Social contact patterns following the COVID-19 pandemic: a snapshot of post-pandemic behaviour from the CoMix study (preprint)

The COVID-19 pandemic led to unprecedented changes in behaviour. To estimate if these persisted a final new round of the CoMix survey was conducted in four countries at a time when all societal restrictions had been lifted for several months. We conducted a survey on a nationally representative sample in the UK, Netherlands (NL), Belgium (BE), and Switzerland (CH). Participants were asked about their contacts and behaviours on the previous day. We calculated contact matrices and compared the contact levels to a pre-pandemic baseline to estimate R0. Data collection occurred from 17 November to 7 December 2022. 7,477 participants were recruited. Some were asked to undertake the survey on behalf of their children. Only 14.4% of all participants reported wearing a facemask on the previous day, varying between 6.7% in NL to 17.8% in CH. Self-reported vaccination rates in adults were similar for each country at around 86%. Trimmed mean recorded contacts were highest in NL with 9.9 (95% confidence interval [CI] 9.0 to 10.8) contacts per person per day and lowest in CH at 6.0 (95% CI 5.4 to 6.6). The number of contacts at home were similar between the countries. Contacts at work were lowest in the UK (1.4 contacts per person per day) and highest in NL at 2.8 contacts per person per day. Other contacts were also lower in the UK at 1.6 per person per day (95% CI 1.4 to 1.9) and highest in NL at 3.4 recorded per person per day (95% CI 4.0 to 4.0). Using the next-generation approach suggests that R0 for a close-contact disease would be roughly half pre-pandemic levels in the UK, 80% in NL and intermediate in the other two countries. The pandemic appears to have resulted in lasting changes in contact patterns that would be expected to have an impact on the epidemiology of many different pathogens. Further post-pandemic surveys are necessary to confirm this finding.

Contact patterns of frail and non-frail elderly persons in the Netherlands during the COVID-19 pandemic (preprint)

Background During the COVID-19 pandemic social distancing measures were imposed to protect the population from exposure, especially elderly and frail persons who have the highest risk for severe outcomes. These restrictions greatly reduced contacts in the general population, but little is known about behaviour changes among elderly and frail persons themselves. Our aim was to quantify how COVID-19 measures affected contact behaviour of elderly and how this differed between frail and non-frail elderly. Methods In 2021 a contact survey was carried out among persons aged 70 years and older in the Netherlands. A random sample of persons per age group (70-74, 75-79, 80-84, 85-89, 90+) and gender was invited to participate, either during a period with stringent (April 2021) or moderate (October 2021) measures. Participants provided general information on themselves including their frailty, and reported characteristics of all persons with whom they had face-to-face contact on a given day, over the course of a full week. Results In total 720 community-dwelling elderly persons were included (overall response rate of 15%), who reported 16,505 contacts. During the survey period with moderate measures, non-frail participants had significantly more contacts outside their household than frail participants. Especially for women, frailty was a more informative predictor for number of contacts than age. During the survey period with stringent measures, frail and non-frail participants had significantly lower numbers of contacts compared to the survey period with moderate measures. The reduction of number of contacts was largest for the eldest non-frail participants. As they likely interact closely with highly aged and highly frail persons, this reduction of number of contacts indirectly protects frail elderly from SARS-CoV-2 exposure. Conclusions The results of this study reveal that social distancing measures during the COVID-19 pandemic differentially affected the contact patterns of frail and non-frail elderly. The reduction of contacts may have led to direct protection of elderly persons in general but also to indirect protection of frail elderly.

Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top exploration (preprint)

Individual-based epidemiological models support the study of fine-grained preventive measures, such as tailored vaccine allocation policies, in silico. As individual-based models are computationally intensive, it is pivotal to identify optimal strategies within a reasonable computational budget. Moreover, due to the high societal impact associated with the implementation of preventive strategies, uncertainty regarding decisions should be communicated to policy makers, which is naturally embedded in a Bayesian approach. We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework in combination with a Bayesian anytime $m$-top exploration algorithm. $m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility, enabling experts to inspect this small set of alternative strategies, along with their quantified uncertainty. The anytime component provides policy advisors with flexibility regarding the computation time and the desired confidence, which is important as it is difficult to make this trade-off beforehand. We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies that minimize the number of infections and hospitalisations. Through experiments we show that our method can efficiently identify the $m$-top policies, which is validated in a scenario where the ground truth is available. Finally, we explore how vaccination policies can best be organised under different contact reduction schemes. Through these experiments, we show that the top policies follow a clear trend regarding the prioritised age groups and assigned vaccine type, which provides insights for future vaccination campaigns.

