The unknown denominator problem in population studies of disease frequency

Morrison, Christopher N.; Rundle, Andrew; Branas, Charles C.; Chihuri, Stanford; Mehranbod, Christina A.; Li, Guohua

doi:10.1016/j.sste.2020.100361

Cited by 12 publications

(13 citation statements)

References 56 publications

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“…Derivative pandemic metrics are widely used in many statistical researches concerning distinct COVID-19 pandemic trajectories, i.e., positive rate [29] and infection rate [30] to uncover the infection-specific characteristic of according trajectories. Besides, the case fatality rate [31], mortality rate [32] and closed case fatality rate [33] are proposed for revealing death-specific characteristic; the discharge rate [34], recovery rate [35] and survival case discharge rate [36,37] for recovery-specific characteristic; in-ICU case rate [37,38] and clinical deterioration rate [38] for worsening-specific characteristic, respectively.…”

Section: Pandemic Metricsmentioning

confidence: 99%

The Retreated Tendency of the COVID-19 Pandemic

Zuo

2021

Preprint

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Background: The novel virus (COVID-19) pandemic threats the global most since the World War II yet difficult to design effective policy to respond it because widely utilized models only support to predict the future tendency within a narrow time-window. Besides, existing pandemic metrics are sensitive to the detecting policy ( p <0.001), which make them hard to reveal the real infection phenomena, because the fluctuation of these metric values may be an outcome of a suddenly changed detecting policy instead of real infection increase or decrease. Methods: We developed a Benchmark, Amendment and Validation model (BAVM) to make the above complex phenomena clearer, and combined with the hypothesis of potential periodicity of COVID-19 to devote jointly the retreated prediction of pandemic within a broad time-window. Onward, we utilized 113 countries’ epidemic data from January 21, 2020 to August 31, 2020 and detecting policies to carry out our methodology, then validated the results through statistical tests involving chi -square test, one t-test and independent t test. Results: Results exhibit the 4-month of potential periodicity between two adjacent peaks of pandemic ( t =–1.56, p =12.4%). Besides, whether or not the peak emergence has no effect on COVID-19 dynamic trajectory, but the time to firstly peak affects. Meanwhile, uprising the quarantine rate exhibits the earlier expedition towards the first peak emergence (9.7% vs. 6.2%, p =4.1%). On the contrary, the delay of first peak increased the infection rate (0.6% vs. 0.3%, p =1.5%) but also the discharge rate (65% vs. 74%, p =3.8%). Moreover, the indication of the retreated tendency of COVID-19 pandemic is that the next peak should emerge but in fact vanished after one periodicity. Otherwise, the pandemic enters into the next worse phase, typical of high mortality (5% vs. 3.4%, p =5.3%) and low discharge rate (65.8% vs. 74.1%, p =4.2%). Conclusion: To recognize the retreated tendency of this pandemic is quite significant for either the global or the local policy-making, especially in the regular epidemic prevention, control measures and work resumption.

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Section: Pandemic Metricsmentioning

confidence: 99%

The Retreated Tendency of the COVID-19 Pandemic

Zuo

2021

Preprint

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“…Derivative pandemic metrics are widely used in many statistical researchs concerning COVID-19, i.e., the proportion of the positive tests (Cordes and Castro, 2020) and positivity rate (Morrison et al, 2020) to uncover the infection-specific characteristic of the COVID-19; case fatality rate (Yang et al, 2020), mortality rate (Baud et al, 2020) and closed case fatality rate (Pueyo et al, 2020) for death-specific characteristic; discharge rate (Tian and Hu et al, 2020), recovery rate (Li and Huang et al, 2020) and survival case discharge rate (Khafaie and Rahim, 2020;Bhatraju et al, 2020) for recovery-specific characteristic; ICU case rate (Bhatraju et al, 2020;Felice et al, 2020) and clinical deterioration rate (Felice et al, 2020) for worsening-specific characteristic, respectively. The details about those metrics calculation are listed in Table S1 and the geometric probability representation of the Bernouli Distribution in investigated pandemic metrics is drawn on Figure S1.…”

Section: Pandemic Metricsmentioning

confidence: 99%

Pandemic Metric with Confidence (PMC) Model to Predict Trustworthy Probability of Utilized COVID-19 Pandemic Trajectory across the Global

Zuo

Yan

Zhao

2021

Preprint

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Lots of works aim to reveal the driving factors of COVID-19 pandemic trajectory yet ignore the confidence of utilized trajectory data, making consequent results suspicious. Hereby, we proposed a pandemic metric with confidence (PMC) model in the hypothesis of Bernoulli Distribution of nine trajectories reported from 113 countries. Results exhibit the average confidence of trajectories across the global not in excess of 12.1% with the error threshold configuration of 1E-5. In contrast, the 95% high confidence setting also failed to predict the trajectory containing the acceptable error not beyond 1E-3. Thus, a proposed trade-off strategy between two contradictory expections (>50% confidence, <1E-3 error) supports 61% of investigated countries to predict the varying trajectory with confidence beyond 50%. Moreover, PMC model recommend the remanent 39% countries to extend the proportion of populaces in COVID-19 detecting-pool to a suggested-value (>1% of populations), ensuing the average confidence up to 70%.

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“…Similarly, studies of traffic safety now adjust for immediate exposure risk which otherwise could bias results. For example, a recent epidemiological analysis found a crude decrease in motor vehicle crash incidence associated with ridesharing ( Morrison et al, 2020 ). However, when accounting for traffic volume exposure, which varied during the day, an increased risk of crashes associated with ridesharing was revealed ( Morrison et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…For example, a recent epidemiological analysis found a crude decrease in motor vehicle crash incidence associated with ridesharing ( Morrison et al, 2020 ). However, when accounting for traffic volume exposure, which varied during the day, an increased risk of crashes associated with ridesharing was revealed ( Morrison et al, 2020 ). Thus, a more nuanced understanding of fall and injury incidence in PLL may be gained by examining fall and injury incidence in the context of a time-variant proxy for risk exposure such as an individual’s average daily step counts varying over time.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the risk of falls and injuries for amputees beyond annual fall rates—A longitudinal cohort analysis based on person-step exposure over time

Chihuri¹,

Youdan

Wong³

2021

Preventive Medicine Reports

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Highlights People with lower-limb loss even with community walking ability have high annual fall and injury rates. True fall and injury risk may be obscured if exposure to risk measured by person-steps over time is not considered. Risk was higher for amputees with limited walking ability per person-step exposure over time. Incorporating person-step exposure over time clarifies fall and injury risk level.

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The unknown denominator problem in population studies of disease frequency

Cited by 12 publications

References 56 publications

The Retreated Tendency of the COVID-19 Pandemic

The Retreated Tendency of the COVID-19 Pandemic

Pandemic Metric with Confidence (PMC) Model to Predict Trustworthy Probability of Utilized COVID-19 Pandemic Trajectory across the Global

Quantifying the risk of falls and injuries for amputees beyond annual fall rates—A longitudinal cohort analysis based on person-step exposure over time

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