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Guo J, Qian Y, Chen C, Liang H, Huang J. Does a GP service package matter in addressing the absence of health management by the occupational population? A modelling study. BMC Health Serv Res 2024; 24:638. [PMID: 38760746 PMCID: PMC11100196 DOI: 10.1186/s12913-024-10954-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 04/04/2024] [Indexed: 05/19/2024] Open
Abstract
OBJECTIVE To assess the influence of supply and demand factors on the contract behavior of occupational populations with general practitioner (GP) teams. METHODS We employed a system dynamics approach to assess and predict the effect of the general practitioner service package (GPSP) and complementary incentive policies on the contract rate for 2015-2030. First, the GPSP is designed to address the unique needs of occupational populations, enhancing the attractiveness of GP contracting services, including three personalized service contents tailored to demand-side considerations: work-related disease prevention (WDP), health education & counseling (HEC), and health-care service (HCS). Second, the complementary incentive policies on the supply-side included income incentives (II), job title promotion (JTP), and education & training (ET). Considering the team collaboration, the income distribution ratio (IDR) was also incorporated into supply-side factors. FINDINGS The contract rate is predicted to increase to 57.8% by 2030 after the GPSP intervention, representing a 15.4% increase on the non-intervention scenario. WDP and HEC have a slightly higher (by 2%) impact on the contract rate than that from HCS. Regarding the supply-side policies, II have a more significant impact on the contract rate than JTP and ET by 3-5%. The maximum predicted contract rate of 75.2% is expected by 2030 when the IDR is 0.5, i.e., the GP receives 50% of the contract income and other members share 50%. CONCLUSION The GP service package favorably increased the contract rate among occupational population, particularly after integrating the incentive policies. Specifically, for a given demand level, the targeted content of the package enhanced the attractiveness of contract services. On the supply side, the incentive policies boost GPs' motivation, and the income distribution motivated other team members.
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Affiliation(s)
- Jing Guo
- School of Social Development and Public Policy of Fudan University, Shanghai, China
| | - Ying Qian
- Business School, University of Shanghai for Science and Technology, Shanghai, China
| | - Chen Chen
- Pengpuxincun Community Health Service Center, Shanghai, China
| | - Hong Liang
- School of Social Development and Public Policy of Fudan University, Shanghai, China
| | - Jiaoling Huang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Osi A, Ghaffarzadegan N. Parameter estimation in behavioral epidemic models with endogenous societal risk-response. PLoS Comput Biol 2024; 20:e1011992. [PMID: 38551972 PMCID: PMC11006122 DOI: 10.1371/journal.pcbi.1011992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 04/10/2024] [Accepted: 03/11/2024] [Indexed: 04/11/2024] Open
Abstract
Behavioral epidemic models incorporating endogenous societal risk-response, where changes in risk perceptions prompt adjustments in contact rates, are crucial for predicting pandemic trajectories. Accurate parameter estimation in these models is vital for validation and precise projections. However, few studies have examined the problem of identifiability in models where disease and behavior parameters must be jointly estimated. To address this gap, we conduct simulation experiments to assess the effect on parameter estimation accuracy of a) delayed risk response, b) neglecting behavioral response in model structure, and c) integrating disease and public behavior data. Our findings reveal systematic biases in estimating behavior parameters even with comprehensive and accurate disease data and a well-structured simulation model when data are limited to the first wave. This is due to the significant delay between evolving risks and societal reactions, corresponding to the duration of a pandemic wave. Moreover, we demonstrate that conventional SEIR models, which disregard behavioral changes, may fit well in the early stages of a pandemic but exhibit significant errors after the initial peak. Furthermore, early on, relatively small data samples of public behavior, such as mobility, can significantly improve estimation accuracy. However, the marginal benefits decline as the pandemic progresses. These results highlight the challenges associated with the joint estimation of disease and behavior parameters in a behavioral epidemic model.
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Affiliation(s)
- Ann Osi
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
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3
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The SEIR model incorporating asymptomatic cases, behavioral measures, and lockdowns: Lesson learned from the COVID-19 flow in Sweden. Biomed Signal Process Control 2023; 81:104416. [PMID: 36438783 PMCID: PMC9676179 DOI: 10.1016/j.bspc.2022.104416] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 11/07/2022] [Indexed: 11/22/2022]
Abstract
The Sweden approach is unique in handling the COVID-19 flow, compared to other European countries. While other countries have practiced the full lockdowns, Sweden has practiced the lighter lockdowns or the partial lockdowns as public spaces such as cafes and restaurants are allowed to serve their customers subject to government recommendations. This study aims to develop an SEIR model for Sweden capturing important issues such as the roles of behavioral measures, partial lockdowns, and undocumented cases. The suggested SEIR model is probably the first SEIR model capturing the roles of behavioral measures, partial lockdowns, hospital preparedness, and asymptomatic cases for Sweden. The SEIR model can successfully reproduce similar main observed outputs, namely documented infected cases and documented death cases. This study finds that the effects of partial lockdowns effectively start 52 days after the first confirmed case. Again, behavioral measures and partial lockdowns reduce possible infected cases about 22% and 70% respectively. This study also suggests that the Sweden government should step up to the full lockdowns by conducting public closures so COVID-19 flow can be curtailed significantly. Likewise, owing to airborne transmission, protecting vulnerable people such as senior citizens should be prioritised.
