COVID-19 is a new emerging infectious disease that, by August 1 st 2020, has caused more than 17 million cases and more than 680 thousand deaths worldwide (World Health Organization 2020b). It has been declared a pandemic by the World Health Organization (WHO) and has prompted lockdowns in most countries for over two months (World Health Organization 2020a).
The virus has the potential to transmit from symptomatic and asymptomatic individuals which has made it difficult to control its spread around the globe.
Colombia identified its first imported case of COVID-19 on March 2 nd , and by July 25 th has reported 240,745 cases and 8,269 deaths (Instituto Nacional de Salud 2020a) which is a low incidence compared to other countries in Latin America (Pan American Health Organization J o u r n a l P r e -p r o o f 2020). It has been postulated that this relative mild behavior of the virus may be partially explained by the lock down and demographic features, considering that 86,4% of Colombian population is under 60 years of age (Departamento Administrativo Nacional de Estadística 2018).
The mild impact of the COVID-19 pandemic in Colombia is unexpected given that it is one of the most unequal countries in a very unequal region of the world, with a health system that, despite having a high insurance coverage of the population (94.6%) (Ministerio de Salud y Protección Social 2018), still struggles to provide good quality healthcare services. In addition, a large number of hospitals are currently underfunded, since financial resources flow from health insurance organizations (HIO) to hospitals through a cumbersome process where HIOs have the upper hand and can leverage multiple strategies in order to block payments after a service has been provided. Currently, HIOs withhold more than US$ 3 billion to hospitals from individual health services provided from 2000 thru 2019, a debt that duplicated over the last five years (Asociación Colombiana de Hospitales y Clínicas 2019).
Given the structural shortcomings of the Colombian health care system, the apparent good standing of the country during the COVID-19 pandemic is a positive but unexpected outcome.
Here, we present the results of a critical analysis of the epidemiological data of the pandemic in Colombia five months after the report of the first case, and try to explore the reasons behind the apparent success of Colombia in maintaining a low number of COVID-19 cases.
This is a descriptive analysis of the Polymerase Chain Reaction (PCR) confirmed cases that have occurred in Colombia from March 2 nd to July 25 th , 2020. This epidemiological data is curated by the Colombian National Institute of Health (INS for its name in Spanish, J o u r n a l P r e -p r o o f Instituto Nacional de Salud). Daily updates of the epidemiological data can be found at https://www.ins.gov.co/Noticias/Paginas/Coronavirus.aspx (Instituto Nacional de Salud 2020a).
The INS is the head of the Colombian National Surveillance system. It provides technical advice on public health surveillance to local healthcare institutions and coordinates the field investigation of cases and the confirmatory laboratory testing for COVID-19 cases.
Case and severity of disease definitions used for surveillance of COVID-19 are provided by the INS and can be fully accessed at the following URL:
http://www.ins.gov.co/Noticias/Coronavirus/Estrategia%20VSP%20COVID-19%2023072020.pdf . From March to June, RT-PCR was predominantly recommended for symptomatic cases with travel history or contact with travelers, despite the fact that local transmission replaced imported transmission since April. Only in July, testing criteria were expanded to include symptomatic and asymptomatic suspected cases with or without risk factors.
The INS database contains the following information: 1. Dates on: symptoms onset, sample taking, epidemiological report, laboratory diagnosis, recovery, and death. 2. Age and sex. 3. City of residence. 4. Severity of disease classified as: asymptomatic, mild, moderate, and severe. 5.
Place where health care is provided stratified by: hospital care, Intensive Care Unit (ICU), or home care.
Data was described according to the level of measurement. Proportions were used for nominal or ordinal variables and means or medians were used for continuous variables. Incidence rates of confirmed COVID-19 cases were estimated by department (state) and for several municipalities J o u r n a l P r e -p r o o f stratified by age and sex. COVID-19 mortality rates were also estimated by departments and for selected municipalities stratified by age and sex.
We assess the capacity of departmental surveillance systems using several indicators: 1.
Cumulated proportion of samples taken by million people by department. 2. Mean number of positive contacts for every imported case. 3. Average interval in days between onset of symptoms and date of diagnosis. 4. Average interval in days from date when case was detected to date of diagnosis.
