Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices
The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dia...
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| Veröffentlicht in: | PloS one 2021-11, Vol.16 (11), p.e0259970-e0259970 |
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| creator | Tofighi, Mohammadali Asgary, Ali Merchant, Asad A Shafiee, Mohammad Ali Najafabadi, Mahdi M Nadri, Nazanin Aarabi, Mehdi Heffernan, Jane Wu, Jianhong |
| description | The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings. |
| doi_str_mv | 10.1371/journal.pone.0259970 |
| format | Article |
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Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. 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This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.</description><subject>Agent-based models</subject><subject>Canada</subject><subject>Computer and Information Sciences</subject><subject>Computer Simulation</subject><subject>Contact Tracing - statistics & numerical data</subject><subject>Control</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - transmission</subject><subject>Dialysis</subject><subject>Disasters</subject><subject>Disease control</subject><subject>Disease transmission</subject><subject>Disease Transmission, Infectious - statistics & numerical data</subject><subject>End-stage renal disease</subject><subject>Epidemics</subject><subject>Evaluation</subject><subject>Health care networks</subject><subject>Health facilities</subject><subject>Health risks</subject><subject>Hemodialysis</subject><subject>Hemodialysis Units, Hospital - statistics & numerical data</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Infections</subject><subject>Kidney diseases</subject><subject>Long term health care</subject><subject>Medicine and Health Sciences</subject><subject>Modelling</subject><subject>Models, Statistical</subject><subject>Mortality</subject><subject>Pandemics</subject><subject>Patients</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Prospective payment systems</subject><subject>Public health</subject><subject>Research and Analysis Methods</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Simulation</subject><subject>Software</subject><subject>Supervision</subject><subject>Viral diseases</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk12L1DAUhoso7rr6D0QLgujFjEmTps2NsIxfAysDfuxtSJPTmSxtMiapuP_edKe7TGUvJBcJ6XPe0_OenCx7jtESkwq_u3KDt7Jb7p2FJSpKziv0IDvFnBQLViDy8Oh8kj0J4QqhktSMPc5OCK14VbPiNIOvTkPXGbvNV5vL9YcF5nn00obehGCczY3NZb6D3mkju-tgQq7ARg_5EMagYPqhk3Ekt2DBywg6V85GqWLIexm9URCeZo9a2QV4Nu1n2c9PH3-sviwuNp_Xq_OLhWK8iIsaI4xa1GrKgWBCCt00CFBNqa5UUXDG6rR4iRHVuCFSNVRRVLWMKiVLDeQse3nQ3XcuiMmhIEZzyioVXSZifSC0k1di700v_bVw0oibC-e3QvpoVAeCtrxtQRWqbSllspG6KijQpiRSc1qipPV-yjY0PegbX2Q3E51_sWYntu63qBmiBFdJ4M0k4N2vAUIUyXWV2iEtuCH9N0OoqFnFSEJf_YPeX91EbWUqwNjWpbxqFBXnrE4pa05popb3UGlp6E3qHbQm3c8C3s4Cxv7Cn7iVQwhi_f3b_7Obyzn7-ojdgeziLrhuGJ9TmIP0ACrvQvDQ3pmMkRin4dYNMU6DmKYhhb04btBd0O3zJ38Bnj0FoA</recordid><startdate>20211119</startdate><enddate>20211119</enddate><creator>Tofighi, Mohammadali</creator><creator>Asgary, Ali</creator><creator>Merchant, Asad A</creator><creator>Shafiee, Mohammad Ali</creator><creator>Najafabadi, Mahdi M</creator><creator>Nadri, Nazanin</creator><creator>Aarabi, Mehdi</creator><creator>Heffernan, Jane</creator><creator>Wu, Jianhong</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-9574-7614</orcidid><orcidid>https://orcid.org/0000-0001-6095-8120</orcidid></search><sort><creationdate>20211119</creationdate><title>Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices</title><author>Tofighi, Mohammadali ; Asgary, Ali ; Merchant, Asad A ; Shafiee, Mohammad Ali ; Najafabadi, Mahdi M ; Nadri, Nazanin ; Aarabi, Mehdi ; Heffernan, Jane ; Wu, Jianhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-81010f0fd49e31332dbb0e0844d7c22966868695104d1b3acb4c407f64cca5de3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Agent-based models</topic><topic>Canada</topic><topic>Computer and Information Sciences</topic><topic>Computer Simulation</topic><topic>Contact Tracing - 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Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>34797862</pmid><doi>10.1371/journal.pone.0259970</doi><tpages>e0259970</tpages><orcidid>https://orcid.org/0000-0001-9574-7614</orcidid><orcidid>https://orcid.org/0000-0001-6095-8120</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Agent-based models Canada Computer and Information Sciences Computer Simulation Contact Tracing - statistics & numerical data Control Coronaviruses COVID-19 COVID-19 - transmission Dialysis Disasters Disease control Disease transmission Disease Transmission, Infectious - statistics & numerical data End-stage renal disease Epidemics Evaluation Health care networks Health facilities Health risks Hemodialysis Hemodialysis Units, Hospital - statistics & numerical data Hospitals Humans Infections Kidney diseases Long term health care Medicine and Health Sciences Modelling Models, Statistical Mortality Pandemics Patients People and Places Physical Sciences Prospective payment systems Public health Research and Analysis Methods Severe acute respiratory syndrome coronavirus 2 Simulation Software Supervision Viral diseases |
| title | Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices |
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