HAL Id: hal-03265983
https://hal.archives-ouvertes.fr/hal-03265983
Submitted on 21 Jun 2021
HAL
is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire
HAL, estdestinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
CONDITIONS: CASE OF COVID-19
Ibrahim Cheaitou, Abdessamad Ait El Cadi, Abdelghani Bekrar, David Duvivier, Anwar Sahili
To cite this version:
Ibrahim Cheaitou, Abdessamad Ait El Cadi, Abdelghani Bekrar, David Duvivier, Anwar Sahili. RE-
SOURCES SCHEDULING IN THE EMERGENCY DEPARTMENT USING SIMULATION IN DIS-
ASTER CONDITIONS: CASE OF COVID-19. MOSIM2020, Nov 2020, Agadir, Morocco. �hal-
03265983�
RESOURCES SCHEDULING IN THE EMERGENCY DEPARTMENT USING SIMULATION IN DISASTER CONDITIONS: CASE OF COVID-19
Ibrahim Cheaitou, Abdessamad Ait El Cadi, Abdelghani Bekrar, David Duvivier Polytechnic University of Hauts-de-France, LAMIH UMR CNRS 8201, Valenciennes, France
ibrahim.cheaitou@etu.uphf.fr Abdessamad.AitElCadi@uphf.fr
Abdelghani.Bekrar@uphf.fr David.Duvivier@uphf.fr
Anwar Sahili
Al-Zahraa Hospital University Medical Center Beirut, Lebanon
anwar.sahili@zhumc.org.lb
ABSTRACT: With the propagation of the Coronavirus (COVID-19), many researchers study the influence of this pandemic on the patient flow at the emergency department (ED). This article proposes a simulation model for the emergency department in the case of COVID-19 period. The model is based on real data. It allows monitoring system performances, like the Length of Stay (LOS), and the percentage of patients that are transferred to another hospital, and it helps managers to face the pandemic. After the validation of the model, we tested studied scenarios depending on the arrival rate and the percentage of patients arriving at the ED with symptoms related to COVID-19. Our contribution is resumed in the evaluation of the preparedness of participation of the studied hospital in the national program in case of an outbreak of Coronavirus (when the rate of arrival is high), so we suggested interventions include beds sharing and adding resources. This leads to minimizing the transfer rate of arrested, critical, and overall cases by 20.06 %, 33.38 %, and 5.98 %, respectively. Besides, the LOS is reduced by 63.43 %. The given results could be presented as recommendations for the managers of the ED to plan the resources in a suitable manner to minimize the transfer of patients in case of outbreaks of this pandemic in the future.
KEYWORDS: Simulation, Healthcare systems, Emergency department, Capacity management, COVID-19.
1 INTRODUCTION
As of March 11, 2020, the world health organization has announced COVID-19 as a pandemic due to the rapid accelerated increase of cases and deaths around the globe, which put a lot of pressure on the national health system (WHO, 2020). In Lebanon, the first confirmed COVID- 19 case was registered on February 21, 2020. Figure 1 shows the evolution of infected patients from February 21, 2020, until May 11, 2020, with cumulative 809 cases, (Epidemiological Surveillance Program, 2020).
An Emergency Department (ED) has to face uncertainty every day; the ED should have sufficient resources to
overcome unexpected injuries and diseases (Weng &
Wang, 2011). In (Aroua & Abdulnour, 2018), two populations were considered: admitted and non-admitted patients. For admitted patients, the improvement of the hospital admission capacity could be addressed by an increase in the number of beds or the creation of an independent unit for patients under observation and short- stay patients. For non-admitted patients, improving the treatment time for patients under observation is the improvement change with the highest influence. In (Gul
& Guneri, 2015a), a comprehensive literature review is carried out to show the ED simulation applications including both normal and disaster conditions. The paper contains studies concerning ED operations published in a global simulation platform-WSC and peer-reviewed journals. Most of the studies are undertaken in the USA, UK, and Canada and include the use of DES (Discrete- Event Simulation) methodology. Many of the studies focus on LOS, resource utilization, the number of patients discharged, and financial analysis as KPIs (Key Performance Indicators). In another study (Gul & Guneri, 2015b) presented a DES model was developed to investigate and analyze an ED under normal conditions and an ED in a disaster scenario which takes into consideration an increased influx of disaster victims- patients. This will allow the early preparedness of emergency departments in terms of physical and human resources. The studied ED is in an earthquake zone in Istanbul. Based on real case study information, the study Figure 1: Evolution of COVID-19 cases in Lebanon
aims to suggest a model on the pre-planning of ED resources for disasters.
