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Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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The coronavirus disease 2019(COVID-19) pandemic spreads across borders with the frequent global population movement. To explore the impact of the COVID-19 pandemic on China's domestic epidemic prevention and control, based on the classical infectious disease dynamics model this paper proposes an infectious disease model that considers oversea imported cases. The model can simulate three situations:national pandemic without imported cases, no domestic cases with only imported cases, and domestic cases with international travellers entering simultaneously. By calculating the peak case number and range of infection spread duration in these situations, as well as the amount of medical resources invested, the model has shown the different results of impact of entry type on the domestic pandemic and different pressures on medical resources. Finally, the paper suggests that testing measures should be taken according to the degree of pandemic risk and resource conditions, that strict prevention and control should be applied to the people not entering through customs, and closed-loop management to the people entering through customs, that entry quarantine measures and quarantine periods should be dynamically adjusted and international exchanges should be gradually resumed in the context of ensuring domestic and overseas epidemic prevention and control in advance, and that it is necessary to integrate medical resources, improve allocation efficiency, and relieve the pressure of resource occupation.
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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.
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Featured ApplicationRadiation thermometry of real objects under real conditions.Despite great technical capabilities, the theory of non-contact temperature measurement is usually not fully applicable to the use of measuring instruments in practice. While black body calibrations and black body radiation thermometry (BBRT) are in practice well established and easy to accomplish, this calibration protocol is never fully applicable to measurements of real objects under real conditions. Currently, the best approximation to real-world radiation thermometry is grey body radiation thermometry (GBRT), which is supported by most measuring instruments to date. Nevertheless, the metrological requirements necessitate traceability;therefore, real body radiation thermometry (RBRT) method is required for temperature measurements of real bodies. This article documents the current state of temperature calculation algorithms for radiation thermometers and the creation of a traceable model for radiation thermometry of real bodies that uses an inverse model of the system of measurement to compensate for the loss of data caused by spectral integration, which occurs when thermal radiation is absorbed on the active surface of the sensor. To solve this problem, a hybrid model is proposed in which the spectral input parameters are converted to scalar inputs of a traditional scalar inverse model for GBRT. The method for calculating effective parameters, which corresponds to a system of measurement, is proposed and verified with the theoretical simulation model of non-contact thermometry. The sum of effective instrumental parameters is presented for different temperatures to show that the rule of GBRT, according to which the sum of instrumental emissivity and instrumental reflectivity is equal to 1, does not apply to RBRT. Using the derived models of radiation thermometry, the uncertainty of radiation thermometry due to the uncertainty of spectral emissivity was analysed by simulated worst-case measurements through temperature ranges of various radiation thermometers. This newly developed model for RBRT with known uncertainty of measurement enables traceable measurements using radiation thermometry under any conditions.
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Purpose: This study aims to create a system dynamics simulation model to forecast the performance of small and medium-sized enterprises (SMEs) if some decision-making is executed to reduce the negative of the coronavirus disease 2019 (COVID-19) pandemic. In particular, this study will focus on SMEs that belong to the furniture industry because the furniture industry is one of the leading industries in Indonesia. Design/methodology/approach: The study develops a system dynamics-based model by using three subsystems, i.e. the "production subsystem,” "demand and revenue subsystem” and "raw material (or wood supply) subsystem.” Findings: The best scenario is the third scenario which increases the capacity to the normal situation and government subsidy during and after the pandemic. This scenario gives the best performance for industry revenue and gross domestic product (GDP). However, for the government, the most significant expenditure occurs in the third scenario. This seems a trade-off for the government whether to save the wooden-based furniture industry by encouraging the industry to continue operating during the pandemic accompanied by high subsidies or limiting the activities of the wooden-based furniture industry to prevent the spread of COVID-19 by providing low subsidies. Research limitations/implications: First, this study does not try to combine the system dynamics (SD) methodology with the other method or use a multi-methodology since SD has several limitations and the other method may have several advantages compared to SD. Second, the models used in this study do not consider the decline in forest area and quality. Third, the demand for wooden-based furniture is obtained from historical data on domestic and foreign sales and fourth, the model does not include the government budget as a constraint to make any subsidy to help the SMEs. Practical implications: This study provides essential insights into implementing the policies in the world pandemic situation when SMEs face lockdown policy. Social implications: The study revealed that relevant policy scenarios could be built after simulating and analyzing each scenario's effect on SMEs' performance during the pandemic. Originality/value: This study will enrich the previous study on the impact of the pandemic on SMEs and the dynamic system modeling on SMEs. The previous study discussed the pandemic's impact on SME performance and the impact's analysis in isolation from the dynamic nature of SME owners' decisions or government policy. In this study, the impact generated from the pandemic situation could be different depending on the decision and policies taken by managers from SMEs and the government. © 2023, Emerald Publishing Limited.
