandemic. Distraction is an unavoidable problem in immersive virtual classes, which has a great detrimental impact on learning. However, how to intervene in students' distraction behaviors in immersive virtual environments has not been thoroughly investigated up to now. In this paper, inspired by teachers' instructional techniques in real-life classes, we propose three intervention strategies, namely eye contact, verbal warning and text warning, and explore the intervening effects of these strategies on the inattention of students seated at the front or back of a virtual classroom via eye tracking. Our results show that all of the proposed intervention strategies have positive impacts on the attention of students. This research gives evidence that teachers' instructional techniques in the real world can be transferred to the virtual class, which provides a new insight for the future design of educational VR. © 2022 ACM.

High Performance Agent-Based Modeling to Study Realistic Contact Tracing Protocols

Contact tracing (CT) is an important and effective intervention strategy for controlling an epidemic. Its role becomes critical when pharmaceutical interventions are unavailable. CT is resource intensive, and multiple protocols are possible, therefore the ability to evaluate strategies is important. We describe a high-performance, agent-based simulation model for studying CT during an ongoing pandemic. This work was motivated by the COVID-19 pandemic, however framework and design are generic and can be applied in other settings. This work extends our HPC-oriented ABM framework EpiHiper to efficiently represent contact tracing. The main contributions are: (i) Extension of EpiHiper to represent realistic CT processes. (ii) Realistic case study using the VA network motivated by our collaboration with the Virginia Department of Health. © 2021 IEEE.