Progress and Perspective of Transmission Dynamics Models in Prevention and Control of Infectious Diseases

The current respiratory infectious disease has expanded over the world, posing a serious threat to people's physical and mental health, as well as their lives. Science and technology immediately united to fight against such deadly infectious disease in the past 100 years. Mathematical models have proved invaluable to understand and help control infectious disease epidemics. By simplifying real world phenomena, these models describe, analyze, and predict disease transmission patterns, producing tractable solutions in the face of quickly changing situations. In this Chapter, we firstly summarized the history and development of the mathematical models in infectious diseases. Afterwards, the specific transmission dynamics models with different model structures used in fitting and forecasting the situation of the current respiratory infectious disease were introduced, aiming different analytical objectives including but not limited to parameter estimation, trend prediction and early warning, prevention and control measures effectiveness evaluation, and transmission uncertainty exploration. Summary in values of transmission dynamics models is followed to illustrate their contribution in understanding and combating infectious disease outbreaks. Despite their utility, however, mathematical models are facing several important challenges which, if ignored, would result in biased estimation of the crucial epidemiological parameters, bad fitting of the data, or misinterpretation of the results. In conclusion, mathematical modeling should be one of the most valuable tools to reflect such huge uncertainties or, on the other hand, warn of the worst situation. An appreciation of models' shortcomings not only clarifies why they cannot do but helps anticipate what they can. © People's Medical Publishing House, PR of China 2022.

COVID-19 Model Based on Conformable Fractional Derivativeand Its Numerical Solution

After the outbreak of COVID-19, it is of great significance to find an appropriate dynamic model of COVID-19 epidemic in order to master its transmission law, predict its development trend, and provide corresponding prevention and control basis. In this paper, the SEIRV chamber model is adopted, and the dynamics model of infectious disease is established by combining the fractional derivative of Conformable. The fractional derivative differential equation of Conformable is discretized by numerical method and its numerical solution is obtained. In addition, numerical simulation was carried out on the confirmed data of Wuhan city from January 23, 2020 to February 11, 2020. At the same time, consider that the Wuhan municipal government revised the epidemic data on February 12, 2020, adding nearly 14,000 people. The order α value of SEIRV model is modified, and then the revised data is simulated. The simulation results are in good agreement with the published data. The results show that compared with the traditional integer order model, the fractional order model can simulate the modified data. This reflects the advantages of fractional infectious disease dynamics model, and can provide certain reference value for the prediction of COVID-19 model. © 2022 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.

Selection of forecasting models for infectious diseases in large warships.

As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Selection of forecasting models for infectious diseases in large warships

As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Dynamic and classifier-based model SARS-CoV-2 Omicron spillover risk assessment in China

The coronavirus disease 2019 (COVID-19) continues to have a huge impact on health care and economic systems around the world. The first question to ponder is to understand the flow of COVID-19 in the spatial and temporal dimensions. We collected 7 Omicron clusters outbreaks in China since the outbreak of COVID-19 as of August 2022, selected outbreak cases from different Provinces and cities, and collected variable indicators that affect spillover outcomes, such as distance, migration index, PHSM index, daily reported cases number and so on. First, variables influencing spillover outcome events were assessed and analyzed retrospectively by constructing an infectious disease dynamics model and a classifier model, and secondly, the association between explanatory variables and spillover outcome events was constructed by fitting a logistics function. This study incorporates 7 influencing factors and classifies the spillover risk level into 3 levels. If different outbreak sites could be classified into different levels of spillover, it may reduce the pressure of epidemic prevention in some cities due to the lack of a uniform standard, which might be more conducive to achieving the goal of "dynamic zero".

A dynamic model for elevator operation-induced spread of a respiratory infectious disease in an apartment building.

Residents have to use elevators to leave and enter their high-rise apartments frequently. An elevator car can easily spread respiratory infectious diseases, as it has a confined and small space. Therefore, studying how elevator operations promote epidemic transmission is of importance to public health. We developed an infectious disease dynamics model. First, we used homemade codes to simulate the operating state of an elevator and the dynamic process of infectious disease transmission in an apartment building due to elevator operations. Second, we analysed the temporal distribution patterns of infected individuals and patients. Finally, we validated the reliability of the model by performing continuous-time sensitivity analysis on important model parameters. We found that elevator operations can cause rapid spread of infectious diseases within an apartment building. Therefore, it is necessary to enhance elevator ventilation and disinfection mechanisms to prevent the outbreak of respiratory infections. Moreover, residents should reduce elevator use and wear masks.

