
==== Front
Lancet Reg Health Am
Lancet Reg Health Am
Lancet Regional Health - Americas
2667-193X
Elsevier

S2667-193X(24)00187-X
10.1016/j.lana.2024.100860
100860
Articles
Effects of mobility, immunity and vaccination on SARS-CoV-2 transmission in the Dominican Republic: a modelling study
Finch Emilie emilie.finch1@lshtm.ac.uk
a∗
Nilles Eric J. bc
Paulino Cecilia Then d
Skewes-Ramm Ronald d
Lau Colleen L. e
Lowe Rachel afg
Kucharski Adam J. a
a Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
b Harvard Humanitarian Initiative, Cambridge, MA, USA
c Brigham & Women's Hospital, Boston, MA, USA
d Ministerio de Salud Pública y Asistencia Social, Santo Domingo, Dominican Republic
e School of Public Health, The University of Queensland, Brisbane, Australia
f Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
g Barcelona Supercomputing Center, Barcelona, Spain
∗ Corresponding author. emilie.finch1@lshtm.ac.uk
30 8 2024
9 2024
30 8 2024
37 10086021 11 2023
25 7 2024
29 7 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Background

COVID-19 dynamics are driven by a complex interplay of factors including population behaviour, new variants, vaccination and immunity from prior infections. We quantify drivers of SARS-CoV-2 transmission in the Dominican Republic, an upper-middle income country of 10.8 million people. We then assess the impact of the vaccination campaign implemented in February 2021, primarily using CoronaVac, in saving lives and averting hospitalisations.

Methods

We fit an age-structured, multi-variant transmission dynamic model to reported deaths, hospital bed occupancy, and seroprevalence data until December 2021, and simulate epidemic trajectories under different counterfactual scenarios.

Findings

We estimate that vaccination averted 7210 hospital admissions (95% credible interval, CrI: 6830–7600), 2180 intensive care unit admissions (95% CrI: 2080–2280) and 766 deaths (95% CrI: 694–859) in the first 6 months of the campaign. If no vaccination had occurred, we estimate that an additional decrease of 10–20% in population mobility would have been required to maintain equivalent death and hospitalisation outcomes. We also found that early vaccination with CoronaVac was preferable to delayed vaccination using a product with higher efficacy.

Interpretation

SARS-CoV-2 transmission dynamics in the Dominican Republic were driven by a substantial accumulation of immunity during the first two years of the pandemic but, despite this, vaccination was essential in enabling a return to pre-pandemic mobility levels without considerable additional morbidity and mortality.

Funding

10.13039/501100000265 Medical Research Council , 10.13039/100010269 Wellcome Trust , 10.13039/501100000288 Royal Society , 10.13039/100000030 US CDC and Australian 10.13039/501100000925 National Health and Medical Research Council .

Keywords

COVID-19
Vaccination
Serology
Immunity
Transmission dynamics
Population mobility
Mathematical modelling
Bayesian inference
==== Body
pmc Research in context

Evidence before this study

Mathematical models have been used throughout the COVID-19 pandemic to provide decision-support to policy makers. However, there has been a lack of in-depth modelling for low- and middle-income countries, and particularly in those that were unable to suppress transmission through non-pharmaceutical interventions before the roll-out of vaccines. We searched PubMed using the terms: SARS-CoV-2 or COVID-19; vaccination, immunity or serology; transmission; and modelling, with search dates from 1/1/2020 until 14/08/2023. Many studies identified used mathematical models to characterise epidemic dynamics in high-income settings, particularly in Europe, North America and Asia. Most studies we identified outside these contexts focused on projecting the impact of future vaccination strategies, and we found few studies that incorporated multiple data streams. One study investigated COVID-19 dynamics in the Philippines during the first wave using an age-structured model incorporating mobility, and estimating the effect of control measures in a low immunity setting. Similarly, two studies aimed to investigate the Delta wave in Nepal and India, with the latter estimating the infections averted by vaccination. In addition to this, there have been several modelling studies aiming to estimate vaccination impact on a global scale. One study fit to reported and excess COVID-19 deaths in 189 countries or territories found vaccination averted 63% of total potential SARS-CoV-2 deaths globally in the first year of vaccination. Another study modelled the effect of vaccine inequities in 20 low- and middle-income countries, finding that more than 50% of deaths that occurred could have been averted had vaccines been available at the speed or quantity seen in high income countries. We found no studies that fit to multiple data streams focused on the Dominican Republic or Latin America and the Caribbean, a global hotspot in the first two years of the pandemic.