The intensive care unit (ICU) beds are considered one of the most important resources for critical care COVID-19 patients. So, (Alban et al., 2020) conducted a simple tool to help estimate the patient throughput rate of COVID-19 patients that can be handled by an ICU unit during the COVID-19 pandemic in a specific geographical region.
The tool is based on queuing theory and simulation. The ICU time is estimated to be similar to the encountered ICU treatment in Wuhan data for COVID-19 patients and Amsterdam ICU data for other patients (Alban et al., 2020). For example, a 60 ICU bed could have a throughput of 5.5 patients/day if there are 6 COVID-19 patients arrive/day where a proportion of the patients are transferred to another hospital due to bed blocking. A simulation study by (IHME COVID-19 health service utilization forecasting team and Murray, 2020) presents a model to study the impact of COVID-19 on hospital bed- days, ICU-days, ventilator-days, and deaths by US state in the next 4 months after the pandemic starting in the US.
At the peak of the pandemic -the second week of April, 2020-, it is estimated that around 64,175 total beds (95%
uncertainty interval (UI) 7,977 to 251,059), while for ICU beds, the estimate is 17,380 (95% UI 2,432 to 57,955), and estimation of ventilator need is 19,481 (95% UI 9,767 to 39,674). The peak is found to differ from state to state and ranges from the second week of April through May.
For deaths, the prediction is to have around 81,114 deaths with 95% UI (38,242 to 162,106). The rate of deaths is expected to reach under 10 deaths per day between May 31 to June 6. SIR (Susceptible, Infectious, or Recovered) and SIS (Susceptible, Infectious, and Susceptible) epidemic models are used to study the propagation of an epidemic (Hugo Falconet & Antoine Jego, 2016). Guinet, A. (2020) proposed a flow model, which calculates by period the people in different disease stages and under medical treatments and which propagates flow of people between periods. To do that, he used a discrete representation of the SIR model over a horizon of six months.
Jee et al. (2020) described the utility of in situ simulation in identifying system errors and latent safety hazards in response to preparation for the expected COVID-19 surge. They also aim to describe the corrective measures taken to improve its outbreak response locally. This result may inspire others to use this information as foresight in preparing their departments for this outbreak. Wu &
McGoogan (2020) Summarize a Report of 72 314 Cases from the Chinese Center for Disease Control and Prevention in mainland China (updated through February 11, 2020). They resumed the key findings from this report and discussed the emerging understanding of and lessons from the COVID-19 epidemic. They showed the distribution of infected cases by age/ spectrum of diseases/ case-fatality rate/ Health care personnel infected. For example, they identified 81% of cases as mild, 14% as moderate, and 5% as critical. We used these
distributions of cases in our model. Williamson et al.
(2020) worked on behalf of NHS England. They, therefore, set out to deliver a secure and pseudonymized analytics platform inside the data center of a major primary care electronic health records vendor establishing coverage across detailed primary care records for a substantial proportion of all patients in England. In their results, they have quantified a range of clinical risk factors for death from COVID-19, some of which were not previously well characterized, in the largest cohort study conducted by any country to date. Docherty et al. (2020) showed that in study participants, mortality was high, independent risk factors were increasing with age, male sex, and chronic comorbidity, including obesity. This study has shown the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks.
Bordehore et al. (2020) aimed to provide an open model that can be customized to any area/region and by any user, allowing them to evaluate the different behavior of the COVID-19 dynamics under different scenarios. They believe that scenarios comparison can be an effective tool to satisfy the society of the need of a huge and extraordinary effort to reduce new infections and eventually, mortalities. Yuan et al. (2020) aimed to report the real-time effective reproduction numbers (R(t)) and case fatality rates (CFR) in Europe. The used data were obtained mainly from the World Health Organization website, up to March 9, 2020. R(t) were estimated by exponential growth rate (EG) and time-dependent (TD) methods. This study provided important findings on the early outbreak of COVID-19 in Europe.