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Korzebor M, Nahavandi N. A system dynamics model of the COVID-19 pandemic considering risk perception: A case study of Iran. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023. [PMID: 36854955 DOI: 10.1111/risa.14115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 11/02/2022] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
The new coronavirus disease 2019 (COVID-19) has become a complex issue around the world. As the disease advancing and death rates are continuously increasing, governments are trying to control the situation by implementing different response policies. In order to implement appropriate policies, we need to consider the behavior of the people. Risk perception (RP) is a critical component in many health behavior change theories studies. People's RP can shape their behavior. This research presents a system dynamics (SD) model of the COVID-19 outbreak considering RP. The proposed model considers effective factors on RP, including different media types, awareness, and public acceptable death rate. In addition, the simplifying assumption of permanent immunity due to infection has been eliminated, and reinfection is considered; thus, different waves of the pandemic have been simulated. Using the presented model, the trend of advancing and death rates due to the COVID-19 pandemic in Iran can be predicted. Some policies are proposed for pandemic management. Policies are categorized as the capacity of hospitals, preventive behaviors, and accepted death rate. The results show that the proposed policies are effective. In this case, reducing the accepted death rate was the most effective policy to manage the pandemics. About 20% reduction in the accepted death rate causes about 23% reduction in cumulative death and delays at epidemic peak. The mean daily error in predicting the death rate is less than 10%.
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Affiliation(s)
- Mohammadreza Korzebor
- Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran
| | - Nasim Nahavandi
- Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran
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5
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Stanovov V, Grabljevec S, Akhmedova S, Semenkin E, Stojanović R, Rozman Č, Škraba A. Identification of COVID-19 spread mechanisms based on first-wave data, simulation models, and evolutionary algorithms. PLoS One 2022; 17:e0279427. [PMID: 36576938 PMCID: PMC9797101 DOI: 10.1371/journal.pone.0279427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 12/07/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The COVID-19 epidemic has shown that efficient prediction models are required, and the well-known SI, SIR, and SEIR models are not always capable of capturing the real dynamics. Modified models with novel structures could help identify unknown mechanisms of COVID-19 spread. OBJECTIVE Our objective is to provide additional insights into the COVID-19 spread mechanisms based on different models' parameterization which was performed using evolutionary algorithms and the first-wave data. METHODS Data from the Our World in Data COVID-19 database was analysed, and several models-SI, SIR, SEIR, SEIUR, and Bass diffusion-and their variations were considered for the first wave of the COVID-19 pandemic. The models' parameters were tuned with differential evolution optimization method L-SHADE to find the best fit. The algorithm for the automatic identification of the first wave was developed, and the differential evolution was applied to model parameterization. The reproduction rates (R0) for the first wave were calculated for 61 countries based on the best fits. RESULTS The performed experiments showed that the Bass diffusion model-based modification could be superior compared to SI, SIR, SEIR and SEIUR due to the component responsible for spread from an external factor, which is not directly dependent on contact with infected individuals. The developed modified models containing this component were shown to perform better when fitting to the first-wave cumulative infections curve. In particular, the modified SEIR model was better fitted to the real-world data than the classical SEIR in 43 cases out of 61, based on Mann-Whitney U tests; the Bass diffusion model was better than SI for 57 countries. This showed the limitation of the classical models and indicated ways to improve them. CONCLUSIONS By using the modified models, the mechanism of infection spread, which is not directly dependent on contacts, was identified, which significantly influences the dynamics of the spread of COVID-19.
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Affiliation(s)
- Vladimir Stanovov
- Siberian Institute of Applied System Analysis Named After A.N. Antamoshkin, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Krasnoyarsk Krai, Russian Federation
- * E-mail:
| | - Stanko Grabljevec
- Department of Anesthesiology and Perioperative Intensive Care, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Shakhnaz Akhmedova
- Siberian Institute of Applied System Analysis Named After A.N. Antamoshkin, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Krasnoyarsk Krai, Russian Federation
| | - Eugene Semenkin
- Siberian Institute of Applied System Analysis Named After A.N. Antamoshkin, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Krasnoyarsk Krai, Russian Federation
| | - Radovan Stojanović
- Department of Electrical Engineering and Computer Technology, Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro
| | - Črtomir Rozman
- Department of Agricultural Economics, Faculty of Agriculture and Life Sciences, University of Maribor, Hoče, Slovenia
| | - Andrej Škraba
- Department of Informatics, Cybernetics & Decision Support Systems Laboratory, Faculty of Organizational Sciences, University of Maribor, Kranj, Slovenia
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Safaie N, Kaveie M, Mardanian S, Mohammadi M, Abdol Mohamadi R, Nasri SA. Investigation of Factors Affecting COVID-19 and Sixth Wave Management Using a System Dynamics Approach. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4079685. [PMID: 36471726 PMCID: PMC9719431 DOI: 10.1155/2022/4079685] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/12/2022] [Accepted: 07/29/2022] [Indexed: 11/04/2023]
Abstract
The COVID-19 pandemic has plunged the world into a health and economic crisis never seen before since the Spanish flu pandemic in 1918. The closure of schools and universities, the banning of rallies, and other social distancing in countries have been done to disrupt the transmission of the virus. Governments have planned to reduce restrictions on corona management by implementing vaccination programs. This research aims to better understand the Coronavirus disease's behavior, identify the prevalent factors, and adopt effective policies to control the pandemic. This study examines the different scenarios of releasing the constraints and returning to normal conditions before Corona to analyze the results of different scenarios to prevent the occurrence of subsequent peaks. The system dynamics approach is an effective means of studying COVID-19's behavioral characteristics. The factors that affect Coronavirus disease outbreak and control by expanding the basic SEIR model, interventions, and policies, such as vaccination, were investigated in this research. Based on the obtained results, the most critical factor in reducing the prevalence of the disease is reducing the behavioral risks of people and increasing the vaccination process. Observance of hygienic principles leads to disruption of the transmission chain, and vaccination increases the immunity of individuals against the acute type of infection. In addition, the closure of businesses and educational centers, along with government support for incomes, effectively controls and reduces the pandemic, which requires cooperation between the people and the government. In a situation where a new type of corona has spread, if the implementation of the policy of reducing restrictions and reopening schools and universities is done without planning, it will cause a lot of people to suffer.