Added value of this study

To our knowledge, this is the first in-depth mathematical modelling study jointly fitting to serological and surveillance data investigating the transmission dynamics of SARS-CoV-2 in a high-incidence, low- and middle-income setting. This study adds to existing evidence by quantifying SARS-CoV-2 epidemic drivers in a setting where non-pharmaceutical interventions alone were unable to suppress transmission. Additionally, we estimate the impact of the vaccination campaign, which primarily administered CoronaVac vaccine, which to our knowledge has not yet been assessed in a context with high seroprevalence before vaccine roll-out. We then quantify the potential effect of using alternative vaccine products with higher efficacy, as well as the impact of delaying the campaign to wait for a higher efficacy product. These findings help to address questions around the impact of iniquities and delays regarding vaccine products available for low- and middle-income countries. Finally, this is the first study we are aware of to quantify the extra mobility (or return-to-normality) afforded by the vaccination campaign.

Implications of all the available evidence

Our analysis emphasizes the importance of setting up reliable data streams at a global level, including serological data, to accurately characterize transmission dynamics and better support public health decision-making in low- and middle-income countries. Furthermore, given the inequities in access to vaccine products and doses throughout the pandemic, there is a need to assess the impact of decisions around the timing, speed and efficacy of vaccination campaigns on COVID-19 burden. We found that, in the context of the Dominican Republic, the timing and speed of the vaccination campaign were crucial in averting deaths and hospitalisations, and relaxing social distancing measures.

Introduction

During 2020–22, many countries experienced a significant burden of COVID-19 and imposed non-pharmaceutical interventions (NPIs) aiming to control SARS-CoV-2 transmission. Despite this, countries experienced markedly different epidemic dynamics, mediated by a complex interplay of factors including population behaviour, government interventions, the introduction of new variants, the roll-out of vaccination campaigns and prior levels of transmission. Serological surveys have proven crucial to understand global and national landscapes of population immunity, as well as indicating the extent of prior exposure to SARS-CoV-2.1 Some countries pursued an elimination strategy throughout the pre-Omicron era, with stringent public health interventions resulting in low levels of seroprevalence towards the end of 2021. For example, in Hong Kong, serosurveillance studies found <1% of sera tested was positive for anti-N SARS-CoV-2 IgG prior to March 2021.2 Other countries, such as the UK and many EU countries, saw epidemic waves linked to the strengthening and relaxing of public health interventions, alongside the emergence of more transmissible variants. In England, approximately 20% of the population were estimated to have been infected with SARS-CoV-2 by July 2021.3 In contrast, SARS-CoV-2 transmission was largely unmitigated in some settings such as in Manaus, Brazil, where 76% of the population are thought to have been infected by October 2020.4

Latin America and the Caribbean was a global hotspot for SARS-CoV-2 transmission during 2020–21, prior to the emergence of the Omicron variant, which caused large epidemics globally.5,6 The Dominican Republic is an upper-middle income country with a population of 10.8 million, which shares the island of Hispaniola in the Caribbean with Haiti. They reported their first case of COVID-19 on 1st March 2020, which was followed by the imposition of strict public health measures including the closure of schools and workplaces, the cancellation of public events and the imposition of curfews. These began to be relaxed in July 2021 and were mostly lifted with the reopening of schools and relaxation of curfew measures in October 2021. In the first two years of the pandemic, the Dominican Republic experienced four waves of transmission: the first peaked in August 2020, with cases increasing again from November 2020 before a second peak in January 2021. A third wave of transmission took place over the summer of 2021, following the introduction of more transmissible variants, including Mu, with cases rising sharply to peak in July 2021. Following the introduction of the Delta variant, a fourth wave took place in October and November 2021.7 A nationally representative serological survey involving 6683 individuals from 3832 households took place between June and October 2021.8 Results from the serological survey estimated that 76.7% (95% CI 70.1–82.5) of the population had been previously infected by the study midpoint. This vastly exceeded earlier estimates constructed from coarse reported data for the Dominican Republic, and the wider region of Latin America and the Caribbean.9

The government of the Dominican Republic launched a national COVID-19 vaccination programme on 16th February 2021 initially focusing on health-care professionals and then following a three-phase age-based approach.10 The campaign first targeted individuals over 60 years of age, then expanding to individuals over 50 on 3rd May 2021 and all adults over 18 on 10th May 2021 (Supplementary Table S1). Booster vaccination for highly vulnerable individuals began in July 2021, with the Dominican Republic being the first country in the Americas to approve vaccination with a third dose.11 During the study period, approximately 90% of vaccine doses administered were Sinovac-CoronaVac (an inactivated viral vaccine), with Oxford/AstraZeneca vaccine (ChAdOx1-S, an adenovirus vector vaccine) and Pfizer/BioNTech (BNT162b2, mRNA vaccine) also administered.8 By the current study's endpoint (15th December 2021), 62% of the population had received at least one dose of a COVID-19 vaccine and 50% had received two doses.

Mathematical models have been used throughout the pandemic to provide decision-support to policy makers through estimation of key epidemiological parameters, forecasts of future incidence, projections of epidemic trajectories under different scenarios, and quantification of the impact of non-pharmaceutical interventions. However, despite regular and in-depth modelling decision-support for high-income countries, there has been a lack of equivalent modelling analysis to understand transmission and control in low- and middle-income countries.12, 13, 14, 15, 16, 17, 18 To address this gap, we used an age-structured transmission dynamic model to quantify the drivers of epidemic dynamics in the Dominican Republic during the first two years of the pandemic, and to assess the impact of the vaccination campaign on COVID-19 hospitalisations and deaths.

