Fizzle Testing: An Equation Utilizing Random Surveillance to Help Reduce COVID-19 Risks

Cullenbine, Christopher A.; Rohrer, Joseph W.; Almand, Erin A.; Steel, J. Jordan; Davis, Matthew; Carson, Christopher M.; Hasstedt, Steven Cm; Sitko, John C.; Wickert, Douglas P

doi:10.3390/mca26010016

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…For comparison purposes, some other published references also used data with less than or equal to six months of the observation in their models and have produced good simulation results (see e.g. [2,4,5,11,13,15,22,[24][25][26]28]).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, various studies about COVID-19 spread analysis using dynamical systems are also available in the literature, which were used to determine the effectiveness of treatments to prevent the transmission of the virus (see e.g. [20][21][22][23][24][25][26]). We summarize the specification of each developed model as well as the proposed model in this paper in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Modeling and Stability Analysis of the COVID-19 Spread by Considering Quarantine and Hospitalize

Widowati¹,

Sutrisno²,

Sasongko³

et al. 2022

MMEP

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A virus that attacks the human respiratory system that first appeared in the province of Wuhan, China is known as COVID-19 (SARS COV2 n-corona virus). In order to anticipate the increasing of the cases, a strategy is needed to inhibit its growth and spread. Seeing the projection of future situations becomes very important, so we can anticipate with a selection of policy scenarios. The dynamical system model approach is very important for predicting future situations as well as selectable scenarios based on simulation results. In this study, we develop a COVID-19 transmission model which can be used to predict the epidemiological outcome and simultaneously to evaluate the effect of quarantine and hospitalization to COVID-19 spread. The mathematical model of the transmission of COVID-19 was developed in the form of the non-linear differential equation system, with seven variables, namely susceptible, exposed, infected, quarantined-1 (exposed individuals who were quarantined), quarantined-2 (infected individuals who were quarantined), hospitalized and recovered. The proposed model has a non-endemic and endemic equilibrium point. Local stability analysis of the non-endemic equilibrium point was investigated using the Routh-Hurwitz criterion, while global stability of the endemic equilibrium point was analyzed by using the Lyapunov method. If the basic reproduction number is less than one, then non-endemic equilibrium point is stable. On the other hand, if the basic reproduction number is greater than one, then endemic equilibrium point is stable. Verification of the developed model was carried out through numerical simulations using data from Central Java Province, Indonesia. We have investigated that parameter related to quarantine and hospitalize affect the number of new infections COVID-19 and the basic reproduction number. From the simulation results, it was found that strict quarantine and hospitalize have the potential to succeed in reducing and inhibiting the transmission of the COVID-19. It can be used by the government in making policies to increase the implementation of quarantine and hospitalize in the community.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and Stability Analysis of the COVID-19 Spread by Considering Quarantine and Hospitalize

Widowati¹,

Sutrisno²,

Sasongko³

et al. 2022

MMEP

View full text Add to dashboard Cite

show abstract

“…Analysts used a stochastic Susceptible–Exposed–Infected–Recovered compartmental model previously validated for our population to predict the spread of SARS-CoV-2 infection throughout the cadet population under various surveillance testing rates. 14 The model included symptomatic and asymptomatic infection compartments, as well as quarantine and isolation compartments, permitting detailed forecasts of possible outcomes from various observed conditions. Model parameters (eg, transmissibility, reproductive number, asymptomatic rates) based on institutional population dynamics laid the modeling groundwork to inform policies related to classroom density, military training activities, and off-base liberties ( Table ).…”

Section: Methodsmentioning

confidence: 99%

“…This reset halted the virus spread and highlighted that, in addition to identifying people who received a positive test result for SARS-CoV-2, random surveillance testing also provides an early warning system to identify increased viral transmission within the tested population. 14 …”

Section: Purposementioning

confidence: 99%

Campus Reset: Dynamic Planning and Response to SARS-CoV-2 Infections at the US Air Force Academy

et al. 2022

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Predominantly asymptomatic infections, such as those for SARS-CoV-2, require robust surveillance testing to identify people who are unknowingly spreading the virus. The US Air Force Academy returned to in-person classes for more than 4000 cadets aged 18-26 years during the fall 2020 semester to meet graduation and leadership training requirements. To enable this sustained cadet footprint, the institution developed a dynamic SARS-CoV-2 response plan using near–real-time data to inform decisions and trigger policies. A surveillance testing program based on mathematical modeling and a policy-driven campus reset option provided a scaled approach to react to SARS-CoV-2 conditions. This program adequately controlled the spread of the virus for the first 2 months of the academic semester but failed to predict or initially mitigate a significant outbreak in the second half of the semester. Although this approach did not completely eliminate SARS-CoV-2 infections in the population, it served as an early warning system to alert public health authorities to potential issues, which allowed timely responses while containment was still possible.

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“…In order to facilitate in-person learning during a global pandemic, a robust testing plan was developed to ensure the safety of students and faculty. This plan included a dynamic mathematical equation to model the minimum amount of testing required each week to prevent COVID-19 outbreaks from occurring [ 6 ]. However, the levels of testing required severely strained the resources of our medical facilities.…”

mentioning

confidence: 99%

Efficiency of Pooled Surveillance Testing in Academic Labs to Detect and Inhibit Covid-19 Outbreaks

et al. 2021

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Robust surveillance testing is a key strategic plan to prevent COVID-19 outbreaks and slow the spread of the SARS-CoV-2 pandemic; however, limited resources, facilities and time often impair the implementation of a widespread surveillance effort. To mitigate these resource limitations, we employed a strategy of pooling samples, reducing reagent cost and processing time. Through utilizing academic faculty and labs, successful pooled surveillance testing was conducted throughout Fall 2020 semester to detect positive SARS-CoV-2 infections in a population of 4400 students. During the semester, over 25,000 individual COVID status evaluations were made by pooling eight individual samples into one quantitative reverse transcription polymerase chain reaction. This pooled surveillance strategy was highly effective at detecting infection and significantly reduced financial burden and cost by $3.6 million.

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Fizzle Testing: An Equation Utilizing Random Surveillance to Help Reduce COVID-19 Risks

Cited by 5 publications

References 19 publications

Mathematical Modeling and Stability Analysis of the COVID-19 Spread by Considering Quarantine and Hospitalize

Mathematical Modeling and Stability Analysis of the COVID-19 Spread by Considering Quarantine and Hospitalize

Campus Reset: Dynamic Planning and Response to SARS-CoV-2 Infections at the US Air Force Academy

Efficiency of Pooled Surveillance Testing in Academic Labs to Detect and Inhibit Covid-19 Outbreaks

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