A compartmental network model for the spread of whooping cough

Ameri, Hakimeh; Cooper, Kathryn

doi:10.1109/bibm.2017.8218004

Cited by 12 publications

(12 citation statements)

References 9 publications

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“…Model accuracy was validated using standard methods, 65,[78][79][80][81][82] cross-validation to each other, and varied national, state, and county empirical data sets from January to July 2020 ( Figure 2). As shown, in all cases model results closely emulate historical data, with accuracy on par with or exceeding norms and results reported elsewhere [83][84][85][86][87][88][89] and with ≤ 1 change dynamic point generally providing good fits, both suggesting good prospective short-term prediction capability. While no model is perfect, for the cases of Dougherty (Georgia) and Suffolk (Massachusetts) countieseach exhibiting different epidemic patterns, magnitudes, and timingsour models sufficiently emulate community-based transmission to help inform policy-making decisions.…”

Section: B Parameter Estimation and Calibrationsupporting

confidence: 77%

Potential Community and Campus Covid-19 Outcomes Under University and College Reopening Scenarios

Benneyan

Gehrke

Ilieş

et al. 2020

Preprint

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Background: Significant uncertainty exists about the safety of, and best strategies for, reopening colleges and universities while the Covid-19 pandemic is not well-controlled. Little also is known about the effects that on-campus outbreaks may have on local non-student and/or higher-risk communities. Model-based analysis can help inform decision and policy making across a wide range of assumptions and uncertainties. Objective: To evaluate the potential range of campus and community Covid-19 exposures, infections, and mortality due to various university and college reopening plans and precautions. Methods: We developed and calibrated campus-only, community-only, and campus-x-community epidemic models using standard susceptible-exposed-infected-recovered differential equation and agent-based modeling methods. Input parameters for campus and surrounding communities were estimated via published and grey literature, scenario development, expert opinion, Monte Carlo simulation, and accuracy optimization algorithms; models were cross-validated against each other using February-June 2020 county, state, and country data. Campus opening plans (spanning various fully open, hybrid, and fully virtual approaches) were identified from websites, publications, communications, and surveys. All scenarios were simulated assuming 16-week semesters and best/worst case ranges for disease prevalence among community residents and arriving students, precaution compliance, contact frequency, virus attack rates, and tracing and isolation effectiveness. Day-to-day student and community differences in exposures, infections, and mortality were estimated under each scenario as compared to regular and no re-opening; 10% trimmed medians, standard deviations, and probability intervals were computed to omit extreme outlier scenarios. Factorial analyses were conducted to identify inputs with largest and smallest impacts on outcomes. Results: As a base case, predicted 16-week student infections and mortality under normal operations with no precautions (or no compliance) ranged from 472 to 9,484 (4.7% to 94.8%) and 2 to 61 (0.02% to 0.61%) per 10,000 student population, respectively. In terms of contact tracing and isolation resources, as many as 17 to 1,488 total exposures per 10,000 students could occur on a given day throughout the semester needing to be located, tested, and if warranted quarantined. Attributable total additional predicted community exposures, infections, and mortality ranged from 1 to 187, 13 to 820, and 1 to 21, respectively, assuming the university takes no additional precautions to limit exposure risk. The mean (SD) number of days until 1% and 5% of on-campus students are infected was 11 (3) and 76 (17) days, respectively; 34.8% of replications resulted in more than 10% students infected by semester end. The diffusion first inflection point occurred on average on day 84 (+/- 20 days, 95% interval). Common re-opening precaution strategies reduced the above consequences by 24% to 26% fewer infections (now 360 to 6,976 per 10,000 students) and 36% to 50% fewer deaths (now 1 to 39 per 10,000 students). Perfect testing and immediate quarantining of all students on arrival to campus at semester start further reduced infections by 58% to 95% (now 200 to 468 per 10,000 students) and deaths by 95% to 100% (now 0 to 3 per 10,000 students). Uncertainties in many factors, however, produced tremendous variability in all median estimates, ranging by -67% to +370%. Conclusions: Consequences of reopening college and university physical campuses on student and community Covid-19 exposures, infections, and mortality are very highly unpredictable, depending on a combination of random chance, controllable (e.g. physical layouts), and uncontrollable (e.g. human behavior) factors. Important implications at government and academic institution levels include clear needs for specific criteria to adapt campus operations mid-semester, methods to detect when this is necessary, and well-executed contingency plans for doing so.

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Section: B Parameter Estimation and Calibrationsupporting

confidence: 77%

Potential Community and Campus Covid-19 Outcomes Under University and College Reopening Scenarios

Benneyan

Gehrke

Ilieş

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Model accuracy was validated using standard methods [65,[78][79][80][81][82], cross-validation, and varied state and county empirical data (January to July 2020) exhibiting different epidemic patterns, magnitudes, and timings (Multimedia Appendix 2). Model results closely emulate historical data across multiple settings, with accuracy on par with or exceeding norms reported elsewhere [83][84][85][86][87][88][89] and with ≤1 change points generally providing good fits, suggesting good prospective short-term prediction capability. 1) used in the community and campus models were estimated using a combination of published and grey literature, expert opinion, and search-based optimization.…”

Section: Parameter Estimation and Model Calibrationsupporting

confidence: 64%

Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis

Benneyan¹,

Gehrke²,

Ilieş³

et al. 2021

JMIR Public Health Surveill

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Background Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities. Objective We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties. Methods We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions. Results Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance. Conclusions Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations.

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“…Whooping cough or pertussis is a highly epidemic bacterium that lives in the mouth, nose, throat and it's life-threatening especially in infants. The widespread disease can easily disperse through coughing and sneezing of an infected individual [15]. Influenza is one of the most common contagious illnesses caused by influenza viruses.…”

Section: Resultsmentioning

confidence: 99%

Epidemic Peak for COVID-19 in India, 2020

Wagh¹,

Mahalle²,

Wagh³

2020

Preprint

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In India the first case of coronavirus disease 2019 (COVID-19) reported on 30 January 2020, and thereafter cases were increasing daily after the last week of Feb. 2020. COVID-19 identified as family member of coronaviridae where previously Middle East Respiratory Syndrome MERS and Severe Acute Respiratory Syndrome SARS belongs to same family. The COVID-19 attacks on respiratory system signing fever, cough and breath shortness, in severe cases may cause pneumonia, SARS or some time death. The aim of this study work is to develop model which predicts the epidemic peak for COVID-19 in India by using the real-time data from 30 Jan to 10 May 2020. There are uncertainties while identifying the population information due to the incomplete and inaccurate data, we initiate the most popular model for epidemic prediction i.e Susceptible, Exposed, Infectious, & Recovered SEIR initially the compartmental model for the prediction. Based on the solution of the state estimation problem for polynomial system with Poisson noise, we estimate that the epidemic peak may reach the early-middle July 2020, initializing recovered R0 to 0 and Infected I0 to 1. The outcomes of the model will help epidemiologist to isolate the source of the disease geospatially and analyze the death. Also government authorities will be able to target their interventions for rapidly checking the spread of the epidemic.

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A compartmental network model for the spread of whooping cough

Cited by 12 publications

References 9 publications

Potential Community and Campus Covid-19 Outcomes Under University and College Reopening Scenarios

Potential Community and Campus Covid-19 Outcomes Under University and College Reopening Scenarios

Community and Campus COVID-19 Risk Uncertainty Under University Reopening Scenarios: Model-Based Analysis

Epidemic Peak for COVID-19 in India, 2020

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