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Since early 2020, the COVID-19 outbreak has disrupted various supply chains including the on-demand food delivery sector. As a result, this service industry has witnessed a tremendous spike in demand that is affecting its delivery operations at the downstream level. Previous research studies have explored one-to-one and many-to-one solutions to the virtual food court delivery problem (VFCDP) to optimize on-demand food delivery services in different cities. However, research efforts have been limited to multiple restaurant orders from only one customer which does not apply to traditional systems where multiple customers request on-demand food delivery from multiple restaurants. This study rigorously analyses multiple restaurants to multiple customers (Many-to-many) food delivery simulation models in ideal weather conditions that are constrained with multiple key performance indicators (KPIs) such as delivery fleet utilization (the number of couriers utilized over the fleet size), average order delivery time, and fuel costs. This research also benchmarks the on-demand food delivery queueing methodologies using system dynamics and agent-based simulation modeling where three on-demand food delivery routing methodologies are simulated including First-in-First-Out (FIFO), Nearest, and Simulated Annealing using AnyLogic. The results suggest that the Many-to-many (Nearest) method outperforms other delivery routing methods which would have positive implications on optimizing existing food delivery systems and managerial decisions.
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Since early 2020, the COVID-19 outbreak has disrupted various supply chains including the on-demand food delivery sector. As a result, this service industry has witnessed a tremendous spike in demand that is affecting its delivery operations at the downstream level. Previous research studies have explored one-to-one and many-to-one solutions to the virtual food court delivery problem (VFCDP) to optimize on-demand food delivery services in different cities. However, research efforts have been limited to multiple restaurant orders from only one customer which does not apply to traditional systems where multiple customers request on-demand food delivery from multiple restaurants. This study rigorously analyses multiple restaurants to multiple customers (Many-to-many) food delivery simulation models in ideal weather conditions that are constrained with multiple key performance indicators (KPIs) such as delivery fleet utilization (the number of couriers utilized over the fleet size), average order delivery time, and fuel costs. This research also benchmarks the on-demand food delivery queueing methodologies using system dynamics and agent-based simulation modeling where three on-demand food delivery routing methodologies are simulated including First-in-First-Out (FIFO), Nearest, and Simulated Annealing using AnyLogic. The results suggest that the Many-to-many (Nearest) method outperforms other delivery routing methods which would have positive implications on optimizing existing food delivery systems and managerial decisions.
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As increasing proportions of the world's population have received at least one dose of the vaccine against COVID-19, everyday activities start to be resumed, including travels. The present study investigates the impact of immunization on the risk of exposure to an infectious disease such as COVID-19, during the boarding process in a commercial airplane. An agent-based simulation model considers different vaccine types and vaccination rates among passengers. The results show significant decrease in the median exposure risk, when the vaccination rate increases from 0% to 100%, but also that people in seats adjacent to an infectious passenger are in much higher risk, for a similar vaccination coverage. Such results provide quantitative evidence of the importance of mass immunization, and also that, when full vaccination is not guaranteed for 100% of passengers, it may be recommendable to avoid full occupancy of the aircraft, by implementing physical distancing when assigning seats. © 2022 IEEE.
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The Covid-19 virus has substantially transformed many aspects of life, impacted industries, and revolutionized supply chains all over the world. System dynamics modeling, which incorporates systems thinking to understand and map complex events as well as correlations, can aid in predicting future outcomes of the pandemic and generate key learnings. As system dynamic modeling allows for a deeper understanding of the manifestation and dynamics of disease, it was helpful when examining the implications of the pandemic on the supply chain of semiconductor companies. This tutorial describes how the system dynamics simulation model was constructed for the Covid-19 pandemic using AnyLogic Software. The model serves as a general foundation for further epidemiological simulations and system dynamics modeling. © 2022 IEEE.
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In large agent-based models, it is difficult to identify the correlate system-level dynamics with individual-level attributes. In this paper, we use inverse reinforcement learning to estimate compact representations of behaviors in large-scale pandemic simulations in the form of reward functions. We illustrate the capacity and performance of these representations identifying agent-level attributes that correlate with the emerging dynamics of large-scale multi-agent systems. Our experiments use BESSIE, an ABM for COVID-like epidemic processes, where agents make sequential decisions (e.g., use PPE/refrain from activities) based on observations (e.g., number of mask wearing people) collected when visiting locations to conduct their activities. The IRL-based reformulations of simulation outputs perform significantly better in classification of agent-level attributes than direct classification of decision trajectories and are thus more capable of determining agent-level attributes with definitive role in the collective behavior of the system. We anticipate that this IRL-based approach is broadly applicable to general ABMs. © 2022 IEEE.
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In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to ‘Jan Aushadhi' (peoples' medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers' workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research.
