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. 2021 May 17;16(5):e0251295. doi: 10.1371/journal.pone.0251295

Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients

Daniel Garzon-Chavez 1,*, Daniel Romero-Alvarez 2,3,‡, Marco Bonifaz 4,#, Juan Gaviria 4,#, Daniel Mero 4,#, Narcisa Gunsha 4,#, Asiris Perez 4,#, María Garcia 4,#, Hugo Espejo 4,#, Franklin Espinosa 4,#, Edison Ligña 4,#, Mauricio Espinel 5,#, Emmanuelle Quentin 6,‡, Enrique Teran 1,‡, Francisco Mora 4,#, Jorge Reyes 4,7,*
Editor: Adriana Calderaro8
PMCID: PMC8128267  PMID: 33999930

Abstract

The World Health Organization (WHO) declared coronavirus disease-2019 (COVID-19) a global pandemic on 11 March 2020. In Ecuador, the first case of COVID-19 was recorded on 29 February 2020. Despite efforts to control its spread, SARS-CoV-2 overran the Ecuadorian public health system, which became one of the most affected in Latin America on 24 April 2020. The Hospital General del Sur de Quito (HGSQ) had to transition from a general to a specific COVID-19 health center in a short period of time to fulfill the health demand from patients with respiratory afflictions. Here, we summarized the implementations applied in the HGSQ to become a COVID-19 exclusive hospital, including the rearrangement of hospital rooms and a triage strategy based on a severity score calculated through an artificial intelligence (AI)-assisted chest computed tomography (CT). Moreover, we present clinical, epidemiological, and laboratory data from 75 laboratory tested COVID-19 patients, which represent the first outbreak of Quito city. The majority of patients were male with a median age of 50 years. We found differences in laboratory parameters between intensive care unit (ICU) and non-ICU cases considering C-reactive protein, lactate dehydrogenase, and lymphocytes. Sensitivity and specificity of the AI-assisted chest CT were 21.4% and 66.7%, respectively, when considering a score >70%; regardless, this system became a cornerstone of hospital triage due to the lack of RT-PCR testing and timely results. If health workers act as vectors of SARS-CoV-2 at their domiciles, they can seed outbreaks that might put 1,879,047 people at risk of infection within 15 km around the hospital. Despite our limited sample size, the information presented can be used as a local example that might aid future responses in low and middle-income countries facing respiratory transmitted epidemics.

Introduction

Originally announced as a pneumonia of an unknown etiology in China [1], the coronavirus disease (COVID-19) had amassed around four million cases worldwide by mid-May 2020 [2, 3]. Ecuadorian implementations to control COVID-19 are relevant as a case study considering its fragmented public health system [4, 5] and the heterogeneous evolution of the pandemic in its different administrative units (i.e., provinces). For instance, two of its main cities, Quito and Guayaquil, applied initial recommended control measures at different times: Guayaquil banned mass gatherings and implemented strict isolation around two weeks later than Quito [6, 7].

The first case of COVID-19 in Ecuador was reported on 29 February 2020 [8]. By 7 April 2020, Ecuador centralized real-time reverse-transcriptase polymerase chain reaction (RT-PCR) testing for SARS-Cov-2 diagnosis in the National Institute of Public Health (INSPI, Spanish). As a consequence, reports were dependent on availability of resources and infrastructure, highly biasing the official case counts [9], misreporting cases, and retaining data due to the lack of processing capacities (S1 Fig; [10]). An initiative to decentralized testing led by university laboratories suggested in 12 March 2020 had the potential to increase RT-PCR diagnosis availability [11], although at 15 April 2020 it was still on plans of implementation [12].

Due to the limitations on testing capacity, on-site approaches for patient triage have been suggested and actively explored using chest computed tomography (CT) and even pure clinical approaches [13, 14]. Chest CT is performed on the majority of hospitalized patients with COVID-19 with main findings including the presence of ground glass opacities (GGO) [15–17]. At least 20% of non-severe COVID-19 infections have shown lack of changes in chest CT scans, while only 3% of severe patients presented normal CTs [15]. Thus, the role of CT in severity screening and diagnosis has been evaluated thoroughly in different parts of the world and recommended as an essential part of COVID-19 diagnosis in different guidelines [18–21].

The Hospital General del Sur de Quito (HGSQ, in Spanish) was inaugurated on 5 December 2017 as a center with 450 beds, providing secondary healthcare with capabilities to solve third level health related complexities. The HGSQ provided medical care to an average of 20,000 monthly patients and performed 1,104 annual surgeries. On 14 February 2020, the hospital was designated to become a COVID-19 specific treatment center. Because this nomination implied that suspected and diagnosed cases of COVID-19 were to be attended exclusively by HGSQ personnel, measures assuring a safe environment for patients and health workers were developed and implemented in a constrained schedule.

Hospital transition to a COVID-19 specific health center

Triage strategies based on Artificial Intelligence (AI)-assisted chest computer tomography (CT)

A pandemic event represents a unique challenge for a hospital response, which should mainly focus on preserving the biosafety of patients and health workers, avoid nosocomial infections, and managing typical diseases and chronic patients [22]. As such, the HGSQ took the advantage of an innovative triage approach based on non-contrast chest CT scans to stratify COVID-19 suspected patients according to an artificial intelligence (AI) scoring system. The software was developed as part of the Huawei Cloud AI services [23] and was implemented for the first time in Latin America for the HGSQ Radiology Department [24].

By calculating internal metrics comparing the predicted lesions from a trained AI (see Methods), with the actual lesions from the CT scan [25], the AI-aided assisted CT screening provided a score that categorized patients in three classes: non-severe (score of 0–30%), moderately severe (30–70%), and severe (>70%), considering the likelihood of being COVID-19 positive in relation with the severity of radiological findings on chest CT [23, 26, 27]. A medical radiologist examined and confirmed these severity scores. Depending on this categorization, patients were distributed in different ‘score rooms’ across three hospital towers, with a specific flow to prevent the spread of the virus (Fig 1). Similar AI-based approaches for imaging recognition on chest CTs have been deployed in China with contrasting results for COVID-19 diagnosis, the majority of them still requiring further evaluation [19, 28, 29].

Fig 1. Schematic representation of the Hospital General del Sur de Quito (HGSQ) highlighting specific COVID-19 designated areas.

Fig 1

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The initial distribution of areas dedicated to COVID-19 patients (i.e., score rooms in red; A) was complemented with newer areas to categorize confirmed, suspected, and negative COVID-19 patients based on laboratory diagnosis and a computer tomography (CT) assisted severity score (B; see text). Different red shading represents areas of high and low COVID-19 transmission risk. Arrows (black) summarize the available patient flow around the hospital with only one elevator available during the first time period (A) and two during the second (B). Areas in grey shading were disabled in order to control movement of personnel. The different floors of the hospital are labeled as F0-F6 in both panels. ICU = intensive care unit; G/O = gynecology and obstetrics; Admin. = administrative offices. CT scan = computer tomography scan.

Hospital distribution for COVID-19 patient attendance

Redistribution of hospital areas for COVID-19 attendance occurred in two phases and was guided mainly by the severity scores provided by the AI assisted chest CT scores, due to the difficulties to obtain timely of RT-PCR testing. The Infection Control Unit designated ‘score rooms’ for different COVID-19 diagnostic categories (Fig 1). Cases with a higher probability of COVID-19 (score >70%) were hosted at Clinic Area 1, patients with moderate probability (score 30–70%) were concentrated at Surgery Area 1. Lastly, patients with a score less than 30%, and therefore a lower likelihood of COVID-19 positivity, were located in Surgery Area 2 and 3 in common rooms separated by gender (Fig 1A). Hospital personnel prevented the CT machine to act as a fomite source of SARS-CoV-2 transmission using disinfection based on conventional cleaning followed by pulsed-xenon ultraviolet room sterilization [30, 31]. A further reorganization was put in place once confirmatory RT-PCR tests became available (Fig 1B), however AI-assisted chest CT scans remained the main approach driving triage considering the lag of laboratory-based testing results, and their application only on patients with higher suspicion of infection. At that point, two elevators were available for patient transportation (Fig 1B). Molecularly confirmed SARS-CoV-2 patients were located exclusively in Clinic Area 1. Surgery Area 1 became exclusively for patients with a score above 70%, surgery area 2 became exclusively for patients with a 30–70% score, as it was Clinic Area 2. Therefore, Clinic and Surgery areas 1 and 2 became SARS-CoV-2 exclusive wards for laboratory confirmed diagnosis and CT-scores >70%. Moreover, other areas of the hospital were accommodated to host patients with a moderate AI-CT score COVID-19 likelihood of infection, including the Gynecology and Obstetrics area, for patients with a score between 30–70% and the Surgery Area 3, divided in two sections: one for patients with a score less than 30% and the other for negative RT-PCR patients in the path of discharge (Fig 1B).