A conservative assessment of the potential underestimation of cases was done using the following approach: 1) We estimated the ratio of samples taken per million people by department and districts and identified the geographical area with the highest ratio. 2) That ratio was projected to the population of every department/district, in order to estimate the "potential number of samples" that the surveillance system would have obtained should they have done the same effort for every geographical area. 3) The positivity ratio of samples by department was obtained dividing the number of positive samples in a department/district, over the total number of samples taken at that department. 4) A "potential number of cases" by department, was obtained by multiplying the "potential number of samples" of a particular department by its positivity ratio. See Table 1S supplementary material.
We did a visual analysis on how COVID-19 was disseminated around the country during the first month of transmission by mapping departments and municipalities where local cases -not linked to imported transmission -were detected during March 2020. Also, we aggregated these territories into geographic regions to describe the differential trends in the number of cases (mild, moderate, severe) and deaths ( Figure 1S ).
All data was analyzed using Microsoft® Excel, EPIINFO 7.2, Stata 12 (Stata Corporation, College Station, TX) and Python v3.6 (packages: pandas, geopandas, matplotlib and seaborn).
By July 25 th , Colombia has confirmed 240,795 cases of COVID-19 and 8,269 deaths, with differential trends by geographic regions (Figure 1 ). Men accounted for 53.6% of cases while 72% occurred in persons aged 0-49 years (Table 1 ). Most cases (90.1%) were asymptomatic or presented with mild clinical manifestations, 0.7% were at ICU and 51.5% have recovered so far.
The cumulated incidence of infection is 478 per 10 5 persons and the average density incidence is 97.6 cases per 10 5 persons-month. Incidence rate is lower among persons < 20 years of age (<50 cases per 10 5 persons-month) and, among adults >20 years of age, it increases to more than 100 cases per 10 5 persons-month. Case incidence was higher in males than in females (524 vs. 434 cases per 10 5 persons, respectively) ( Table 2 ).
The case fatality ratio (CFR) is 3.4% and the specific cumulated mortality rate was 16.4 per 10 5 persons. The CFR increases by age groups, from less than 1% among younger people (0 to 39 years) to more than 30% among the eldest (80 years and older). The specific mortality rate also increased from less than 1 per 10 5 among younger people (< 20 years old) to more than 200 per 10 5 people among the eldest (>80 years old). CFR was higher among men compared to women (4.3% vs 2.4% respectively) and the same trend was observed for the mortality rate (21.3 vs 11.8 cases per 10 5 ) ( Table 2 ).
All departments have reported cases, but 292 municipalities are apparently free of COVID-19 (26%) and 373 (33.2%) have had limited transmission. The incidence rate by department ranges from 1.8 to 3,160 cases per 10 5 people, with the highest rate reported in Amazonas (7 times the average national rate), a southern region that shares borders with Brazil and Peru. Other 4 departments (Atlántico, Caquetá, Cesar, and Sucre ) and 4 special districts (Bogotá DC, J o u r n a l P r e -p r o o f Barranquilla DE, Buenaventura DE, and Santa Marta DT) exceeded the national average rate (Table 3) . Figure 1 shows the epidemic curve for the country and for the five main geographical areas. It suggests that widespread transmission started in June, after the lockdown was relaxed and a gradual commercial opening started. By May 16 th , after 14 weeks of transmission, Colombia had reported 15,000 cases and 560 deaths and by June 15 th , five weeks later, the number of cases had tripled (47,000 cases and 1,545 deaths). Early peaks of transmission were detected in the Amazon, which started while the lock down was still in place.
Specific mortality rates by department ranged from 0 in Vichada to 1,278 in Amazonas which has 7.8 times the national rate (IRR=7.8 Confidence interval (CI) 95% 6.4-9.5). Eight additional areas (Atlántico, Barranquilla DE, Bogotá DC, Buenaventura DE, Caquetá, Choco, Santa Marta DT, and Sucre) surpassed the national mortality rate (Table 3 ).
