ABSTRACT
Reducing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infections among healthcare workers is critical. We ran Monte Carlo simulations modeling the spread of SARS-CoV-2 in non-COVID-19 wards, and we found that longer nursing shifts and scheduling designs in which teams of nurses and doctors co-rotate no more frequently than every 3 days can lead to fewer infections.
Subject(s)
COVID-19 , Health Workforce/organization & administration , Infection Control/methods , Medical Staff, Hospital , Personnel Staffing and Scheduling , Safety Management/standards , COVID-19/epidemiology , COVID-19/prevention & control , Connecticut/epidemiology , Humans , Medical Staff, Hospital/organization & administration , Medical Staff, Hospital/statistics & numerical data , Occupational Exposure/prevention & control , Organizational Innovation , Personnel Staffing and Scheduling/organization & administration , Personnel Staffing and Scheduling/standards , Personnel Staffing and Scheduling/trends , SARS-CoV-2 , Safety Management/organization & administrationABSTRACT
At the beginning of 2020, the spread of a new strand of Coronavirus named SARS-CoV-2 (COVID-19) raised the interest of the scientific community about the risk assessment related to the viral infection. The contagion became pandemic in few months forcing many Countries to declare lockdown status. In this context of quarantine, all commercial and productive activities are suspended, and many Countries are experiencing a serious crisis. To this aim, the understanding of risk of contagion in every urban district is fundamental for governments and administrations to establish reopening strategies. This paper proposes the calibration of an index able to predict the risk of contagion in urban districts in order to support the administrations in identifying the best strategies to reduce or restart the local activities during lockdown conditions. The objective regards the achievement of a useful tool to predict the risk of contagion by considering socio-economic data such as the presence of activities, companies, institutions and number of infections in urban districts. The proposed index is based on a factorial formula, simple and easy to be applied by practitioners, calibrated by using an optimization-based procedure and exploiting data of 257 urban districts of Apulian region (Italy). Moreover, a comparison with a more refined analysis, based on the training of Artificial Neural Networks, is performed in order to take into account the non-linearity of the phenomenon. The investigation quantifies the influence of each considered parameter in the risk of contagion useful to obtain risk analysis and forecast scenarios.