Introduction

For the past few decades, the world has been exposed to a series of threats from viral outbreaks caused by emerging zoonotic diseases and in particular by a family of viruses known as coronaviruses [1]. The World Health Organization recognizes at least three types of coronavirus capable of generating epidemic outbreaks, including SARS-CoV, MERS-CoV and the recently discovered SARS-CoV-2 virus [2]. These viruses are responsible for causing severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and the most recently described coronavirus disease (COVID-19) [3].

Since the first reports of a cluster of atypical pneumonia cases in Wuhan, China on December 2019, the SARS-CoV-2 virus and COVID-19 has quickly spread across the globe [1,4], infecting more than 40 million people and causing more than 1.1 million deaths worldwide until October 20^th, 2020. One of the main reasons the virus has spread so rapidly is due to droplet transmission from both symptomatic and asymptomatic people, making it difficult to test, trace and isolate new cases effectively [2].

Understanding the transmission dynamics in different settings can provide important clues about the advance of the pandemic, especially in areas with unequal access to health services, high population density and a high burden of neglected tropical diseases [5,6].

In this study, we present the findings of an interim analysis of the epidemiological situation in Ecuador, describing the clinical characteristics and epidemiological behavior of the first 9,468 confirmed COVID-19 cases using the reverse transcription polymerase chain reaction (RT-PCR) method.

Methods

Results

There were 9,468 positive cases of COVID-19 and 474 officially reported deaths in Ecuador from 27 February– 18 April 2020 (54-day period). 99.3% of COVID-19 patients were Ecuadorians (n = 9,400), 0.30% (n = 44) were from other countries in Latin America and the other 0.40% (n = 24) were either from Europe, North America or Asia.

Age and sex analysis

Men accounted for 55.4% (n = 5,247) of all cases with an incidence rate of 60.5 per 100,000, while women accounted for 44.6% of cases (n = 4,221) and an incidence of 47.2 per 100,000. The median age of COVID-19 patients was 42 (IQR: 32–56) in men and 39 (IQR: 30–54) in women. The median age of patients who had died from COVID-19 was 62 (IQR: 51–70) in men and 65 (IQR: 56–74) in women.

We also found that men were more likely to die from COVID-19 in almost every age group (Fig 1), although case fatality rate for patients aged between 85–94 was higher amongst women (Table 1).

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Fig 1. COVID-19 cases and deaths in Ecuador by age and sex.

Top panel: Age pyramids for confirmed cases and officially reported deaths. Lower panel: Case fatality rate (%) and risk likelihood of dying from COVID-19, for females (blue) and males (purple).

https://doi.org/10.1371/journal.pntd.0008958.g001

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Table 1. Case fatality rate (CFR%) due to COVID-19 and relative risk (RR) in Ecuador among women and men from different age groups (N = 9,468).

*P-values below or equal to 0.05 were considered significant.

https://doi.org/10.1371/journal.pntd.0008958.t001

Socio-demographic variables

Ethnicity.

The majority of COVID-19 cases occurred among Mestizos 78% (n = 7,367), followed by indigenous 0.79% (n = 75), Caucasians 0.84% (n = 40) and Afro-Ecuadorians/Black with ~0.1% (n = 16) (Table 2).

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Table 2. Total number of deaths, patients alive and percentage of total (%) and case fatality rate (%) for women and men in different ethnic groups, type of healthcare provision, presence of comorbidities and travel history.

https://doi.org/10.1371/journal.pntd.0008958.t002

Education attainment and marital status.

In Ecuador, the results of the demographic survey (n = 856) show that 0.7% (n = 6) of COVID-19 patients had not completed their elementary school, 1.9% (n = 16) reported a complete elementary school education, 5.8% (n = 50) had not completed their high school education and 22.5% (n = 193) had completed their entire high school cycle. At least 6.8% (n = 58) are technicians, 46.3% (n = 396) have completed their undergraduate degree and 16.0% (n = 137) have formal postgraduate training. In terms of civil status 39.3% (n = 336) are single, 38% (n = 327) are married, 13% (n = 114) are cohabitating with their partners, 5.4% are divorced (n = 46), 1.3% widows (n = 11) and other responses 3% (n = 22).

Occupation and work-related risk.

The results of COVID-19 mortality by occupation showed that 19% of infected people (n = 1,800) are health professionals (Table 3). From this group medical doctors (n = 876) were the most affected professionals in Ecuador, representing at least 9.3% of all reported cases (Fig 2)

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Fig 2. Relative risk calculation by occupation compared with white-collar workers (Engineers, lawyer, auditors, psychologists, chefs, architects, communicator, teachers, managers, chief executive officers, entrepreneurs, analysts, administrator).