Attitudes towards booster, testing and isolation, and their impact on COVID-19 response in winter 2022/2023 in France, Belgium, and Italy (preprint)

European countries are focusing on testing, isolation, and boosting strategies to counter the 2022/2023 winter surge due to Omicron subvariants. However, widespread pandemic fatigue and limited compliance potentially undermine mitigation efforts. To establish a baseline for interventions, we ran a multicountry survey to assess respondents' willingness to receive booster vaccination and comply with testing and isolation mandates. The vast majority of survey participants (N=4,594) was willing to adhere to testing (>91%) and rapid isolation (>88%) across the three countries. Pronounced differences emerged in the declared senior adherence to booster vaccination (73% in France, 94% in Belgium, 86% in Italy). Next, we inferred the vaccine-induced population immunity profile at the winter start from prior vaccination data, immunity waning, and declared booster uptake. Integrating survey and estimated immunity data in a branching process epidemic spreading model, we evaluated the effectiveness and costs of current protocols in France, Belgium, and Italy to manage the winter wave. Model results estimate that testing and isolation protocols would confer significant benefit in reducing transmission (17-24%) with declared adherence. Achieving a mitigating level similar tothe French protocol, the Belgian protocol would require 30% fewer tests and avoid the long isolation periods of the Italian protocol (average of 6 days vs. 11). A cost barrier to test would significantly decrease adherence in France and Belgium, undermining protocols' effectiveness. Simpler mandates for isolation may increase awareness and actual compliance, reducing testing costs, without compromising mitigation. High booster vaccination uptake remains key for the control of the winter wave.

The influence of COVID-19 risk perception and vaccination status on the number of social contacts across Europe: insights from the CoMix study (preprint)

The SARS-CoV-2 transmission dynamics have been greatly modulated by human contact behaviour. To curb the spread of the virus, global efforts focused on implementing both Non-Pharmaceutical Interventions (NPIs) and pharmaceutical interventions such as vaccination. This study was conducted to explore the influence of COVID-19 vaccination status and risk perceptions related to SARS-CoV-2 on the number of social contacts of individuals in 16 European countries. This is important since insights derived from the study could be utilized in guiding the formulation of risk communication strategies. We used data from longitudinal surveys conducted in the 16 European countries to measure social contact behaviour in the course of the pandemic. The data consisted of representative panels of participants in terms of gender, age and region of residence in each country. The surveys were conducted in several rounds between December 2020 and September 2021. We employed a multilevel generalized linear mixed effects model to explore the influence of risk perceptions and COVID-19 vaccination status on the number of social contacts of individuals. The results indicated that perceived severity played a significant role in social contact behaviour during the pandemic after controlling for other variables. More specifically, participants who perceived COVID-19 to be a serious illness made fewer contacts compared to those who had low or neutral perceptions of the COVID-19 severity. Additionally, vaccinated individuals reported significantly higher number of contacts than the non-vaccinated. Furthermore, individual-level factors played a more substantial role in influencing contact behaviour than country-level factors. Our multi-country study yields significant insights on the importance of risk perceptions and vaccination in behavioural changes during a pandemic emergency. The apparent increase in social contact behaviour following vaccination would require urgent intervention in the event of emergence of an immune escaping variant. Hence, insights derived from this study could be taken into account when designing, implementing and communicating COVID-19 interventions.

Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands (preprint)

The COVID-19 pandemic was in 2020 and 2021 for a large part mitigated by reducing contacts in the general population. To monitor how these contacts changed over the course of the pandemic in the Netherlands, a longitudinal survey was conducted where participants reported on their at-risk contacts every two weeks, as part of the European CoMix survey. The survey included 1659 participants from April to August 2020 and 2514 participants from December 2020 to September 2021. We categorized the number of unique contacted persons excluding household members, reported per participant per day into six activity levels, defined as 0, 1, 2, 3-4, 5-9 and 10 or more reported contacts. After correcting for age, vaccination status, risk status for severe outcome of infection, and frequency of participation, activity levels increased over time, coinciding with relaxation of COVID-19 control measures.

Reconstruction of SARS-CoV-2 outbreaks in a primary school using epidemiological and genomic data (preprint)

Mathematical modeling studies have shown that repetitive screening can be used to mitigate SARS-CoV-2 transmission in primary schools while keeping schools open. However, not much is known about how transmission progresses within schools and whether there is a risk of importation to households. In this study, we reconstructed outbreaks observed during a prospective study in a primary school and associated households in Liege (Belgium) during the academic year 2020-2021. In addition we performed a simulation study to investigate how the accuracy of estimated weekly positivity rates in a school depends on the proportion of a school that is sampled in a repetitive screening strategy. We found that transmission occurred mainly within the school environment and that observed positivity rates are a good approximation to the true positivity rate, especially in children. This study shows that it is worthwile to implement repetitive testing in school settings, which in addition to reducing infections can lead to a better understanding of the extent of transmission in schools during a pandemic and importation risk at the community level.

