**4. Discussion**

The present study describes the development and validation of a novel sentinel surveillance system allowing for the prompt risk assessment of a COVID-19 outbreak in a large European network of dialysis clinics over a 2-week forecasting horizon. The model had a stable accuracy over time and was able to consistently discriminate outbreak risk in dialysis units across all European countries at every stage of the current pandemic, i.e., during epidemic growth and decay phases. The design of our ML prediction model enables administrators and developers to quickly retrain this tool in case the visual inspection of AUC values over time suggests a trend toward a decrease in its discrimination ability.

Nosocomial transmission has greatly contributed to an increase in the global burden of COVID-19 pandemic by extremely affecting the capacity of the health system, not only to provide medical support to patients, but also to protect healthcare professionals [30,31]. Dialysis centers are particularly vulnerable to outbreak development [11,12,32] in that mitigation strategies are not entirely feasible due to the necessity of in-person encounters to provide a life-saving treatment such as hemodialysis [11]. Considering the peculiar frailty of ESKD patients, all scientific nephrology societies have provided guidance on COVID-19 transmission prevention in dialysis facilities [33–35]. In this regard, surveillance and early contagion detection are essential to reduce the risk of local outbreaks developing into epidemics.

Clinics of the FMC European Nephrocare Network have implemented multiple nonpharmacological interventions to limit viral spreading among the CKD community, including stringent hygiene procedures, social distancing, and the identification and isolation of suspected cases. In addition, dialysis facilities have established recording pathways to report any infection event in the EuCliD® TIR System. Such data are used to monitor the effectiveness of non-pharmacological intervention and to detect high-risk patients needing special attention [36–38].

One important feature of our modeling strategy entailed the combined use of open source and clinical data collected in standard clinical practice. In fact, we exploited the interconnection of the European Nephrocare clinics to augment background epidemic data with a surveillance system based on incident reports and practice pattern variation at each dialysis unit. Information about local epidemic status in a given clinic was then propagated through distance-weighting metrics to the surrounding facilities. An ML method was used to integrate all information into a summary score metric. Remarkably, variables related to the epidemic dynamics in the clinic and to the regional epidemic status, as well as to the risk proxies propagated from adjacent clinics, were all important predictors of outbreak occurrence. Such an approach is particularly relevant because it enabled us to capture local disease spread beyond the registry data compiled for the general population, which does not capture the heterogeneity of viral transmission in a setting where frequent and multiple human interactions necessarily occur. Indeed, as the basic reproduction index (R0) is a function of both the transmissibility of a disease and the contact patterns that underlie transmission [39], the regional/provincial R0 cannot be translated in dialysis facilities in that ESKD patients' biological and socio-behavioral factors significantly differ from those of the general population [40]. The occurrence of SSEVs further complicates the picture, making generalizations of regional epidemic trends that are not entirely appropriate for the reliable prediction of viral spreading in healthcare settings [41,42].

The interconnection of the FMC network allows for the collection and subsequent central integration of a bulk of information provided by facilities distributed throughout European countries. This particular setting offers the advantage to perform the real-time monitoring of sentinel sensors that are likely to provide timely and accurate indications of epidemic activity [43], while considering the heterogeneity underlying transmission dynamics. Sentinel surveillance in outpatient settings was previously shown to provide a robust approach to oversee SARS-CoV-2 spreading [44]. In general, the monitoring of community transmission in nodes distributed across different regions was reported to ensure efficient disease detection in networked populations [45]. It is important to highlight that the analytic strategy adopted in this study is general and can be applied to any epidemic communicable disease, as all naturally occurring, clustering units where social promiscuity, density, and duration of interactions are substantially different compared to the general population. Henceforth, this method may be applied to social contexts with a high risk of outbreak generation, including schools, hospitals, and workplaces from which the provided infection data are promptly captured and conferred to a central database, even in aggregated form. Monitoring of the pandemic situation within the network allows for the timely implementation of infection control procedures in the adjacent networked unit and efficiently anticipates resource needs.

Finally, variable importance analysis has indicated that trends in clinical practice patterns are among the top predictors. This observation indicates that the tracking of physicians' prescription behavior can provide valuable information to assess epidemic dynamics also during explosive growth, when surveillance and laboratory resources are limited and COVID-19 cases may be recorded with some delay due to the emergency situation [46].

#### **5. Conclusions**

Our sentinel surveillance system allows for a prompt risk assessment and timely response to the challenges posed by the COVID-19 epidemic throughout FMC European dialysis clinics. This tool can have significant implications for public health practice in that it represents a robust strategy to assess the level of community transmission of COVID-19 and to guide the selection and implementation of mitigation measures. The same framework can be applied in other networked settings, such as healthcare facilities or schools to improve early detection and forecasting of SARS-CoV-2 transmission. Finally, the implementation of our surveillance system can guide preparedness efforts for future pandemics.

**Author Contributions:** Conceptualization: F.B. and L.N.; data curation: M.G. and F.M.-M.; formal analysis: P.C., M.G. and F.M.-M.; supervision: S.S.; validation: F.B., L.N. and S.S.; writing—original draft: P.C. and C.L.; writing—review and editing: F.B., P.C., C.L. and L.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Fresenius Medical Care Deutschland GmbH.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki. IRB approval was not necessary because all input data for modeling were aggregated statistics concerning COVID-19 infection distribution and practice patterns across dialysis centers (i.e., cumulative number of infections in countries, number of prescribed laboratory tests in dialysis centers, number of new cases in dialysis centers). No patients' personal information has been used for the present study.

**Informed Consent Statement:** No patients' personal information has been used for the present study since all input data for modeling were aggregated statistics concerning COVID-19 infection distribution and practice patterns across dialysis centers (i.e., cumulative number of infections in countries, number of prescribed laboratory tests in dialysis centers, number of new cases in dialysis centers).

**Data Availability Statement:** Open source datasets adopted for the study have been referenced throughout the manuscript. Restrictions apply to the availability of these data. Data was obtained from Fresenius Medical Care and may be available for specific, well-motivated requests, from the corresponding author with the permission of Fresenius Medical Care.

**Conflicts of Interest:** C.L. received consultancy fees from Fresenius Medical Care Deutschland GmbH. All remaining authors are full time employees of Fresenius Medical Care Deutschland GmbH.

#### **Appendix A**

**Table A1.** Variables included in the model.


**Table A1.** *Cont.*


**Table A1.** *Cont.*