Methods

Data

For this analysis we incorporated multiple data streams. Aggregated daily reported deaths were collected from the Dominican Republic's COVID-19 Dashboard. Aggregated daily hospital and intensive care unit (ICU) bed occupancy were scraped from daily COVID-19 bulletins published by the Ministerio de Salud Pública y Asistencia Social, available from 19th September 2020 onward.19 We also used serological data from a nationally representative multistage SARS-CoV-2 seroprevalence survey undertaken between June and October 2021 (Fig. 1). This survey employed a multistage sampling method which assigned clusters to provinces, taking into account population, urban-rural divide, and the spatial dispersal of clusters. Individuals aged ≥5 years old were eligible to enroll in the study and 6683 individuals were surveyed in total. SARS-CoV-2 antibodies were measured using the Roche Elecsys SARS-CoV-2 electrochemiluminescence immunoassays. Large non-manufacture-sponsored studies demonstrated specificities and sensitivities of 99.8% (confidence interval, CI, 99.3–100) and 98.2% (CI 96.5–99.2) for the Elecsys anti-S assay and 99.6% (CI 98.9–100) and 90.8% (CI 81.3–95.7) for the anti-NC assay.20,21 Seroprevalence estimates were adjusted for study design, national demographics and assay characteristics. Further details of survey methodology and findings are available elsewhere.8Fig. 1 Map of the study setting and time-series of COVID-19 cases. Figure showing a map of the Caribbean with the location of the Dominican Republic shown in a box (a), a map of the Dominican Republic showing clusters sampled in the 2021 serosurvey (b), and daily COVID-19 cases in the Dominican Republic (bars) and the 7-day moving average (line) from March 2020 to January 2022 (c). The shaded grey area indicates the timing of the serological survey.

To obtain estimates of age stratified vaccination rates, we used data on the daily number of second vaccine doses distributed in the population and assumed that daily doses were evenly distributed between eligible age groups as per the government's vaccination program,22 Supplementary Table S1). If an eligible age group became fully vaccinated during the vaccination allocation, remaining vaccine doses were distributed between the remaining eligible age groups or, if all were fully vaccinated, between adults >20, mimicking the vaccination of younger health care workers or those with chronic health conditions. Estimated vaccination coverage by age group over time is shown in Fig. 2.Fig. 2 Google mobility data, SARS-CoV-2 sequence data, and estimated vaccination coverage by age. Panel a shows Google mobility data of the proportional change in population mobility in different locations relative to a pre-pandemic baseline, panel b shows the frequency of SARS-CoV-2 sequences from the Dominican Republic on GISAID by variant and panel c shows estimated vaccination coverage by age assuming vaccine doses were evenly distributed between eligible age groups.

Transmission dynamic model

We used covidM, an age-stratified, deterministic, compartmental model originally developed to model the effects of NPIs on SARS-CoV-2 transmission in the UK, and described fully elsewhere.23, 24, 25 In brief, covidM is structured into 5-year age groups, with individuals moving from a susceptible state (S) to an exposed state (E) and then either to a pre-clinical and clinical infected state (Ip followed by Ic) or a sub-clinical infected state (Is) and finally to a recovered state (R). The model explicitly considers two variants of SARS-CoV-2; wild-type and B.1.617.2 (Delta), while the introduction of other variants in the Dominican Republic in the intervening period (which include Mu, Gamma and Alpha) is captured through a gradual increase in transmissibility in the first half of 2021, following a logistic function. We fit the model to daily reported deaths, daily hospital and ICU bed occupancy and a cross-sectional seroprevalence estimate from June to October 2021. We fit the model using data until 15th December 2021, when cases began to increase due to the Omicron variant, and only simulate epidemic trajectories until this point.

Hospitalisation, ICU admission and death are modelled as observation processes according to age-specific infection-severe ratios, infection-critical ratios and infection-fatality ratios based on estimates from the literature. These are adjusted on the log odds scale by several fitted parameters and delays from infection to hospitalisation, ICU admission and death are estimated during the model fitting process. We assume that the observed number of deaths, hospital bed occupancy and ICU bed occupancy are distributed according to a negative binomial distribution, with the overdispersion parameter estimated during the model fitting process. We used a skew-normal likelihood for seroprevalence with the same mean and 95% confidence interval as reported for the data evaluated for the period of the serosurvey.

We consider a central waning assumption corresponding to 15% loss of post-infection protection after 1 year. Full details on model equations, fixed and fitted model parameters can be found in Supplementary Tables S2–S6.