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Affiliation(s)
- Nasser Safaie
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Maryam Kaveie
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Siroos Mardanian
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mina Mohammadi
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Rasoul Abdol Mohamadi
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Seyed Amir Nasri
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
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7
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Zandavi SM, Rashidi TH, Vafaee F. Dynamic Hybrid Model to Forecast the Spread of COVID-19 Using LSTM and Behavioral Models Under Uncertainty. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:11977-11989. [PMID: 34735351 DOI: 10.1109/tcyb.2021.3120967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
To accurately predict the regional spread of coronavirus disease 2019 (COVID-19) infection, this study proposes a novel hybrid model, which combines a long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arising from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries at the time of the study. The results show that the proposed model closely replicates the test data, such that not only it provides accurate predictions but it also replicates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model while accounting for data limitation. The parameters of the hybrid models are optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict the short-term to medium-term daily spreading of the COVID-19 infection, it is capable of being used for policy assessment, planning, and decision making.
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8
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Rahmandad H, Xu R, Ghaffarzadegan N. A missing behavioural feedback in COVID-19 models is the key to several puzzles. BMJ Glob Health 2022; 7:bmjgh-2022-010463. [PMID: 36283733 PMCID: PMC9606737 DOI: 10.1136/bmjgh-2022-010463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/22/2022] [Indexed: 11/11/2022] Open
Affiliation(s)
| | - Ran Xu
- Department of Allied Health Sciences, University of Connecticut, Storrs, Connecticut, USA
| | - Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, Virginia, USA
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9
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Bushaj S, Yin X, Beqiri A, Andrews D, Büyüktahtakın İE. A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-33. [PMID: 36187178 PMCID: PMC9512996 DOI: 10.1007/s10479-022-04926-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/28/2022] [Indexed: 05/12/2023]
Abstract
In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas.
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Affiliation(s)
- Sabah Bushaj
- Department of Management Information Systems and Analytics, School of Business and Economics, SUNY Plattsburgh, Plattsburgh, NY USA
| | | | - Arjeta Beqiri
- Department of Management Information Systems and Analytics, School of Business and Economics, SUNY Plattsburgh, Plattsburgh, NY USA
| | - Donald Andrews
- Trinity College Dublin, School of Natural Sciences, Dublin, Ireland
| | - İ. Esra Büyüktahtakın
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA USA
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10
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Safety archetypes identification and behavior simulation for nuclear power plant operation human reliability improvement. ANN NUCL ENERGY 2022. [DOI: 10.1016/j.anucene.2022.109189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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11
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Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID-19: A systematic review. SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE 2022; 40:SRES2897. [PMID: 36245570 PMCID: PMC9538520 DOI: 10.1002/sres.2897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/23/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
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.
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Affiliation(s)
- Weiwei Zhang
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social SciencesBeijing Normal University at ZhuhaiZhuhaiChina
| | - Nathaniel Osgood
- Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
- Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
| | - Hongli Zhu
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Ying Qian
- Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Peng Jia
- School of Resource and Environmental SciencesWuhan UniversityWuhanHubeiChina
- International Institute of Spatial Lifecourse HealthWuhan UniversityWuhanHubeiChina
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Rahmandad H, Xu R, Ghaffarzadegan N. Enhancing long-term forecasting: Learning from COVID-19 models. PLoS Comput Biol 2022; 18:e1010100. [PMID: 35587466 PMCID: PMC9119494 DOI: 10.1371/journal.pcbi.1010100] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs). Long-term projections of COVID-19 trajectory have been used to inform various policies and decisions such as planning intensive care capacity, selecting clinical trial locations, and deciding on economic policy packages. However, these types of long-term forecasts are challenging as epidemics are complex: they include reinforcing contagion mechanisms that create exponential growth, are moderated by randomness in environmental and social determinants of transmission, and are subject to endogenous human responses to evolving risk perceptions. In this study we take a step towards systematically examining the modeling choices that regulate COVID-19 forecasting accuracy in two complementary studies. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. Second, we design a very simple forecasting model that only incorporates the key features identified in the first study, and show that the long-term prediction accuracy of this model is comparable with the best models in the CDC set. We conclude that forecasting models responding to future epidemics would benefit from starting small: first incorporating key mechanistic features, important behavioral feedbacks, and simple state-resetting approaches and then expanding to capture other features. Our study shows that the key to the long-term predictive power of epidemic models is an endogenous representation of human behavior in interaction with the evolving epidemic.
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Affiliation(s)
- Hazhir Rahmandad
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ran Xu
- Department of Allied Health Sciences, University of Connecticut, Storrs, Connecticut, United States of America
| | - Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, Virginia, United States of America
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13
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Armenia S, Arquitt S, Pedercini M, Pompei A. Anticipating human resilience and vulnerability on the path to 2030: What can we learn from COVID-19? FUTURES 2022; 139:102936. [PMID: 35382386 PMCID: PMC8972982 DOI: 10.1016/j.futures.2022.102936] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 01/11/2022] [Accepted: 03/24/2022] [Indexed: 05/16/2023]
Abstract
The COVID-19 pandemic is causing unprecedented damage to our society and economy, globally impacting progress towards the SDGs. The integrated perspective that Agenda 2030 calls for is ever more important for understanding the vulnerability of our eco-socio-economic systems and for designing policies for enhanced resilience. Since the emergence of COVID-19, countries and international institutions have strengthened their monitoring systems to produce timely data on infections, fostering data-driven decision-making often without the support of systemic-based simulation models. Evidence from the initial phases of the pandemic indicates that countries that were able to implement effective policies before the number of cases grew large (e.g. Australia) managed to contain COVID-19 to a much greater extent than others. We argue that prior systemic knowledge of a phenomenon provides the essential information to correctly interpret data, develop a better understanding of the emerging behavioural patterns and potentially develop early qualitative awareness of how to react promptly in the early phases of destructive phenomena, eventually providing the ground for building more effective simulation models capable of better anticipating the effects of policies. This is even more important as, on its path to 2030, humanity will face other challenges of similar dynamic nature. Chief among these is Climate Change. In this paper, we show how a Systems Thinking and System Dynamics modelling approach is useful for developing a better understanding of these and other issues, and how systemic lessons learned from the COVID-19 case can help decision makers anticipate the destructive dynamics of Climate Change by improving perceptions of the potential impacts of reinforcing feedback and delays, ultimately leading to more timely interventions to achieve the SDGs and mitigate Climate Change risks.