Comparison of the transmission of COVID -19 between Hebei and Heilongjiang in early 2021

Objective To compare the diversity of transmission of COVID -19 in Hebei and Heilongjiang Province in early 2021, and to provide theoretical support for the formulation of prevention and control strategies for COVID -19. Methods A dynamical model with staged control strategies was constructed based on the number of existing asymptomatic cases, the number of existing confirmed cases and the cumulative number of removed cases in Hebei and Heilongjiang at the beginning of 2021. Parameters of the model were estimated by the nonlinear least square method. Sensitivity analysis was used to explore the impact of key parameters on the peak number and peak time of existing confirmed cases in the two regions. We respectively analyzed the influence of the change for the number of initial contacts, the probability of initial contacts, the relative infectivity correction factor of the latent and the composition ratio of the symptomatic infection on the number of existing asymptomatic cases, the number of existing confirmed cases and the number of cumulative cases in the two regions. Results The model fitting results of the two regions were good. Compared the results of Hebei with those of Heilongjiang, there was a larger proportion of asymptomatic infected persons. When the number of initial contacts, the probability of initial contacts, the relative infectivity correction factor of the latent and the composition ratio of the symptomatic infection separately decreased by 10%, the average decrease for the peak number of existing asymptomatic and existing confirmed cases, and the cumulative removed cases in Heilongjiang were more than those in Hebei. Conclusions In early 2021, the transmissions of COVID -19 in Hebei and Heilongjiang were significantly different. In particular, the impact of control measures on the development of the epidemic is different in different areas. © 2023, Publication Centre of Anhui Medical University. All rights reserved.

Epidemiological Modeling with Sir and Arima Dynamic Models and Machine Learning to Predict the Future Behavior of Covid-19 in Panama

Mathematical models SIR and ARIMA were used, within an epidemiological approach, to adjust them to the COVID-19 pandemic data in Panama to establish a scientific criterion for taking decisions for the effects control that this pandemic has brought. Based on the predictions made from the adjustments of these models, it was concluded that they can be adjusted correctly to the data, allowing to make short-term predictions in a satisfactory way, however, if a more accurate model were to be carried out, independent variables could be included, besides time, such as mobility restrictions. This work lays down the foundations for future investigations of epidemiological models in Panama due to its exposition of mathematical model's comparison used to analyze the behavior of the COVID-19 Pandemic. Jupyter Notebook, GitHub, Machine Learning libraries and mathematical software such as Wolfram Mathematica were used. Adjustment of data was performed through statistical techniques and, for this prediction, statistical software Minitab and E-Views were also used. © 2022 IEEE.

Comparison of the transmission of COVID -19 between Hebei and Heilongjiang in early 2021.

Objective To compare the diversity of transmission of COVID -19 in Hebei and Heilongjiang Province in early 2021, and to provide theoretical support for the formulation of prevention and control strategies for COVID -19. Methods A dynamical model with staged control strategies was constructed based on the number of existing asymptomatic cases, the number of existing confirmed cases and the cumulative number of removed cases in Hebei and Heilongjiang at the beginning of 2021. Parameters of the model were estimated by the nonlinear least square method. Sensitivity analysis was used to explore the impact of key parameters on the peak number and peak time of existing confirmed cases in the two regions. We respectively analyzed the influence of the change for the number of initial contacts, the probability of initial contacts, the relative infectivity correction factor of the latent and the composition ratio of the symptomatic infection on the number of existing asymptomatic cases, the number of existing confirmed cases and the number of cumulative cases in the two regions. Results The model fitting results of the two regions were good. Compared the results of Hebei with those of Heilongjiang, there was a larger proportion of asymptomatic infected persons. When the number of initial contacts, the probability of initial contacts, the relative infectivity correction factor of the latent and the composition ratio of the symptomatic infection separately decreased by 10%, the average decrease for the peak number of existing asymptomatic and existing confirmed cases, and the cumulative removed cases in Heilongjiang were more than those in Hebei. Conclusions In early 2021, the transmissions of COVID -19 in Hebei and Heilongjiang were significantly different. In particular, the impact of control measures on the development of the epidemic is different in different areas.Copyright © 2023, Publication Centre of Anhui Medical University. All rights reserved.