Vaccination parameters

We also incorporated information on SARS-CoV-2 vaccination in the Dominican Republic, using data collated by Our World in Data (10). Fully vaccinated individuals moved to a vaccinated model compartment (V) from which, subject to vaccine waning parameters, they can move to the exposed state (E) or directly to a sub-clinical infection (Is). We assume vaccinated individuals have a lower probability of clinical or sub-clinical infection but that, once infected, they have the same infectiousness as non-vaccinated individuals. We model vaccine efficacy against infection (VEinf) and vaccine efficacy against severe disease given infection (VEsd|inf) as described in Fig. 3 and Supplementary Table S5. We used vaccine efficacy against severe disease rather than symptomatic disease as we fit directly to hospitalisations, ICU admissions and deaths. However, we conducted sensitivity analysis around the vaccination parameters used, as well as waning assumptions (Supplementary Tables S7–S9).Fig. 3 Model schematic showing a two-variant SEIR model structure with a vaccination compartment (V). Model compartments Si,Ei,Ipi,Ic,i,Isi represent the number of individuals who are susceptible, exposed, infected (pre-clinical), infected (clinical) and infected (sub-clinical or asymptomatic) in age group i and either the wild-type or Delta variant respectively. Parameters are defined as follows: λi is the force of infection for age group i; vi is the number of daily vaccinations for age group i; σ is 1/dE (where dE is the duration in the exposed compartment or the latent period); γp is 1/dP (where dP is the duration of preclinical infectiousness); γs is 1/dS (where dS is the duration of clinical infectiousness); γc is 1/dC (where dC is the duration of sub-clinical infectiousness). yi is the probability of clinical symptoms given infection for age group iwn is the rate of waning of post-infection immunity and wv is the rate of waning for post-vaccination immunity. Further description of model equations and parameter values is given in the Supplementary Tables S2–S6.

Approximately 90% of the vaccine doses administered during the study period were Sinovac-CoronaVac, with Oxford/AstraZeneca and Pfizer/BioNTech also administered.8 As a result of this we used vaccination efficacy parameters based on the literature available for Sinovac-CoronaVac. We model differing vaccine efficacy by strain but assume the same vaccine efficacy across age groups.26,27 Note that we do not directly model vaccine efficacy against hospitalisation or death.

Mobility

In this analysis, we used separate age-stratified social contact matrices for contacts in the home, at work, in school, or in other locations (for instance leisure or transport), simulated from contact surveys and demographic data.28 To estimate changes in contact rates during the pandemic, we used population mobility data captured by Google's COVID-19 Community Mobility Reports as a proxy for changes in population behaviour. We mapped changes in mobility to changes in contact rates using the relationship between mobility and contact survey data found in the UK.23,29 School contact rates were set to zero during school closures and school holiday periods. The representativeness of Google mobility data is dependent on the proportion of the population with smartphones using Google products. As this differs between the UK and the Dominican Republic, we infer a weighting between UK-adjusted contact rates and pre-pandemic baseline contact rates in the Dominican Republic, fitting a separate weighting parameter for each year of the simulation period.28 We found the UK-adjusted value was given more weight in the first year of the pandemic than the second, suggesting that the relationship between measured population mobility and contact rates changed during the pandemic.

Model fitting

We performed Bayesian inference using Markov chain Monte Carlo to estimate model parameters. We used the Differential Evolution Markov Chain Monte Carlo (DE-MCMC) algorithm which combines a genetic algorithm (Differential Evolution) with MCMC.30 Here, multiple Markov chains are run in parallel and learn from one another to determine the scale and orientation of the proposal distribution, which allows for more efficient exploration of a complex parameter space than traditional Metropolis–Hastings algorithms, particularly when considering correlated parameters. Model convergence was assessed using trace plots of MCMC chains and the Rhat statistic.31 Posterior estimates of fitted parameters and a histogram of residuals are shown in Supplementary Figures S1 and S2.

Counterfactual analysis

We conducted counterfactual scenario analysis using the fitted model to simulate epidemic trajectories under different scenarios. We considered five key scenarios with changes applied from the beginning of the vaccination campaign (15th February 2021) until the end of the analysis period:1. No vaccination

2. Vaccination using a vaccine with a Pfizer/BioNTech efficacy profile

3. Delayed vaccination using a vaccine with a Pfizer/BioNTech efficacy profile

4. Vaccination using a vaccine with an Oxford/AstraZeneca efficacy profile

5. Delayed vaccination using a vaccine with an Oxford/AstraZeneca efficacy profile

In Scenario 1 we assume no vaccine doses are distributed. In Scenarios 2 and 3 we assume the same timing of vaccine distribution, but with a Pfizer/BioNTech or Oxford/AstraZeneca efficacy profile (Supplementary Table S7). For Scenarios 3 and 5 we consider a vaccination programme using a Pfizer/BioNTech or Oxford/AstraZeneca efficacy profile with a delay of two months. Here vaccination would begin on 15th April 2021, aligning with initial deliveries of Oxford/AstraZeneca vaccine doses through the COVID-19 Vaccines Global Access initiative (COVAX).32

For each scenario we ran 500 simulations, drawing parameters from the posterior distribution of each fitted parameter, and calculated the difference in hospital admissions, ICU admissions and deaths from the original model fit to estimate the impact of the scenario considered.