Key performance indicators of COVID-19 contact tracing in Belgium from September 2020 to December 2021 (preprint)

Background Contact tracing aims to prevent onward transmission of infectious diseases and data obtained during tracing provide unique information on transmission characteristics. A key performance indicator that has been proposed to evaluate contact tracing is the proportion of cases arising from known contacts. However, few empirical studies have investigated the effectiveness of contact tracing. Methods Using data collected between September 2020 and December 2021 in Belgium, we investigated the impact of contact tracing on SARS-CoV-2 transmission. We compared confirmed cases that were previously identified as a close contact to those that were not yet known, in terms of their traced contacts and secondary cases as well as the serial interval. In addition, we established contact and transmission patterns by age. Findings Previously traced, hence 'known', cases comprised 20% of all cases and they were linked to relatively fewer close contacts as well as fewer secondary cases and a lower secondary attack rate compared to cases that were not already known. In addition we observed a shorter serial interval for 'known' cases. There was a relative increase in transmission from children to adults during circulation of the Delta and Omicron variants, without an increase in the extent of contact between these age groups. Interpretation These results suggest that contact tracing in Belgium has been effective in reducing onward transmission and that individuals aware of their exposure to SARSCoV- 2 seemed more reserved in their social contact behaviour. Data from a reference period or region are needed to measure the impact of contact tracing in terms of the number of cases and deaths averted.

Bayesian nowcasting with Laplacian-P-splines (preprint)

During an epidemic, the daily number of reported (infected/death) cases is often lower than the actual number of cases due to underreporting. Nowcasting aims to estimate the cases that have not yet been reported and combine it with the already reported cases to obtain an estimate of the daily cases. In this paper, we present a fast and flexible Bayesian approach to nowcasting combining P-splines and Laplace approximations. The main benefit of Laplacian-P-splines (LPS) is the flexibility and faster computation time compared to Markov chain Monte Carlo (MCMC) algorithms that are often used for Bayesian inference. In addition, it is natural to quantify the prediction uncertainty with LPS in the Bayesian framework, and hence prediction intervals are easily obtained. Model performance is assessed through simulations, and the method is applied to the Belgian COVID-19 mortality cases for the year 2021. Simulation results show that our model has good predictive performance except when the nowcast date is near the peak date, where it has lower prediction interval coverage.

Social contact patterns during the COVID-19 pandemic in 21 European countries: evidence from a two-year study (preprint)

Most countries have enacted some restrictions to reduce social contacts to slow down disease transmission during the COVID-19 pandemic. For nearly two years, individuals likely also adopted new behaviours to avoid pathogen exposure based on personal circumstances. We aimed to understand the way in which different factors affect social contacts, a critical step to improving future pandemic responses. The analysis was based on repeated cross-sectional contact survey data collected in 21 European countries between March 2020 and March 2022. We calculated the mean daily contacts reported using a clustered bootstrap by country and by settings (at home, at work, or in other settings). Where data were available, contact rates during the study period were compared with rates recorded prior to the pandemic. We fitted censored individual-level generalized additive mixed models to examine the effects of various factors on the number of social contacts. The survey recorded 463,336 observations from 96,456 participants. In all countries where comparison data were available, contact rates over the previous two years were substantially lower than those seen prior to the pandemic (approximately from over 10 to <5), predominantly due to fewer contacts outside the home. Government restrictions imposed immediate effect on contacts, and these effects lingered after the restrictions were lifted. Across countries, the relationships between national policy, individual perceptions, or personal circumstances determining contacts varied. Our study, coordinated at the regional level, provides important insights into the understanding of the factors associated with social contacts to support future infectious disease outbreak responses.

Impact of tiered measures on social contact and mixing patterns in Italy during the second wave of COVID-19 (preprint)