‘Virus in movement’ protocol

To complement the triage approach, from 10 to 28 March 2020, the Unit of Infection Control, in coordination with multiple departments at the HGSQ, developed the ‘Virus in Movement (VM)’ protocol for COVID-19 crisis management. Every time that a patient suspected of COVID-19 needed transit to any area of the hospital, the VM alert was activated and triggered three coordinated steps: (a) evacuation of all health personnel and patients from the movement areas (e.g., corridors, halls, etc), (b) blocking doors to prevent people transit, and (c) designation of exclusive elevators (Fig 1A and 1B). After movement, areas occupied by suspected/confirmed patients were cleaned with pulsed-xenon ultraviolet room disinfection according to Jinadatha et al. (2015) and Kovach et al. (2017) [30, 31]. The average duration of the implementation of the full VM protocol was about 1 hour. During the next 18 days of transition, a total of 75 inpatients were transferred to different portions of the hospital a total of 432 times. No health care workers or other personnel transiting within the hospital were infected with SARS-CoV-2 during this period.

In the remainder of the manuscript, we quantify the ability of the AI-assisted chest CT for patient screening and describe the socio-demographic and clinical characteristics of the first 75 patients tested and treated for SARS-CoV-2 at the HGSQ, which represent the first outbreak of Quito city. Moreover, due to the high risk of asymptomatic carriers and super-spreader events associated with coronavirus infections [17, 32, 33], we plotted the geographic distribution of 126 health care workers attending COVID-19 cases at the HGSQ, plus 54 laboratory tested COVID-19 cases, to suggest how epidemic surveillance might be organized via geographical information systems (GIS) in Quito, Ecuador.

Methods

Ethic statement

This study was reviewed and approved by the Institutional Review Board at Universidad San Francisco de Quito (2020-023M). Information from patients was anonymized before analysis. Patients offered their oral consent for gathering demographic data. Health-care personnel followed intra-hospital guidelines to fill information forms considering their home addresses according to HGSQ policies.

Artificial intelligent (AI)-assisted chest computed tomography (CT) screening for patients’ triage

The Huawei Cloud AI-assisted CT diagnosis software can be described as a deep-learning neuronal network approach for automated medical image segmentation for identification of abnormalities on chest CTs [34, 35], it was released on 17 March 2020 and uses MindSpore as its AI deep-learning algorithm framework, which was developed entirely by Huawei [36, 37]. For calibration purposes, the AI has been trained with ~4,000 chest CT images from confirmed positive COVID-19 cases from China [23]. Scanned CT images were uploaded to the HGSQ picture archiving and communication system (PAC) and then examined with the Huawei AI to detect the presence of GGOs and lung consolidations [23, 25].

Information regarding the Huawei AI-assisted CT screening COVID-19 score system for patient categorization was lacking, with absence of specific technical details about image categorization; thus, we had to implement the system as a black box. We calculated sensitivity and specificity indexes for the Huawei AI-assisted CT screening tool to correctly identify cases as highly likely to be COVID-19 positives (i.e., score >70%) as confirmed by molecular diagnosis via RT-PCR.

Clinical and epidemiological characteristics of COVID-19 patients

Epidemiological (e.g., hospitalization time, risk factors, source of infection), clinical (e.g., symptoms and signs), laboratory data and drug treatment schemes, were recovered from medical records of hospitalized patients with respiratory symptoms above 18 years old with a RT-PCR test for SARS-CoV-2. We collected data from patients admitted in the HGSQ between 10 to 28 March 2020.

Spatial distribution of households of patients and health personnel

Health personnel attending inpatients with presumptive or confirmed diagnosis of COVID-19 at the HGSQ completed online forms disclosing attending time, home address, and use of personal protective equipment (PPE) on a daily basis. This was required due to the risk of health workers spreading COVID-19 to other hospitalized patients or the community as asymptomatic SARS-CoV-2 carriers, and the lack of reliable testing for antibody detection [32, 33, 38]. Health workers with symptoms related to COVID-19 were treated by the Department of Occupational Medicine and immediately notified to the Infection Control Unit, to suspend their activities during 14 days after symptom resolution and RT-PCR negative tests as suggested by different guidelines [20, 21, 39].

We used the information on these forms to identify potential clusters of COVID-19 surveillance outside hospital settings by georeferencing addresses of health workers using Google Maps (https://www.google.com/maps/), calculating the distance to the hospital (i.e., HGSQ), and estimating the amount of people at risk of infection using the 2010–2020 population projections from the official Ecuadorian census (https://www.ecuadorencifras.gob.ec/proyecciones-poblacionales/). We built three buffers of 0–5, 5–10, and 10–15 km distances centered at the hospital and calculated the number of people at risk within each buffer boundary. Distance calculation and population at risk was calculated using TerrSet (version 18.39; https://clarklabs.org/terrset/). Coordinates and results of this analysis were plotted in maps using QGIS (3.4 Madeira; https://qgis.org/es/site/forusers/download).

Results

Efficacy of the AI-assisted chest CTs for COVID-19 triage

We obtained chest CTs for 75 patients with laboratory confirmed SARS-CoV-2 diagnosis (Table 1). Images showed that the distribution of the GGOs in the lungs were most peripheral (30/61, 49.18%) than central, the latter detected in 21 of the 61 laboratory-confirmed positive cases (34.43%). Bilateral lesions were predominant. Five laboratory-confirmed infected patients showed an absence of GGOs patterns in the lungs (8.2%). Seven SARS-CoV-2 negative tested cases, showed peripheral GGOs lesions (7/14, 50%) while four showed central GGOs (4/14, 28.57%).

Table 1. General characteristics, risk factors, and symptoms/signs of the first laboratory tested COVID-19 cases (n = 75) attended in the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador—March 2020.

Patient characteristics Positive (%) Negative (%) Totals (%)
RT-PCR confirmed 61 (81.3) 14 (18.7) 75 (100)*
Median age (range) 50 (25–76) 49 (27–77) 50 (25–77)
Male 39 (63.9) 8 (57.1) 47 (62.7)
Female 22 (36.1) 6 (42.9) 28 (37.3)
ICU 14 (23) 4 (28.6) 18 (24)
 • ICU male 11 (18.1) 2 (14.3) 13 (17.3)
 • ICU female 3 (4.9) 2 (14.3) 5 (6.7)
Non-ICU 47 (77) 10 (71.4) 57 (76)
 • Non-ICU male 28 (45.9) 6 (42.9) 34 (45.3)
 • Non-ICU female 19 (31.1) 4 (28.5) 23 (30.7)
Risk factors
Contact with European tourist 3 (4.9) 1 (7.1) 4 (5.3)
Contact with COVID-19 case 10 (16.4) 4 (28.6) 14 (18.6)
Unknown contact 14 (23) 6 (42.9) 20 (26.6)
Travel to Guayaquil 26 (42.6) 3 (21.4) 29 (38.6)
Travel to other Ecuadorian cities 5 (8.2) 0 (0) 5 (6.6)
Travel to European countries 2 (3.3) 0 (0) 2 (2.6)
Health worker 1 (1.6) 0 (0) 1 (1.3)
Symptoms/signs
Fever 59 (96.7) 10 (71.4) 69 (92)
Cough 48 (78.7) 13 (92.9) 61 (81.3)
Odynophagia 36 (54) 5 (35.7) 41 (54.7)
Headache 13 (21.3) 2 (14.3) 15 (20)
Diarrhea 12 (19.7) 1 (7.1) 13 (17.3)
Vomiting 5 (8.2) 0 (0) 5 (6.7)
Anosmia 4 (6.6) 1 (7.1) 5 (6.7)
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Cases categorized as positive/negative by laboratory testing (i.e., RT-PCR) from 10 to 28 March 2020.

*Percentages from the first row are calculated in relation to the total cases (n = 75). Percentages of the following rows are calculated with positives, negatives, and totals from the first row, respectively.

We obtained severity scores for 37 laboratory-tested patients (49.3%). Sensitivity corresponded to 21.4% and specificity to 66.7% when considering the likelihood to classify a patient as COVID-19 positive with a score over 70% (S1 Table). Thus, 7/28 positive and 3/9 negative laboratory-tested cases (n = 10) were allocated in 70% score rooms; 10/20 positive and 1/9 negative laboratory-tested cases (n = 11), in score rooms for the 30–70% category; and 11/28 positive and 5/9 negative laboratory-tested cases (n = 16) were allocated in rooms for scores less 30%.