The current study contributes to the literature of resource planning for COVID-19 pandemic targeting different types of patients severity including arrested (having cardiac attack), critical, and moderate. We use simulation to evaluate the pandemic preparedness and discuss the scenarios of resource scheduling in case of outbreaks. In this perspective, we study the planning to receive and treat patients that are suspected as COVID-19 infected cases in Al-Zahraa Hospital University Medical Center (ZHUMC) at Beirut. We aim to evaluate the preparedness of participation of this hospital in the national program in case of an outbreak of Coronavirus. Our main objective is managing the resource in such a way to minimize or avoid the transfer of infected patients to another hospital without treatment, especially the critical and arrested patients.
Another considered performance key is the patient length of stay (LOS).
The remainder of this paper goes as follows. Section 2 presents the proposed workflow for the Corona ED for patients with COVID-19 patients, while section 3 presents the simulation model results for the proposed system and some recommendations for performance improvement.
Finally, section 4 presents conclusions.
2 WORKFLOW IN THE ED OF CORONA Figure 2 shows the planned patient flow for patients arriving at the ED at Al-Zahraa Hospital University Medical Center (ZHUMC) with a focus on the path for patients with COVID-19 symptoms which is moved to the Corona ED unit. The workflow has been identified through multiple interviews with the COVID-19 committee within the ZHUMC which has been formed for preparedness for possible participation in the national program in the “Coronavirus Disease 2019 (COVID- 2019) Health Strategic Preparedness and Response Plan”
developed by the ministry of public health.
The Corona unit consists of three main sections, namely, the main Corona ED with two isolation rooms (beds) and two ventilators, the Corona intensive care unit (ICU Corona) with three ICU beds with three ventilators, and the Corona regular floor with five beds. Besides, there is a triage room with one bed. Arrival rates to the system are assumed to be the total arrival rate of regular patients (patients with symptoms) and the Corona suspected ones (patients with symptoms).
As the patient arrives, he goes by a triage process; a nurse determines his severity level as in standard ED practice.
If the patient is arrested, he would pass the triage and go directly to the Corona ED to have first aid and check if he needs a ventilator. At triage, the patient is classified either mild, moderate, or critical. Different patient classes will have to go through different trajectories in the ED Corona system. If the patient does not show known Covid-19 symptoms, he will be moved to the normal ED, otherwise, he will be routed to the Corona ED. At the Corona ED, mild patients are having the PCR test and are advised to stay isolated at home. They will be informed of the PCR result after that. Around 80% of critical patients will need a ventilator as given from the hospital experts, however, moderate patients usually do not need a ventilator. Critical patients are assumed to have a first aid treatment by a physician and a nurse. It is assumed in the system that the first aid must be performed for patients in need, even though the two isolation beds at the ED unit are busy as this is considered as a lifesaving task. Next, the Corona ED staff will check for available beds patients at the Corona ED either the ICU Corona or Corona regular floor for further treatment. Arrested and critical patients will be assigned to an ICU bed if available, otherwise, they will have the PCR test and wait for the result. If the result is negative, they will be directed to the regular ICU outside the Corona Unit, however, if the result is positive, the patient is transferred to another hospital and would be counted into the number of transferred patients. Moderate patients will be assigned to a Corona regular floor bed, if available. If there is no available bed at the Corona regular floor, an available ICU bed will be used. Otherwise, the moderate corona will also have the PCR test, and depending on the result, they will be admitted to the hospital (regular ICU or regular floor) or transferred to
another hospital and would be counted into the number of transferred patients.
3 SIMULATION MODEL AND RESULTS This section describes the simulation model for the Corona Unit and its process, in addition to the suggested modifications to share beds between the ICU and the regular floor of Corona.
3.1 Simulation model
The simulation model was constructed (using the Rockwell Arena simulator V15) based on a deep understanding of the Corona Unit processes. It mimics the workflow of Figure 2. Figure 3 shows the design of a part of the simulation model (the treatment at ICU Corona).
