Unveiling Pandemic Patterns: A Detailed Analysis of Transmissibility and Severity Parameters Across Four COVID-19 Waves in Bogotá, Colombia

doi:10.21203/rs.3.rs-3914714/v1

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Unveiling Pandemic Patterns: A Detailed Analysis of Transmissibility and Severity Parameters Across Four COVID-19 Waves in Bogotá, Colombia

https://doi.org/10.21203/rs.3.rs-3914714/v1

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This retrospective study on COVID-19's four waves in Bogotá, Colombia, scrutinises 1.77 million cases from March 2020 to April 2022, revealing significant shifts in both transmissibility and severity. The study highlights dynamic changes in the instantaneous reproduction number (Rt), with the highest values (> 2.5) corresponding to the ancestral and Omicron variants. There was a notable 88% decrease in the Case Fatality Ratio (CFR) from the first to the fourth wave, emphasising changing severity levels. The third wave, marked by the Mu variant, saw the highest case and death counts, yet paradoxically showed a decrease in CFR and an increase in the hospitalisation fatality ratio. Conversely, the fourth wave, dominated by Omicron, had the lowest severity despite higher hospitalisation rates in children. Additionally, the study records a consistent reduction in average hospital and ICU stay durations, from 10.84 days to 7.85 days and from 16.2 days to 12.4 days respectively, across the waves. These findings underscore the importance of ongoing epidemiological surveillance and adaptable public health strategies in lower-middle-income regions like Bogotá, deepening our understanding of COVID-19's impact in Latin America.

Understanding the statistical characteristics of an epidemic is crucial for modelling and managing public health emergencies. During the COVID-19 pandemic, early estimates based on preliminary reports[1],[2] were crucial for projecting spreading scenarios across different geographies. This pandemic exhibited marked geographical heterogeneity, influenced initially by variations in population demographics and health system capacities, and later by the diversity in interventions and contact patterns[3]. The evolving epidemiological scenario was further complicated by the advent of novel SARS-CoV-2 variants and unequal vaccination rates, presenting challenges in comprehending the regional disease dynamics[4].

In Latin America, especially in major urban centres like Bogotá, Colombia, distinct epidemiological patterns emerged during the COVID-19 pandemic. Some of these trends were initially identified in the early phases of the outbreak[5], yet a comprehensive retrospective characterization remains lacking. While numerous studies have detailed epidemiological parameters across various high-income countries[6, 7], there is a notable deficit in holistic retrospective analyses that integrate epidemiological, clinical, and genomic data on a global scale, particularly in the Latin American context.

Addressing this void, our study presents a nuanced statistical analysis and comparative examination of the transmissibility and severity of the first four COVID-19 waves in Bogotá. Covering March 2020 to April 2022, our work distinguishes itself by synthesising diverse data sources to elucidate the pandemic's multifaceted dynamics in a key Latin American urban setting.

Data

Confirmed cases: the confirmed cases database of the District Health Secretary of Bogotá (SDS) stores individual information on dates: symptoms onset, admission to general hospitalisation and intensive care units (ICU), discharge from hospitalisation services, and death. It also contains information on the condition of patients, the level of severity of the infection, and demographic details such as age and sex[8]. This database is maintained and updated with the daily report of confirmed cases from PCR and antigen tests and the information on patients' status provided by the National Epidemiology Surveillance System (SIVIGILA).

To compute stays at general hospital beds we created end dates using the following hierarchy of available dates: ICU entrance, discharge, and death. Similarly, we calculated stays in ICU creating the end dates from discharge and death dates, following the same hierarchy. To validate the estimated end dates, we recreated daily occupancy curves from the confirmed cases report. We compared them with the official report of ICU occupancy for both services available on the open data websites: Datos Abiertos Bogotá[9] and Saludata Bogotá[10].

Genomic data

the genomic surveillance data was published by the Global Initiative on Sharing All Influenza Data (GISAID)[11],[12]; it contains the genomic sequences for SARS-CoV-2, processed by different laboratories all across the country. We classified the viral lineages using the nomenclature presented in the literature[13] and counted the sequences grouped by epidemic week, starting from the 12th week of 2021 which is the date earliest date available.