Epidemiological characteristics and description of clinical cases

At the moment of data collection, we have attended 2,590 patients with respiratory symptoms, 93 with potential SARS-CoV-2 infection (i.e., 3.5% prevalence) and 18 deaths. We present clinical, epidemiological, and laboratory data, together with treatment schemes for 61 patients with confirmed SARS-CoV-2 infection and 14 patients with a negative test but COVID-19 suspected (n = 75; Tables 1–4). The overall age of patients is 50 years old with a male majority (male/female ratio: 47/28 = 1.67; Table 1).

Table 4. Treatment schemes applied to laboratory confirmed COVID-19 cases (n = 75) by Intensive Care Unit (ICU) admission status in the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador—March 2020.

Therapy Laboratory confirmed positive (n = 61) Laboratory confirmed negative (n = 14) Totals (positive+ negative, n = 75) Hospitalized (n = 28) Hospital discharged (n = 47)
ICU
AZT + CEF + CLOR 1 - 1 1 -
AZT + CEF + CLOR + LOP/RITO + OSEL 3 - 3 3 -
AZ + CLOR + LOP/RITO + OSEL + PIP/TAZ** 8 4 12 7** 2
CLA + CLOR + LOP/RITO + PIP/TAZ 2 - 2 2 -
Totals 14 (22.9%) 4 (28.6%) 18 (24%) 13+3** = 16 2
Non-ICU
AZT + AMOX/CLA + CLOR 1 - 1 - 1
AZT + CEF + CLOR 38 8 46 10 36
AZT + CLOR + LEV 2 - 2 - 2
AZT + CEF + CLOR + LOP/RITO 3 - 3 - 3
AZT + CEF + CLOR + LOP/RITO + OSEL 1 - 1 1 -
AZT + CLOR + LOP/RITO + MER + OSEL 1 - 1 1 -
AZT + CLOR + LOP/RITO + OSEL + PIP/TAZ 1 - 1 - 1
CEF + CLA - 1 1 - 1
LEVO - 1 1 - 1
Totals 47 (%) 10 (%) 57 (%) 12 45
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Outputs considered up to the end of data collection for intensive care unit (ICU; n = 14) or non-ICU (n = 47) cases that remained in the hospital or were discharged.

Percentages are calculated in relation to the total number of laboratory confirmed cases (n = 75).

AMOX/CLA = Amoxicilin/Clavulanic Acid, AZT = Azithromicyn, CEF = Ceftriaxone, CLAR = Clarithromycin, CLOR = Chloroquine, LEVO = Levofloxacin, LOP/RIT = Lopinavir/Ritonavir, MER = Meropenem OSEL = Oseltamivir, PIP/TAZ = Piperaciline/Tazobactam.

**Three patients with this treatment scheme died.

From the patients with a positive laboratory test, 42.6% (n = 26) reported having traveled to Guayaquil, the city with more cases of COVID-19 in Ecuador during the studied period [9] (Table 1). Moreover, 16.4% (n = 10) laboratory confirmed positive cases reported having a history of close contact with known COVID-19 patients (Table 1). In general, fever, cough, and odynophagia were the most prevalent symptoms, while anosmia was the least common (Table 1). At least ten negative patients also had fever (71.4%) and 13 presented cough (92.9%). One of the negative cases also referred anosmia (7.1%). The average number of days from onset of respiratory symptomatology until hospital attention was eight days (ranging from zero to 20 days).

All cases admitted to the intensive care unit (ICU) presented values of C-reactive protein (CRP) above 10 mg/L and LDH above 250 UI/L. Median values from non-ICU patients also presented higher values for LDH and borderline values of CRP (Table 2). From laboratory positive SARS-CoV-2 cases, those admitted at the ICU presented elevated values of D-dimer than those attended outside the ICU; in both classes the median was higher than normal. The median value for levels of transaminases AST and ALT was elevated only in ICU patients (Table 2).

Table 2. Blood chemical values for laboratory-confirmed COVID-19 cases (n = 61) by Intensive Care Unit (ICU) admission status in the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador—March 2020.

ICU/Non-ICU D-Dimer (0–0.5 ng (EUF)/mL) CPK (<25 UI/L) LDH (125–243 UI/L) CRP (<3 mg/L) PCT (<0.05 ng/mL) CR (<1.2 mg/dL) ALT (9–50 UI/L) AST (15–40 UI/L)
ICU (n = 14) 333 (0.09–7567) 205.5 (35–822) 481.5 (130–917) 202 (88.7–395) 0.43 (0.06–13.9) 0.88 (0.5–2.85) 62 (17–95) 59 (21–117)
Non-ICU (n = 47) 155 (0.11–946 68 (16–898) 272.5 (87–1143) 48 (2.1–211) 0.05 (0.01–0.59) 0.87 (0.53–2.37) 43.5 (10–240) 34 (12–106)
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Data collected from 10 to 28 March 2020. Normal ranges of each laboratory parameter are shown in parenthesis in the headers of each column.

Values of each cell represent medians. Ranges are depicted in parenthesis.

CPK = Creatine phosphokinase, LDH = Lactate dehydrogenase, CRP = C-reactive protein, PCT = Procalcitotin, CR = Creatinine; ALT = Alanine transaminase, AST = Aspartate transaminase.

Procalcitonin was higher in patients admitted to the ICU with lesser values on patients attended outside this unit. Creatinine was within normal range for both ICU and non-ICU cases (Table 2). One case at ICU presented Candida spp. in trachea, other patients in the ICU with normal values of procalcitonin presented Klebsiella pneumoniae and Pseudomonas aeruginosa-antibiotic-sensitive in blood, and Candida spp. in urine. Leucocytes and platelet counts for both case categories were within normal values, but lymphocytes were lower for ICU patients (Table 3).

Table 3. Leucocyte, lymphocyte, and platelet counts for laboratory confirmed (n = 61) COVID-19 cases by Intensive Care Unit (ICU) admission status in the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador—March 2020.

ICU/Non-ICU Leucocytes (3,500–9,500/ml) Lymphocytes (1,100–3,200/ml) Lymphocytes (20–40%) Platelets count (125,000–350,000/ml)
ICU (n = 14) 8,900 (4,800–15,000) 1,000 (500–1,800) 11 (4.2–21) 183,000 (145,000–277,000)
Non-ICU (n = 47) 5,700 (1,900–13,300) 1,440 (500–2,490) 22.5 (8.9–61.7) 210,000 (114,000–452,000)
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Data collected from 10 to 28 March 2020. Normal ranges of each laboratory parameter are shown in the headers of each column.

Values of each cell represent medians. Ranges are depicted in parenthesis.

Only two laboratory confirmed negative patients received treatments without chloroquine (Table 4). Most schemes were associated with beta-lactam antibiotics such as ceftriaxone, meropenem, or piperacillin/tazobactam (Table 4). A total of 45/75 patients were discharged. The average hospital stay was of 10 days (range = 8–20 days); these patients received the scheme based on azithromycin, ceftriaxone, and chloroquine for 7 days (Table 4).

Health personnel and COVID-19 cases domiciliary georeferencing

The first COVID-19 case had contact with at least 51 health workers. Our first ten patients had contact with a median of 15 health workers (range = 9–51). After the transition to a COVID-19 exclusive hospital, we reduced contact to a mean of five health personnel (range = 3–7) as those taking care of COVID-19 patients. By georeferencing domiciles of health workers, we identified that 43 live between 0–5 km from the hospital in an urban area with 783,291 inhabitants. Similarly, 39 health workers lived around 5–10 km from the hospital in an area with 647,655 inhabitants. Finally, 22 health workers lived between 10–15 km around the hospital in an area with 448,101 inhabitants, for a total of 1,879,047 people in risk within 15 km around the hospital. The majority of health workers came from the south and northernmost metropolitan area. By this analysis, we also identified that some health workers (n = 4) traveled up to 85 km to get to the HGSQ, crossing other provinces including Imbabura to the north and Cotopaxi to the south (Fig 2).

Fig 2. Distribution of 54 COVID-19 cases and 126 health workers from the Hospital General del Sur de Quito (HGSQ), in Quito, Ecuador.

Fig 2

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Buffers represent 0–5, 5–10, and 10–15 km distances around the HGSQ (red cross) and encompass the amount of population living in those areas according to Ecuadorian estimates for 2020. (A) Some health care workers (orange circles) traveled across different provinces to the HGSQ. For the date of data collection, COVID-19 cases (blue triangles) came from different regions of the entire metropolitan zone of Quito municipality (B).

Domicile georeferencing was completed for 54 out of 75 (72%) patients with COVID-19 positive diagnosis. At the beginning of the epidemic, the majority of patients came from the south of Quito city. The geographic distribution of cases changed once the hospital became exclusive for COVID-19, with cases broadening to encompass the whole Quito metropolitan area and its surroundings including one case arriving from the southern province of Cotopaxi (Fig 2).