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Van Oorschot KE, Van Wassenhove LN, Jahre M. Collaboration-competition dilemma in flattening the COVID-19 curve. PRODUCTION AND OPERATIONS MANAGEMENT 2022; 32:POMS13709. [PMID: 35601840 PMCID: PMC9115479 DOI: 10.1111/poms.13709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 02/28/2022] [Indexed: 06/15/2023]
Abstract
Testing for COVID-19 is a key intervention that supports tracking and isolation to prevent further infections. However, diagnostic tests are a scarce and finite resource, so abundance in one country can quickly lead to shortages in others, creating a competitive landscape. Countries experience peaks in infections at different times, meaning that the need for diagnostic tests also peaks at different moments. This phase lag implies opportunities for a more collaborative approach, although countries might also worry about the risks of future shortages if they help others by reallocating their excess inventory of diagnostic tests. This article features a simulation model that connects three subsystems: COVID-19 transmission, the diagnostic test supply chain, and public policy interventions aimed at flattening the infection curve. This integrated system approach clarifies that, for public policies, there is a time to be risk-averse and a time for risk-taking, reflecting the different phases of the pandemic (contagion vs. recovery) and the dominant dynamic behavior that occurs in these phases (reinforcing vs. balancing). In the contagion phase, policymakers cannot afford to reject extra diagnostic tests and should take what they can get, in line with a competitive mindset. In the recovery phase, policymakers can afford to give away excess inventory to other countries in need (one-sided collaboration). When a country switches between taking and giving, in a form of two-sided collaboration, it can flatten the curve, not only for itself but also for others.
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Affiliation(s)
- Kim E. Van Oorschot
- Department of Accounting and Operations ManagementBI Norwegian Business SchoolOsloNorway
| | | | - Marianne Jahre
- Department of Accounting and Operations ManagementBI Norwegian Business SchoolOsloNorway
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15
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Moradi A. An analysis of the social distancing effects on global economy and international finance using causal loop diagrams. DECISION ANALYTICS JOURNAL 2022. [PMCID: PMC8762924 DOI: 10.1016/j.dajour.2022.100023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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16
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Sharifi H, Jahani Y, Mirzazadeh A, Ahmadi Gohari M, Nakhaeizadeh M, Shokoohi M, Eybpoosh S, Tohidinik HR, Mostafavi E, Khalili D, Hashemi Nazari SS, Karamouzian M, Haghdoost AA. Estimating COVID-19-Related Infections, Deaths, and Hospitalizations in Iran Under Different Physical Distancing and Isolation Scenarios. Int J Health Policy Manag 2022; 11:334-343. [PMID: 32772007 PMCID: PMC9278464 DOI: 10.34172/ijhpm.2020.134] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/14/2020] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Iran is one of the first few countries that was hit hard with the coronavirus disease 2019 (COVID-19) pandemic. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios. METHODS We developed a susceptible-exposed-infected/infectious-recovered/removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UIs). RESULTS Under scenario A, we estimated 5 196 000 (UI 1 753 000-10 220 000) infections to happen till mid-June with 966 000 (UI 467 800-1 702 000) hospitalizations and 111 000 (UI 53 400-200 000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (ie, 550 000) and change the epidemic peak from 66 000 on June 9, to 9400 on March 1, 2020. Scenario E also reduces the hospitalizations by 92% (ie, 74 500), and deaths by 93% (ie, 7800). CONCLUSION With no approved vaccination or therapy available, we found physical distancing and isolation that include public awareness and case-finding and isolation of 40% of infected people could reduce the burden of COVID-19 in Iran by 90% by mid-June.
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Affiliation(s)
- Hamid Sharifi
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
- Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Yunes Jahani
- Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Ali Mirzazadeh
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
- Department of Epidemiology and Biostatistics, Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Milad Ahmadi Gohari
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Mehran Nakhaeizadeh
- Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Mostafa Shokoohi
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Sana Eybpoosh
- Department of Epidemiology and Biostatistics, Research Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran
| | - Hamid Reza Tohidinik
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
- Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Ehsan Mostafavi
- Department of Epidemiology and Biostatistics, Research Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Saeed Hashemi Nazari
- Prevention of Cardiovascular Disease Research Center, Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Karamouzian
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
- School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Ali Akbar Haghdoost
- HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
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17
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Cunha LRA, Antunes BBP, Rodrigues VP, Ceryno PS, Leiras A. Measuring the impact of donations at the Bottom of the Pyramid (BoP) amid the COVID-19 pandemic. ANNALS OF OPERATIONS RESEARCH 2022; 335:1-31. [PMID: 35039706 PMCID: PMC8754524 DOI: 10.1007/s10479-021-04378-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/21/2021] [Indexed: 06/14/2023]
Abstract
The governments' isolation measures to contain the transmission of COVID-19 imposed a dilemma for the people at the bottom of the pyramid. Since these people have very unreliable sources of income, a dilemma arises: they must either work under risky conditions or refrain from work and suffer from income cuts. Emergency donations of food and cleaning supplies in a pandemic context might be overlooked by government and civil society actors. This paper aims to model the effects of donations on mitigating the negative effects of COVID-19 on vulnerable communities. Applying the system dynamics method, we simulated the behaviour of the pandemic in Rio de Janeiro (Brazil) communities and the impacts that donations of food and cleaning supplies have in these settings. We administered surveys to the beneficiaries and local organisations responsible for the final distribution of donations to gather information from the field operations. The results show that increasing access to cleaning supplies in communities through donations can significantly reduce coronavirus transmission, particularly in high-density and low-resource areas, such as slums in urban settings. In addition, we also show that food donations can increase the vulnerable population's ability to afford necessities, alleviating the stress caused by the pandemic on this portion of the population. Therefore, this work helps decision-makers (such as government and non-governmental organisations) understand the impacts of donations on controlling outbreaks, especially under COVID-19 conditions, in a low-resource environment and, thus, aid these hard-to-reach populations in a pandemic setting.