*Diplomatic, City Mayor, political advisor and city/provincial councilors, ** Hospital and Minister of Public Health administrative personnel, Medical Student nursing assistant, hospital cellars, Medical Assistant, Therapists, Laboratory, X-ray and Pharmacy Technician, Diagnostic Medical Sonographer, primary health care technicians (TAPS), physiotherapist, obstetricians and paramedics. °Military soldiers and army staff, Navy personal, Transit agents, Policemen, Security guards, Jail security agents. †Informal merchants, kitchen staff, Housework, Domestic Services employees, informal driver, Farmers, Craftsman, Machine operators, Cleaning Services employees, mechanics, carpenters and construction workers.

https://doi.org/10.1371/journal.pntd.0008958.g002

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Table 3. Case fatality rate (%), relative risk and risk likelihood due to COVID-19 in Ecuador by occupation.

Relative risk was estimated using White-Collar jobs as a reference group. P-values below or equal to 0.05 were considered significant.

https://doi.org/10.1371/journal.pntd.0008958.t003

Nurses are the second most affected group with 3.3% representing at least 309 people (Table 2). The majority of people testing positive work in manual labor (n = 1,390). In terms of fatality, the small number of inmates seem to be at the highest risk of dying, with a RR of 7.75 [p value: 0.020, 1.3–44.0], followed by retired elderly people with a RR of 5.6 [p value: <0.0001, 3.5–9.0] and unemployed with 4.47 [p value: <0.0001, 2.8–6.9]. The results from the likelihood of dying is increasingly higher among ‘blue collar’ workers, civil servants (police officers, soldiers, members of the navy, firefighters and other agents), unemployed, retirees and prisoners (Table 3). Although a small number of cases were reported among politicians (n = 18), the CFR% for this group was zero, followed by nurses (0.3%), other health care professionals (0.7%) and medical doctors with a CFR% of 1.6%.

Vaccination history, comorbidities and pregnancy history among COVID-19 patients.

The current analysis of the immunization history of influenza showed that 99.1% of patients (n = 9,384) did not report any vaccination history in the last year and only 0.9% (n = 84) of patients reported having been vaccinated. From the not vaccinated group, 471 patients died while from the vaccinated group, only 3 died. The risk of dying among the not vaccinated group was higher than the vaccinated group, although the difference was not statistically significant (1.40 [95% CI: 0.46 to 4.28]) (Table 2).

In our study, 1.4% of women (n = 60) were pregnant and 4 of them died (6.7%) due to COVID-19. The RR of pregnant women dying was not found to be statistically significant (RR: 2.05 [0.785 to 5.36] p value: 0.14).

The median age of COVID-19 patients reporting comorbidities in Ecuador was 59 (IQR: 49–68) years old,while those with no comorbidities had a median age of 42 years old (IQR: 32–55). When the presence of comorbidities was incorporated in an adjusted logistic regression model, the presence of comorbidities, being male and older than 65 years old increased the risk of dying by almost 130% (OR: 2.27 [1.72–3.00, p value <0.001]), 100% (OR: 2.03[1.65–2.50, p value < 0.001]) and 470% (OR: 5.74 [4.7–7.0, p value <0.001]) (Table 4).

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Table 4. Adjusted logistic regression using final outcome (Dead/Alive) as a dependent variable and the presence of comorbidities, sex and age of the patients as a covariate.

https://doi.org/10.1371/journal.pntd.0008958.t004

Symptoms assessment.

The most common symptom reported among COVID-19 patients in Ecuador was fatigue or general tiredness (53.2%), followed by headaches (43%), and dry cough (41.7%). 37.1% of the patients reported loss of taste (ageusia), 36.1% reported loss of smell (anosmia) and 35% reported muscle and joint pain (S2 Fig). The median time elapsed between the onset of symptoms and the time before receiving medical attention was four days (IQR: 1–8 days). The average time between onset of symptoms in patients and case notification was nine days (IQR: 5–14 days). When observed retrospectively using the date of the onset of symptoms, 29 undocumented patients were already sick but only 6 were diagnosed, a trend that continued to be high until March 24^th, the day when strict government interventions were implemented in Ecuador (Fig 3).

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Fig 3. Epidemiological curves of COVID-19 cases by date of onset of symptoms (blue) and date of diagnosis (red), from 14 February to 18 April 2020.

The first case in Ecuador was diagnosed on 27^th February but notified on February 29^th.

https://doi.org/10.1371/journal.pntd.0008958.g003

Discussion

Ecuador has been the worst hit country in the Latin American region from the COVID-19 pandemic [13]. Since the first case confirmed in Ecuador on 27 Feb 2020, at least 9,468 positive COVID-19 cases and 474 deaths were officially registered over a 54-day period.

