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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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Kong L, Duan M, Shi J, Hong J, Chang Z, Zhang Z. Compartmental structures used in modeling COVID-19: a scoping review. Infect Dis Poverty 2022; 11:72. [PMID: 35729655 PMCID: PMC9209832 DOI: 10.1186/s40249-022-01001-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 06/10/2022] [Indexed: 12/23/2022] Open
Abstract
Background The coronavirus disease 2019 (COVID-19) epidemic, considered as the worst global public health event in nearly a century, has severely affected more than 200 countries and regions around the world. To effectively prevent and control the epidemic, researchers have widely employed dynamic models to predict and simulate the epidemic’s development, understand the spread rule, evaluate the effects of intervention measures, inform vaccination strategies, and assist in the formulation of prevention and control measures. In this review, we aimed to sort out the compartmental structures used in COVID-19 dynamic models and provide reference for the dynamic modeling for COVID-19 and other infectious diseases in the future. Main text A scoping review on the compartmental structures used in modeling COVID-19 was conducted. In this scoping review, 241 research articles published before May 14, 2021 were analyzed to better understand the model types and compartmental structures used in modeling COVID-19. Three types of dynamics models were analyzed: compartment models expanded based on susceptible-exposed-infected-recovered (SEIR) model, meta-population models, and agent-based models. The expanded compartments based on SEIR model are mainly according to the COVID-19 transmission characteristics, public health interventions, and age structure. The meta-population models and the agent-based models, as a trade-off for more complex model structures, basic susceptible-exposed-infected-recovered or simply expanded compartmental structures were generally adopted. Conclusion There has been a great deal of models to understand the spread of COVID-19, and to help prevention and control strategies. Researchers build compartments according to actual situation, research objectives and complexity of models used. As the COVID-19 epidemic remains uncertain and poses a major challenge to humans, researchers still need dynamic models as the main tool to predict dynamics, evaluate intervention effects, and provide scientific evidence for the development of prevention and control strategies. The compartmental structures reviewed in this study provide guidance for future modeling for COVID-19, and also offer recommendations for the dynamic modeling of other infectious diseases. Graphical Abstract
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-01001-y.
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Affiliation(s)
- Lingcai Kong
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China
| | - Mengwei Duan
- Department of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China
| | - Jin Shi
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Jie Hong
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China
| | - Zhaorui Chang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhijie Zhang
- Department of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
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Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling study. PLoS One 2022; 17:e0263432. [PMID: 35421094 PMCID: PMC9009677 DOI: 10.1371/journal.pone.0263432] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 01/19/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND During the first wave of the COVID-19 pandemic, the United Kingdom experienced one of the highest per-capita death tolls worldwide. It is debated whether this may partly be explained by the relatively late initiation of voluntary social distancing and mandatory lockdown measures. In this study, we used simulations to estimate the number of cases and deaths that would have occurred in England by 1 June 2020 if these interventions had been implemented one or two weeks earlier, and the impact on the required duration of lockdown. METHODS Using official reported data on the number of Pillar 1 lab-confirmed cases of COVID-19 and associated deaths occurring in England from 3 March to 1 June, we modelled: the natural (i.e. observed) growth of cases, and the counterfactual (i.e. hypothetical) growth of cases that would have occurred had measures been implemented one or two weeks earlier. Under each counterfactual condition, we estimated the expected number of deaths and the time required to reach the incidence observed under natural growth on 1 June. RESULTS Introducing measures one week earlier would have reduced by 74% the number of confirmed COVID-19 cases in England by 1 June, resulting in approximately 21,000 fewer hospital deaths and 34,000 fewer total deaths; the required time spent in full lockdown could also have been halved, from 69 to 35 days. Acting two weeks earlier would have reduced cases by 93%, resulting in between 26,000 and 43,000 fewer deaths. CONCLUSIONS Our modelling supports the claim that the relatively late introduction of social distancing and lockdown measures likely increased the scale, severity, and duration of the first wave of COVID-19 in England. Our results highlight the importance of acting swiftly to minimise the spread of an infectious disease when case numbers are increasing exponentially.
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Caro JJ, Möller J, Santhirapala V, Gill H, Johnston J, El-Boghdadly K, Santhirapala R, Kelly P, McGuire A. Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2021; 24:1570-1577. [PMID: 34711356 PMCID: PMC8339677 DOI: 10.1016/j.jval.2021.05.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 04/22/2021] [Accepted: 05/26/2021] [Indexed: 06/07/2023]
Abstract
OBJECTIVES To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.
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Affiliation(s)
- J Jaime Caro
- Department of Health Policy, London School of Economics and Political Science, London, England, UK; Evidera, London, England, UK.
| | | | - Vatshalan Santhirapala
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Harpreet Gill
- Department of Health Policy, London School of Economics and Political Science, London, England, UK; Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Jessica Johnston
- Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Kariem El-Boghdadly
- Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Ramai Santhirapala
- Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Paul Kelly
- Department of Theatres, Anaesthesia, and Perioperative Care, Guy's and St Thomas' NHS Foundation Trust, London, England, UK
| | - Alistair McGuire
- Department of Health Policy, London School of Economics and Political Science, London, England, UK
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Datta S, Saratchand C. Non-pharmaceutical interventions in a generalized model of interactive dynamics between COVID-19 and the economy. NONLINEAR DYNAMICS 2021; 105:2795-2810. [PMID: 34305316 PMCID: PMC8294325 DOI: 10.1007/s11071-021-06712-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/05/2021] [Indexed: 06/13/2023]
Abstract
We use a simple general model of interactive dynamics between the COVID-19 pandemic and the economy to examine the impact of various non-pharmaceutical interventions in the form of restrictions on socio-economic activities like lockdowns, travel restrictions, etc. We mathematically demonstrate that these restrictions might be useful in preventing repeated waves of infection recurrence in the pandemic. These results are general and not dependent on choice of specific functional forms or parameter configurations. We set out briefly the implications of these results for public health interventions.
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Affiliation(s)
- Soumya Datta
- Faculty of Economics, South Asian University, Akbar Bhawan, Chanakyapuri, New Delhi, 110021 India
| | - C. Saratchand
- Department of Economics, Satyawati College (University of Delhi), Ashok Vihar Phase-III, Delhi, 110052 India
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Balmford A, Fisher B, Mace GM, Wilcove DS, Balmford B. Analogies and lessons from COVID-19 for tackling the extinction and climate crises. Curr Biol 2020; 30:R969-R971. [PMID: 32898490 PMCID: PMC7321052 DOI: 10.1016/j.cub.2020.06.084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
As environmental scientists working in countries whose COVID-linked deaths already exceed their military casualties from all campaigns since 1945, we believe there are significant messages from the handling of this horrific disease for efforts addressing the enormous challenges posed by the ongoing extinction and climate emergencies.
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Affiliation(s)
- Andrew Balmford
- Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
| | - Brendan Fisher
- Environmental Program, Gund Institute for Environment, Rubenstein School of Environment & Natural Resources, University of Vermont, Burlington, VT 05405, USA
| | - Georgina M Mace
- Centre of Biodiversity & Environment Research, Medawar Building, University College London, London WC1E 6BT, UK
| | - David S Wilcove
- Woodrow Wilson School of Public & International Affairs, Princeton University, Princeton, NJ 08544, USA
| | - Ben Balmford
- Land, Environment, Economics & Policy Institute, University of Exeter Business School, Prince of Wales Road, Exeter EX4 4PJ, UK
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