Determinants of COVID-19 Infection Among Employees of an Italian Financial Institution Determinants of COVID-19 Infection
Main Article Content
Keywords
COVID-19; transition probabilities; COVID-19 infection trends; multi-state model; COVID-19 transmission; risk assessment.
Abstract
Background: Understanding the trend of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is becoming crucial. Previous studies focused on predicting COVID-19 trends, but few papers have considered models for disease estimation and progression based on large real-world data. Methods: We used de-identified data from 60,938 employees of a major financial institution in Italy with daily COVID-19 status information between 31 March 2020 and 31 August 2021. We consider six statuses: (i) concluded case, (ii) confirmed case, (iii) close contact, (iv) possible-probable contact, (v) possible contact, and (vi) no-COVID-19 or infection. We conducted a logistic regression to assess the odds ratio (OR) of transition to confirmed COVID-19 case at each time point. We also fitted a general model for disease progression via the multi-state transition probability model at each time point, with lags of 7 and 15 days. Results: Employment in a branch versus in a central office was the strongest predictor of case or contact status, while no association was detected with gender or age. The geographic prevalence of possible-probable contacts and close contacts was predictive of the subsequent risk of confirmed cases. The status with the highest probability of becoming a confirmed case was concluded case (12%) in April 2020, possible-probable contact (16%) in November 2020, and close contact (4%) in August 2021. The model based on transition probabilities predicted well the rate of confirmed cases observed 7 or 15 days later. Conclusion: Data from industry-based surveillance systems may effectively predict the risk of subsequent infection.
References
2. Worldometer – COVID-19 CORONAVIRUS PANDEMIC. Available online: https://www.worldometers.info/coronavirus/.
3. Dowd JB, Andriano L, Brazel DM, et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proc Natl Acad Sci USA. 2020;117(18):9696-9698. Doi: 10.1073/pnas.2004911117/-/DCSupplemental
4. Aabed K, Lashin MMA. An analytical study of the factors that influence COVID-19 spread. Saudi J Biol Sci. 2021;28(2):1177-1195. Doi: 10.1016/j.sjbs.2020.11.067
5. Lowen AC, Steel J. Roles of Humidity and Temperature in Shaping Influenza Seasonality. J Virol. 2014;88(14):7692-5. Doi: 10.1128/jvi.03544-13
6. M. Moriyama, Hugentobler WJ, Iwasaki A. Seasonality of Respiratory Viral Infections. Annu Rev Virol. 2020;7(1):83-101. Doi: 10.1146/annurev-virology-012420
7. Thai PQ, Choisy M, Duong TN, et al. Seasonality of absolute humidity explains seasonality of influenza-like ill-ness in Vietnam. Epidemics. 2015:13:65-73. Doi: 10.1016/j.epidem.2015.06.002
8. Martin A, Markhvida M, Hallegatte S, Walsh B. Socio-Economic Impacts of COVID-19 on Household Consump-tion and Poverty. Econ Disaster Clim Chang. 2020;4(3):453-479. Doi: 10.1007/s41885-020-00070-3
9. Fernandes N. Economic effects of coronavirus outbreak (COVID-19) on the world economy. 2020. Available online: https://ssrn.com/abstract=3557504
10. Fama EF, French KR. The Cross‐Section of Expected Stock Returns. J Finance. 1992;47(2):427-465. Doi: 10.1111/j.1540-6261.1992.tb04398.x.
11. Liu S. Analysis of COVID-19 on service industry based on fama and French five-factor model. Proceedings – 2020 Management Science Informatization and Economic Innovation Development Conference, MSIEID 2020, Dec. pp. 154-157. Doi: 10.1109/MSIEID52046.2020.00035
12. Priyadarshini I. A Survey on some of the Global Effects of the COVID-19 Pandemic. 2020. Doi: 10.21203/rs.3.rs-20842/v1
13. Chinazzi M, Davis JT, Ajelli M, et al. The effect of travel restrictions on the spread of the 2019 novel corona-virus (COVID-19) outbreak. 2020;36B(6489):395-400. Doi: 10.1126/science.aba97
14. Qiu J, Shen B, Zhao M, Wang Z, Xie B, Xu Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy recommendations. Gen Psychiatr. 2020 ;33(2):e100213. Doi: 10.1136/gpsych-2020-100213
15. Wong JEL, Leo YS, Tan CC COVID-19 in Singapore-Current Experience: Critical Global Issues That Require At-tention and Action. JAMA. 2020;323(13):1243-1244. Doi: 10.1001/jama.2020.2467
16. Koh D. Occupational risks for COVID-19 infection. Occup Med. 2020;70(1):3-5. Doi: 10.1093/occmed/kqaa036
17. DeCaprio D, Gartner J, Burgess T et al. Building a COVID-19 Vulnerability Index. Arxiv 2020. Available online: http://arxiv.org/abs/2003.07347
18. Jiang Z, Hu M, Fan L, et al. Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device. Arxiv 2020. Available online: http://arxiv.org/abs/2004.06912