The images of corpses in the streets and the difficulty of burying the dead occupied the main pages of all the newspapers around the world [13,14]. Overcrowded hospitals and laboratories were overwhelmed by an excessive number of cases, causing a confirmed toll of 474 COVID-19 related deaths and 809 registered suspicious deaths up to April 18^th 2020 [15]. On April 18, the National Emergency Operations Committee reported 1,061 patients discharged from hospital, 369 hospitalized patients, and 7,564 COVID-19 positives cases, who were stable in-home isolation [16]. Data from the region indicates that Ecuador, along with Brazil, Mexico and Peru are among the top ten most affected countries in Latin America, but when excess mortality is taking into account, Ecuador ranks 1^st worldwide in the numbers of deaths per million inhabitants [17–19].

At the beginning of the pandemic, Ecuador registered a single case on February 27, when in reality, looking retrospectively, there were already 29 undetected cases. Only two weeks later, only 11 cases were officially registered, but data shows that at least 119 cases were actually circulating within the country (Fig 3). After 54 days, a total of 9,468 positive COVID-19 cases and 474 officially reported deaths, with a sex distribution (55.4% men) that was similar to that reported in China (58% men) and Italy (59.8% men) [20–22].

According to the high PTR% found in Ecuador, the excess mortality that was not correctly diagnosed and reported as COVID-19 and other variables; we inferred that the epidemiological surveillance system and contact tracing strategies were not correctly implemented. At the beginning of the outbreak, every confirmed patient was followed and tracked by the health authority. However, when the number of new COVID-19 cases began to increase exponentially, the system’s capacity was overstretched. At the same time, testing capacities were collapsed and only those patients who presented symptoms, those who were in contact with any positive case or those lucky enough to get tested under the surveillance program obtained the diagnosis.

The mortality rate for women (3.35%) is almost half of that for men (6.86%). The median age was 42 in men and 39 in women and the age group most affected was the group from 19–50 years old representing 59.6% of the entire cohort, almost doubling the same age group reported in Italy (24.0%) and very similar to the age distribution from China [21,22]. The reason behind these trends might be due to the fact that Italy has one of the oldest populations in the world. According to the latest data, Italy has 14 million residents over the age of 65 (22%) with an average age of 45.7 years, while in Ecuador the average population is 26.6 years old [7,23,24].

In terms of age, the patients between 0 and 50 years old reported a CFR of 1.6%, compared to 0.4% in Italy, 0.4% in China and 0.6% in Spain from the same age groups [24–26]. When adjusting for sex, age and the presence of comorbidities, mortality increased significantly among elderly men, which is consistent with other regional studies [21,27].

The existence of comorbidities is linked with augmented age and therefore increased risk of mortality in COVID-19 patients. The age of patients reporting comorbidities in Ecuador was higher than those without comorbidities. In terms of risk, patients with comorbidities had a CFR% of 10.3% in women and 16.9% in men, higher than those without comorbidities and in both sexes CGF% averaged 4%. These findings are equivalent to previous studies that have shown that the presence of comorbidities increases the risk in COVID-19 patients to be admitted to the ICU or die due to this disease [27,28].

In terms of ethnicity, it is important to point out that self-identified Montubios and Indigenous had a CFR% of 14% and 9% respectively, which is surprisingly higher than Mestizos (6%) and other ethnic groups living in Ecuador. This is probably due to reduced healthcare access for vulnerable groups [29]. For influenza, ethnic minorities have the highest estimated fatality rate, most likely due to their social determinants of health, social inequalities and reduced access to health care, especially in rural areas [30,31].

Although the clinical features of COVID-19 are widely studied in moderate and severe hospitalized patients, information on patients with a less severe symptoms is scarce. We collected self-reported data on symptoms from a sample of patients in home isolation. 53% presented with fatigue, 43% headaches and 42% a dry cough which is in agreement with studies from other settings. However a higher proportion of patients reported ageusia (37%) and anosmia (36%), when compared to studies from China (5.1% and 5.6%, respectively) and Italy reporting anosmia in 19.4% of patients [32,33].