Using simulation modelling and systems science to help contain COVID-19: A systematic review.

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

Machine learning of flow cytometry data reveals the delayed innate immune responses correlate with the severity of COVID-19.

Introduction: The COVID-19 pandemic has posed a major burden on healthcare and economic systems across the globe for over 3 years. Even though vaccines are available, the pathogenesis is still unclear. Multiple studies have indicated heterogeneity of immune responses to SARS-CoV-2, and potentially distinct patient immune types that might be related to disease features. However, those conclusions are mainly inferred by comparing the differences of pathological features between moderate and severe patients, some immunological features may be subjectively overlooked. Methods: In this study, the relevance scores(RS), reflecting which features play a more critical role in the decision-making process, between immunological features and the COVID-19 severity are objectively calculated through neural network, where the input features include the immune cell counts and the activation marker concentrations of particular cell, and these quantified characteristic data are robustly generated by processing flow cytometry data sets containing the peripheral blood information of COVID-19 patients through PhenoGraph algorithm. Results: Specifically, the RS between immune cell counts and COVID-19 severity with time indicated that the innate immune responses in severe patients are delayed at the early stage, and the continuous decrease of classical monocytes in peripherial blood is significantly associated with the severity of disease. The RS between activation marker concentrations and COVID-19 severity suggested that the down-regulation of IFN-γ in classical monocytes, Treg, CD8 T cells, and the not down-regulation of IL_17a in classical monocytes, Tregs are highly correlated with the occurrence of severe disease. Finally, a concise dynamic model of immune responses in COVID-19 patients was generalized. Discussion: These results suggest that the delayed innate immune responses in the early stage, and the abnormal expression of IL-17a and IFN-γ in classical monocytes, Tregs, and CD8 T cells are primarily responsible for the severity of COVID-19.

Modeling Cross-Regional Transmission and Assessing the Effectiveness of Restricting Inter-Regional Population Movements in Controlling COVID-19 - Xi'an City, Shaanxi Province, China, 2021.

Introduction: The aim of this study was to construct an assessment method for cross-regional transmission of coronavirus disease 2019 (COVID-19) and to provide recommendations for optimizing measures such as interregional population movements. Methods: Taking Xi'an City as the example subject of this study's analysis, a Cross-Regional-Gravitational-Dynamic model was constructed to simulate the epidemic in each district of Xi'an under three scenarios of controlled population movement (Scenario 1: no intensive intervention; Scenario 2: blocking Yanta District on December 18 and blocking the whole region on December 23; and Scenario 3: blocking the whole region on December 23). This study then evaluated the effects of such simulated population control measures. Results: The cumulative number of cases for the three scenarios was 8,901,425, 178, and 474, respectively, and the duration of the epidemic was 175, 18, and 22 days, respectively. The real world prevention and control measures in Xi'an reduced the cumulative number of cases for its outbreak by 99.98% in comparison to the simulated response in Scenario 1; in contrast, the simulated prevention and control strategies set in Scenarios 2 (91.26%) and 3 (76.73%) reduced cases even further than the real world measures used in Xi'an. Discussion: The constructed model can effectively simulate an outbreak across regions. Timely implementation of two-way containment and control measures in areas where spillover is likely to occur is key to stopping cross-regional transmission.

Mitigating personal protective equipment (PPE) supply chain disruptions in pandemics - a system dynamics approach

