Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model
When formulating countermeasures to epidemics such as those generated by COVID-19, estimates of the benefits of a given intervention for a specific population are highly beneficial to policy makers. A recently introduced tool, known as the "dynamic-spread" SIR model, can perform population...
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-07-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022445?viewType=HTML |
| _version_ | 1811225705289089024 |
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| author | Gavin D'Souza Jenna Osborn Shayna Berman Matthew Myers |
| author_facet | Gavin D'Souza Jenna Osborn Shayna Berman Matthew Myers |
| author_sort | Gavin D'Souza |
| collection | DOAJ |
| description | When formulating countermeasures to epidemics such as those generated by COVID-19, estimates of the benefits of a given intervention for a specific population are highly beneficial to policy makers. A recently introduced tool, known as the "dynamic-spread" SIR model, can perform population-specific risk assessment. Behavior is quantified by the dynamic-spread function, which includes the mechanisms of droplet reduction using facemasks and transmission control due to social distancing. The spread function is calibrated using infection data from a previous wave of the infection, or other data felt to accurately represent the population behaviors. The model then computes the rate of spread of the infection for different hypothesized interventions, over the time window for the calibration data. The dynamic-spread model was used to assess the benefit of three enhanced intervention strategies – increased mask filtration efficiency, higher mask compliance, and elevated social distancing – in four COVID-19 scenarios occurring in 2020: the first wave (i.e. until the first peak in numbers of new infections) in New York City; the first wave in New York State; the spread aboard the Diamond Princess Cruise Liner; and the peak occurring after re-opening in Harris County, Texas. Differences in the efficacy of the same intervention in the different scenarios were estimated. As an example, when the average outward filtration efficiency for facemasks worn in New York City was increased from an assumed baseline of 67% to a hypothesized 90%, the calculated peak number of new infections per day decreased by 40%. For the same baseline and hypothesized filtration efficiencies aboard the Diamond Princess Cruise liner, the calculated peak number of new infections per day decreased by about 15%. An important factor contributing to the difference between the two scenarios is the lower mask compliance (derivable from the spread function) aboard the Diamond Princess. |
| first_indexed | 2024-04-12T09:11:55Z |
| format | Article |
| id | doaj.art-aba2726db7d54312b386ef67f87dbd73 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-12T09:11:55Z |
| publishDate | 2022-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-aba2726db7d54312b386ef67f87dbd732022-12-22T03:38:57ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-07-011999571958910.3934/mbe.2022445Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function modelGavin D'Souza 0Jenna Osborn 1Shayna Berman2Matthew Myers3Division of Applied Mechanics, U. S. FDA/CDRH, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USADivision of Applied Mechanics, U. S. FDA/CDRH, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USADivision of Applied Mechanics, U. S. FDA/CDRH, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USADivision of Applied Mechanics, U. S. FDA/CDRH, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USAWhen formulating countermeasures to epidemics such as those generated by COVID-19, estimates of the benefits of a given intervention for a specific population are highly beneficial to policy makers. A recently introduced tool, known as the "dynamic-spread" SIR model, can perform population-specific risk assessment. Behavior is quantified by the dynamic-spread function, which includes the mechanisms of droplet reduction using facemasks and transmission control due to social distancing. The spread function is calibrated using infection data from a previous wave of the infection, or other data felt to accurately represent the population behaviors. The model then computes the rate of spread of the infection for different hypothesized interventions, over the time window for the calibration data. The dynamic-spread model was used to assess the benefit of three enhanced intervention strategies – increased mask filtration efficiency, higher mask compliance, and elevated social distancing – in four COVID-19 scenarios occurring in 2020: the first wave (i.e. until the first peak in numbers of new infections) in New York City; the first wave in New York State; the spread aboard the Diamond Princess Cruise Liner; and the peak occurring after re-opening in Harris County, Texas. Differences in the efficacy of the same intervention in the different scenarios were estimated. As an example, when the average outward filtration efficiency for facemasks worn in New York City was increased from an assumed baseline of 67% to a hypothesized 90%, the calculated peak number of new infections per day decreased by 40%. For the same baseline and hypothesized filtration efficiencies aboard the Diamond Princess Cruise liner, the calculated peak number of new infections per day decreased by about 15%. An important factor contributing to the difference between the two scenarios is the lower mask compliance (derivable from the spread function) aboard the Diamond Princess.https://www.aimspress.com/article/doi/10.3934/mbe.2022445?viewType=HTMLcovid-19sir modelinfection-spread modelfacemaskdynamic-spread modelrisk-assessment model |
| spellingShingle | Gavin D'Souza Jenna Osborn Shayna Berman Matthew Myers Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model Mathematical Biosciences and Engineering covid-19 sir model infection-spread model facemask dynamic-spread model risk-assessment model |
| title | Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model |
| title_full | Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model |
| title_fullStr | Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model |
| title_full_unstemmed | Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model |
| title_short | Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model |
| title_sort | comparison of effectiveness of enhanced infection countermeasures in different scenarios using a dynamic spread function model |
| topic | covid-19 sir model infection-spread model facemask dynamic-spread model risk-assessment model |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022445?viewType=HTML |
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