We also conducted a counterfactual analysis to examine the trade-off between differing levels of vaccination coverage and changes in population mobility on deaths, hospital admissions and ICU admissions in the time period between 16th February and 16th August 2021. We considered 11 vaccination coverage scenarios with coverage by 16th August 2021 ranging from 0 to 100% in 10% increments, and 9 population mobility scenarios changing ‘Work’ and ‘Other’ mobility by an extra −40% to +40% compared with the Google mobility data during the simulation period. Vaccination allocation for each scenario was performed in a similar way as described above, where daily vaccinations are multiplied by a factor equal to CoveragesimulationCoverageactual. Vaccination doses are then distributed between eligible age groups and, once these are fully vaccinated, between age groups >20 and then finally between age groups <20. We then used the fitted model to simulate epidemic trajectories for 99 scenarios, considering all combinations of vaccination coverage and population mobility change, and estimated the impact of the scenario considered as above.

Ethical approval

Ethical approval for this research was given by the London School of Hygiene and Tropical Medicine Ethics Committee (reference: 26214). Ethical approval for the seroprevalence study was given by the National Council of Bioethics in Health, Santo Domingo (013–2019), the Institutional Review Board of Pedro Henríquez Ureña National University, Santo Domingo, and the Mass General Brigham Human Research Committee, Boston, USA (2019P000094).

Role of the funding source

The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or decision to submit. The corresponding authors had full access to all the data and the final responsibility to submit for publication.

Results

COVID-19 transmission dynamics between 2020 and 2022

Our modelling analysis suggests that, after an initial decline in transmission following a sharp reduction in social interactions, COVID-19 dynamics were driven by substantial accumulation of immunity throughout 2020–2022 (Fig. 4), as well as the spread of novel variants such as Mu in mid-2021 and Delta in late 2021. By jointly fitting to reported deaths, hospital bed occupancy, ICU bed occupancy and seroprevalence data, the model reproduced the overall observed epidemic dynamics. Estimated deaths did not track closely with observed deaths during the first wave from March to September 2020, and deaths and hospitalisations were slightly underestimated during the second wave in January 2021.Fig. 4 Comparison of model fit to observed data in the Dominican Republic from February 2020 to December 2021. Panels show comparison between modelled and observed hospital bed occupancy (a), ICU bed occupancy (b), reported deaths (c) and proportion of the population previously infected (d). Black lines show observed data, with horizontal dashed lines in panels (a) and (b) indicating the point at which hospitalisation data became available. For panel (d) the black cross shows the duration of the serosurvey (horizontal line) and the 95% confidence interval around the central estimate (vertical line). Modelled hospital bed occupancy, ICU bed occupancy, deaths, and proportion previously infected, are shown in orange, red, purple and green respectively, with associated 50% and 95% credible intervals in surrounding ribbons. Note that uncertainty in the observation process is included in modelled outputs for surveillance data streams (a, b and c) but not for the proportion previously infected (d).

Reconstructing the underlying epidemic dynamics, we found that changes in the effective reproduction number, Rt, reflected changes in contact rates derived from Google mobility data during 2020, but were less strongly associated with contact rates during 2021 (Fig. 5). We estimated that 33.4% (95% CrI: 33.3–33.5) of the population had been infected by the end of 2020 (Fig. 5), ranging from around 45% in those aged 20–39 to around 20% in those aged under 19 and above 70 (Supplementary Figures S3 and S4). This accumulation of population immunity contributed to a decline in transmission, with Rt remaining around 1 despite gradual increases in contact rates from May 2020. By the end of 2021, we estimated that 82.1% (95% CrI: 81.9–82.4%) of the population had been infected, ranging from above 90% in those aged 20–39 to around 55% in those over 70 (Supplementary Figures S3 and S4). Again, high levels of post-infection immunity resulted in reduced levels of transmission despite contact rates approaching pre-pandemic baseline levels, except during the emergence of more transmissible variants in May and September 2021.Fig. 5 Immune status, contact rates and reproduction number estimates from January 2020 to December 2021. Panel a shows the modelled distribution of immune states in the Dominican Republic over time with the proportion of the population that are: currently infected (brown), susceptible (beige), protected post-infection (blue) and protected post-vaccination (dark blue). Note that the vaccinated area (dark blue) does not include individuals that were vaccinated post-infection and so does not correspond with observed vaccination coverage. Panel b shows inferred contact rates in home, work, school and other settings relative to a pre-pandemic baseline. Panel c shows estimated R0 and Rt. R0, the basic reproduction number, is defined as the average number of secondary cases generated by a primary case in a susceptible population, while Rt, is its time-varying equivalent. Here, changes in R0 reflect changing contact rates while changes in Rt incorporate both changing contacts and the build-up of immunity over time. The vertical lines on Panel c show the time at which Mu and Delta sequences start to increase, according to GISAID, the global data science initiative.36