Statistical methods

Start and end dates of waves: we computed the start and end date of each wave using the first derivative criteria for change of convexity applied on the daily series of new cases. For that purpose, we differentiated this series and calculated its roots using simple linear interpolation. To avoid multiple roots generated by the typical oscillations of series, we smoothed both series using the Gaussian smoothing method with a kernel width of 10 days.

Reproduction number

we estimated the time-varying instantaneous reproduction number using the epidemiological R package EpiEstim[14]. We used the report of daily new cases from the confirmed cases database, grouped by onset date, distinguishing imported from local cases. We assumed an incubation period of 5 days and a serial interval of 6.48 days with a standard deviation equal to 3.83 days[15],[16]. Additionally, we estimated the delay time of the database using the percentile 0.9 of the distribution of differences between reporting and onset dates of cases. We ran these estimations for the total confirmed population in Bogotá and for adults 60 years of age or older to compute the possible effects of changes in the focalization of massive testing strategies in Bogotá.

Transmissibility advantage

we evaluated the transmissibility advantage using a multinomial logistic regression with a single explanatory variable given by

$$f(v,t)=\alpha +{\beta }_{v,0}t$$

where $\alpha$ is the intercept of the model and ${\beta }_{v,0}$ is the variant-specific parameter for the time covariate, which is computed with respect to a reference (or pivot) variant. For simplicity, we chose Alpha as the pivot variant, which is the first observation in time that we have.

In general terms, the coefficients ${\beta }_{v,0 }$ can be used to calculate the transmissibility advantage of a variant $v$ with respect to the pivot variant (in our case Alpha) by means of the following relation[17].

$${T}_{v,0}=exp\left(\frac{{\beta }_{v,0}}{7}{g}_{0}\right)$$

Where ${g}_{0 }= 4.5 days, (3.7–5.4)$ is the generation time of Alpha[18], and the coefficient was divided by seven to normalise its value to daily scale. Thus, we can compute the transmissibility advantages between any two different variants $w$ and $v$, using the transmissibilities ${T}_{w,0}$ and ${T}_{v,0}$ as follows:

$${T}_{w,v}= \frac{{T}_{w,0}}{{T}_{v,0}}$$

Severe outcomes

we computed the Hospitalisation Case Ratio (HCR), ICU Case Ratio (ICU-CR) (HCR), Case Fatality Ratio (CFR), Hospitalisation Fatality Ratio (HFR) and ICU Fatality Ratio (ICU-FR) disaggregated by sub-populations where is the number of wave

and $g\in \{0-9, 9-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+\}$ the age-group. For the case ratios we used:

$$XC{R}_{i,g} =\frac{{X}_{i,g}}{{C}_{i,g}}$$

Where ${X}_{i,g}\in \{{H}_{i,g} ,IC{U}_{i,g}\}$ is the cumulative number of hospitalised patients (${H}_{i,g}$) and the cumulative number of patients at ICU ($IC{U}_{i,g}$) in a subpopulation $(i,g)$, and ${C}_{i,g}$ is the cumulative number of cases by sub-population. On the other hand, the fatality ratios were calculated as:

$$XF{R}_{i,g} =\frac{{D|X}_{i,g}}{{X}_{i,g}}$$

In this case ${X}_{i,g}\in \{{C}_{i,g} , {H}_{i,g} ,IC{U}_{i,g}\}$ and $D|{X}_{i,g}$ is the cumulative number of deaths given that they belong to the population ${X}_{i,g}$[19].

We also computed the percentages of Hospitalisation, ICU admission and Deaths per age group and wave:

$$Y{\%}_{i, g} = 100\times \frac{{Y}_{i,g}}{{Y}_{i}}$$

Where ${Y}_{i,g}\in \{{H}_{i,g}, IC{U}_{i,g}, {D}_{i,g}\}$ is the number of cases for each outcome per wave and age group and ${Y}_{i}\in \{{H}_{i}, IC{U}_{i}, {D}_{i}\}$ is the total number of cases for each outcome per wave. In all cases we estimated confidence intervals of 95% using binomial proportions.