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Affiliation(s)
- Luiza Ribeiro Alves Cunha
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Marquês de São Vicente St., 225 – Gávea, Rio de Janeiro, RJ 22541-041 Brazil
| | - Bianca B. P. Antunes
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Marquês de São Vicente St., 225 – Gávea, Rio de Janeiro, RJ 22541-041 Brazil
| | | | - Paula Santos Ceryno
- Department of Production Engineering, Federal University of the State of Rio de Janeiro, Pasteur Av., 296 – Urca, Rio de Janeiro, RJ 22290-240 Brazil
| | - Adriana Leiras
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Marquês de São Vicente St., 225 – Gávea, Rio de Janeiro, RJ 22541-041 Brazil
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18
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Khairulbahri M. Lessons learned from three Southeast Asian countries during the COVID-19 pandemic. JOURNAL OF POLICY MODELING 2021; 43:1354-1364. [PMID: 34690384 PMCID: PMC8526120 DOI: 10.1016/j.jpolmod.2021.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 09/12/2021] [Accepted: 09/30/2021] [Indexed: 05/05/2023]
Abstract
Several scholars have focused on the COVID-19 case studies in Europe and USA, leaving the people in Southeast Asia with little information about the lesson learned from their own case studies. This study aims to analyses case studies through the SEIR model in three Southeast Asia countries including Singapore, Malaysia, and Indonesia. The SEIR model incorporates two types measures including social behavior and lockdowns as well as hospital preparedness. The SEIR model reveals that Malaysia, despite its relatively low testing capacity but with the application of the national lockdown, can slash the coronavirus transmission while Indonesia has still struggled to contain the COVID-19 flow owing to partial lockdowns. Singapore, at one hand, can successfully contain the coronavirus due to the national lockdowns, and the better healthcare system. With this point in mind, it is not surprising that Singapore has very low fatality rates and significantly low cases after lockdowns. Better preparedness lockdowns, and sufficient testing capacity are keys to controlling the COVID-19 flow, especially if the development of vaccines or distribution of respective vaccines is under progress.
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Affiliation(s)
- Muhamad Khairulbahri
- Bandung Institute of Technology, Postgraduate of Development Studies, Ganesha 10, Bandung, West Java, Indonesia
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19
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Xu R, Rahmandad H, Gupta M, DiGennaro C, Ghaffarzadegan N, Amini H, Jalali MS. Weather, air pollution, and SARS-CoV-2 transmission: a global analysis. Lancet Planet Health 2021; 5:e671-e680. [PMID: 34627471 PMCID: PMC8497024 DOI: 10.1016/s2542-5196(21)00202-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 06/12/2021] [Accepted: 07/19/2021] [Indexed: 05/21/2023]
Abstract
BACKGROUND Understanding how environmental factors affect SARS-CoV-2 transmission could inform global containment efforts. Despite high scientific and public interest and multiple research reports, there is currently no consensus on the association of environmental factors and SARS-CoV-2 transmission. To address this research gap, we aimed to assess the relative risk of transmission associated with weather conditions and ambient air pollution. METHODS In this global analysis, we adjusted for the delay between infection and detection, estimated the daily reproduction number at 3739 global locations during the COVID-19 pandemic up until late April, 2020, and investigated its associations with daily local weather conditions (ie, temperature, humidity, precipitation, snowfall, moon illumination, sunlight hours, ultraviolet index, cloud cover, wind speed and direction, and pressure data) and ambient air pollution (ie, PM2·5, nitrogen dioxide, ozone, and sulphur dioxide). To account for other confounding factors, we included both location-specific fixed effects and trends, controlling for between-location differences and heterogeneities in locations' responses over time. We built confidence in our estimations through synthetic data, robustness, and sensitivity analyses, and provided year-round global projections for weather-related risk of global SARS-CoV-2 transmission. FINDINGS Our dataset included data collected between Dec 12, 2019, and April 22, 2020. Several weather variables and ambient air pollution were associated with the spread of SARS-CoV-2 across 3739 global locations. We found a moderate, negative relationship between the estimated reproduction number and temperatures warmer than 25°C (a decrease of 3·7% [95% CI 1·9-5·4] per additional degree), a U-shaped relationship with outdoor ultraviolet exposure, and weaker positive associations with air pressure, wind speed, precipitation, diurnal temperature, sulphur dioxide, and ozone. Results were robust to multiple assumptions. Independent research building on our estimates provides strong support for the resulting projections across nations. INTERPRETATION Warmer temperature and moderate outdoor ultraviolet exposure result in a slight reduction in the transmission of SARS-CoV-2; however, changes in weather or air pollution alone are not enough to contain the spread of SARS-CoV-2 with other factors having greater effects. FUNDING None.
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Affiliation(s)
- Ran Xu
- Department of Allied Health Sciences, University of Connecticut, Storrs, CT, USA
| | - Hazhir Rahmandad
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marichi Gupta
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA
| | - Catherine DiGennaro
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA
| | - Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, VA, USA
| | - Heresh Amini
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Mohammad S Jalali
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA; MGH Institute for Technology Assessment, Harvard Medical School, Harvard University, Boston, MA, USA.