Impact of vaccination campaign

To estimate the impact of vaccination, we used our calibrated model to simulate counterfactual epidemic trajectories under different scenarios. First, we considered a ‘no vaccination scenario’, estimating deaths, hospital and ICU admissions in the absence of any vaccination during 2021 (Fig. 6). By comparing these counterfactual outcomes to the original model estimates we were able to estimate the burden averted by the vaccination campaign in the 6 and 10 months following its launch in February 2021 (Table 1). It should be noted that this analysis assumes all other factors remain constant and, in particular, we assume the same changes in contact rates and variant transmissibility as in the original model fitting displayed in Fig. 4. This is a simplifying assumption, as in reality it is possible countries would respond to rising COVID-19 cases with impositions of further restrictions or would see accompanying changes in population behaviour.Fig. 6 Impact of vaccination campaign. Figure showing modelled deaths (a), hospital admissions (b) and ICU admissions (c) from the original model fit (blue line) and from a ‘no vaccination’ counterfactual (red line). Lines show the median value from 500 simulations. To facilitate comparison between scenarios, modelled deaths do not include uncertainty generated through the observation process and are therefore higher than those shown in the model fit in Fig. 4.

Table 1 Estimated total hospital admissions, ICU admissions and deaths under different counterfactual vaccination scenarios.

Scenario	Additional hospital admissions in next 6 months	Additional hospital admissions in next 10 months	Additional ICU admissions in next 6 months	Additional ICU admissions in next 10 months	Additional deaths in next 6 months	Additional deaths in next 10 months	
No vaccination	7210 (6830–7600)	8330 (7850–8830)	2180 (2080–2280)	2500 (2380–2640)	766 (694–859)	863 (773–978)	
Pfizer efficacy or equivalent	−565 (−597 to −535)	−2070 (−2320 to −1840)	−149 (−157 to −143)	−599 (−660 to −541)	−48 (−54.2 to −43.5)	−179 (−239 to −129)	
Pfizer efficacy or equivalent and delay	4840 (4540–5140)	2070 (1550–2580)	1380 (1310–1450)	533 (415–658)	475 (412–548)	228 (101–344)	
AZ efficacy or equivalent	−88.8 (−107 to −71.5)	−1410 (−1600 to −1240)	−11.9 (−15.8 to −7.99)	−408 (−452 to −365)	−0.447 (−4.21 to 2.53)	−115 (−161 to −78.7)	
AZ efficacy or equivalent and delay	4970 (4670–5280)	2470 (1970–2960)	1420 (1360–1500)	654 (540–776)	490 (429–564)	267 (146–379)	
Median values and 95% credible intervals are shown from 500 simulations. Estimates are split into the cumulative burden estimated during the 6 months following the beginning of the vaccination campaign (until 16th August 2021, aligning with the Mu wave) and the 10 months following the beginning of the vaccination campaign (until 15th December 2021, aligning with the Mu and Delta waves).

We estimated that the vaccination campaign averted 7210 hospital admissions (95% CrI: 6830–7600) hospital admissions, 2180 ICU admissions (95% CrI: 2080–2280) and 766 deaths (95% CrI (694–859) in the 6 months following its launch. This is equivalent to averting 27.0% (95% CrI: 25.6–28.5) of hospital admissions, 33.2% (95% CrI: 31.8–34.8) of ICU admissions and 36.2% (95% CrI: 32.8–40.6) of reported deaths considering the median values expected under a ‘no vaccination’ scenario. Notably, we estimated hospital bed and ICU bed capacity would have been exceeded under a ‘no vaccination’ scenario, given population behaviour and variant introductions observed in 2021.19

Given the challenges and inequities of vaccine availability in real-time, with some products available at scale before others, we evaluated the impact of using alternative vaccine products with or without a delay in the vaccination programme on deaths, hospital and ICU admissions.

We estimated that while vaccination with a more efficacious product would have reduced hospitalisations, ICU admissions and deaths, delaying the vaccination campaign to vaccinate with a more efficacious product would have resulted in a higher overall burden in subsequent waves in 2021 (Table 1 and Supplementary Figure S5). This is due both to the speed of vaccination rollout, with 50% of the population receiving a two-dose primary series by the end of 2021, as well as the introduction of variants of concern or interest (particularly the Mu variant) in the summer of 2021.22

Trade-off between vaccination and population mobility

Finally, we investigated the trade-off between levels of vaccination coverage and population mobility on hospitalisations, ICU admissions and deaths. Here, we explore different counterfactual combinations of vaccination coverage and population mobility change to understand how much relaxation of social distancing measures vaccination could ‘buy’ in the later stages of a pandemic.