The age average increased with the severity of clinical presentation, being 37.5 years (standard deviation -SD-: 17.6) for mild disease, 51.9 years (SD: 20.5) for moderate disease, 54.6 (SD: 19.7) for severe clinical presentations, and 68.1 (SD:16.5) for people who died. These differences persisted by geographical area (Table 4) .
The Colombian surveillance system has taken 1,370,271 samples (27,203 samples per million people), but there is wide variability in the number of samples taken by department (range 2,664 to 158,681/10 6 ). The positivity ratio (number of cases/number of samples) varied from 0.2% to 79.3% with a national average of 17% (Table 1S supplementary material) .
It took a median of 11 days to confirm a case from the date of beginning of symptoms (Interquartile range -IQR-: 2 days), and 5.6 days from the date of case detection by the health J o u r n a l P r e -p r o o f system (IQR:1.7 days). A median of 5 days passed from the beginning of symptoms to case detection (IQR:1.9 days) ( In March, local transmission (cases without travel history or contact with travelers) were identified in 14 different geographic areas covering the most populated departments. Only the Eastern area of the country did not report such cases at that time ( Figure 2 ).
Using a conservative approach to assess the potential underestimation of cases, it was estimated that, by July 25 th , Colombia should have detected 1,328,175 cases instead of the actual 240,795 observed, an underestimation of 82% (Table 1S , Supplemental material).
After approximately 140 days of COVID-19 transmission in Colombia, the number of cases and deaths are extremely low compared to projections. A mathematical model estimated that 21,237,000 cases and 212,000 deaths would occur in the first 100 days of the epidemic without J o u r n a l P r e -p r o o f interventions (Instituto Nacional de Salud 2020b). However, only 0.3% and 0.7% of forecasted cases and deaths were reported after those first 100 days, respectively.
Compared to other Latin American countries, Colombia has a lower incidence rate per 10 5 people (478/10 5 ), than Peru (1,212/10 5 ), Panama (1,507/10 5 ), Chile (1,819/10 5 ), Brazil
(1,157/10 5 ), and Bolivia (636/10 5 ). Its mortality rate (17/10 5 ) is lower than Brazil (42/10 5 ), Chile (49/10 5 ), Panama (33.8/10 5 ), Mexico (33/10 5 ), and Ecuador (32.8/10 5 ). In addition, it has a higher rate of sampling than most Latin American countries excepting Chile, Panama, and Uruguay (Hasell et al. 2020 ).
One factor explaining Colombia's seemingly success in containing the pandemic, is the enforcement of a strict lockdown early on. On March 18 th 2020, the Colombian government Colombia, which may be explained by differences in the efficacy of the lockdown, differences in testing, and social and climatic conditions, some of which will need to be studied in the future (Villalobos et al. 2020 ) (Perez-Brumer and Silva-Santisteban 2020).
There is no experimental evidence on the effectiveness of social distancing measures, but modelling approaches consistently show that they are followed by a sharp decline in cases and deaths (Institute for Health Metrics and Evaluation (IHME) 2020; Kennedy et al. 2020) . With our data, we may cite the low number of cases in the department of Cundinamarca as an evidence of the success of the lockdown imposed in Bogotá DC, the country's capital. Bogotá DC is surrounded by a myriad of Cundinamarca's municipalities, containing around 3 million people and there is continuous mobility of people from and to Bogotá DC. The lock down stopped this mobility and was so far, Cundinamarca has a rate incidence ~5 times lower than Bogotá DC.
Two additional factors may influence the low incidence and mortality rates: weather and demographic profile. In Colombia, peaks of respiratory virus transmission and related mortality occur mostly during rainy seasons (Porras Ramírez et al. 2009; Cotes et al. 2012 ). However, since October 2019 the country has experienced an unusually longer dry season that has extended through June 2020 (El Tiempo 2020). This year, the rainy season started late in June for most regions of the country, coinciding with the end of the lockdown. The potential role of both factors in the increase of cases and deaths observed after May has yet to be ascertained. In addition, Colombia has a relatively young population which may have attenuated the SARS-CoV-2 impact on mortality (Amariles et al. 2020) .