The whole model encompasses six essential processes (Patient arrival, Triage_T0, First Aid, PCR_Test, ICU treatment, Regular Floor Corona treatment). Each process needs a fixed number of resources, in this model we consider two resources as the most important: beds and ventilators. The flow of patients from one process to another is defined using decision components which are implemented respecting the probabilistic model given from collected data from the Ministry of Public Health in Lebanon. The arrival of the patient is simulated using an entity that follows an exponential distribution, which is effective for modeling the arrival process in such a case.
In the case of an outbreak, around 10% of patients are assumed to be symptomatic (Ministry of Public Health in Lebanon). The different probabilities used in the system, arrival rates, and service times have been inferred from the hospital, government sources related to COVID-19, or from hospital experts.
PCR time is assumed to need around 6 hours. PCR positive rate is assumed to be 21.35% hospitalized symptomatic patients as reported in a recent study (Tang et al., 2020). A similar positivity rate has been reported in a recent study that compares the positivity rate of symptomatic hospitalized and community-based patients.
We run the simulation model for 100 days, 32 hours warm-up period, and 12 replicated times. The warm-up period is set for the simulation run to eliminate any bias at the early stages of the process. The realized model was validated by domain experts and by the supervisors of the committee of Corona at ZHUMC. In this paper, we evaluate a model to allocate resources in the corona unit of ZHUMC that includes three parts: ED Corona, ICU Corona, and Regular floor Corona. So, we built our model using statistical distributions to use them as input parameters for the simulation model (see Table 1).
To monitor the performance of the proposed Corona Unit at ZHUMC, we consider, in the simulation model some key performance indicators. These KPIs are the length of stay of the patients (treatment time, diagnostic time, waiting time) and the percentage of blocking beds (or the
rate of transferred patients in each category of patients:
arrested, critical and moderate). Note that we do not have a transfer state for those who are mild or without symptoms.
3.2 Scenarios
The current study aims to test studied scenarios to schedule the resources in the Corona Unit at ZHUMC and
Figure 2: patient flow in the ED system
to be prepared to face the COVID-19. We consider six scenarios depending on the arrival rate of patients (high:
exponential(10) and low: exponential(60)) and the percentage of patients that arrive at the ED with Corona symptoms (cough, fever...) with three rates: high (10%), medium (5%) and low (1%). The outbreak scenario is represented by a high arrival rate and a high rate of symptomatic patients (10%).
Table 1: Simulation parameters Parameters Probability
Distribution (min) Description Patient arrival
rate
Exponential (10): high rate
Exponential (60): low rate
Time between two patient’s arrivals has a
mean of a variable.
Triage_T0 time Triangular(5, 10, 15)
Duration of triage operation: 5, 10, 15
minutes.
First Aid Triangular(15, 45, 90)
First Aid for critical and arrested patients and 10% of moderate
patients.
PCR_Test 480 Time of PCR Test is 6
hours.
ICU treatment Triangular(20160, 30240, 40320)
ICU Treatment for critical and arrested patients and 10% of moderate patients is Triangular distribution.
Regular Floor Corona treatment
Triangular(10080, 14400, 20160)
Regular Corona Treatment for 90% of
moderate patients is Triangular distribution
3.3 Results and recommendations
In this section, we present the results of the above scenarios with a low/high arrival rate of patients and we consider that the percentage of patients who arrive with symptoms in case of an outbreak of corona is about 10%
(Ministry of Public Health, Lebanon, 2020).
Table 2 shows the simulation results for the studied scenarios in terms of the average LOS and the transfer rate percentage. It is important to note that the reported
transfer rate is calculated relative to the total number of arrived patients in each category, namely: arrested, critical, and moderate. The total transfer rate is calculated relative to the total number of symptomatic patients which include mild patients too. It can be seen that the LOS and transfer rate is increasing when the arrival rate and symptomatic rate increase. It is shown in table 2 that, the rate of transfer of arrested, critical, and moderate patients ranges from 8.22% to 18.94%, from 6.6% to 20.19%, and from 0% to 7.66% as the arrival and symptomatic rate progresses, respectively. For a fixed arrival rate, the total transfer rate is shown to be decreasing with increasing symptoms rate which could be attributed to that large portion of total patients are mild which do not contribute to transfer patients. The LOS is increasing with the rate of symptomatic patient, it passes from 128 min to 180 min in case of low arrival, and from 191 min to 227 min in case of high arrival rate.