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We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020, to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals at the beginning of the pandemic. While the KCDC data offer a wealth of information, they are also by their nature limited. To compensate for their limitations, we use detailed mobility data from Berlin, Germany after observing that mobility of individuals is surprisingly similar in both Berlin and Seoul. Using information from the Berlin mobility data, we cross-fertilize the KCDC Seoul data set and use it to parameterize an agent-based simulation that models the spread of the disease in an urban environment. After validating the simulation predictions with ground truth infection spread in Seoul, we study the importance of each input parameter on the prediction accuracy, compare the performance of our model to state-of-the-art approaches, and show how to use the proposed model to evaluate different what-if counter-measure scenarios. IEEE
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The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.
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A mathematical model to study the transmission dynamics of COVID-19 incorporating public enlightenment campaign as control is presented. The effective reproduction number (Rc) was computed using the next generation method. Using the Lyapunov method, the global stability of the disease-free equilibrium was found to be globally asymptotically stable whenever (Rc 1). Sensitivity analysis was conducted on the effective reproduction number in order to determine parameters of the model that are most sensitive and targeted by way of intervention strategies. Numerical simulations of the COVID-19 model shows that if of both treatment and public enlightenment campaign is achieved, the pandemic will be greatly controlled and subsequently eradicated in the population.
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During the COVID-19 pandemic, a rise of (agent-based) simulation models for predicting future developments and assessing intervention scenarios has been observed. At the same time, dashboarding has become a popular way to aggregate and visualise large quantities of data. The AScore Pandemic Management Cockpit brings together multiagent-based simulation (MABS) and analysis functionalities for crisis managers. It combines the presentation of data and forecasting on the effects of containment measures in a modular, reusable architecture that streamlines the process of use for these non-researcher users. In this paper, the most successful features and concepts for the simplification of simulation usage are presented: definition of scenarios, limitation of parameters, and integrated result visualisation, all bundled in a web-based service to offer a low-barrier entry to the usage of MABS in decision-making processes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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We present an individual-centric agent-based model and a flexible tool, GeoSpread, for studying and predicting the spread of viruses and diseases in urban settings. Using COVID-19 data collected by the Korean Center for Disease Control & Prevention (KCDC), we analyze patient and route data of infected people from January 20, 2020, to May 31, 2020, and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of population mobility and is used to parameterize GeoSpread to capture the spread of the disease. We validate simulation predictions from GeoSpread with ground truth and we evaluate different what-if counter-measure scenarios to illustrate the usefulness and flexibility of the tool for epidemic modeling. © 2022 Owner/Author.
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The transport sector has to be widely decarbonized by 2050 to reach the targets of the Paris Agreement. This can be performed with different drive trains and energy carriers. This paper explored four pathways to a carbon-free transport sector in Germany in 2050 with foci on electricity, hydrogen, synthetic methane, or liquid synthetic fuels. We used a transport demand model for future vehicle use and a simulation model for the determination of alternative fuel vehicle market shares. We found a large share of electric vehicles in all scenarios, even in the scenarios with a focus on other fuels. In all scenarios, the final energy consumption decreased significantly, most strongly when the focus was on electricity and almost one-third lower in primary energy demand compared with the other scenarios. A further decrease of energy demand is possible with an even faster adoption of electric vehicles, yet fuel cost then has to be even higher or electricity prices lower.
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The boarding process is the role activity to maintain the airline's efficiency in the turnaround process on the ground. One of the scenarios to optimize the boarding process is the arrangement of passengers who enter the plane based on the amount of carry-on luggage, adjusted to the selected boarding strategy. This research aims to develop an agent-based simulation model to increase the effectiveness of passengers' boarding process by applying the luggage arrangement method for an airplane with a 180-seat configuration. The simulation results showed that applying the Ascending luggage arrangement method reduced the overall boarding process performance by 6.12%, while the Descending method increased boarding performance by 2.50%, compared to the standard Random method.
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An efficient and functional supply chain is essential for economies to prosper. Pandemics, however, have proven to be a global challenge that has disrupted the supply chain's routine operations. Inspired by the impact of the COVID-19 pandemic, this paper is studies the effects of COVID-19 on the food supply chain as a result of living in close quarters. To determine how much the food supply chain has been impacted by the COVID-19 pandemic, we employ an agent-based simulation model, combined with an SEISR (Susceptible, Exposed, Infectious, Symptomatic, and Recovered) disease model, to quantify the impact on the food supply chain in terms of productivity, disruption time, and the number of sick workers. In relation to how many contacts workers have in a day, five social distance metrics were varied taking into account infection probabilities. A key finding is that social distance practices and the level of contacts that occur at a time along with the level of infection probability define the level of impact the pandemic has on the food supply chain. Essentially it is seen that the pandemic indeed has a disruptive effect on the food supply chain and workers living in close quarters. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.
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Objective: To explore the epidemic trend of coronavirus disease 2019 (COVID-19) and the effect of prevention and control measures in Wuhan and Guangdong province. Method: A Joinpoint regression model was used to fit the time-series of the daily number of new cases of COV1D- 19 in Wuhan and Guangdong provinces. and the daily average rate of change in the number of new cases at different stages was calculated to estimate the critical point of epidemic trend.