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.

Purpose: Discharge or follow up of confirmed coronavirus disease 2019 (COVID-19) cases depend on accurate interpretation of RT-PCR. Currently, positive/negative interpretations are based on amplification instead of quantification of cycle threshold (Ct) values, which could be used as proxies of patient infectiousness. Here, we measured Ct values in hospitalized confirmed COVID-19 patients at different times and its implications in diagnosis and follow up. Patients and Methods: Observational study between March 17th-May 12th, 2020 using multiple RT-PCR testing. A cohort of 118 Hispanic hospitalized patients with confirmed COVID-19 diagnosis in a reference hospital in Quito, Ecuador. Multiple RT-PCR tests were performed using deep nasal swab samples and the assessment of SARS-CoV-2 genes N, RdRP, and E. Results: Patients’ median age was of 49 years (range: 24– 91) with a male majority (62.7%). We found increasing levels of Ct values in time, with a mean Ct value of 29.13 (n = 61, standard deviation (sd) = 5.55) for the first test and 34.38 (n = 60, sd = 4), 35.52 (n = 20, sd = 2.85), and 36.12 (n = 6, sd = 3.28), for the second, third, and fourth tests, respectively. Time to RT-PCR lack of amplification for all tests was of 34 days while time to RT-PCR Ct values > 33 was of 30 days. Conclusion: Cycle thresholds can potentially be used to improve diagnosis, management and control. We found that turnover time for negativity can be large for hospitalized patients and that 11% cases persisted with infectious Ct values for more time than the current isolation recommendations.

The Bacillus Calmette–Guérin (BCG) is a well-known vaccine with almost a century of use, with the apparent capability to improve cytokine production and epigenetics changes that could develop a better response to pathogens. It has been postulated that BCG protection against SARS-CoV-2 has a potential role in the pandemic, through the presence of homologous amino acid sequences. To identify a possible link between BCG vaccination coverage and COVID-19 cases, we used official epidemic data and Ecuadorian Ministry of Health and Pan American Health Organization vaccination information. BCG information before 1979 was available only at a national level. Therefore, projections based on the last 20 years were performed, to compare by specific geographic units. We used a Mann–Kendall test to identify BCG coverage variations, and mapping was conducted with a free geographic information system (QGIS). Nine provinces where BCG vaccine coverage was lower than 74.25% show a significant statistical association (χ² Pearson’s = 4.800, df = 1, p = 0.028), with a higher prevalence of cases for people aged 50 to 64 years than in younger people aged 20 to 49 years. Despite the availability of BCG vaccination data and the mathematical models needed to compare these data with COVID-19 cases, our results show that, in geographic areas where BCG coverage was low, 50% presented a high prevalence of COVID-19 cases that were young; thus, low-coverage years were more affected.

Chronic hepatitis B virus (HBV) infection affects 257–291 million people worldwide. The World Health Organization reported 890,000 HBV‐related deaths in 2019, higher than reported previously. There are 10 HBV genotypes (A–J) subdivided into several subgenotypes that differ considerably by geography. Various virologic factors, including genotype and subgenotype, impact the odds of acquiring a chronic HBV infection, the type of treatment prescribed, and the risk of developing hepatocarcinoma. Information on the HBV genotypes and subgenotypes that circulate in Ecuador remains low. To address this gap, the current study took a preliminary look at HBV‐infected human samples from this region to identify the most common genotypes and subgenotypes. Samples from 44 patients in the Andean, Coastal, and Amazon regions of Ecuador were amplified and two major genotypes were identified, genotype F (42/44; 95.5%) and genotype E (2 patients; 4.5%). The genotype F subgenotypes were F3 (35/42; 83.33%), F4 (6/42; 14.28%), and F1b (1/42, 2.39%). This is the first epidemiological study to assess the distribution of HBV genotypes in Ecuador. The findings can inform antiviral drug effectivity studies specific to HBV genotypes prevalent in South America.

SUMMARY: In low- and middle-income countries (LMICs), close cohabitation with animals and limited access to water, sanitation, and hygiene (WASH) infrastructure increase the risk of zoonotic enteric pathogen transmission to young children. This mixed-methods study combined (A) microbiological analysis of 120 animal fecal samples, and (B) go-along, semi-structured interviews with 35 mothers of children under two years across urban, intermediate, and rural communities in Ecuador to investigate: (Q1) What zoonotic enteric pathogens are present in animal feces and at what concentrations? (Q2) How are children exposed to animals and their feces? and (Q3) Which animals may serve as key sources of child? Microbiological analysis revealed high prevalence and concentrations of zoonotic pathogens, most commonly E. coli aEPEC (57%), Salmonella sp. (36%), and E. coli STEC (25%), with frequent co-infections (33%) and concentrations (4.97-9.29 log10 gc/g) often exceeding infectious dose thresholds. Qualitative findings showed risks from free-roaming animals, poor feces management, and frequent direct and indirect child–animal contact, often via caregivers and siblings. Triangulation identified chickens and dogs as major exposure sources due to their behaviors, proximity to children, and pathogen carriage. These findings highlight the need for targeted interventions to limit animal roaming, improve feces management, and increase caregiver awareness, while demonstrating the value of mixed-methods approaches to inform context-specific strategies for protecting child health in high-exposure environments.

Cutaneotrichosporon debeurmannianum is a yeast-like anamorphic rare fungus commonly found in tropical areas. This case report is the first one located in South America. A 67-year-old patient presented with a 5-year history of right foot pain attributed to foot trauma while at sea 5 years prior. During surgical exploration, an impressive whitish cerebriform-like cyst was encountered. Genetic analysis using the genes ITS1, ITS4, LSU-R, and LSU-F was performed, and a phylogenetic tree identified C. debeurmannianum. A complete characterization of this fungus which causes human disease has not yet been achieved and more research is needed.