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20
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Leerapan B, Kaewkamjornchai P, Atun R, Jalali MS. How systems respond to policies: intended and unintended consequences of COVID-19 lockdown policies in Thailand. Health Policy Plan 2021; 37:292-293. [PMID: 34435199 PMCID: PMC8499753 DOI: 10.1093/heapol/czab103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/30/2021] [Accepted: 08/13/2021] [Indexed: 11/12/2022] Open
Affiliation(s)
- Borwornsom Leerapan
- Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Ratchathewi, Bangkok, Thailand
| | - Phanuwich Kaewkamjornchai
- Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Road, Ratchathewi, Bangkok, Thailand
| | - Rifat Atun
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA.,Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA 02115, USA
| | - Mohammad S Jalali
- Harvard Medical School, MGH Institute for Technology Assessment, 101 Merrimac St, Boston, MA 02114, USA.,Sloan School of Management, Massachusetts Institute of Technology, 100 Main St, Cambridge, MA 02142, USA
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21
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Martínez-Rodríguez D, Gonzalez-Parra G, Villanueva RJ. Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach. EPIDEMIOLOGIA 2021; 2:140-161. [PMID: 35141702 PMCID: PMC8824484 DOI: 10.3390/epidemiologia2020012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 03/25/2021] [Indexed: 02/07/2023] Open
Abstract
The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020. Currently, there are only a few approved vaccines, each with different efficacies and mechanisms of action. Moreover, vaccination programs in different regions may vary due to differences in implementation, for instance, simply the availability of the vaccine. In this article, we study the impact of the pace of vaccination and the intrinsic efficacy of the vaccine on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. Then we study different potential scenarios regarding the burden of the COVID-19 pandemic in the near future. We construct a compartmental mathematical model and use computational methodologies to study these different scenarios. Thus, we are able to identify some key factors to reach the aims of the vaccination programs. We use some metrics related to the outcomes of the COVID-19 pandemic in order to assess the impact of the efficacy of the vaccine and the pace of the vaccine inoculation. We found that both factors have a high impact on the outcomes. However, the rate of vaccine administration has a higher impact in reducing the burden of the COVID-19 pandemic. This result shows that health institutions need to focus on increasing the vaccine inoculation pace and create awareness in the population about the importance of COVID-19 vaccines.
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Affiliation(s)
- David Martínez-Rodríguez
- Insituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain; (D.M.-R.); (R.-J.V.)
| | | | - Rafael-J. Villanueva
- Insituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain; (D.M.-R.); (R.-J.V.)
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22
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Impact of a New SARS-CoV-2 Variant on the Population: A Mathematical Modeling Approach. MATHEMATICAL AND COMPUTATIONAL APPLICATIONS 2021. [DOI: 10.3390/mca26020025] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Several SARS-CoV-2 variants have emerged around the world, and the appearance of other variants depends on many factors. These new variants might have different characteristics that can affect the transmissibility and death rate. The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020 and in some countries the vaccines will not soon be widely available. For this article, we studied the impact of a new more transmissible SARS-CoV-2 strain on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. We studied different scenarios regarding the transmissibility in order to provide a scientific support for public health policies and bring awareness of potential future situations related to the COVID-19 pandemic. We constructed a compartmental mathematical model based on differential equations to study these different scenarios. In this way, we are able to understand how a new, more infectious strain of the virus can impact the dynamics of the COVID-19 pandemic. We studied several metrics related to the possible outcomes of the COVID-19 pandemic in order to assess the impact of a higher transmissibility of a new SARS-CoV-2 strain on these metrics. We found that, even if the new variant has the same death rate, its high transmissibility can increase the number of infected people, those hospitalized, and deaths. The simulation results show that health institutions need to focus on increasing non-pharmaceutical interventions and the pace of vaccine inoculation since a new variant with higher transmissibility, such as, for example, VOC-202012/01 of lineage B.1.1.7, may cause more devastating outcomes in the population.
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23
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Ghaffarzadegan N. Simulation-based what-if analysis for controlling the spread of Covid-19 in universities. PLoS One 2021; 16:e0246323. [PMID: 33524045 PMCID: PMC7850497 DOI: 10.1371/journal.pone.0246323] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 01/18/2021] [Indexed: 12/18/2022] Open
Abstract
A simulation model is developed to analyze the spread of covid-19 in universities. The model can be used to conduct a what-if analysis and estimate infection cases under different policies. For proof-of-concept, the model is simulated for a hypothetical university of 25,000 students and 3,000 faculty/staff in a U.S. college town. Simulation results show that early outbreaks are very likely, and there is no silver bullet to avoid them. Instead, a combination of policies should be carefully implemented. The results suggest (almost) full remote university operations from the beginning of the semester. In a less-preferred alternative, if universities decide to have students attend in person, they should encourage remote operations for high-risk individuals, conduct frequent rapid tests, enforce mask use, communicate with students and employees about the risks, and promote social distancing. Universities should be willing to move to remote operations if cases rise. Under this scenario, and considering implementation challenges, many universities are still likely to experience an early outbreak, and the likelihood of having a case of death is worrisome. In the long run, students and faculty react to the risks, and even if universities decide to continue operations, classes are likely to have very low in-person attendance. Overall, our analysis depicts several sources of system complexities, negative unintended consequences of relying on a single policy, non-linear incremental effects, and positive synergies of implementing multiple policies. A simulation platform for a what-if analysis is offered so marginal effectiveness of different policies and different decision-making thresholds for closure can be tested for universities of varying populations.
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Affiliation(s)
- Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, Virginia, United States of America
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24
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Pourmalek F, Rezaei Hemami M, Janani L, Moradi-Lakeh M. Rapid review of COVID-19 epidemic estimation studies for Iran. BMC Public Health 2021; 21:257. [PMID: 33522928 PMCID: PMC7848865 DOI: 10.1186/s12889-021-10183-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 01/06/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND To inform researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review. METHODS We searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 131 studies and included 29 of them. RESULTS The included studies provided outputs for a total of 84 study-model/scenario combinations. Sixteen studies used 3-4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 1,777 (388,951) for cumulative deaths, 20,588 (2,310,161) for cumulative cases, and at the end of month four (2020-06-20), were 3,590 (1,819,392) for cumulative deaths, and 144,305 (4,266,964) for cumulative cases. Highest estimates of cumulative deaths (and cases) for latest date available in 2020 were 418,834 on 2020-12-19 (and 41,475,792 on 2020-12-31). Model estimates predict an ominous course of epidemic progress in Iran. Increase in percent population using masks from the current situation to 95% might prevent 26,790 additional deaths (95% confidence interval 19,925-35,208) by the end of year 2020. CONCLUSIONS Meticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed. Not accounting for under-reporting drives the models' results misleading.