Underreporting and surveillance weaknesses may also contribute to hide the real number of cases and deaths in Colombia. Colombia kept a conservative case definition (see methods) for the first J o u r n a l P r e -p r o o f 2 and a half month of transmission because of shortage in biological and laboratory supplies to perform PCR tests. While stringent criteria for sampling and testing may succeed in keeping demand at bay, it also contributes to the underestimation of cases.
Some surveillance indicators suggest that Colombia may have missed a substantial number of cases. First, the sampling ratio differs widely by department. Results in Table 1S suggest that if the Amazonas' sampling ratio would have been reached by other departments, the number of cases would have increased 8-fold. Second, the ratio of COVID-19 positive contacts per imported case suggests that some departments were unable to track most imported cases. In three departments/districts with the highest incidence rates, the ratio of positive contacts to imported case was 4 to 12 times the national average (19 to 1). Although super spreaders of COVID-19 may exist, large ratios of positive contacts to imported cases suggest a lack of capacity to detect and track all imported cases (Pung et al. 2020) .
Although the first imported case was detected on March 2 nd , it is highly likely that transmission started in February or even in late January. The map in Figure 2 shows that there was simultaneous detection of local cases without known links to imported cases in several distant areas in March. One of the first cases reported symptoms on February 29 th , several days before the first imported case was detected. The role of undetected transmission during February in the subsequent pattern of disease spread is difficult to stablish given the limitations in testing during March and April.
Colombia has increased its surveillance capacity. It jumped from taking less than 3,000 samples/week in the first week to > 600,000 in July (Table 4S ). But wide gaps between departments remain. The three largest cities have 60% (36/60) of the laboratories able to perform PCR testing, whereas 12 out of 33 departments have no such facilities. Public health officials J o u r n a l P r e -p r o o f must wait up to 15 days to confirm cases, which hampers their ability to detect and track contacts.
As reported by others, age is one of the strongest predictors of mortality (A and A 2020; Immovilli et al. 2020) . Patients who died were, on average, older (68 years) than mild cases (37 years). In Wuhan, average age among deceased people was 68 years ). Wu et al., first reported that CFR increases with age. But CFR in people aged 70 years or older, was lower in Wuhan than in Colombia (14% vs. 25%) (Wu and McGoogan 2020) .
Mortality in Colombia varied by geographic region and may be related to availability of ICU beds and quality of care. Public discussions have centered around the number of ICUs with ventilators available in the country, and whether there will be enough to cope with the potential demand produced by COVID-19 (Amariles et al. 2020) . Another caveat is the availability of healthcare workers. For example, the only hospital covering the Amazonas population (79,020 inhabitants) has five ICU beds with ventilator but lacks trained personnel on critical care. The impact of these shortcomings is reflected in the high mortality rate observed there (~8 times the national average). Surpassing even the mortality rates observed in Peru (21/10 5 ), Brazil (20/10 5 ), and Ecuador (23/10 5 ). Regional inequalities in the pandemic response capacity and their relationship to mortality in Colombia, mirror those described in China (Ji et al. 2020 ).
Our analysis has limitations. We did not have access to clinical records of infected individuals. Therefore, we are unable to assess the role of chronic underlying diseases in the mortality by COVID-19. The INS has published a list of the frequency of comorbidities in COVID-19 patients who died, including hypertension (28%), diabetes (15%), chronic obstructive pulmonary diseases (12%), obesity (8%), history of smoking (5%), hypothyroidism (4%), and 13% without underlying diseases. However, it is not possible to know how many concurrent diseases were present in each patient, or how they compare to individuals with milder presentations.
One strength of the present study is that it provides a critical overview of the potential explanations for the apparent Colombian success in mitigating the effect of the pandemic.
Besides discussing the effect of the preventive measures adopted by the government, we discuss the role that weaknesses in the surveillance system, as well as sociodemographic and climatic factors, may have had in the unexpected positive results. This analysis provides a baseline for monitoring the impact that changes in containment strategy, such as relaxing lockdown measures on May 15 th , may have on COVID-19 transmission. It may also help to assess the impact of the ongoing improvements in laboratory capacity that Colombia is implementing.
The authors received no financial support for this research.
The ethical approval or individual consent was not applicable. 