Discussion

Ecuador faced the current COVID-19 pandemic following WHO recommendations [8], however, despite being supported by the central government, main cities managed their health crisis differently. Despite the National Emergency Operations Committee (COE, Spanish) recommended social isolation and stopping all mobility on 16 March 2020 [6], Guayaquil had an underemployed working population of 16.2% [40] with urbanistic features (e.g. slums and lack of water provision) that complicated the implementation of these measures [41]. Quito stopped all face-to-face academic activities earlier than the COE recommendations (12 March 2020; [42, 43]), and followed guidelines of constrained mobility and mass gatherings strictly, buying the HGSQ time to develop the aforementioned approaches, which effectively allowed it to transit to a COVID-19 exclusive attention center. In the hospital, strategies of management, including change in distribution of suspected COVID-19 patients (Fig 1), has allowed us to halt the high incidence of nosocomial infections reported elsewhere [17], which remained in zero until data collection (28 March 2020).

This is the first time that the Huawei Cloud AI-assisted CT screening for COVID-19 is assessed as a categorization tool in Ecuador at the HGSQ, which received the software for free [27]. The same tool has been deployed for hospitals in other countries including China, Malaysia, and the Philippines [44, 45]. For our particular case, the tool was used for initial triage of suspected patients due to the lag between RT-PCR testing and result availability (range 28–120 hours), concentrating all efforts to stop a potential COVID-19 nosocomial outbreak. However, even for the most severe cases, with a higher likelihood of COVID-19 infection, we obtained unacceptable test accuracy values for sensitivity and specificity (i.e., 21.4% and 66.7% respectively). This has two immediate consequences, first, from a research perspective, the performance of the AI-assisted CT screening for COVID-19 using Huawei technology is too poor to recommend, something that has also been cautioned by the Philippine College of Radiology [46]. Second, from a pragmatic perspective, the HGSQ continue to rely on this method for patient triage because it relieves a health care bottleneck in the current burgeoning epidemic: patients can be allocated to specific areas depending on the severity score to prevent COVID-19 spread (Fig 1), a crucial endeavor considering the potential correlation between increasing health burden and mortality [47].

In order to fully test the ability of this AI-based screening system, a strict study design should evaluate both laboratory confirmed positive and negative cases [15, 18, 19, 48]. We currently lack information from the latter since we cannot justify using the unique CT facilities of the hospital to expose non-suspected patients to infection. Nevertheless, during the development of this study, zero cases have been associated with exposure of patients or health workers to the CT area, which shows preliminary evidence that the routine cleaning plus pulsed-xenon ultraviolet disinfection approaches, effectively prevent the CT to become a fomite source of SARS-CoV-2 contagion [30, 31]. It is important to emphasize that the Huawei Cloud AI-assisted CT screening software was donated freely to the HGSQ and we took the advantage to scientifically evaluate it. Currently, there are many other AI-medical image processors that have been deployed, especially in China, and that merit further assessment as well [28].

Patients attended here were part of the first COVID-19 outbreak in Quito. Despite our limited sample of laboratory confirmed positive cases (n = 61), demographic and clinical characteristics of COVID-19 infection were similar to that of previous reports [17, 49, 50], namely, a majority of male individuals with a median age of 50 years presenting fever, cough, and odynophagia (Table 1). Although less frequent, we also found cases describing anosmia, which has been correlated with positive COVID-19 infections [51]. In the present study we did not find cutaneous manifestations of the disease [52]. Blood chemical markers such as CRP, LDH, CPK, etc., were elevated in patients admitted to ICU in comparison with non-ICU patients (Table 2). Values for D-dimer, were above normality for ICU and non-ICU patients but higher for the former than the latter (Table 2); coagulopathies have been incriminated as drivers of mortality for patients with a laboratory confirmed COVID-19 infection [17, 53]. From our variables considered for blood count, only lymphopenia was apparent for ICU cases (Table 3).

In Ecuador, RT-PCR laboratory testing for SARS-CoV-2 was centralized in few hospitals, institutes, and private laboratories, up to 46 days since the beginning of the epidemic [12]. This factor influenced diagnosis delay, epidemic spread, and the urgency to implement out-of-the-box triage approaches such as the one presented here [13, 14, 54]. By the time of this study, testing was limited to 7.46 per 10,000 inhabitants [4], thus, clinical suspicion of patients with respiratory symptoms with elevation of CRP, LDH, and lymphopenia (Tables 2 and 3), can be useful markers for triage in settings unable to rely on molecular or radiological tests [13, 14].

In this study, the majority of laboratory confirmed cases received treatment schemes based on the combination of chloroquine plus azithromycin except for two negative cases, which received clarithromycin plus ceftriaxone or levofloxacin (Table 4). Gautret et al. (2020), showed that the treatment based in hydroxychloroquine plus azithromycin was associated with viral load reduction/disappearance in a small sample size of COVID-19 patients [55]. Two recent clinical trials assessing the safety and efficacy of hydroxychloroquine and chloroquine recommended to avoid COVID-19 treatment schemes with any of these drugs due to the detected increased mortality and lack of benefit [56–58]. Regardless, a more recent study showed a lack of association between treatments including hydroxychloroquine and development of poor clinical outcomes [59]. We were unable to investigate electrocardiogram QT alterations as previously reported [60]. Due to the lack of control groups, our findings should be interpreted as preliminary and by no means as evidence to support any treatment scheme, which is still a topic largely debated with no consensus [21]. Lopinavir/Ritonavir treatment was exclusively used in ICU patients. A case control study published on 18 March 2020, suggested a lack of effect in death reduction [61, 62], however the ICU Department from this hospital decided to continue with the antiviral treatment scheme due to the lack of literature consensus and an apparent clinical improvement still on quantification.

During the progression of the epidemic in Ecuador, officers from the Ministry of Public Health have reported ~1,500 health workers getting infected with SARS-CoV-2 while minimizing the need of full body personal protection [63]. Assuming that infected health workers might act as vectors of SARS-CoV-2 [32], we estimated that at least 1,879,047 people within our designated buffers at the metropolitan area of Quito, might be at risk of infection (Fig 2). Our findings reiterate the need to protect health workers and provide them adequate personal protective equipment to avoid seeding COVID-19 outbreaks [64]. We believe that novel surveillance approaches as the one suggested here should be leveraged and encouraged to complement efforts of epidemiological surveillance to better improve epidemic control.

Our manuscript presents data from the first COVID-19 outbreak in Quito attended in the HGSQ. Despite our limited sample size, we offer a local perspective on how a hospital might be reorganized in a limited amount of time to respond against an emergent epidemic. Further, we show how in spite of its actual limited capacity to discriminate infectious individuals, the AI-assisted chest CT became a key component of the HGSQ transition and triage strategy (i.e., sensitivity = 21.4%). Hospitals in low and middle-income countries might follow a similar approach if there is a lag of evidence-based information with respect to actual response needs. Future analysis should include larger samples sizes and should be shared in a timely matter.

It is important to note that the case of the HGSQ is uncommon in comparison to other Ecuadorian health facilities. Health sector in Ecuador is fractured, encompassing public, social security, military, police, and private health providers [5]; thus, hospital management and protocols are far from standardized [65]. The information published here might aid the implementation of protocols in other regions of Ecuador and also other regions of Latin America, where overrun health systems aiming to control the current epidemic, or potentially future emergent respiratory transmitted diseases, are in need of local perspectives [66].

Supporting information

S1 Fig. Official cumulative and daily case counts of COVID-19 in Ecuador from 29 February to 24 April 2020.

(DOCX)

Click here for additional data file. (334.3KB, docx)
S1 Table. Contingency table evaluating Artificial Intelligence (AI)-assisted chest computer tomography (CT) system for COVID-19 triage.

(DOCX)

Click here for additional data file. (14.7KB, docx)
S2 Table. Daily and cumulative number of cases per province in Ecuador from the first case on February 29th to April 24th, 2020.

(XLSX)

Click here for additional data file. (17.5KB, xlsx)
S3 Table. Clinical, epidemiological, radiological, and treatment data for 75 patients attended at the Hospital General Sur de Quito (HGSQ) between March 13th to March 28th, 2020.

(XLSX)

Click here for additional data file. (34.9KB, xlsx)
S4 Table. Chemical values and white-blood cell counts for COVID-19 patients attended outside of the intensive care unit (non-ICU) from the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador.

(XLSX)

Click here for additional data file. (15.4KB, xlsx)
S5 Table. Chemical values and white-blood cell counts for COVID-19 patients attended at the Intensive Care Unit (ICU) from the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador.