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Affiliation(s)
| | | | - Leila Janani
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Maziar Moradi-Lakeh
- Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Community and Family Medicine Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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25
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Rahmandad H, Lim TY, Sterman J. Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations. SYSTEM DYNAMICS REVIEW 2021; 37:5-31. [PMID: 34230767 PMCID: PMC8250772 DOI: 10.1002/sdr.1673] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/17/2021] [Accepted: 02/14/2021] [Indexed: 05/03/2023]
Abstract
Effective responses to the COVID-19 pandemic require integrating behavioral factors such as risk-driven contact reduction, improved treatment, and adherence fatigue with asymptomatic transmission, disease acuity, and hospital capacity. We build one such model and estimate it for all 92 nations with reliable testing data. Cumulative cases and deaths through 22 December 2020 are estimated to be 7.03 and 1.44 times official reports, yielding an infection fatality rate (IFR) of 0.51 percent, which has been declining over time. Absent adherence fatigue, cumulative cases would have been 47 percent lower. Scenarios through June 2021 show that modest improvement in responsiveness could reduce cases and deaths by about 14 percent, more than the impact of vaccinating half of the population by that date. Variations in responsiveness to risk explain two orders of magnitude difference in per-capita deaths despite reproduction numbers fluctuating around one across nations. A public online simulator facilitates scenario analysis over the coming months. © 2021 System Dynamics Society.
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Affiliation(s)
| | - Tse Yang Lim
- Massachusetts Institute of TechnologyCambridgeMAUSA
| | - John Sterman
- Massachusetts Institute of TechnologyCambridgeMAUSA
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26
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Sadeghi M, Greene JM, Sontag ED. Universal features of epidemic models under social distancing guidelines. ANNUAL REVIEWS IN CONTROL 2021; 51:426-440. [PMID: 33935582 PMCID: PMC8063609 DOI: 10.1016/j.arcontrol.2021.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/22/2021] [Accepted: 04/02/2021] [Indexed: 05/06/2023]
Abstract
Social distancing as a form of nonpharmaceutical intervention has been enacted in many countries as a form of mitigating the spread of COVID-19. There has been a large interest in mathematical modeling to aid in the prediction of both the total infected population and virus-related deaths, as well as to aid government agencies in decision making. As the virus continues to spread, there are both economic and sociological incentives to minimize time spent with strict distancing mandates enforced, and/or to adopt periodically relaxed distancing protocols, which allow for scheduled economic activity. The main objective of this study is to reduce the disease burden in a population, here measured as the peak of the infected population, while simultaneously minimizing the length of time the population is socially distanced, utilizing both a single period of social distancing as well as periodic relaxation. We derive a linear relationship among the optimal start time and duration of a single interval of social distancing from an approximation of the classic epidemic SIR model. Furthermore, we see a sharp phase transition region in start times for a single pulse of distancing, where the peak of the infected population changes rapidly; notably, this transition occurs well before one would intuitively expect. By numerical investigation of more sophisticated epidemiological models designed specifically to describe the COVID-19 pandemic, we see that all share remarkably similar dynamic characteristics when contact rates are subject to periodic or one-shot changes, and hence lead us to conclude that these features are universal in epidemic models. On the other hand, the nonlinearity of epidemic models leads to non-monotone behavior of the peak of infected population under periodic relaxation of social distancing policies. This observation led us to hypothesize that an additional single interval social distancing at a proper time can significantly decrease the infected peak of periodic policies, and we verified this improvement numerically. While synchronous quarantine and social distancing mandates across populations effectively minimize the spread of an epidemic over the world, relaxation decisions should not be enacted at the same time for different populations.
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Affiliation(s)
- Mahdiar Sadeghi
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - James M Greene
- Department of Mathematics, Clarkson University, Potsdam, NY, United States
| | - Eduardo D Sontag
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
- Department of Bioengineering, Northeastern University, Boston, MA, United States
- Departments of Mathematics and Chemical Engineering, Northeastern University, Boston, MA, United States
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
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27
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Rahmandad H, Lim TY, Sterman J. Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations. SYSTEM DYNAMICS REVIEW 2021. [PMID: 34230767 DOI: 10.1101/2020.06.24.20139451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Effective responses to the COVID-19 pandemic require integrating behavioral factors such as risk-driven contact reduction, improved treatment, and adherence fatigue with asymptomatic transmission, disease acuity, and hospital capacity. We build one such model and estimate it for all 92 nations with reliable testing data. Cumulative cases and deaths through 22 December 2020 are estimated to be 7.03 and 1.44 times official reports, yielding an infection fatality rate (IFR) of 0.51 percent, which has been declining over time. Absent adherence fatigue, cumulative cases would have been 47 percent lower. Scenarios through June 2021 show that modest improvement in responsiveness could reduce cases and deaths by about 14 percent, more than the impact of vaccinating half of the population by that date. Variations in responsiveness to risk explain two orders of magnitude difference in per-capita deaths despite reproduction numbers fluctuating around one across nations. A public online simulator facilitates scenario analysis over the coming months. © 2021 System Dynamics Society.