We found that, overall, changes in population mobility resulted in greater variation in disease burden than vaccination, where an increase in population mobility resulted in more additional deaths, hospitalisation and ICU admissions than a corresponding decrease in vaccination (Fig. 7). For instance, our analysis suggests an increase of 20% in population mobility would result in around 7200 extra hospital admissions, 1700 extra ICU admissions and 570 extra deaths, while a reduction of 20% in vaccination coverage would result in around 2400 extra hospital admissions, 690 extra ICU admissions and 230 extra deaths (Fig. 7).Fig. 7 Modelled total additional hospital admissions, ICU admissions and deaths in the 6 months following the vaccination campaign launch (16th February 2021–16th August 2021). These contour plots show tbe number of additional deaths (a), hospital admissions (b) and ICU admissions (c) compared to the original model fit under different levels of simulated vaccination coverage reached by 16th August 2021 (y-axis) and changes in population mobility during the 6 month period (x-axis). The actual vaccination coverage observed on 16th August 2021 is shown by a cross (vaccination coverage = 43% and mobility change = 0).

We also compared combinations of population mobility reduction and vaccination coverage that resulted in the same outcomes (looking at lines of equivalence in Fig. 7). We found that in the absence of vaccination, an additional 10–20% reduction in population mobility would have been required to obtain the same hospitalisation and death outcomes seen in this period, quantifying the ‘return-to-normality’ associated with the first 6 months of vaccination in this setting.

Additionally, if population mobility had remained as measured in this period but perfect vaccination coverage had been achieved, an estimated an additional 4530 (95% CrI: 4250–4840), 976 ICU admissions (95% CrI: 923–1035) and 288 deaths (95% CrI: 252–331) would have been averted. This additional burden averted (going from the observed coverage of 43% to 100%) is lower than the burden averted that we estimated in the earlier ‘no vaccination’ scenario (going from 0% coverage to the observed 43% coverage, Table 1). This illustrates the importance of an age-targeted approach in reducing morbidity and mortality. Finally, a simulated change in mobility of +30% would have returned contacts in February 2021 back to (or slightly above) pre-pandemic baseline levels. This suggests that a return to baseline mobility at the beginning of the vaccination campaign would have resulted in an extra 8210 hospital admissions (95% CrI: 6530–9840), 2070 ICU admissions (95% CrI: 1760–2370) and 678 deaths (95% CrI: 380–976).

Discussion

We used an age-structured transmission dynamic model to quantify the drivers of SARS-CoV-2 transmission in the Dominican Republic and investigate the impact of the vaccination campaign and other counterfactual vaccination scenarios. We found that despite substantial prior accumulation of post-infection immunity, the vaccination campaign had an important impact on disease burden in 2021 and was essential in enabling a return to pre-pandemic mobility levels without incurring substantial additional burden. In addition to this, we found that earlier vaccination with Sinovac-Coronavac was preferable to delayed vaccination with a higher efficacy product, resulting in a lower burden over the subsequent waves of 2021. While these findings are contingent on local epidemiological factors in the Dominican Republic (particularly the speed of vaccine roll-out and the introduction of the Mu variant in the summer of 2021), they illustrate the importance of timely and equitable access to vaccines during pandemics and large-scale outbreaks.

From 2020 to 2021 the Dominican Republic experienced four distinct waves of SARS-CoV-2 transmission. We found that, after the initial emergence of SARS-CoV-2 in March 2020, the first wave was largely controlled by the imposition of NPIs and the associated sharp drop in contact rates. The subsequent build up of immunity in the following months maintained an estimated Rt of around 1 until the end of the 2020, despite the gradual increase of social contact rates from their trough. This contrasts with other settings with well-characterised transmission dynamics, such as the United Kingdom, where SARS-CoV-2 dynamics were largely driven by the imposition of NPIs before the widespread rollout of vaccination (19). The second wave from November to February 2021 was driven by a spike in contacts in December 2020, while the third wave during the summer of 2021 is best explained by gradually increasing contact rates back to pre-pandemic levels alongside the emergence of more transmissible variants, including the Mu variant. Finally, the fourth wave between September and December 2021 was driven by the Delta variant, alongside the reopening of schools and the relaxation of NPIs including curfews.

We estimated that the 43% two-dose coverage achieved by the vaccination campaign by mid-August 2021 would have offset a 10–20% increase in mobility—a proxy for social interactions—in this period, quantifying the ‘return-to-normality’ enabled by the vaccination campaign. Indeed, from July 2021 the Dominican Republic began to reopen the economy, culminating in the removal of curfew measures in October 2021 with population mobility almost returning to pre-pandemic baseline levels. This contrasts with other settings where higher levels of vaccination coverage were required to lift measures, such as the United Kingdom, which relaxed many measures in the summer of 2021 with a two-dose vaccine coverage of around 60% and population mobility still well below baseline.22,33 Many other countries were unable to lift measures before intense Omicron transmission generated substantial population immunity or until very high vaccination coverage was achieved.34 The trade-off between vaccination and population mobility on disease burden is likely to differ depending on the setting and epidemiological context. For instance, countries with lower levels of post-infection immunity would likely see greater changes in burden associated with changes in vaccination coverage. This balance will also be affected by the emergence of new variants which may be more transmissible or exhibit immune evasive properties.