Probability distributions of epidemiological delays

we fitted the probability density functions to the observed distributions for onset to death, general hospitalisation, and ICU entrance; and for stays at general hospital and ICU beds. We used a Bayesian hierarchical model[20] to fit the parameters of each distribution. In this order, we assumed that the set of parameters of the -th wave was normally distributed as follows

$${q}_{i,j} \sim N({q}_{i, Bog},{\sigma }_{i})$$

where $i=\text{1,2},3, .., n$ runs over $n$ parameters of certain PDF, $j=\text{1,2},\text{3,4}$ is the wave, ${q}_{i, Bog}$ is the value of the $i$-th parameter of the PDF estimated for Bogotá and ${\sigma }_{i } \sim {N}^{+}\left(\text{0,1}\right)$ is the standard deviation which is assumed to be distributed as a truncated normal distribution. For simplicity, we assumed normal truncated distributions ${N}^{+}\left(\text{0,1}\right)$ as prior probabilities for the parameters at the district level. All the parameters were estimated within a confidence interval of 95%.

We ran all the estimations of posterior samples using the Hamiltonian Monte Carlo (HMC) algorithm implemented in Stan, setting four chains of 2000 iterations (1000 for warming up and 1000 for sampling). To get the best fitting for each epidemiological distribution, we compared the models by pairs using the Bayes Factor (BF), as follows:

$${B}_{ij}=\frac{z\left(y\right|{M}_{i})}{z\left(y\right|{M}_{j})}$$

Here $z\left(y\right|{M}_{i})$ is the evidence of the model ${M}_{i}$, computed using the Laplace approximation corrected with Thermodynamic Integration[21]^,[22].

We fitted the multinomial regressions and the Bayesian hierarchical models using the Hamiltonian Monte Carlo algorithm implemented in Stan. In all the cases, we used a typical number of 2000 iterations (1000 for warming up and 1000 for sampling) and sampled over 4 chains.

Overview

Between March 2020 and April 2022, Bogotá reported a total of around 1.77 million confirmed cases and 112,985 hospitalisations, including 38,088 ICU admissions, and 29,512 deaths associated with COVID-19. Across this period, there were four discrete “waves” of transmission (Fig. 1a, and details in Table S1, also see Statistical methods section for how “waves” were defined). Across all waves, a larger number of COVID-19 cases were reported in females than in males (Fig. 1b). The highest R(t) values were registered at the beginning of the first wave and close to the peak of the fourth wave (Fig. 1c). The former is associated with the original virus lineage from Wuhan and the latter with the Omicron variant (Fig. 1d). Genomic sequencing data is only available from March 2021 onwards and shows the third wave (the largest and deadliest, with 781,276 cases and 13,188 deaths during this period) was dominated by the Mu variant. Although the Delta variant dominated between August and September 2021, this did not lead to the resurgence of transmission and caused another wave. The fourth wave was dominated by Omicron (Fig. 1d).

Transmissibility advantage

Analysis of genomic sequencing data collated from cases in Bogotá over the period from March 2021 onwards highlights a dynamic pattern of establishment and replacement of variant lineages (Fig. 2). During the initial period following the availability of sequencing data, we observed the replacement of the Gamma variant by the Mu variant (mainly associated with the third wave). The Mu variant was subsequently replaced by the Delta variant (though this establishment occurred without leading to a resurgence of transmission). Following this, Delta was replaced by the Omicron variant (which caused the fourth wave of transmission in Bogotá) (Fig. 2a).