(XLSX)

Click here for additional data file. (14.7KB, xlsx)

Acknowledgments

The authors acknowledge Jose Hector Cadena who helped us with English revisions.

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

Daniel Romero-Alvarez DRA was supported by a grant from the National Science Foundation (DMS 2028297).

References

Decision Letter 0

Adriana Calderaro

5 Feb 2021

PONE-D-20-15485

Adapting in middle of COVID-19 pandemic in Ecuador, a Hospital strategies and patients characterization

PLOS ONE

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The manuscript suffers for a lot of bias that should be addressed to have chance to be published. In particular, English Language needs a strong revision by a native english speaker. Furthermore, it needs a major reorganization: in particular, the sections on how the hospital was transformed into a COVID-19 reference hospital, as well as protocol changes in patient management should be moved to the introduction section from the methods and the results.

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Reviewer #1: Partly

**********

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Reviewer #1: N/A

**********

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Reviewer #1: Yes

**********

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Reviewer #1: Overall, I think this manuscript is promising and contains important information for other hospitals in LMIC may find useful in managing COVID-19 patients (specifically the triaging of patients and the software used to identify potential cases). The study is somewhat ambitious and covers many topics and can feel "scattered" at times. However, with some reorganization, I think this would make for a strong descriptive paper. Please see the attached document for specific comments by section.

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Partly

As this is a descriptive paper without regression modeling, the methods are adequate, but the authors should describe the study samples a bit more clearly, as well as tue quantitative methods for determining the reliability of the AI-assisted CT chest scans. I have included specific comments in the attachment.

*2. Has the statistical analysis been performed appropriately and rigorously?

N/A

*3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Yes

*4. Is the manuscript presented in an intelligible fashion and written in standard English?

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No

I strongly recommend that the authors use professional English language editing services prior to submitting their revision. The errors are too many to list here. There are several instances of uncommon terminology or inconsistent usage (for example, "altered" instead of "elevated" when discussing elevated lab values), or "department" vs "service" when discussing specific departments within the hospital. As an intermediate Spanish speaker, I was able to understand some sentences that appeared to be literal translations from Spanish to English, but those who do not understand Spanish may not be able to glean some of these contexts.

**********

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Reviewer #1: No

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Attachment

Submitted filename: PONE-D-20-15485_reviewer comments.docx

Click here for additional data file. (27.3KB, docx)
PLoS One. 2021 May 17;16(5):e0251295. doi: 10.1371/journal.pone.0251295.r002

Author response to Decision Letter 0

2 Mar 2021

Response to reviewers 1

Title: Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients

Authors: Daniel Garzon-Chavez, Daniel Romero-Alvarez, Marco Bonifaz, Juan Gaviria, Daniel Mero, Narcisa Gunsha, Asiris Perez, María Garcia, Hugo Espejo, Franklin Espinosa, Edison Ligña, Mauricio Espinel, Emmanuelle Quentin, Enrique Teran, Francisco Mora, Jorge. Reyes

NOTE: Lines mentioned in this response refer to the version of the manuscript with track changes.

Reviewer #1

Comment 1. The description of the creation of the COVID-19 management center should be moved to the introduction section, along with the protocol for triaging patients.

Answer: We thank the reviewer for this recommendation. We have moved the corresponding sections to introduction (see lines starting 177).

Comment 2. The methods section should be devoted strictly to the quantitative methodology (assessment of the sensitivity and specificity of AI-assisted CT software, description of the patients, and geo-distribution of health care workers), as should the results section.

Answer: We agree with the reviewer, in this version, methods section only describes the three mentioned categories with the entire protocol of patient classification transferred to the introduction (see lines starting 177 and methods section).

Comment 3. I recommend that the abstract be revised so that it contains all the expected parts of an abstract (brief introduction, purpose/objective, methods, results discussion). The current abstract outlines the purpose of the study but leaves out study results and conclusions. At 178 words, the authors should have room to make use of the full 300 words allotted to include these important findings, such as the sensitivity/specificity of the imaging software to detect true cases, and other salient findings.

Answer: We thank the reviewer for this comment and agree with him/her. The new version has 284 words and includes the most important results obtained in the manuscript (see abstract).

Comment 4. I recommend moving the protocol for hospital transformation and patient management into the introduction section. Sub-section headings will be helpful here.

Answer: We have moved this section as recommended and also used specific subheadings. Thanks! (see lines starting 177).

Comment 5. Line 58: Please revise. Is this referring to 97,209 cumulative cases or 97,209 cases reported in the three days following the release of 2,195 cases? The figures do not support the 97,209 number mentioned here.

Answer: We apologize with the reviewer for the typo. There was an extra space between 97 and 209 that was missed in the final edition of the manuscript. We were trying to emphasize how, after the release of 2,195 cases, official case counts only reported 97, and then 209, and then 63 cases. Because this information is reporting early states of the epidemic in Ecuador, we have moved this section to the supplementary material. Thanks! (see supplementary material).

Comment 6. Line 60: These figures are not clear. Data are shown on two scales: Bars for daily case counts and dots for cumulative cases. Neither scale goes up to 22,000

Answer: We agree with the reviewer, none of the scales reach 22,000 cases because, as mentioned in the text, we were depicting the epidemiological curves up to one day before the release of the 11,000 cases. We have amended this problem and now, by including the data for 24 April 2020, supplementary figure 1A shows the correct scales. As mentioned before, we have moved this information to the supplementary material. Thanks!

Comment 7. The authors should briefly describe the study participants, as there are two groups (patients and health care workers). For patients, please clarify the inclusion criteria; several dates were mentioned in the introduction section, but I was not sure what the dates meant. The text (line 157-158) reads, “We considered data from patients either death or discharged admitted in the HGSQ during February 29th to March 28th.” Does the Feb 29-Mar 28 range refer to the admission date or the death/discharge dates?

Answer: The present study has only one group for the COVID-19 epidemiological assessment: patients above 18 years with respiratory symptomatology, and laboratory testing. We collected 75 cases meeting these criteria between 10 to 28 March. The previous date, 29 February 2020, referred to the first COVID-19 patient detected in Ecuador and was the source of the confusion. We have amended this wording across the manuscript (see lines 423 and 716-717).

Comment 8. The author should briefly outline how they calculated the reliability of the AI-assisted CT scan software.

Answer: Thanks for this observation. Sadly, the AI-assisted chest CT software has been presented as a ‘black box’ deep learning algorithm with no technical details on the parameters chosen to develop the scoring system. We now have emphasized this detail in the methods section (see lines 412-414).

Comment 9. Line 149: Unclear what is meant by a “score room,” although further below, in the results, the authors described how patients were separated by score into 3 categories, so I’m assuming that’s what that means. Moving the narrative on patient management and triangulation from the results to the introduction should help clarify things.

Answer: We have followed these recommendations providing more information regarding score rooms and moving all this description to the introduction section. Thanks! (see lines starting 177).

Comment 10. In the sub-section on spatial distribution (beginning line 158), please also include the frequency that information was collected from the health-care workers. Did they complete the online forms daily, weekly, or just once over the study period (lines 163-164)?

Answer: Health care workers collected this information daily. We have added this detail in the main text. Thanks! (see line 427).

Comment 11. Line 172: Please list the distances for each buffer zone.

Answer: We have added this information in the figure, the methods section and the legend of the figure. Thanks for the suggestion (see lines 525-526, 975-976, and Fig. 3).

Comment 12. Description of the creation of the COVID-19 management center (lines 194-236) should be moved to the introduction.

Answer: We have moved this section to introduction (see lines starting 177).

Comment 13. Line 200: Unclear what is meant by “the VM protocol was activated four times.” Does it mean there were 4 patients that needed to be managed in the first 24 hours? Without context/knowing what the average or expected number of activations is (for example—is a higher number good or bad?), this number is somewhat meaningless.

Answer: We agree with the reviewer. We have eliminated this number and now we are plainly describing how it was implemented (see lines 268-272).

Comment 14. Line 207 and throughout: Please be consistent in using the term “service” or “department.”

Answer: We have eliminated the word service and are using department across the text. Thanks!

Comment 15. Line 240 and throughout: Please be consistent in the decimal points chosen when expressing a number. Sometimes 1 decimal point is used and sometimes 2. For percentages, 1 decimal point is enough precision.

Answer: We have reviewed the manuscript and decided to use 1 decimal point as recommended by the reviewer. Thanks!

Comment 16. Lines 253-260: There should be a 2 x 2 table to accompany the information in this paragraph.

Answer: We agree with the reviewer, but also believe that the text is conveying the information accurately. Regardless, we have included the table for sensitivity and specificity calculation as part of the supplementary material. Thanks!

