1
|
Nguyen LKN, Megiddo I, Howick S. Hybrid simulation modelling of networks of heterogeneous care homes and the inter-facility spread of Covid-19 by sharing staff. PLoS Comput Biol 2022; 18:e1009780. [PMID: 35020731 PMCID: PMC8789158 DOI: 10.1371/journal.pcbi.1009780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 01/25/2022] [Accepted: 12/20/2021] [Indexed: 12/17/2022] Open
Abstract
Although system dynamics [SD] and agent-based modelling [ABM] have individually served as effective tools to understand the Covid-19 dynamics, combining these methods in a hybrid simulation model can help address Covid-19 questions and study systems and settings that are difficult to study with a single approach. To examine the spread and outbreak of Covid-19 across multiple care homes via bank/agency staff and evaluate the effectiveness of interventions targeting this group, we develop an integrated hybrid simulation model combining the advantages of SD and ABM. We also demonstrate how we use several approaches adapted from both SD and ABM practices to build confidence in this model in response to the lack of systematic approaches to validate hybrid models. Our modelling results show that the risk of infection for residents in care homes using bank/agency staff was significantly higher than those not using bank/agency staff (Relative risk [RR] 2.65, 95% CI 2.57-2.72). Bank/agency staff working across several care homes had a higher risk of infection compared with permanent staff working in a single care home (RR 1.55, 95%CI 1.52-1.58). The RR of infection for residents is negatively correlated to bank/agency staff's adherence to weekly PCR testing. Within a network of heterogeneous care homes, using bank/agency staff had the most impact on care homes with lower intra-facility transmission risks, higher staff-to-resident ratio, and smaller size. Forming bubbles of care homes had no or limited impact on the spread of Covid-19. This modelling study has implications for policy makers considering developing effective interventions targeting staff working across care homes during the ongoing and future pandemics.
Collapse
Affiliation(s)
- Le Khanh Ngan Nguyen
- Department of Management Science, Strathclyde Business School, University of Strathclyde, Glasgow, United Kingdom
| | - Itamar Megiddo
- Department of Management Science, Strathclyde Business School, University of Strathclyde, Glasgow, United Kingdom
| | - Susan Howick
- Department of Management Science, Strathclyde Business School, University of Strathclyde, Glasgow, United Kingdom
| |
Collapse
|
2
|
Sy C, Bernardo E, Miguel A, San Juan JL, Mayol AP, Ching PM, Culaba A, Ubando A, Mutuc JE. Policy Development for Pandemic Response Using System Dynamics: a Case Study on COVID-19. PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2020; 4. [PMCID: PMC7388738 DOI: 10.1007/s41660-020-00130-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The coronavirus disease 2019 (COVID-19) outbreak has burdened several countries. Its high transmissibility and mortality rate have caused devastating impacts on human lives. This has led countries to implement control strategies, such as social distancing, travel bans, and community lockdowns, with varying levels of success. However, a disease outbreak can cause significant economic disruption from business closures and risk avoidance behaviors. This paper raises policy recommendations through a system dynamics modeling approach. The developed model captures relationships, feedbacks, and delays present in a disease transmission system. The dynamics of several policies are analyzed and assessed based on effectiveness in mitigating infection and the resulting economic strain.
Collapse
Affiliation(s)
- Charlle Sy
- Industrial Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
- Center for Engineering and Sustainable Development Research, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
| | - Ezekiel Bernardo
- Industrial Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
| | - Angelimarie Miguel
- Industrial Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
| | - Jayne Lois San Juan
- Industrial Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
- Center for Engineering and Sustainable Development Research, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
| | - Andres Philip Mayol
- Mechanical Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
| | - Phoebe Mae Ching
- Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Alvin Culaba
- Center for Engineering and Sustainable Development Research, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
- Mechanical Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
| | - Aristotle Ubando
- Center for Engineering and Sustainable Development Research, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
- Mechanical Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
| | - Jose Edgar Mutuc
- Industrial Engineering Department, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines
| |
Collapse
|
3
|
Wallentin G, Neuwirth C. Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model. Ecol Modell 2017. [DOI: 10.1016/j.ecolmodel.2016.11.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|
4
|
Laperrière V, Brugger K, Rubel F. Cross-scale modeling of a vector-borne disease, from the individual to the metapopulation: The seasonal dynamics of sylvatic plague in Kazakhstan. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2016.09.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
5
|
Chowell G, Sattenspiel L, Bansal S, Viboud C. Mathematical models to characterize early epidemic growth: A review. Phys Life Rev 2016; 18:66-97. [PMID: 27451336 PMCID: PMC5348083 DOI: 10.1016/j.plrev.2016.07.005] [Citation(s) in RCA: 175] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 07/01/2016] [Accepted: 07/02/2016] [Indexed: 10/21/2022]
Abstract
There is a long tradition of using mathematical models to generate insights into the transmission dynamics of infectious diseases and assess the potential impact of different intervention strategies. The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts. More refined models are needed however, in particular to account for variation in the early growth dynamics of real epidemics and to gain a better understanding of the mechanisms at play. Here, we review recent progress on modeling and characterizing early epidemic growth patterns from infectious disease outbreak data, and survey the types of mathematical formulations that are most useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth dynamics. Specifically, we review mathematical models that incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing. In this process, we also analyze simulation data stemming from detailed large-scale agent-based models previously designed and calibrated to study how realistic social networks and disease transmission characteristics shape early epidemic growth patterns, general transmission dynamics, and control of international disease emergencies such as the 2009 A/H1N1 influenza pandemic and the 2014-2015 Ebola epidemic in West Africa.