We applied a multinomial regression methodology to calculate the relative transmissibility of each variant (see the Methods section). Our analyses highlight that Delta, Gamma, Mu, and Omicron were more transmissible than Alpha, being 1.84 (1.63, 2.13), 1.35 (1.19, 1.56), 1.45 (1.28, 1.68), and 3.92 (3.35, 4.61) times more transmissible, respectively. Omicron exhibited the highest transmissibility advantage with respect to all the variants, being 2.13 (1.95, 2.33), 2.91 (2.65, 3.2), and 2.7 (2.46, 2.96) times more transmissible than Delta, Gamma, and Mu, respectively.

Note that, after the third wave, Delta became dominant in Bogotá, leading to the extinction of Mu. This fact makes it necessarily more transmissible than Mu (1.3 times its transmissibility advantage). Despite this, Delta did not cause a new outbreak after the end of the third wave.

Severe outcomes

The Hospitalisation Case Ratio (HCR), the ICU Case Ratio (ICU-CR), and the Case Fatality Ratio (CFR) decreased dramatically across the four waves of the pandemic (See Fig. 3a-c).

During the first wave, the all-ages CFR was noted to be 2.70% (95% Credible interval 2.60–2.80%). In the second wave, there was a substantial decrease at 1.80% (1.80–1.90%). The third wave saw a further marginal reduction in the CFR, at 1.70% and a significant change was observed in the fourth wave, where the CFR dropped substantially to 0.60% (0.50–0.60%). It means an 88% reduction of CRF from the first to the fourth wave (See details in Table S6)

Contrastingly, the Hospitalisation Fatality Ratio (HFR) showed varying trends across the four waves of the pandemic. In the first wave, the HFR was 23.90% (23.50–24.40%), and slightly higher in the second wave, at 24.30% (23.80–24.90%). A notable increase was observed in the third wave, where the HFR rose to 31.00% (30.50–31.40%), while the fourth wave saw a significant decrease in the HFR, dropping to 18.90% (18.00–19.70%). Similar trends were observed for ICU-Fatality Rate with the highest values during the third wave (See Fig. 3 and Table S6)

The age group with the highest difference to the overall trend of the other three waves was 50–59, with an HFR and ICU-FR of: 26.00% (25.10%, 26.80%) and 40.00% (38.50%, 41.50%), respectively. Interestingly, the values of the ICU-FR showed an increasing trend with age up to the 70–79 age group and then a decline for the 80 + age group during the first to the third waves (Fig. 3b)

In addition to the severe outcome ratios, we calculated the percentages of the population in general hospital and ICU services, as well as the distribution of deaths by age group for each wave (Fig. 4). The overall behaviour of the first three waves (panels a-c) was similar, yet the fourth wave exhibited drastic changes. Despite a significant decrease in the number of the population presenting severe outcomes during the fourth wave (Fig. S4), the percentage of children below 10 years of age in general hospitalisation and ICUs (Fig. 4 panels a and b) increased in comparison to the other waves. Moreover, the percentage of deaths predominantly occurred in the population aged 80 years and older, reversing the decreasing trend observed in this age group during the first three waves.

It is noteworthy that, except for the fourth wave, the hospitalisation and ICU percentages per wave were highest among those aged between 50 and 69 years, and lowest for individuals under 20 years old. For example, in the third wave, the least hospitalised age group was 10–19 years, and the most hospitalised was 50–59 years, with 0.85% (0.85%, 0.86%) and 23.02% (23.01%, 23.03%) of the hospitalised cases in that wave, respectively (Table S6).

Furthermore, during the fourth wave, which corresponded to a period when the Omicron variant was prevalent in the city (Fig. 1d), there was a marked increase in the percentage of hospitalised cases and ICU admissions for the age group 0–9 years. The percentages rose from 2.70% (2.70%, 2.71%) in the first wave to 16.17% (16.15%, 16.19%) in the fourth wave for general hospitalisation, and from 1.32% (1.31%, 1.33%) in the first wave to 10.20% (10.15%, 10.26%) for ICU admissions. However, these drastic increases did not result in a corresponding increase in the percentage of deaths for this age group.