Comment 17. Line 267: Table shows median, not mean, but text says “mean.” Please confirm which one is the correct one.

Answer: We have corrected to ‘median’ in the main text which is the statistic used for this description (see line 723).

Comment 18. Line 270: Figure 1 is referred to in the sentence on the % of people who travelled to Guayaquil, but that figure doesn’t convey that information, Table 1 does. In addition, Supplementary file 1 appears to contain raw data, which is not necessary to refer to in the narrative.

Answer: Thanks for noticing this problem. We now refer the reader to Table 1. Thanks! (see line 807).

Comment 19. Line 280 and throughout: The authors use the term “altered” but I think what they meant was “elevated.”

Answer: We have reviewed and changed the verbose across the manuscript. Thanks! (see lines 830 and 832).

Comment 20. Line 320: Should be “health personnel.”

Answer: Amended (see line 944)

Comment 21. Line 321: Median may be more appropriate here than mean.

Answer: We agree with the comment. We have changed the statistic as indicated (see line 946).

Comment 22. Line 324: Not clear if these were all health workers or just the ones who took care of COVID-19 patients.

Answer: Only those in charge of COVID-19 patients. We have added this information (see line 948).

Comment 23. Overall comment: Ensure that the captions of all tables and figures are descriptive enough such that readers can understand the content of the tables and figures without having to read the text. This usually means describing what the table shows (“distribution of…” “sensitivity and specificity of…” etc.), any other variables that are being crosstabbed, the sample/sub-sample and sample size, and dates (if applicable).

Answer: Thanks for pointing this detail. We have made the legends of tables and figures more descriptive and now the reader will be able to understand them without need of the main text (see figures and tables).

Comment 24. Please use boldface for table superheaders, headers, and sub-headers (within table) so readers can tell easily where different sections of a table starts/ends.

Answer: We have applied this suggestion for tables in the main text and supplementary material (see tables).

Comment 25. Removing vertical lines and minimizing the amount of horizontal lines in the table would improve its readability.

Answer: Amended (see tables).

Comment 26. Table 1. The caption needs to be revised as the content of this table is unclear. The narrative states there were 75 patients with laboratory-confirmed SARS-CoV-2 infection, the column headers say “positive” and “negative,” so are these referring to the positive or negative determination based on the chest CT scan? If so, this needs to be stated explicitly, as well as the number of people (75)

Answer: We agree with the reviewer and for this version we have amended the confusing wording. We referred to laboratory tested for SARS-CoV-2, not positive for infection. As shown in Table 1, the column ‘positive’ and ‘negative’ refers to the status of the patient according to their laboratory RT-PCR result; we have emphasized this detail in the legend as well. Patients from tables 1-4 refer to those either positive/negative based on RT-PCR. The strategy based on AI-assisted chest CT was used only for triage purposes (see table).

Comment 27. Table 1. The first column needs a header. If these are patient characteristics, that needs to be stated.

Answer: We have added the suggested header. Thanks (see table 1).

Comment 28. Table 1. There needs to be a superheader for the column stating exactly what “positive” and “negative” refer to (lab test or CT scan scoring result?).

Answer: We have clarified the positive/negative status in the legend. They refer to SARS-CoV-2 laboratory tested cases (see table 1 legend and comment 26).

Comment 29. Table 1. Please check the percentages in this table again. For the row on RT-PCR confirmation, that’s clear that row percentages are shown. However for male and female (for example), it is not. 63.9 + 57.1 > 100%, while 57.1 + 42.3 does not add up to 100%.

Answer: In the legend we specify that for the RT-PCR confirmed cases, we calculate percentages based on the total of that row, and how for the rest of the table, we used the totals of each positive, negative and totals from the first row to calculate percentages. Thanks for noticing the typo for the percentages of male and females; it was 57.1 + 42.9 = 100%, respectively. We have reviewed all cells and now we have made sure that no errors are present in the table. Thanks! (see table 1).

Comment 30. Table 2. Please revise the caption to be descriptive of the information contained in the table, including the sample size (e.g. “Blood chemical values comparing XXX to XXX among YYYY patients (n=ZZZ), dates”).

Answer: We have updated the table caption as suggested. Thanks! (see table 2).

Comment 31. Table 3. It would be helpful to use thousand separators for the numbers (e.g. 2,000 instead of 2000).

Answer: We have amended this detail across the manuscript. Thanks! (see table 3).

Comment 32. Table 3. Same comment as above to revise the caption to be descriptive of the information contained in the table.

Answer: Amended (see table 3).

Comment 33. Table 4. Same comment as above to revise the caption to be descriptive of the information contained in the table.

Answer: Amended (see table 4).

Comment 34. Table 4. See my comment on formatting the tables using boldface and minimize use of lines in the table to make it easier to read. In particular, the sub-sections on ICU and non-ICU patients should be made more prominent.

Answer: All the suggestions have been implemented (see table 4).

Comment 35. Table 4. The column headers on “remain or transfer to ICU” and “hospital discharged” should have (n)s listed. Please also clarify what “remain” means. Are these patients that remained in their score room? Perhaps the people who remained in their score room should be presented separately from people who were transferred to the ICU (e.g. split the data in this column into two separate columns).

Answer: We apologize with the reviewer for the confusing headers used for the previous version of the manuscript. For the current version we have clarified patient status to ‘Hospitalized’ since we were referring to patients that remained hospitalized at the end of the study period. They are already stratified as ICU or non-ICU in the table. Thanks! (see table 4).

Comment 36. Table 4. It may be helpful to list treatment regimens that contain the same medications close to one another, but in the order of increasing medicine. For example, if the treatments can contain medicines A, B, C, and D, list A + B first, then list A + B + C or A + B + D. That way, the common medications are obvious, so other clinicians easily see what other medications are in other regimens.

Answer: Thank you very much for this comment. We have reordered drug names by alphabetical order and rearranged the rows to reflect the progressive inclusion of an additional drug. By this way, treatment schemes are now more traceable (see table 4).

Comment 37. Table 4. -It may also be helpful to list these in order of descending frequency if the goal is to show which treatment regimen was most common for ICU and non-ICU patients. Note that this recommendation may conflict with the recommendation immediately above, so the authors should decide on which presentation would be most helpful to other clinicians.

Answer: See comment 36.

Comment 38. Figure 1. I recommend giving a letter to each of the 4 panels and referring to the panels specifically, for example “Figure 1A,” so that it is clear which one is being referenced.

Answer: We have added a letter to each panel and legend now refers to these panels. We have moved the table to the supplementary material since the information is outdated (see supplementary material).

Comment 39. Figure 2. Ensure correct English spelling of room names

Answer: We have redesign Figure 2 to improve color-wise interpretability and fix the errors regarding English name of different hospital sections. Thanks! (see figure 2).

Comment 40. Figure 3. The colors on this map are hard to read, especially for people with color blindness. Recommend using different shapes instead of different colors.

Answer: Thanks for pointing this detail. We have updated the figure to use colorblind safe colors (see figure 3).

Comment 41. Figure 3. The hospital marker needs to be more prominent.

Answer: We agree with the reviewer, the marker for the hospital is clearer in this version (see figure 3).

Comment 42. Figure 3. I strongly recommend including the distance for each buffer zone radius somewhere in the figure

Answer: We have added buffer distances in the legend of the figure together with different shades of yellow to denote distances with respect to the hospital. Thanks! (see figure 3).

Comment 43. Overall, the discussion was well-organized, and synthesized information from the results with the current literature. The authors should expand upon their discussion of geographic risk of populations exposed to health care workers caring for COVID-19 patients (lines 432-438), with an emphasis on the population at risk in the metro area.

Answer: We have added a paragraph emphasizing the number of people at risk within Quito Metropolitan Area due to the potential infected health workers seeding external COVID-19 outbreaks. Thanks! (see lines 1107-1113).

Comment 44. The authors should include a paragraph describing the limitations of the current study.

Answer: We have noticed our limitations in a paragraph as suggested (see lines 1135-1143).

Comment 45. The manuscript should conclude with a discussion of the strengths of the current study, as well as what future studies should consider.

Answer: We have included the strengths of our manuscript in a paragraph as suggested (see lines 1135-1143).

Comment 46. The references appear to be up-to-date. However, the authors need to ensure that the formatting follows Journal guidelines.

Answer: We have reviewed the references making sure that they follow PLoS One guidelines. (see references).

Comment 47. References for internet sources of information, including news websites, or any official websites (ministry of health, WHO, PAHO, etc.) include the URL and the date that the resource was accessed.

Answer: We have reviewed the references from Internet sources and now all of them include its corresponding URL and date of access. Thanks! (see references).