In January, alarms went off with the first suspected COVID-19 case in Ecuador, alarms that denoted the poor readiness of the public health system that took more than 15 days to rule-out a highly suspicious COVID-19 patient, who died with the diagnosis of hepatitis B and atypical pneumonia [34,35]. At the beginning of the outbreak, the World Health Organization (WHO) emphasized the importance of testing capabilities in order to improve contact tracing and diseases detection worldwide (S5 Fig). Nevertheless, Ecuador has not enough capabilities to perform molecular diagnosis (RT-PCR), limiting epidemiological surveillance strategies and contact tracing [17,36]. For this reason, the number of samples taken exceeds the local molecular diagnosis capabilities, resulting in testing delays [13,14]. Despite the limitations, in some areas of the country, especially richer areas, such as Samborondon, testing rates are as high as those seen in Iceland or even close to those seen in the US (S1 Table and S1 Data) [37]. This trend might be driven by those patients who were able to pay for their diagnosis and their medical treatment. Therefore, their high attack rate is interpreted as better access to health resources and that is why mortality is also low among these cantons. Although social status and monthly income was not assessed, in Ecuador, blue collar workers have less monetary income than health care and white-collar workers [38,39]. In Ecuador, it is hypothesized that labor workers and the unemployed have less access to health services, therefore the high case fatality rates among these groups is significantly higher than those in more privileged positions such as politicians, medical doctors or health workers (Table 2). These results could indicate that poor working conditions, poverty and therefore limited access to health services could be linked to the high mortality rates reported among the most vulnerable groups, a situation described previously [40]. The working environment and the confinement was also associated with a high prevalence of persons presenting at least mild symptoms of anxiety (58.1%) and depression (52.6%)[41].

Low testing capabilities and high number of suspicious COVID-19 deaths likely distort the calculation of age-specific attack and mortality rates across cantons. We found a median delay of two days (IQR: 1–7) between the day of sampling to the day of notification and a low number of reported tests (test per million people and the overall count) in the region [17]. Therefore, the lack of molecular testing capabilities causes a high positive test rate comparing with other countries in the region [17]. This impacts the ability of contact tracing and other prevention strategies to interrupt SARS-CoV-2 transmission [42]. As evidenced by the high R0 in the provinces closest to the start of the pandemic, where prevention measures were weak and scarce, due to multiple social and political factors. Likewise, other Latin American countries with deficient health policies and lack of timely actions, such as Peru and Brazil, obtained a higher average transmission potential of 2.97[43,44] and 3.1[45,46], respectively.

In terms of elevation we found that people living in cantons and provinces located in the highlands had lower attack rates and lower mortality rates than the populations living at lower altitudes. We suggest that the virus spreads faster at lower altitudes due to demographic and cultural differences. We hypothesized that harsh weather (cold and windy) invites people to stay indoors while at sea level and due to warmer tropical climate, people tend to be more socially active and have greater mobility than the highlands [47]. Additionally, demographic density is lower in the highlands according to the latest INEC data in Ecuador, a situation that might influence the speed of the spread in terms of viral transmission.

Analyzing the years of life lost due to SARS-CoV-2 is challenging due to the uncertainty around the length of the different phases of the disease and the clinical spectrum of the severity of the disease. These uncertainties make it difficult to estimate disability weights that are a crucial component for the DALY calculation. In our study 99.5% of the burden was attributable to the years of life lost due to premature mortality among the study population, with an average of 25.4 YLL per death, and 1.27 DALY per each case of COVID-19. Even though COVID-19 related deaths are higher among elderly populations in developing countries with weakened health systems, mortality is also overstretched among younger populations [48].

The limitations of this study are the lack of specific data during the given time period regarding the type of comorbidities and the presence of clinical manifestations during the initial evaluation. This limitation is also evident when reviewing the follow-up file, which includes only the date of death and the date when the case was discharged and closed. Moreover, the database does not indicate the severity of the disease and whether or not they required hospitalization. Another important fact to consider is that testing was done mainly in symptomatic people diagnosed COVID-19 cases, probably because of limited test availability.

Conclusions

This is the first study of the epidemiological trends of COVID-19 in Ecuador. The results demonstrate the vulnerability of the health system to contain, mitigate, treat and adequately diagnose this type of new viral disease that spread across the country at a speed that exceeds the speed of response. We found that there were a high number of COVID-19 infections among medical personnel, which likely occurred during the first weeks of the outbreak in the Ecuador. Although the attack rate was high, mortality in this group was very low, which could be due to greater access to healthcare. We also found that the occupations of patients and whether they were unemployed was strongly associated with overall mortality due to COVID-19 in Ecuador, suggesting that poverty is an important driver of the final outcome for this disease. Lastly, strengthening of community-based surveillance and contact tracing response capacities is essential to prevent further advance of the disease within the community.

Supporting information

Acknowledgments

The authors thank Rebeca Bravo who was very keen in editing our maps for this publication as well as the staff from the National Direction of Epidemiology for sharing the official database after fulfilling their requirements. We greatly appreciate the work carried out by Dr. Ignacia Paez and her team of Community Mental Health professionals from the Ministry of Public Health, for their role of distributing the self-reporting symptomology tool among the entire cohort of patients who were under constant monitoring from the official epidemiological department of the MoH.