Conversely, the percentage of deaths among the elderly (80 + years) dramatically increased in the fourth wave to 54.11% (54.03%, 54.19%). This age group accounted for 928 of the 1,715 deaths that occurred during the fourth wave (Table S1), but this rise in the contribution of deaths from this age group did not reflect an increase in the total number of deaths compared to previous waves, due to the overall lower severity of the Omicron variant as indicated by the reported fatality ratios (Fig. 3).

Epidemiological delay distributions

We utilised a previously developed hierarchical Bayesian framework to fit different statistical distributions to data describing the delays between key epidemiological outcomes (e.g. COVID-19 symptom onset and death). Our results highlight marked variation in the different epidemiological distributions across the four waves, with a clear downward trend for the fourth wave compared to previous ones. The highest values were observed for the second wave for the parameters onset to hospitalisation, and onset to death. Also, narrower distributions were observed for the fourth wave (Fig. 5). In most cases, we found evidence in favour of the Generalised Log-Normal distribution as the best model, except for the onset-to-death distributions, for which the best model was the Gamma distribution (Fig. S3 and Tables S4 and S5).

For the time from symptoms onset to hospitalisation, mean values of around 7–8 days were observed for the first three waves, with a significant reduction for the fourth wave to 5.54 (5.49, 5.57) days. For the time from symptoms onset to ICU admission, the mean values were reduced from the first to the fourth waves and went from 12.31 (12.22, 12.38) days to 7.84 (7.55, 8.17). For the time from symptoms onset to death, the mean value decreased from the first to the fourth waves, from 17.42 (17.33, 17.5) to 14.87 (14.65, 15.03) days.

For the duration of the hospital stay, a reduction of around 2 days, from 10.84 (18.83 (8.74, 8.93) to 8.83 (8.74, 8.93) days, was observed from the first to the second wave. No further changes were observed for the third wave. However, a further reduction of almost one day, from 8.77 (8.69, 8.86) to 7.85 (7.7, 8.01) days, was observed for the fourth wave with respect to the third wave. Interestingly, for the duration of the ICU stay, a reduction of about 1 day was observed in each consecutive wave, going from 16.2 (15.91, 16.52) to 15.4 (15.16, 15.67) and to 14.41 (14.25, 14.61), during the first, second, and third waves respectively. A further decrease of 2 days was observed for the fourth wave for which the mean ICU stay time was 12.4 (11.9, 13.11).

In summary, the fourth wave exhibited the shortest epidemiological timelines of the pandemic. The duration from symptom onset to hospitalisation and death remained relatively stable throughout the first three waves. Meanwhile, other hospital stay durations showed a consistent decrease from one wave to the next.

This study constitutes the first detailed exploration of the COVID-19 pandemic's progression in Colombia, employing an extensive dataset that includes 1,77 million cases, 105,831 deaths, 36,313 hospital admissions, and 28,274 ICU admissions from Bogotá. It uniquely compares transmissibility and severity parameters across four distinct waves, offering critical insights into the dynamic nature of the pandemic.

The analysis of transmissibility, as indicated by the instantaneous reproduction number (Rt), revealed significant fluctuations. During the early days of the pandemic in March 2020, linked to the ancestral strain, the Rt reached 2.8, denoting high transmissibility. A similar peak in transmissibility (Rt = 2.7) occurred in the fourth wave, around November 2021, corresponding with the Omicron variant's emergence. Genomic data, available from the latter part of the second wave, disclosed a sequence of variant displacement: Gamma initially overtook Alpha, followed by Mu's dominance in the third wave, the most extensive of the pandemic. This prevalence of Mu between April and July 2021 is hypothesised to have led to a significant proportion of the local population contracting the virus. The transition from the third to the fourth wave saw the Delta variant ascend to dominance, causing the decline of Mu. This trend mirrors observations from India and Europe, underscoring Delta's heightened transmissibility[23]. However, Delta's rise in Bogotá did not trigger a new outbreak, likely due to the reduced pool of susceptible individuals after the third wave. The emergence of Omicron towards the end of 2021 then swiftly replaced Delta, instigating a large wave of infections, attributable to its ability to evade immune responses.