Comment 48. Ensure consistency of spelling throughout (COVID-19 vs COVID19).

Answer: In this version we are using COVID-19 for the entire manuscript. Thanks!

Comment 49. Recommend professional English language editing for grammar and spelling.

Answer: We have improved the English correcting multiple sections of the manuscript, tables, and figures, and revising the entire text by a native English speaker. We believe that the current version has good English quality.

Comment 50. For dates, no comma after month (e.g. “December 2019”, not “December, 2019”).

Answer: We have formatted the date to use day month and year without commas (e.g., 28 February 2020) across the manuscript. Thanks!

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (134.4KB, docx)

Decision Letter 1

Adriana Calderaro

6 Apr 2021

PONE-D-20-15485R1

Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients

PLOS ONE

Dear Dr. Garzon,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The Authors have addressed to all the comments, however they should amed some grammar errors as suggested by the Reviewer.

Please submit your revised manuscript by May 21 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Adriana Calderaro

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I am very pleased with the authors’ responsiveness to my earlier comments. I appreciate the authors’ taking the time to address all of the reviewer's concerns. I believe the flow of the narrative is much clearer after the reorganization. I do not have any major concerns, but I have specific grammar comments, as the PLOS One review system asked reviewers to point out those specific errors. This was not something I had looked at closely in the first review. The specific recommended changes are in the attached Word document. Please be sure to have the English language reviewer do a final pass before you submit your second revisions.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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PLoS One. 2021 May 17;16(5):e0251295. doi: 10.1371/journal.pone.0251295.r004

Author response to Decision Letter 1

10 Apr 2021

Response to reviewers 2

Title: Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients

Authors: Daniel Garzon-Chavez, Daniel Romero-Alvarez, Marco Bonifaz, Juan Gaviria, Daniel Mero, Narcisa Gunsha, Asiris Perez, María Garcia, Hugo Espejo, Franklin Espinosa, Edison Ligña, Mauricio Espinel, Emmanuelle Quentin, Enrique Teran, Francisco Mora, Jorge. Reyes

Reviewer #1

Comment 1. I am very pleased with the authors’ responsiveness to my earlier comments. I appreciate the authors’ taking the time to address all of the comments. I believe the flow of the narrative is much clearer after the reorganization. I do not have major concerns, but I have specific grammar comments, as the PLOS One review system asked reviewers to point out those specific errors. This was not something I had looked at closely in the first review. Please be sure to have the English language reviewer do a final pass before you submit your second revisions.

Answer: We thank the reviewer for his favorable response to our previous revisions. In this version we have incorporated all the minor changes suggested.

Comment 2. Line 25: Change “overrun” to “overran.

Answer: Amended (see line 25).

Comment 3. Line 48: Consider changing “rallied” to “amassed”

Answer: Amended (see line 49).

Comment 4. Line 49: Should be “case study” instead of “study case”

Answer: Corrected (see line 50).

Comment 5. Line 53-54: Consider changing this clause: “Guayaquil allowed mass gatherings and delayed strict isolation around two weeks in relation to Quito” to “Guayaquil banned mass gatherings and implemented strict isolation two weeks later than Quito”

Answer: We thank the reviewer for its detailed correction, which we have applied (see lines 54-55).

Comment 6. Line 56: Consider changing “Ecuador managed diagnosis of SARS-Cov-2 mainly centralizing real-time reverse transcriptase” to “Ecuador centralized real-time reverse transcriptase”

Answer: Correction applied (see lines 57-58).

Comment 7. Line 92: Change “considering the likelihood” to “with the likelihood”

Answer: Thanks for this suggestion. We decided to not apply this suggestion because in this case, we are talking about three categories of severity and how they are related with COVID-19 positivity, thus, the word ‘considering’ here is clearly relating both sentences (see line 100-104).

Comment 8. Section on hospital distribution for COVID-19 patient attendance (114-135): Recommend capitalizing the names of the clinic area and surgery areas in the narrative and also in Figure 1. For example, instead of “clinical area 1” it should be “Clinical Area 1” so readers understand that it is the formal/proper name of those areas. Same goes for “surgery area 1” � “Surgery Area 1”

Answer: We have applied this suggestion in this section, in the figure, and across the main text. Thanks! (see for example lines 132-135 and Fig. 1).

Comment 9. Line 116: Is “Infection control” a department within the hospital? If so, consider changing to “Infection Control Department”.

Answer: Infection control is a multidisciplinary assembly formed with specialists from different departments. In this version we are calling it Infection Control Unit. We have applied this rewording across the manuscript (see for example line 132 and 169).

Comment 10. Lines 133, 135, and throughout manuscript: Change all instances of “an score” to “a score”.

Answer: Thank you for noticing this detail. We have corrected it across the manuscript (e.g., see lines 164-165).

Comment 11. Line 144: Consider changing “suspected/positive” to “suspected/confirmed”.

Answer: Amended (see line 175).

Comment 12. Line 148: Change “got infected” to “was infected”

Answer: We changed the wording to ‘were infected’ considering the meaning of the entire sentence. Thanks! (see line 179).

Comment 13. Line 183-184: Please clarify: Are these people who had laboratory-confirmed SARS-CoV-2 infection or were these simply people who had the RT-PCR done (regardless of results)?

Answer: We are referring to the people that had the RT-PCR test done regardless of the results. Notice that from these 75 patients, 61 are laboratory confirmed positive cases and 14 are laboratory confirmed negative, as explained later in the text.

Comment 14. Line 190-191: Change “in the context of asymptomatic” to “as asymptomatic”

Answer: Changed as suggested (see lines 234-235).

Comment 15. Line 193: Here, and throughout, capitalize the proper names of hospital departments, e.g. “Department of Occupational Medicine,” “Infection Control Department”

Answer: Amended (e.g., lines 99, 236-247).

Comment 16. Line 196: Change “to suggest potential clusters” to “to identify potential clusters”

Answer: Fixed (see line 244).

Comment 17. Line 202: Change “and calculated the people at risk” to “and calculated the number of people at risk”

Answer: Fixed (see line 250).

Comment 18. Line 222: Change “93 positive to SARS-CoV-2” to “93 with laboratory-confirmed SARS-CoV-2 infection”

Answer: We changed ‘93 positive to SARS-CoV-2’ to ‘93 with potential SARS-CoV-2 infection’ since from this 93, 18 patients passed and the remaining 75 are the ones described in the following lines. Thanks for pointing this out.

Comment 19. Line 225: Change “from both groups” to “overall,” since the overall values are what is being discussed, not the 2 groups separately, as the median was slightly lower for those with a (-) test result.

Answer: Fixed. Thanks (see line 282).

Comment 20. Line 234: Change “positive patients” to “patients with laboratory-confirmed SARS-CoV-2 infection” or “patients with positive test results” here and throughout. “Positive” is an attribute of the test result or infection status, not of the patients.

Answer: Thanks for this comment. We have amended this mistake across the manuscript (e.g., 273-275, 318).

Comment 21. Line 236: See comment above. Instead of “positive cases,” use “confirmed cases”.

Answer: We have amended this wording across the entire manuscript. Thanks! (e.g., 273-275, 318).

Comment 22. Line 239: Instead of “negative patients,” use “patients with negative test results”

Answer: Thanks. We have fixed the wording in the entire text (e.g., 273-275, 318).

Comment 23. Line 299: Change “22-health workers” to “22 health workers”

Answer: Fixed (see line 451).

Comment 24. Perhaps I should have been clearer when I made the recommendation for a more detailed caption. I think the current boldface part of the captions for each table should stay as captions, whereas the non-boldface parts should be table footnotes. What I meant by a more descriptive caption is that captions typically have a descriptor of the sample, the locale (“Quito, Ecuador” or if is the Quito metropolitan area, you may indicate it as “Quito Metropolitan Area, Ecuador”), and some time indicator when the data were collected or sampled (e.g. “February-March 2020”).

For example, for Table 1:

This would be the table caption (note additions highlighted in aqua)

Table 1. General characteristics, risk factors, and symptoms/signs of the first 75 COVID-19

laboratory tested cases attended in the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador -- March 2020

This would be the table footnote

Cases categorized as positive/negative by laboratory testing (i.e., RT-PCR) from 10 to 28 March 2020.

Percentages from the first row are calculated in relation to the total cases (n = 75). Percentages of the

following rows are calculated with positives, negatives, and totals from the first row, respectively.