19. Knight SR, Ho A, Pius R, et al. Risk stratification of patients admitted to hospital with covid-19 using the ISA-RIC WHO Clinical Characterisation Protocol: Development and validation of the 4C Mortality Score. BMJ. 2020:370:m3339. Doi: 10.1136/bmj.m3339
20. Zhang H, Shi T, Wu X, et al. Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK. medRxiv. 2020; Doi: 10.1101/2020.04.28.20082222
21. Barda N, Riesel D, Akriv A, Levi J. Performing risk stratification for COVID-19 when individual level data is not available – the experience of a large healthcare organization. medRxiv. 2020. Doi: 10.1101/2020.04.23.20076976
22. Xie J, Hungerford D, Chen H, et al. Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19. medRxiv. 2020. Doi: 10.1101/2020.03.28.20045997
23. Coronavirus. La situazione/desktop. Available online: https://mappe.protezionecivile.gov.it/it/mappe-emergenze/mappe-coronavirus/situazione-desktop
24. Gentleman RC, Lawless JF, Lindsey JC, Yan P. Multi-state Markov models for analysing incomplete disease his-tory data with illustrations for HIV disease. Stat Med. 1994;13(8):805-21. Doi: 10.1002/sim.4780130803
25. Andersen PK. Multistate models in survival analysis: a study of nephropathy and mortality in diabetes. Stat Med. 1988;7(6):611-70. Doi: 10.1002/sim.4780070605
26. Satten GA, Longini Jr IM. Markov chains with measurement error: Estimating the ‘true’ course of a marker of the progression of human immunodeficiency virus disease. J R Stat Soc: Series C (Applied Statistics). 1996;45(3):275-295.
27. Jackson CH, Sharples LD, Thompson SG, Duffy SW, Couto E. Multistate Markov models for disease progression with classification error. J R Stat Soc: Series D (The Statistician). 2003;52(2):193-209.
28. Cox DR and Mille HD. The theory of stochastic processes. Routledge, 2017.
29. Kalbfleisch JD, Lawless JF. The analysis of panel data under a Markov assumption. JASA. 1985;80(392):863-871.
30. Kay R. A Markov model for analysing cancer markers and disease states in survival studies. Biometrics. 1986;42:855-865.
31. MSMBuilder. Available online: http://msmbuilder.org/3.8.0/
32. SciPy. Available online: https://docs.scipy.org/doc/scipy-1.5.4/reference/
33. Statsmodels. Available onlne: https://www.statsmodels.org/devel/release/version0.12.1.html
34. Rabiolo A, Alladio E, Morales E, et al. Forecasting the COVID-19 epidemic by integrating symptom search be-havior into predictive models: Infoveillance study. J Med Internet Res. 2021;23(8):e28876. Doi: 10.2196/28876
35. Yu C-S, Chang S-S, Chang T-H, et al. A COVID-19 pandemic artificial intelligence–based system with deep learn-ing forecasting and automatic statistical data acquisition: development and implementation study. J Med In-ternet Res. 2021;23(5):e27806. Doi: 10.2196/27806
36. Panovska-Griffiths J, Kerr CK, Stuart RM, et al. Determining the optimal strategy for reopening schools, the impact of test and trace interventions, and the risk of occurrence of a second COVID-19 epidemic wave in the UK: a modelling study. Lancet Child Adolesc Health. 2020;4(11):817-827. Doi: 10.1016/S2352-4642(20)30250-9
37. Coccia M. Preparedness of countries to face COVID-19 pandemic crisis: strategic positioning and factors sup-porting effective strategies of prevention of pandemic threats. Environ Res. 2022;203:111678.
38. De Nadai M, Roomp K, Lepri B, Oliver N. The impact of control and mitigation strategies during the second wave of coronavirus infections in Spain and Italy. Sci Rep. 2022;12(1):1073.
39. Bleier BS, Ramanathan M, Lane AP. COVID-19 Vaccines May Not Prevent Nasal SARS-CoV-2 Infection and Asymptomatic Transmission. Otolaryngol Head Neck Surg. 2021;164(2):305-307. Doi: 10.1177/0194599820982633
40. Planas D, Veyer D, Baidaliuk, et al. Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutraliza-tion. 2021;596(7871):276-280. Doi: 10.1038/s41586-021-03777-9.
41. Palladino R, Bollon J, Ragazzoni L, Barone-Adesi F. Excess Deaths and Hospital Admissions for COVID-19 Due to a Late Implementation of the Lockdown in Italy. Int J Environ Res Public Health. 2020;17(16):5644.
42. Caristia S, Ferranti M, Skrami E, et al. Effect of national and local lockdowns on the control of COVID-19 pan-demic: a rapid review. Epidemiol Prev. 2020;44(5-6 Suppl 2):60-68.
43. Bontempi E. The europe second wave of COVID-19 infection and the Italy ‘strange’ situation. Environ Res. 2021;193vol. 193:110476. Doi: 10.1016/j.envres.2020.110476.
Article Sidebar
Article Details
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reproductions with commercial intent will require written permission and payment of royalties.
This work is licensed under a CC BY-NC-SA 4.0 Creative Commons Attribution-NonCommercial ShareAlike 4.0 International License.
How to Cite