Table 2: Simulation results
Arrival rate Symptomatic rate (%) LOS (min)
Transfer rate (%)
Arrested Critical Moderate Total
Low
1 128 8.22 24.81 0 6.52
5 151 9 6.6 0 3.63
10 180 9.16 8.2 0 2.61
High
1 191 17.99 16.98 0 13.91
5 223 18.12 19 1.38 7.75
10 227 18.94 20.19 7.66 6.19
The transfer rate is qualified as very high by the hospital management (with the evaluation of Corona committee) and requires a procedure to solve this problem. Besides, the transfer of a critical or arrested patient to another hospital could be dangerous for his life (if we have a disaster case or outbreak). Therefore, it is important to improve the proposed model by some adjustments like the idea of shared beds and the increase of the number of some resources as discussed in the following section.
3.4 Improvements
We modify the model by the use of shared beds (between ICU and Regular Corona Floor) to show the efficiency of this proposal in case of the Outbreak of Corona to Figure 3: Simulation sub-model of the ICU corona treatment
under the Rockwell Arena
minimize the percentage of transferred patients. After analyzing the report of this model, we show that the waiting time at triage is high (134 min as average).
Therefore, we propose another scenario by adding a nurse and a bed in the triage room of this model.
Table 3 presents the improvements obtained by applying the two scenarios. It shows the LOS and transfer rate for the worst scenario named baseline (high arrival rate and high symptomatic rate) and the results of two proposed scenarios. Also, it shows the percentage of change in the performance measures in the two scenarios relative to the tested improvement scenarios.
Table 3 : Results of improvements adjustments
Scenario LOS in minutes (% of improvements) Transfer rate
(% of improvements)
Arrested Critical Moderate Total
Baseline
227 18.94 20.34 7.66 6.19
1st scenario
212 (-6.6)
15.46 (-18.3)
15.5 (-23.7)
16.68 (+117.7)
6.28 (-1.4)
2nd scenario 83
(- 63.4)
15.14 (-20.0)
13.55 (-33.3)
15.15 (+97.7)
5.82 (-5.98)
In the first scenario, we share the beds between ICU Corona (three beds) and Regular Corona floor (five beds) to obtain a Corona zone treatment to receive critical, arrested, and moderate patients. In this step, we improved the total transfer rate (from 6.19% in the baseline to 6.28%
and 5.82% in the first and the second scenario respectively) and we decreased the average patient LOS from 227 to 212 min. The transfer rate has decreased from 18.94% to 15.46% and from 20.34% to 15.5% for arrested and critical patients, respectively. Notably, the transfer rate for moderate cases has increased from 7.66% to 16.68% due to the shared beds for all patients types.
The second scenario includes the same bed sharing policy as in scenario 1, in addition to adding one more nurse and one bed at the triage process. It is shown that average LOS
has dropped from 212 to 83 minutes with a 63.4%
reduction. In addition, the rate of transfer from total patients has decreased by 5.98% relative to the baseline scenario. With relative to the first scenario, the transfer rate has decreased from 15.46% to 15.14%, from 15.50%
to 13.55%, and from 16.68% to 15.15% for arrested and critical, and moderate patients, respectively.
4 CONCLUSIONS AND PERSPECTIVES
This article presents a simulation study for ED at ZHUMC (Lebanon) in the case of disasters like the pandemic of Corona. After the validation of the simulation model by the experts, the validated simulation is used to test two scenarios of sharing beds and adding resources (nurse and bed in triage process) and their effects on LOS and the percentage of transferred patients.
After the validation of the model, we change the arrival rate and the percentage of symptomatic patients. The tested scenario with a high arrival rate and a 10%
symptomatic rate is considered the baseline scenario (worst case or outbreak). This scenario is characterized by excessive waiting time at the triage and high transfer rate of arrested and critical patients.
We show potential improvements, considering sharing beds and adding resources to the triage process. The transfer rate for critical, arrested, and overall cases could be reduced by 20%, 33.3%, and 5.98% respectively. This could have a significant impact on patients’ lives. Also, the average LOS could be reduced by 63.4% by improving access to the ED Corona.