In examining severity parameters, a consistent reduction across the waves was noted in the likelihood of hospitalisation, ICU admission, or death among confirmed cases. This trend aligns with observations from other nations, such as the UK, where a shift in severity estimates from the first wave was noted, primarily due to advancements in medical care[24]. Nonetheless, some intriguing patterns in severity emerged during the third and fourth waves that are worth mentioning.

The third wave, though characterised by high case and mortality rates at the population level, exhibited a lower Case Fatality Rate (CFR) but a higher risk of death post-hospitalisation or ICU admission. This pattern does not necessarily point to an inherently more severe Mu variant, but rather to a greater influx of severe cases into hospitals due to the largest infection surge. The strain on hospital capacity during this period reached an all-time high, as evidenced by peak hospital and ICU occupancy rates (Fig. S2).

On the other hand, the fourth wave, predominantly driven by Omicron, presented the lowest values of all severity parameters during the pandemic: CFR, Hospitalisation Case Ratio (HCR), ICU Case Ratio (ICU-CR), Hospitalisation Fatality Rate (HFR), and ICU Fatality Rate (ICU-FR). The marked decrease in severity was accompanied by a notable reduction in delay times across various metrics (onset to hospitalisation, onset to death and hospitalisation stays), potentially reflecting the overall less severe nature of this wave. This reduction could be a consequence of both the substantial vaccination coverage (60% with at least the first dose) and the reduced severity of the Omicron variant. Intriguingly, despite lower severity metrics, this wave witnessed an increase in mortality among those over 80 and a rise in hospitalisations in the under-10 age group.

This research underscores the crucial role of retrospective analytics in enhancing our understanding of the rapidly changing epidemiological parameters during a pandemic. It highlights the need for continued collaboration between governmental and academic institutions, which has been instrumental in developing the technological infrastructure necessary for accessing comprehensive information and facilitating robust data analysis. Such collaborative efforts are vital for enriching our understanding of the pandemic's trajectory and for informing future public health strategies.

Author Contribution

DSQ and ZMC designed the study. DSQ and NTD prepared data for analysis and conducted data analysis and visualisations. DO, AC, FA and DM collated the data, cleaned, organised, and verified the quality of the datasets. CW advised and supervised the statistical analysis. ZMC: conceived project idea, revised analysis, administered and supervised the project. All authors have revised the manuscript, read it, and approved the final version. All authors confirm full access to all the data in the study and accept responsibility to submit for publication.

Acknowledgements:

We gratefully acknowledge all data contributors, i.e., the Authors and their Originating laboratories responsible for obtaining the specimens, and their Submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. We also thank all the public health professionals who reported, cleaned, verified and submitted the information we used for this analysis. DSQ, NT and ZMC are funded in whole or in part by the TRACE-LAC project (Enhancing Tools for Response, Analytics and Control of Epidemics in Latin America and the Caribbean funded by the International Development Research Center (IDRC), grant number: 109848-002. C.W. was supported by Sir Henry Wellcome Postdoctoral Fellowship, reference 224190/Z/21/Z. This research was funded in whole, or in part, by the Wellcome Trust (reference 224190/Z/21/Z). This work was supported by the Ministerio de Ciencia, Tecnología e Innovación de Colombia, MinCiencias, AGORA Project: “Alianza para la Generación de evidencia sobre Covid-19, su Respuesta y lecciones Aprendidas para la postpandemia y futuras epidemias” (Contract No. 637–2022).

Data availability:

Datasets used are available at https://github.com/TRACE-LAC/covid19-waves-bogota/

Code availability:

Codes for this paper are available at: https://github.com/TRACE-LAC/covid19-waves-bogota/

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No competing interests reported.

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Unveiling Pandemic Patterns: A Detailed Analysis of Transmissibility and Severity Parameters Across Four COVID-19 Waves in Bogotá, Colombia

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Transmissibility advantage

Severe outcomes

Epidemiological delay distributions

Discussion

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