For Table 2:

This would be the table caption (note additions highlighted in aqua)

Table 2. Blood chemical values, laboratory-confirmed COVID-19 cases (n=61) by intensive care unit (ICU) admission status, Quito, Ecuador -- March 2020

This would be the table footnote

Blood chemical values for 61 COVID-19 positive cases categorized as intensive care unit (ICU; n = 14) and non-ICU (n = 47) from 10 to 28 March 2020. Values represent medians and ranges are depicted in parenthesis. Normal ranges are shown in the headers of each column. CPK = Creatine phosphokinase, LDH = Lactate dehydrogenase, CRP = C-reactive protein, PCT = Procalcitotin, CR = Creatinine; ALT = Alanine transaminase, AST = Aspartate transaminase.

Answer: Thank you very much for your detailed explanation on how to improve the tables. We have applied your suggestions for all the tables (see Tables).

Comment 25. Table 3. Same comment as above to add a locale and brief sample description to the caption, and separate the non-boldface text to the footnote

Answer: See comment 24.

Comment 26. Table 4. Same comment as above to add a locale and brief sample description to the caption, and separate the non-boldface text to the footnote

Answer: See comment 24.

Comment 27. Figure 1 A and B. The red arrow on a red box is difficult to see. I recommend changing the color of the boxes to a medium blue (such as this color) instead of red and the color of the arrows to black instead of red. Individuals with color blindness may have an especially hard time seeing the red arrows inside the red boxes.

Answer: We agree that the red arrows are going to be difficult to discriminate. We have changed the color of the arrows and lighted colors of the boxes so everything is more noticeable. We decided to avoid changing the color to the suggested blue because the boxes (rooms) represent infectious sources, and therefore dangerous objects, which traditionally are represented with reddish colors. Thanks! (see Fig. 1).

Comment 28. Line 324: Change all instances of “sub-employed”/“sub-employment” to “underemployed”/”underemployment”.

Answer: Fixed (see line 479).

Comment 29. Line 334: Please clarify this line “represents the first attempt to explore the ability of this tool”: the ability of the tool to do what? Also, the first attempt by HGSQ or by whom?

Answer: Thank you very much for this comment; we have reworded the sentence (see lines 488-489).

Comment 30. Line 337: Change “results availability” to “result availability”

Answer: Fixed (see line 492).

Comment 31. Lines 342-343: Change “poor enough to actually recommend it” to “too poor to recommend”

Answer: Amended (see lines 497-498).

Comment 32. Line 343: “adverted” is not the correct word here. Perhaps “cautioned” by the Philippine College of Radiology?

Answer: You are totally right. Thank you very much for noticing this miswording. We have changed it as recommended (see line 498).

Comment 33. Line 345: Awkward wording in “bottlenecks of attention.” Perhaps you meant “bottleneck of patients needing attention”

Answer: We agree with the reviewer, we have changed the sentence (see lines 499-501).

Comment 34. Lines 351-352: Change “since it is impossible to have the luxury of using the only” to “since we cannot justify using the only”

Answer: Amended (see line 520).

Comment 35. Line 353: Change “exposition” to “exposure”

Answer: Fixed (see line 522).

Comment 36. Line 385: Change “trials)” to “trials”

Answer: Done (see line 557).

Comment 37. Line 394: Should “ICU attendant” be plural (i.e. “ICU attendants”)?

Answer: Thanks for this comment, we were refereeing to the main director of ICU, we have changed it accordingly (see line 571).

Comment 38. Line 402: Consider changing “We remark the need” to “Our findings reiterate the need”

Answer: Amended (see lines 578-579).

Comment 39. Lines 411-413: Consider changing “Hospitals in low and middle-income countries might follow a similar approach regardless of the scientific evidence in favor or against a particular policy because in emergency contexts, evidence-based information lag with respect to actual response needs.” To “Hospitals in low and middle-income countries might follow a similar approach if there is evidence-based information lag with respect to actual response needs.”

Answer: Thanks for this comment. We have made the change as suggested (see lines 589).

Comment 40. Line 415: Change “to remark” to “to note”

Answer: Fixed (see line 591).

Comment 41. 416: Change “Health sector in Ecuador is fractionated” to “The health sector in Ecuador is fractured”

Answer: Amended. Thanks! (see line 592).

Attachment

Submitted filename: response to reviewers2.docx

Click here for additional data file. (28.7KB, docx)

Decision Letter 2

Adriana Calderaro

15 Apr 2021

PONE-D-20-15485R2

Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients

PLOS ONE

Dear Dr. Garzon,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Only few grammar errors should be amended by the Authors.

Please submit your revised manuscript by May 30 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Adriana Calderaro

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear authors,

I appreciate the effort you have put in to respond to my suggestions in all rounds of reviews. I have reviewed the most recent draft, and I accept the reasons you provided for not making some of the recommended changes.

I have three more minor grammatical changes to recommend in this round that I missed last time:

Line 393 -- There should not be a comma between "trials" and "assessing"

Line 406 -- Change "referred to" to "reported"

Line 421 -- Change "being" to "be"

The paper should be good to go after this. Again, thank you for being so responsive to the comments. This is an important and timely paper not just for this outbreak for future outbreaks.

Warm regards,

Reviewer 1

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2021 May 17;16(5):e0251295. doi: 10.1371/journal.pone.0251295.r006

Author response to Decision Letter 2

15 Apr 2021

Response to reviewers 3

Title: Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients

Authors: Daniel Garzon-Chavez, Daniel Romero-Alvarez, Marco Bonifaz, Juan Gaviria, Daniel Mero, Narcisa Gunsha, Asiris Perez, María Garcia, Hugo Espejo, Franklin Espinosa, Edison Ligña, Mauricio Espinel, Emmanuelle Quentin, Enrique Teran, Francisco Mora, Jorge. Reyes

Reviewer #1: Dear authors,

I appreciate the effort you have put in to respond to my suggestions in all rounds of reviews. I have reviewed the most recent draft, and I accept the reasons you provided for not making some of the recommended changes.

I have three more minor grammatical changes to recommend in this round that I missed last time:

Line 393 -- There should not be a comma between "trials" and "assessing"

Line 406 -- Change "referred to" to "reported"

Line 421 -- Change "being" to "be"

The paper should be good to go after this. Again, thank you for being so responsive to the comments. This is an important and timely paper not just for this outbreak for future outbreaks.

Warm regards,

Reviewer 1

Answer: We thank the reviewer for his/her efforts in improve the paper and for his/her comments. In this version we have incorporated all the minor changes suggested and we did a detailed examination of the paper. Also, in Line 402 we change “Deparment” to “Department”.

Thank you so much and kind regards.

Attachment

Submitted filename: response to reviewers3.docx

Click here for additional data file. (18KB, docx)

Decision Letter 3

Adriana Calderaro

26 Apr 2021

Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients

PONE-D-20-15485R3

Dear Dr. Garzon,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Acceptance letter

Adriana Calderaro

7 May 2021

PONE-D-20-15485R3

Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients

Dear Dr. Garzon-Chavez:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

MD, PhD, Associate Professor Adriana Calderaro

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Official cumulative and daily case counts of COVID-19 in Ecuador from 29 February to 24 April 2020.

    (DOCX)

    Click here for additional data file. (334.3KB, docx)
    S1 Table. Contingency table evaluating Artificial Intelligence (AI)-assisted chest computer tomography (CT) system for COVID-19 triage.

    (DOCX)

    Click here for additional data file. (14.7KB, docx)
    S2 Table. Daily and cumulative number of cases per province in Ecuador from the first case on February 29th to April 24th, 2020.

    (XLSX)

    Click here for additional data file. (17.5KB, xlsx)
    S3 Table. Clinical, epidemiological, radiological, and treatment data for 75 patients attended at the Hospital General Sur de Quito (HGSQ) between March 13th to March 28th, 2020.

    (XLSX)

    Click here for additional data file. (34.9KB, xlsx)
    S4 Table. Chemical values and white-blood cell counts for COVID-19 patients attended outside of the intensive care unit (non-ICU) from the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador.

    (XLSX)

    Click here for additional data file. (15.4KB, xlsx)
    S5 Table. Chemical values and white-blood cell counts for COVID-19 patients attended at the Intensive Care Unit (ICU) from the Hospital General del Sur de Quito (HGSQ), Quito, Ecuador.

    (XLSX)

    Click here for additional data file. (14.7KB, xlsx)
    Attachment

    Submitted filename: PONE-D-20-15485_reviewer comments.docx

    Click here for additional data file. (27.3KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (134.4KB, docx)
    Attachment

    Submitted filename: PONE-D-20-15485_revisions_reviewer 1 comments.docx

    Click here for additional data file. (24.6KB, docx)
    Attachment

    Submitted filename: response to reviewers2.docx

    Click here for additional data file. (28.7KB, docx)
    Attachment

    Submitted filename: response to reviewers3.docx

    Click here for additional data file. (18KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting information files.

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