Purpose The coronavirus disease (COVID-19) pandemic has emerged as an unprecedented health crisis worldwide and heavily disrupted the healthcare supply chain. This study focuses on analysing the different types of disruptions occurring in personal protective equipment (PPE) supply chains during the COVID-19 pandemic and on proposing mitigation strategies that are fit to the global scale and many interdependencies that are characteristic for this pandemic. The authors construct a conceptual system dynamics model (SD) based on the literature and adjusted with the use of empirical data (interviews) to capture the complexity of a global supply chain and identify leverage points (mitigation strategies). Design/methodology/approach This research follows a mix-methods approach. First, the authors developed a conceptual framework based on four types of disruptions that usually occur during health emergencies (direct effect, policy, supply chain strategy, and behaviourally induced disruptions). Second, the authors collected and analysed data from interviews with experts in the PPE supply chain. Based on the interviews data, the authors developed a conceptual system dynamics (SD) model that allows to capture the complex and dynamic interplay between the elements of the global supply chain system, by highlighting key feedback loops, delays, and the way the mitigation strategies can impact on them. From this analysis, the authors developed four propositions for supply chain risk management (SCRM) in global health emergencies and four recommendations for the policy and decision makers. Findings The SD model highlights that without a combination of mitigation measures, it is impossible to overcome all disruptions. As such, a co-ordinated effort across the different countries and sectors that experience the disruptions is needed. The SD model also shows that there are important feedback loops, by which initial disruptions create delays and shortages that propagate through the supply chain network. If the co-ordinated mitigation measures are not implemented early at the onset of the pandemic, these disruptions will be persistent, creating potential shortages of PPE and other critical equipment at the onset of a pandemic - when they are most urgently needed. Originality/value This research enriches the understanding of the disruptions of PPE supply chains on the systems level and proposes mitigation strategies based on empirical data and the existing literature.

Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges.

Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.

Computing R 0 of dynamic models by a definition-based method.

Objectives: Computing the basic reproduction number (R 0) in deterministic dynamical models is a hot topic and is frequently demanded by researchers in public health. The next-generation methods (NGM) are widely used for such computation, however, the results of NGM are usually not to be the true R 0 but only a threshold quantity with little interpretation. In this paper, a definition-based method (DBM) is proposed to solve such a problem. Methods: Start with the definition of R 0, consider different states that one infected individual may develop into, and take expectations. A comparison with NGM has proceeded. Numerical verification is performed using parameters fitted by data of COVID-19 in Hunan Province. Results: DBM and NGM give identical expressions for single-host models with single-group and interactive R ij of single-host models with multi-groups, while difference arises for models partitioned into subgroups. Numerical verification showed the consistencies and differences between DBM and NGM, which supports the conclusion that R 0 derived by DBM with true epidemiological interpretations are better. Conclusions: DBM is more suitable for single-host models, especially for models partitioned into subgroups. However, for multi-host dynamic models where the true R 0 is failed to define, we may turn to the NGM for the threshold R 0.

The Chinese plug-in electric vehicles industry in post-COVID-19 era towards 2035: Where is the path to revival?

The sudden Coronavirus Disease reported at the end of 2019 (COVID-19) has brought huge pressure to Chinese Plug-in Electric Vehicles (PEVs) industry which is bearing heavy burden under the decreasing fiscal subsidy. If the epidemic continues to rage as the worst case, analysis based on System Dynamics Model (SDM) indicates that the whole PEVs industry in China may shrink by half compared with its originally expected level in 2035. To emerge from the recession, feasible industrial policies include (1) accelerating the construction of charging infrastructures, (2) mitigating the downtrend of financial assistance and (3) providing more traffic privilege for drivers. Extending the deadline of fiscal subsidy by only 2 years, which has been adopted by the Chinese central government, is demonstrated to achieve remarkable effect for the revival of PEVs market. By contrast, the time when providing best charging service or most traffic privilege to get the PEVs industry back to normal needs to be advanced by 10 years or earlier. For industrial policy makers, actively implementing the other two promoting measures on the basis of existing monetary support may be a more efficient strategy for Chinese PEVs market to revive from the shadow in post-COVID-19 era.

Prediction and management of household solid waste generation during COVID-19 pandemic in Bandung City using system dynamics model

WHO declared a novel coronavirus in humans as Coronavirus Disease 2019 (COVID-19) on February 2020, and Indonesia as well as Bandung City have been suffering from COVID-19 since the first case in March, 2020. Currently, the outbreak of COVID-19 has occurred for more than a year. The COVID-19 pandemic had a severe impact on the environment, like the enhancement of household solid waste as a result of work and school from home policies to decrease the rates of COVID-19 cases. This study aimed to predict the amount of household solid waste generation and analyze the waste management during COVID-19 in Bandung City using a system dynamics model. Data for model input was obtained from questionnaire to 200 respondents from Bandung spread across the sub-districts. The results revealed that the household solid waste generation was increased by 1.3 to 3.8% compared to the year before the COVID-19 pandemic. The composition of household solid waste was dominated by food and plastic waste, which have increased to 76.43% and 25.81%, respectively. The system dynamics model has predicted the household waste generation from three management scenarios for 30 years of model simulation. Scenario I: the household solid waste was totally managed by a local sanitary agency (existing condition);Scenario II: each household manage their waste by recycling;and Scenario III: the utilization of a local waste bank to manage the waste. The scenario III showed the most effective waste management to reduce the amount of household solid waste generation up to 24% by using waste bank. This scenario can be applied for more prolonged landfill operations up to 17 years.

Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19?

The COVID-19 pandemic has induced significant transit ridership losses worldwide. This paper conducts a quantitative analysis to reveal contributing factors to such losses, using data from the Chicago Transit Authority's bus and rail systems before and after the COVID-19 outbreak. It builds a sequential statistical modeling framework that integrates a Bayesian structural time-series model, a dynamics model, and a series of linear regression models, to fit the ridership loss with pandemic evolution and regulatory events, and to quantify how the impacts of those factors depend on socio-demographic characteristics. Results reveal that, for both bus and rail, remote learning/working answers for the majority of ridership loss, and their impacts depend highly on socio-demographic characteristics. Findings from this study cast insights into future evolution of transit ridership as well as recovery campaigns in the post-pandemic era.

A study on the impact of extreme weather events on the ceramic manufacturing in Egypt

Nowadays, global economic production is organized around a complex system of highly interdependent supply chains that are currently enormously disrupted due to COVID 19. What would happen if a fast-growing risk could pose a more significant threat to our supply chains? Are our supply chains resilient to climate change? Even though governments, businesses, and climate change organizations in developed countries are forced to work together trying to mitigate and adapt to this fast-moving phenomenon, developing countries like Egypt are less concerned about this topic. This study has developed a system dynamic model based on a four-phase mixed methodology approach;we captured the complex interconnected interactions between supply chain performance and climate change physical risks. A cognitive map was first developed to capture the relationship between the climate change physical risks variables and the supply chains. Then, historical climate data and data from a ceramic manufacturing company were analyzed using the Statistical Package for the Social Sciences (SPSS). A case study of a ceramic manufacturing company located in Egypt is provided to show the applicability of our developed system dynamic model. Lastly, we simulated different scenarios to assess the ramifications and consequences of climate change extreme weather-related events on the manufacturing process of the selected case company. We have observed a negative impact;a decrease in the manufacturing inventory level and production rate, total received orders and sales. As far as our knowledge, our study is the first to investigate the impacts of climate change extreme weather events on supply chains located in Egypt. Our main contribution is to prove and establish awareness among business owners, organizations, decision-makers and the Egyptian government that climate change and related extreme weather events exist and disruptions due to this fast-moving phenomenon must be considered.

Modeling the COVID-19 Epidemic in PR China

This study is to investigate the impacts of the strategies against COVID-19 epidemic in China, so as to provide a solid reference to control its spread in the world. A two-stage dynamics transmission model is proposed using 'lockdown of Wuhan city' as the time line. The first stage is a SEIR derived model that considers the contagious of the exposed ones. It simulates the COVID-19 epidemic in Hubei Province before 'lockdown of Wuhan city'. The second stage is the new transmission dynamics model proposed in this paper and referred to as SEIRQH. It takes into account the influence over the COVID-19 epidemic from the series of strategies taken by Chinese government, such as travel restriction, contact tracing, centralized treatment, the asymptomatic infected patients, hospitalized patients and so on. It simulates the COVID-19 epidemic in China after 'lockdown of Wuhan city'. The least square method is used to estimate the parameters of the SEIR derived model and the SEIRQH model based on the collected data of COVID-19 from Hubei Province and the mainland of China before April 30, 2020. The experimental results found that the SEIR derived model simulates the actual data in Hubei Province before 'lockdown of Wuhan city', and the basic reproduction number of COVID-19 epidemic in Hubei Province is 3.2035. The SEIRQH model simulates the number of the hospitalized persons of COVID-19 in Hubei Province and the mainland of China after the 'lockdown of Wuhan city' perfectly. The control reproductive number is 0.11428 and 0.09796 in Hubei Province and the mainland of China, respectively. Our two-stage dynamics transmission model simulates the COVID-19 epidemic in China, especially our SEIRQH model simulates the actual data very well. The strategies taken by Chinese government are effective, and plays significant role in preventing the spread of COVID-19 epidemic in China. This study gives the reference to World Health Organization and other countries against the COVID-19 epidemic. © 2021 IEEE.