Interpreting SARS-CoV-2 seroprevalence, deaths, and fatality rate — Making a case for standardized reporting to improve communication

Cavataio, Joseph P.; Schnell, Santiago

doi:10.1016/j.mbs.2021.108545

Cited by 21 publications

(18 citation statements)

References 52 publications

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“…Next, nonlinear dynamics researchers have proposed several sophisticated extensions to the classical predictive SIR model, including analytic techniques to find explicit solutions [18] , [19] , modifications to the SIR model with additional variables [20] , [21] , [22] , [23] , [24] , incorporation of Hamiltonian dynamics [25] or network models [26] and a closer analysis of uncertainty in the SIR approach [27] . Other mathematical approaches to prediction and analysis include power-law models [28] , [29] , [30] , distance analysis [31] , [32] , network models [33] , [34] , [35] , [36] , analyses of the dynamics of transmission and contact [37] , [38] , forecasting models [39] , Bayesian methods [40] , clustering [41] , [42] and many others [43] , [44] , [45] , [46] .…”

Section: Introductionmentioning

confidence: 99%

Trends in COVID-19 prevalence and mortality: A year in review

James

Menzies

2021

Physica D: Nonlinear Phenomena

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This paper introduces new methods to study the changing dynamics of COVID-19 cases and deaths among the 50 worst-affected countries throughout 2020. First, we analyse the trajectories and turning points of rolling mortality rates to understand at which times the disease was most lethal. We demonstrate five characteristic classes of mortality rate trajectories and determine structural similarity in mortality trends over time. Next, we introduce a class of virulence matrices to study the evolution of COVID-19 cases and deaths on a global scale. Finally, we introduce three-way inconsistency analysis to determine anomalous countries with respect to three attributes: countries’ COVID-19 cases, deaths and human development indices. We demonstrate the most anomalous countries across these three measures are Pakistan, the United States and the United Arab Emirates.

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Section: Introductionmentioning

confidence: 99%

Trends in COVID-19 prevalence and mortality: A year in review

James

Menzies

2021

Physica D: Nonlinear Phenomena

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“…Next, nonlinear dynamics researchers have proposed several sophisticated extensions to the classical predictive SIR model, including analytic techniques to find explicit solutions [12] , [13] , modifications to the SIR model with additional variables [14] , [15] , [16] , [17] , [18] , [19] , incorporation of Hamiltonian dynamics [20] or network models [21] , and a closer analysis of uncertainty in the SIR equations [22] . Other mathematical approaches to prediction and analysis include power-law models [23] , [24] , [25] , forecasting models [26] , fractal approaches [27] , [28] , [29] , neural networks [30] , Bayesian methods [31] , distance analysis [32] , network models [33] , [34] , [35] , [36] , analyses of the dynamics of transmission and contact [37] , [38] , clustering [39] , [40] and many others [41] , [42] , [43] , [44] , [45] . Finally, numerous articles have been devoted to understanding the spatial components of the virus’ spread, in numerous countries [46] , [47] , [48] , [49] .…”

Section: Introductionmentioning

confidence: 99%

Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil

James¹,

Menzies

Bondell³

2022

Physica D: Nonlinear Phenomena

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This paper compares and contrasts the spread and impact of COVID-19 in the three countries most heavily impacted by the pandemic: the United States (US), India and Brazil. All three of these countries have a federal structure, in which the individual states have largely determined the response to the pandemic. Thus, we perform an extensive analysis of the individual states of these three countries to determine patterns of similarity within each. First, we analyse structural similarity and anomalies in the trajectories of cases and deaths as multivariate time series. Next, we study the lengths of the different waves of the virus outbreaks across the three countries and their states. Finally, we investigate suitable time offsets between cases and deaths as a function of the distinct outbreak waves. In all these analyses, we consistently reveal more characteristically distinct behaviour between US and Indian states, while Brazilian states exhibit less structure in their wave behaviour and changing progression between cases and deaths.

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“…One of the challenges of studying DoW effects is the need for assuring data quality, the absence of structural missingness or spikes (say during weekends or holidays), and consistency in reporting of time-referenced records. Now almost 2 years into the ongoing pandemic, several reports and commentaries have discussed limitations of publicly available COVID-19 surveillance data and the need for improvement as the pandemic persists [24][25][26][27][28][29][30][31]. For example, many statewide agencies use a variety of dates for reported cases such that a positive test result could be reported by the date of symptom onset, specimen collection, positively identified culture, or date of reporting [27,31].…”

Section: Introductionmentioning

confidence: 99%

“…For example, many statewide agencies use a variety of dates for reported cases such that a positive test result could be reported by the date of symptom onset, specimen collection, positively identified culture, or date of reporting [27,31]. Similarly, the definition of SARS-CoV-2 deaths may rely on a positive test shortly before or after death (which would underestimate deaths depending on testing availability) or include individuals who were not tested but were suspected of having SARS-CoV-2 (likely overestimating deaths) [29]. Despite these criticisms, few studies have quantified patterns to reporting inconsistencies or missing data in commonly used publicly available surveillance data [32,33].…”

Section: Introductionmentioning

confidence: 99%

Critical Periods, Critical Time Points and Day-of-the-Week Effects in COVID-19 Surveillance Data: An Example in Middlesex County, Massachusetts, USA

Simpson

Lauren

Schipper

et al. 2022

IJERPH

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Critical temporal changes such as weekly fluctuations in surveillance systems often reflect changes in laboratory testing capacity, access to testing or healthcare facilities, or testing preferences. Many studies have noted but few have described day-of-the-week (DoW) effects in SARS-CoV-2 surveillance over the major waves of the novel coronavirus 2019 pandemic (COVID-19). We examined DoW effects by non-pharmaceutical intervention phases adjusting for wave-specific signatures using the John Hopkins University’s (JHU’s) Center for Systems Science and Engineering (CSSE) COVID-19 data repository from 2 March 2020 through 7 November 2021 in Middlesex County, Massachusetts, USA. We cross-referenced JHU’s data with Massachusetts Department of Public Health (MDPH) COVID-19 records to reconcile inconsistent reporting. We created a calendar of statewide non-pharmaceutical intervention phases and defined the critical periods and timepoints of outbreak signatures for reported tests, cases, and deaths using Kolmogorov-Zurbenko adaptive filters. We determined that daily death counts had no DoW effects; tests were twice as likely to be reported on weekdays than weekends with decreasing effect sizes across intervention phases. Cases were also twice as likely to be reported on Tuesdays-Fridays (RR = 1.90–2.69 [95%CI: 1.38–4.08]) in the most stringent phases and half as likely to be reported on Mondays and Tuesdays (RR = 0.51–0.93 [0.44, 0.97]) in less stringent phases compared to Sundays; indicating temporal changes in laboratory testing practices and use of healthcare facilities. Understanding the DoW effects in daily surveillance records is valuable to better anticipate fluctuations in SARS-CoV-2 testing and manage appropriate workflow. We encourage health authorities to establish standardized reporting protocols.

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Interpreting SARS-CoV-2 seroprevalence, deaths, and fatality rate — Making a case for standardized reporting to improve communication

Cited by 21 publications

References 52 publications

Trends in COVID-19 prevalence and mortality: A year in review

Trends in COVID-19 prevalence and mortality: A year in review

Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil

Critical Periods, Critical Time Points and Day-of-the-Week Effects in COVID-19 Surveillance Data: An Example in Middlesex County, Massachusetts, USA

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