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Affiliation(s)
| | - Tse Yang Lim
- Massachusetts Institute of Technology Cambridge MA USA
| | - John Sterman
- Massachusetts Institute of Technology Cambridge MA USA
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Poustchi H, Darvishian M, Mohammadi Z, Shayanrad A, Delavari A, Bahadorimonfared A, Eslami S, Javanmard SH, Shakiba E, Somi MH, Emami A, Saki N, Hormati A, Ansari-Moghaddam A, Saeedi M, Ghasemi-Kebria F, Mohebbi I, Mansour-Ghanaei F, Karami M, Sharifi H, Pourfarzi F, Veisi N, Ghadimi R, Eghtesad S, Niavarani A, Ali Asgari A, Sadeghi A, Sorouri M, Anushiravani A, Amani M, Kaveh S, Feizesani A, Tabarsi P, Keyvani H, Markarian M, Shafighian F, Sima A, Sadjadi A, Radmard AR, Mokdad AH, Sharafkhah M, Malekzadeh R. SARS-CoV-2 antibody seroprevalence in the general population and high-risk occupational groups across 18 cities in Iran: a population-based cross-sectional study. THE LANCET. INFECTIOUS DISEASES 2020; 21:473-481. [PMID: 33338441 PMCID: PMC7833828 DOI: 10.1016/s1473-3099(20)30858-6] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/15/2020] [Accepted: 10/26/2020] [Indexed: 02/03/2023]
Abstract
Background Rapid increases in cases of COVID-19 were observed in multiple cities in Iran towards the start of the pandemic. However, the true infection rate remains unknown. We aimed to assess the seroprevalence of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 18 cities of Iran as an indicator of the infection rate. Methods In this population-based cross-sectional study, we randomly selected and invited study participants from the general population (from lists of people registered with the Iranian electronic health record system or health-care centres) and a high-risk population of individuals likely to have close social contact with SARS-CoV-2-infected individuals through their occupation (from employee lists provided by relevant agencies or companies, such as supermarket chains) across 18 cities in 17 Iranian provinces. Participants were asked questions on their demographic characteristics, medical history, recent COVID-19-related symptoms, and COVID-19-related exposures. Iran Food and Drug Administration-approved Pishtaz Teb SARS-CoV-2 ELISA kits were used to detect SARS-CoV-2-specific IgG and IgM antibodies in blood samples from participants. Seroprevalence was estimated on the basis of ELISA test results and adjusted for population weighting (by age, sex, and city population size) and test performance (according to our independent validation of sensitivity and specificity). Findings From 9181 individuals who were initially contacted between April 17 and June 2, 2020, 243 individuals refused to provide blood samples and 36 did not provide demographic information and were excluded from the analysis. Among the 8902 individuals included in the analysis, 5372 had occupations with a high risk of exposure to SARS-CoV-2 and 3530 were recruited from the general population. The overall population weight-adjusted and test performance-adjusted prevalence of antibody seropositivity in the general population was 17·1% (95% CI 14·6–19·5), implying that 4 265 542 (95% CI 3 659 043–4 887 078) individuals from the 18 cities included were infected by the end of April, 2020. The adjusted seroprevalence of SARS-CoV-2-specific antibodies varied greatly by city, with the highest estimates found in Rasht (72·6% [53·9–92·8]) and Qom (58·5% [37·2–83·9]). The overall population weight-adjusted and test performance-adjusted seroprevalence in the high-risk population was 20·0% (18·5–21·7) and showed little variation between the occupations included. Interpretations Seroprevalence is likely to be much higher than the reported prevalence of COVID-19 based on confirmed COVID-19 cases in Iran. Despite high seroprevalence in a few cities, a large proportion of the population is still uninfected. The potential shortcomings of current public health policies should therefore be identified to prevent future epidemic waves in Iran. Funding Iranian Ministry of Health and Medical Education. Translation For the Farsi translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Hossein Poustchi
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Darvishian
- Cancer Control Research, BC Cancer Research Centre, Vancouver, BC, Canada
| | - Zahra Mohammadi
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Amaneh Shayanrad
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Delavari
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ayad Bahadorimonfared
- Department of Health & Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saeid Eslami
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Shaghayegh Haghjooy Javanmard
- Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Ebrahim Shakiba
- Department of Clinical Biochemistry, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mohammad Hossein Somi
- Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Amir Emami
- Microbiology Department, Burn & Wound Healing Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nader Saki
- Hearing Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Ahmad Hormati
- Gastroenterology and Hepatology Disease Research Center, Qom University of Medical Science, Qom, Iran
| | | | - Majid Saeedi
- Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
| | - Fatemeh Ghasemi-Kebria
- Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
| | - Iraj Mohebbi
- Social Determinants of Health Center, Urmia University of Medical Sciences, Urmia, Iran
| | - Fariborz Mansour-Ghanaei
- Division of Gastroenterology & Hepatology, Gastrointestinal & Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Manoochehr Karami
- Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Hamid Sharifi
- HIV/STI Surveillance Research Center and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Farhad Pourfarzi
- Digestive Disease Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | | | - Reza Ghadimi
- Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
| | - Sareh Eghtesad
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmadreza Niavarani
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Ali Asgari
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Anahita Sadeghi
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Majid Sorouri
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Anushiravani
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Amani
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Soudeh Kaveh
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Akbar Feizesani
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Payam Tabarsi
- Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Disease, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hossein Keyvani
- Department of Virology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Melineh Markarian
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Shafighian
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Sima
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Sadjadi
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Maryam Sharafkhah
- Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Malekzadeh
- Digestive Oncology Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran; Digestive Diseases Research Center, Digestive Diseases Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
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Struben J. The coronavirus disease (COVID-19) pandemic: simulation-based assessment of outbreak responses and postpeak strategies. SYSTEM DYNAMICS REVIEW 2020; 36:247-293. [PMID: 33041496 PMCID: PMC7537277 DOI: 10.1002/sdr.1660] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 07/26/2020] [Accepted: 07/31/2020] [Indexed: 05/14/2023]
Abstract
It is critical to understand the impact of distinct interventions on the ongoing coronavirus disease pandemic. I develop a behavioral dynamic epidemic model for multifaceted policy analysis comprising endogenous virus transmission (from severe or mild/asymptomatic cases), social contacts, and case testing and reporting. Calibration of the system dynamics model to the ongoing outbreak (31 December 2019-15 May 2020) using multiple time series data (reported cases and deaths, performed tests, and social interaction proxies) from six countries (South Korea, Germany, Italy, France, Sweden, and the United States) informs an explanatory analysis of outbreak responses and postpeak strategies. Specifically, I demonstrate, first, how timing and efforts of testing-capacity expansion and social-contact reduction interplay to affect outbreak dynamics and can explain a large share of cross-country variation in outbreak pathways. Second, absent at-scale availability of pharmaceutical solutions, postpeak social contacts must remain well below prepandemic values. Third, proactive (targeted) interventions, when complementing general deconfinement readiness, can considerably increase admissible postpeak social contacts. © 2020 System Dynamics Society.
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