We are working on the extension of this work to study the evolution of cases (from moderate to critical for example).
Future work would include the use of other important resources like the doctor and the nurse in the treatment process, especially that these treatments need a lot of time (between 7 and 28 days). Besides, we aim to study the propagation algorithm of epidemic viruses like Corona using popular algorithms such as SIR and SIS epidemic models.
5 REFERENCES
Alban, A., S. E. Chick, D. A. Dongelmans, A. P. J. Vlaar, D. Sent, A. F. van der Sluijs, & W. J. Wiersinga (2020). ICU capacity management during the COVID-19 pandemic using a process simulation, Intensive Care Medicine, 2020, 7–9.
Aroua, A., & G. Abdulnour (2018). Optimization of the emergency department in hospitals using simulation and experimental design: Case study, Procedia Manufacturing, 2018, 17, 878–885.
Bordehore, C., Navarro, M., Herrador, Z., & Fonfria, E.
S. (2020). Understanding COVID-19 spreading
through simulation modeling and scenarios comparison: preliminary results. MedRxiv, (2020).
https://doi.org/10.1101/2020.03.30.20047043 Docherty, A. B., Harrison, E. M., Green, C. A., Hardwick,
H. E., Pius, R., Norman, L., Holden, K. A., Read, J.
M., Dondelinger, F., Carson, G., Merson, L., Lee, J., Plotkin, D., Sigfrid, L., Halpin, S., Jackson, C., Gamble, C., Horby, P. W., Nguyen-Van-Tam, J. S., Dunning, J., Openshaw, P. JM., Baillie, J. K., &
Semple, M. G, (2020). Features of 16,749 hospitalised UK patients with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol. MedRxiv, (2020).
2020.04.23.20076042.
https://doi.org/10.1101/2020.04.23.20076042 Epidemiological Surveillance Program. (2020). COVID-
19 Coronavirus Lebanon Cases. Retrieved June 13, 2020, from https://www.moph.gov.lb/maps/COVID- 19.php
Guinet, A. (2020). A modelling of COVID-19 outbreak with a linear compartmental model To cite this version: HAL Id: hal-02624165.
Gul, M., & A. F. Guneri (2015). A comprehensive review of emergency department simulation applications for normal and disaster conditions, Computers and Industrial Engineering, 2015a, 83, 327–344.
Gul, M., & A. F. Guneri (2015). Simulation modelling of a patient surge in an emergency department under disaster conditions, Croatian Operational Research Review, 2015b, 6:2, 429–443.
Hugo Falconet, & Antoine Jego (2016). Modéliser la propagation d’une épidémie. Interstice. Info.
Retrieved from https://www.fondation- lamap.org/fr/page/35700/epidemie-recherche-4- eclairages-scientifiques
IHME COVID-19 health service utilization forecasting team, & Murray, C. J. (2020). Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator- days and deaths by US state in the next 4 months.
MedRxiv, 114, 2020.03.27.20043752.
https://doi.org/10.1101/2020.03.27.20043752 Jee, M., Khamoudes, D., Brennan, A. M., & O’Donnell,
J. (2020). COVID-19 Outbreak Response for an Emergency Department Using In Situ Simulation.
Cureus. https://doi.org/10.7759/cureus.7876
Ministry of Public Health, L. (2020). Coronavirus Disease 2019 (COVID-2019) Health Strategic Preparedness and Response Plan. Retrieved from https://www.moph.gov.lb/userfiles/files/News/Leb
nCoV Strategic Response Plan MARCH 2020- converted.pdf
Tang, J. W., Young, S., May, S., Bird, P., Bron, J., Mohamedanif, T., Bradley, C., Patel, D., Holmes, C.
W., & Kwok, K. O. (2020). Comparing hospitalised, community and staff COVID-19 infection rates during the early phase of the evolving COVID-19 epidemic.
Journal of Infection.
https://doi.org/https://doi.org/10.1016/j.jinf.2020.05.0 29
Yuan, J., Li, M., Lv, G., & Lu, Z. K. (2020). Monitoring transmissibility and mortality of COVID-19 in Europe. International Journal of Infectious Diseases,
95, 311–315.
https://doi.org/10.1016/j.ijid.2020.03.050
