Abstract

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.

Highlights

  • One of the most important lessons of the novel coronavirus 2019 pandemic (COVID-19)has been the importance and utility of real-time infectious disease surveillance for monitoring, tracking, and reducing emerging infectious outbreaks [1,2,3]

  • We examined DoW effects using publicly available SARS-CoV-2 surveillance data reported from 2 March 2020 through 7 November 2021

  • While JHU data had 100% completeness, we found several discrepancies in daily counts including 3 days with negative values in cases

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Summary

Introduction

One of the most important lessons of the novel coronavirus 2019 pandemic (COVID-19)has been the importance and utility of real-time infectious disease surveillance for monitoring, tracking, and reducing emerging infectious outbreaks [1,2,3]. Unlike most publicly available infectious disease surveillance data, SARS-CoV-2 reported tests, cases, and deaths have been curated at a daily temporal resolution and state- and county-level geographic areas. These reporting protocols create unique opportunities for public health professionals to. Top of each plot to signify onsetacceleration timing (O1–O5, short (pink), deceleration periods (blue), steady-state periods (grey) defined independently forarrow) each health dashed arrow), peak timingand (P1–P5, solid arrow), and resolution timing We identified a global maximum of cases for the primary peak of Wave 2 (2 January 2021; 591.37 cpm), which trailed peak tests and a return to more-stringent intervention Phase F by ~1 month. Wave 3 onset (23 June 2021; 9.80 cpm) returned to similar magnitudes as Wave 2 onset (4 July 2020; 21.29 cpm) ~1 year later

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