Time Analysis of an Emergent Infection Spread Among Healthcare Workers: Lessons Learned from Early Wave of SARS-CoV-2

Abstract

Aim: To find whether an emergent airborne infection is more likely to spread among healthcare workers (HCW) based on data of SARS-CoV-2 and whether the number of new cases of such airborne viral disease can be predicted using a method traditionally used in weather forecasting called Autoregressive Fractionally Integrated Moving Average (ARFIMA).

Methods: We analyzed SARS-CoV-2 spread among HCWs based on outpatient nasopharyngeal swabs for real-time polymerase chain reaction (RT-PCR) tests and compared it to non-HCW in the first and the second wave of the pandemic. We also generated an ARFIMA model based on weekly case numbers from February 2020 to April 2021 and tested it on data from May to July 2021.

Results: Our analysis of 8998 tests in the 15 months period showed a rapid rise in positive RT-PCR tests among HCWs during the first wave of pandemic. In the second wave, however, positive patients were more commonly non-HCWs. The ARFIMA model showed a long-memory pattern for SARS-CoV-2 (seven months) and predicted future new cases with an average error of ±1.9 cases per week.

Conclusion: Our data indicate that the virus rapidly spread among HCWs during the first wave of the pandemic. Review of published literature showed that this was the case in multiple other areas as well. We therefore suggest strict policies early in the emergence of a new infection to protect HCWs and prevent spreading to the general public. The ARFIMA model can be a valuable forecasting tool to predict the number of new cases in advance and assist in efficient planning.

Keywords: COVID-19; SARS-COV-2; diagnosis; dispersion; health care worker; prediction.

Figure 1

Comparison of occupation type over time. (A) Count of all patients based on occupation type. Healthcare worker (HCW) predominance in the beginning months of the pandemic versus non-HCW in the final months of the data gathering. HCW-D = HCW with direct patient contact, HCW-A = HCW with indirect contact. The time of each public intervention is shown in parallel; lockdown was between March 23rd and May 30th 2020, start of vaccination of HCWs was in January 2021, and start of vaccination of non-HCWs was in April 2021. Face mask use was mandated for public with the start of lockdown. (B) Percent HCW (both direct and indirect) in all positive patients declined rapidly (tau=−0.52, p<0.0001) in the first months of the pandemic, then it stays relatively steady over the rest of the data gathering (blue line and dots, tau =+0.10, p=0.62).

Figure 2

Autoregressive Fractionally Integrated Moving Average (ARFIMA) Forecasting of COVID-19 cases (A) Autocorrelation Function (ACF) plot or correlogram shows calculated autocorrelation coefficients per time lag, revealing a long memory process of both positive and negative cases. (B) The fractal dimensions plot shows a long memory process (6–7 months) for COVID-19. The black line is the actual fractal dimension and the blue line is the LOESS smoothing of the black line of positive cases for each month. (C) Count of all positive cases over the course of our data gathering and the ARFIMA prediction of the final 12 weeks (purple). (D) Testing our forecast model: Comparison of predicted count of positive cases (red line) versus count of actual cases (blue lines) in the past 3 months. The average error was 1.9 cases per week. The model only deviates from actual in the “far future” (late June), which is expected from any prediction of future. The unexpected rise in the number of actual cases may also be the result of the new delta variant.