Predictive value of clinical symptoms for COVID-19 diagnosis in young adults

Kunkel, Deborah; Stuenkel, Mackenzie; Sivaraj, Laksika Banu; Colenda, Christopher C.; Pekarek, Lesslie; Rennert, Lior

doi:10.1080/07448481.2022.2068963

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…However, many of these models rely on input parameters derived from broad geographic regions which can lead to inaccurate projections for local populations 7 . When models are not tailored to local populations, uncertainty in local-level input parameters, including initial model states (e.g., population immunity) 21 , disease transmission (e.g., vaccine protection) 9 , human behavior (e.g., voluntary testing compliance) 22 , and the unpredictable nature of the pandemic 23 , further amplify model inaccuracy 24 . While predictive models can be useful for comparing the relative effectiveness of interventions 13 , 25 , 26 , inaccurate point estimates for disease incidence can ultimately complicate institutional decision making and policy 27 .…”

Section: Introductionmentioning

confidence: 99%

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study

Ma,

Rennert

2024

Sci Rep

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Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

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Section: Introductionmentioning

confidence: 99%

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study

Ma,

Rennert

2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…[18][19][20] However, many of these models rely on input parameters derived from broad geographic regions which can lead to inaccurate projections for local populations. 7 When models are tailored to local populations, uncertainty in local-level input parameters including initial model states (e.g., population immunity), 21 disease transmission (e.g., vaccine protection), 9 human behavior (e.g., voluntary testing compliance), 22 and the unpredictable and changing nature of the pandemic 23 further amplify model inaccuracy. 24 While predictive models can be useful for comparing the relative effectiveness of interventions, 13,25,26 inaccurate point estimates for disease incidence can ultimately complicate institutional decision making and policy formation.…”

Section: Introductionmentioning

confidence: 99%