Collapse
Affiliation(s)
- Gerardo Chowell
- School of Public Health, Georgia State University, Atlanta, GA, USA; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
| | - Lisa Sattenspiel
- Department of Anthropology, University of Missouri, Columbia, MO, USA
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington DC, USA; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
6
|
Vincenot CE, Carteni F, Mazzoleni S, Rietkerk M, Giannino F. Spatial Self-Organization of Vegetation Subject to Climatic Stress-Insights from a System Dynamics-Individual-Based Hybrid Model. FRONTIERS IN PLANT SCIENCE 2016; 7:636. [PMID: 27252707 PMCID: PMC4877523 DOI: 10.3389/fpls.2016.00636] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 04/25/2016] [Indexed: 05/13/2023]
Abstract
In simulation models of populations or communities, individual plants have often been obfuscated in favor of aggregated vegetation. This simplification comes with a loss of biological detail and a smoothing out of the demographic noise engendered by stochastic individual-scale processes and heterogeneities, which is significant among others when studying the viability of small populations facing challenging fluctuating environmental conditions. This consideration has motivated the development of precise plant-centered models. The accuracy gained in the representation of plant biology has then, however, often been balanced by the disappearance in models of important plant-soil interactions (esp. water dynamics) due to the inability of most individual-based frameworks to simulate complex continuous processes. In this study, we used a hybrid modeling approach, namely integrated System Dynamics (SD)-Individual-based (IB), to illustrate the importance of individual plant dynamics to explain spatial self-organization of vegetation in arid environments. We analyzed the behavior of this model under different parameter sets either related to individual plant properties (such as seed dispersal distance and reproductive age) or the environment (such as intensity and yearly distribution of precipitation events). While the results of this work confirmed the prevailing theory on vegetation patterning, they also revealed the importance therein of plant-level processes that cannot be rendered by reaction-diffusion models. Initial spatial distribution of plants, reproductive age, and average seed dispersal distance, by impacting patch size and vegetation aggregation, affected pattern formation and population survival under climatic variations. Besides, changes in precipitation regime altered the demographic structure and spatial organization of vegetation patches by affecting plants differentially depending on their age and biomass. Water availability influenced non-linearly total biomass density. Remarkably, lower precipitation resulted in lower mean plant age yet higher mean individual biomass. Moreover, seasonal variations in rainfall greater than a threshold (here, ±0.45 mm from the 1.3 mm baseline) decreased mean total biomass and generated limit cycles, which, in the case of large variations, were preceded by chaotic demographic and spatial behavior. In some cases, peculiar spatial patterns (e.g., rings) were also engendered. On a technical note, the shortcomings of the present model and the benefit of hybrid modeling for virtual investigations in plant science are discussed.
Collapse
Affiliation(s)
- Christian E. Vincenot
- Biosphere Informatics Laboratory, Department of Social Informatics, Graduate School of Informatics, Kyoto UniversityKyoto, Japan
| | - Fabrizio Carteni
- Dipartimento di Agraria, Università degli Studi di Napoli Federico IIPortici, Italy
| | - Stefano Mazzoleni
- Dipartimento di Agraria, Università degli Studi di Napoli Federico IIPortici, Italy
| | - Max Rietkerk
- Environmental Sciences Group, Copernicus Institute of Sustainable Development, Utrecht UniversityUtrecht, Netherlands
| | - Francesco Giannino
- Dipartimento di Agraria, Università degli Studi di Napoli Federico IIPortici, Italy
| |
Collapse
|
7
|
Vincenot CE, Mazzoleni S, Moriya K, Cartenì F, Giannino F. How spatial resource distribution and memory impact foraging success: A hybrid model and mechanistic index. ECOLOGICAL COMPLEXITY 2015. [DOI: 10.1016/j.ecocom.2015.03.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
8
|
Rees EE, Pond BA, Tinline RR, Bélanger D. Modelling the effect of landscape heterogeneity on the efficacy of vaccination for wildlife infectious disease control. J Appl Ecol 2013. [DOI: 10.1111/1365-2664.12101] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Erin E. Rees
- Département de Pathologie et Microbiologie; Le Groupe de Recherche en Épidémiologie des Zoonoses et Santé Publique; Université de Montréal; 3200 Sicotte; C.P. 5000; Saint-Hyacinthe; QC; J2S 7C6; Canada
| | - Bruce A. Pond
- Wildlife Research and Development Section; Ontario Ministry of Natural Resources; Trent University; 2140 East Bank Drive; Peterborough; ON; K9J 7B8; Canada
| | - Rowland R. Tinline
- Department of Geography; Queen's University; Kingston; ON; K7L 3N6; Canada
| | - Denise Bélanger
- Département de Pathologie et Microbiologie; Le Groupe de Recherche en Épidémiologie des Zoonoses et Santé Publique; Université de Montréal; 3200 Sicotte; C.P. 5000; Saint-Hyacinthe; QC; J2S 7C6; Canada
| |
Collapse
|
9
|
|