There are several limitations to this analysis. During the first wave (March–September 2020), the model struggled to reproduce the observed pattern in reported deaths. This may reflect limits in testing infrastructure and COVID-19 death reporting during this period, as observed in many countries globally.35 Hospitalisation and ICU data were only publicly reported from September 2020, and deaths were probably under-reported early in the pandemic. While we partially accounted for changes in COVID-19 death reporting by allowing the infection-fatality ratio to vary over time, there remains substantial uncertainty in the modelled size and timing of the first wave. As hospital occupancy data were only available aggregated to the national level, we were unable to investigate transmission at finer spatial scales or stratification.

Our modelling framework only incorporates protection from full primary vaccination with two doses and does not incorporate protection from a single dose. This would result in an underestimation of the impact of vaccination. However, as estimated protection of a single dose of Coronavac is low (particularly against the Delta strain), we do not expect this to have an important impact on our results.27 Additionally, we assume fully vaccinated individuals are immediately afforded protection according to vaccine efficacy estimates used. We do not consider the impact of booster vaccination, which had begun in the Dominican Republic by late 2021 and we do not consider any additional benefit afforded by vaccination for individuals with post-infection immunity. Due to limited information on the introduction and epidemiological characteristics of variants introduced in early 2021, we parameterised the model for wild-type and Delta variants and modelled the effect of Mu and other variants of concern or interest in mid 2021 through a fitted sinusoidal increase in transmissibility over this period. We therefore do not capture the effect of immune evasion of Mu (or other variants such as Gamma) on the epidemic dynamics. Assuming increased transmissibility of Mu or other variants, rather than immune evasion, would result in an underestimation of the impact of the vaccination campaign, as fewer individuals would remain susceptible during vaccine roll-out and therefore able to benefit from post-vaccination rather than post-infection protection.

Despite this, our analysis untangles the complex interactions between population behaviour, the introduction of variants and changes in population immunity in the Dominican Republic, enabling us to estimate the impact of vaccination in a complex immunological landscape and consider other counterfactual scenarios. We quantify the impact of these epidemic drivers in a setting with high seroprevalence during vaccination rollout, providing alternative insights to much comparable modelling in high-income countries. Our conclusions are therefore likely to be relevant to many other countries that were unable to suppress transmission through NPIs prior to vaccination roll-out. We also highlight the importance of having multiple data streams available to accurately characterise transmission dynamics during an epidemic. A particular strength of this study is the representativeness of the serological data used to parameterise the model and estimate population infection history, which was generated through one of the few national SARS-CoV-2 serological studies conducted in Latin America and the Caribbean during the pandemic using a rigorous multistage study design.8 Similarly, the availability of hospital and ICU occupancy data is crucial for understanding how the relationship between infection, severe outcomes and death modulates during the epidemic due to improved treatment, the introduction of VOC/VOIs, and vaccination. Understanding these dynamics in real-time is essential to avoid potential problems such as reopening the economy too late when the population has high levels of immunity or delaying the re-imposition of NPIs when new variants emerge or contact rates increase unexpectedly. Ensuring that reliable data streams can be set-up quickly across both high income and low- and middle income countries should be a priority for future pandemic planning and preparedness.

Contributors

Conceptualization: EF, AJK, RL, EJN, CLL, RS-R, CTP. Funding acquisition: AJK, RL, EJN, CLL. Methodology: EF, AJK, RL. Project administration: EF. Investigation: EF. Supervision: AJK, RL. Data analysis: EF. Figures: EF. Writing–original draft: EF, AJK, RL. Writing–review and editing: all authors. EF and AJK accessed and verified the data. All authors had full access to the data in the study and accept responsibility to submit for publication.

Data sharing statement

All code and data used for this analysis are available at: https://github.com/EmilieFinch/DR-covid19.

Editorial disclaimer

The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.

Declaration of interests

EF was supported by the Medical Research Council (MR/N013638/1); AJK was supported by Wellcome Trust (206250/Z/17/Z) and the US CDC (U01GH002238); RL was supported by a Royal Society Dorothy Hodgin Fellowship; EJN was supported by the US CDC (U01GH002238), CLL was supported by an Australian National Health and Medical Research Council Investigator Grant (APP1158469) and the US CDC (U01GH002238). AJK, EF, CLL have received salaries, consultancy fees, or travel paid through the US CDC award (U01GH002238). CTP and RS-R are employees of the Ministry of Health and Social Assistance, Dominican Republic, that was subcontracted with funds from the US CDC award.

Appendix A Supplementary data

Supplementary-Materials

Acknowledgements

We would like to thank the participants of the serosurvey in the Dominican Republic and the field teams that collected the data as well as the Dominican Republic Ministry of Health and Social Assistance for their support for this work. We would also like to thank Nicholas G. Davies, Rosana C. Barnard and Lloyd A.C. Chapman for helpful discussions in the completion of this work.

Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.lana.2024.100860.
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