A closer look at the spreaders of COVID-19 in Wisconsin and the US

Scott, Sherry; Cook, Keisha; Barley, Kamal

doi:10.3934/mbe.2021188

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…According to the epidemiological data, by the end of the blind clinical sample collection period (March 16, 2022-April 07, 2022), around 65% of the WI population had at least one COVID-19 infection [20][21][22] . From the infection between the start of the pandemic and the end of the sample collection period, around 30% were the Omicron variant infections.…”

Section: Comparison Between Our Results Predicted By ML Algorithm And...mentioning

confidence: 99%

Rapid COVID-19 immunity screening by machine learning aided multiplexed nanoplasmonic biosensor

Beisenova,

Adi,

Bashar

et al. 2023

Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XXI

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Surveillance of population immunity against infectious diseases is critical for pandemic management and risk assessment of future outbreaks. However, current serological tests fall short in accurately identifying immunity profiles from diverse populations. We present a label-free, rapid, multiplexed, and variant-sensitive nanoplasmonic biosensor to quantify antibodies against multiple SARS-CoV-2 antigens from small human blood samples. We combined a machine learning model with antigen-specific antibody patterns measured from four different cohorts with known COVID-19 immunity. Subsequently, we tested our model on 100 blind human samples and determine that our findings are consistent with public epidemiological data showing that our nanobiosensor can help monitor population health during a pandemic.

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Section: Comparison Between Our Results Predicted By ML Algorithm And...mentioning

confidence: 99%

Rapid COVID-19 immunity screening by machine learning aided multiplexed nanoplasmonic biosensor

Beisenova,

Adi,

Bashar

et al. 2023

Plasmonics: Design, Materials, Fabrication, Characterization, and Applications XXI

View full text Add to dashboard Cite

show abstract

“…5(a)). Furthermore, by using available epidemiological data for the period, it was estimated that 65% of the WI population had at least one COVID-19 infection [41][42][43] (Fig. 5(b), for calculation of estimated cases, see Materials and methods).…”

Section: Comparison Between Our Nanobiosensor's Covid-19 Immunity Sta...mentioning

confidence: 99%

“…40. For the total number of Omicron cases, the weekly reported cases in Wisconsin 41 (see Fig. 5(b), red curve) from the beginning of the pandemic until the end of the sample collection period is multiplied with the prevalence of Omicron as a function of time 44 (see Fig.…”

Section: Machine-learning Modelmentioning

confidence: 99%

“…Therefore, the number was extrapolated to two weeks before the end of sample collection (March 14th, 2022) as follows: by the end of February 2022, the cumulative number of reported cases in WI is 1 571 977, 42 whereas by March 14th 2022, the number grew to 1 578 362. 41 The ratio between these two numbers is calculated to be 1.004 and is multiplied with the aforementioned CDC's prediction to obtain the extrapolated number of 3 869 654 cases. This comprises around 65% of Wisconsin's population at the time.…”

Section: Machine-learning Modelmentioning

confidence: 99%

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