Background: The COVID-19 pandemic has significantly affected social contact patterns worldwide. Particularly during the first epidemic wave, because of the lack of specific treatment or vaccine, most countries around the world enforced non-pharmaceutical interventions. Italy was one of the first countries to be strongly affected by the pandemic, imposing in the first epidemic wave a hard lockdown. During the second wave, the country implemented color-coded, progressively restrictive tiers at the regional level according to weekly epidemiological risk assessments. Methods: We analyze longitudinal surveys of a representative sample of the Italian population by age, gender, and region of residence, collected during the second epidemic wave. After presenting a statistical description of the sample, we compare variations in contact patterns according to a color-coded tier of interventions experienced by the participants. In particular, we use contact matrices to quantify the reduction in the number of contacts by age group and contact settings, focusing on the adult population. We also compare the results with the pre-pandemic baseline assessing the impact of tiered restrictions on contacts. Finally, we compute the reproduction number to evaluate the impact of the restrictions on the spreading of the disease.Results: The comparison with the pre-pandemic baseline, shows a significant decrease in the number of contacts, independently from the age group or contact settings. Moreover, we show that the decrease in the number of contacts significantly depends on the strictness of the non-pharmaceutical interventions. For all levels of strictness considered, the reduction in social mixing results in a reproduction number smaller than one. In particular, the impact of the restriction on the number of contacts decreases with the severity of the interventions. Conclusions: We showed that the progressive restriction tiers implemented in Italy reduced overall the reproduction number, with stricter interventions associated with higher reductions. Readily collected contact data can promptly inform the implementation of mitigation measures at the national level in epidemic emergencies to come.

Pregnancy during COVID-19: social contact patterns and vaccine coverage of pregnant women from CoMix in 19 European countries (preprint)

BackgroundEvidence and advice for pregnant women evolved during the COVID-19 pandemic. We studied social contact behaviour and vaccine uptake in pregnant women between March 2020 and September 2021 in 19 European countries. MethodsIn each country, repeated online survey data were collected from a panel of nationally-representative participants. We calculated the mean adjusted contacts reported with an individual-level generalized additive mixed model, modelled using the negative binomial distribution and a log link function. Mean proportion of people in isolation or quarantine, and vaccination coverage by pregnancy status and gender were calculated using a clustered bootstrap. FindingsWe recorded 4,129 observations from 1,041 pregnant women, and 115,359 observations from 29,860 non-pregnant individuals aged 18-49. Pregnant women made slightly fewer contacts (3.6, 95%CI=3.5-3.7) than non-pregnant women (4.0, 95%CI=3.9-4.0), driven by fewer work contacts but marginally more contacts in non-essential social settings. Approximately 15-20% pregnant and 5% of non-pregnant individuals reported to be in isolation and quarantine for large parts of the study period. COVID-19 vaccine coverage was higher in pregnant women than in non-pregnant women between January and April 2021. Since May 2021, vaccination in non-pregnant women began to increase and surpassed that in pregnant women. InterpretationSocial contacts and vaccine uptake protect pregnant women and their newborn babies. Recognition of maternal social support need, and efforts to promote the safety and effectiveness of the COVID-19 vaccines during pregnancy are high priorities in this vulnerable group.

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning (preprint)

Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models. Current research focuses on optimizing policies w.r.t. a single objective, such as the pathogen's attack rate. However, as the mitigation of epidemics involves distinct, and possibly conflicting criteria (i.a., prevalence, mortality, morbidity, cost), a multi-objective approach is warranted to learn balanced policies. To lift this decision-making process to real-world epidemic models, we apply deep multi-objective reinforcement learning and build upon a state-of-the-art algorithm, Pareto Conditioned Networks (PCN), to learn a set of solutions that approximates the Pareto front of the decision problem. We consider the first wave of the Belgian COVID-19 epidemic, which was mitigated by a lockdown, and study different deconfinement strategies, aiming to minimize both COVID-19 cases (i.e., infections and hospitalizations) and the societal burden that is induced by the applied mitigation measures. We contribute a multi-objective Markov decision process that encapsulates the stochastic compartment model that was used to inform policy makers during the COVID-19 epidemic. As these social mitigation measures are implemented in a continuous action space that modulates the contact matrix of the age-structured epidemic model, we extend PCN to this setting. We evaluate the solution returned by PCN, and observe that it correctly learns to reduce the social burden whenever the hospitalization rates are sufficiently low. In this work, we thus show that multi-objective reinforcement learning is attainable in complex epidemiological models and provides essential insights to balance complex mitigation policies.

Different forms of superspreading lead to different outcomes: heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2 (preprint)

Superspreading events play an important role in the spread of SARS-CoV-2 and several other pathogens. Hence, while the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness and susceptibility, variations in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, our understanding of the effect that these different forms of superspreading have on the spread of pathogens and the effectiveness of control measures remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in the Belgian population. We considered its impact on viral spread as well as on the effectiveness of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are very different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in less outbreaks occurring following the introduction of one infected individual. Outbreaks were also slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to smaller final sizes, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. Determining the contribution of both source of heterogeneity is therefore important but left to be explored further. Author summaryTo investigate the effect of different sources of superspreading on disease dynamics, we implemented superspreading driven by heterogeneity in infectiousness and heterogeneity in contact behavior into an individual-based model for the transmission of SARS-CoV-2 in the Belgian population. We compared the impact of both forms of superspreading in a scenario without interventions as well as in a scenario in which a period of strict social distancing (i.e. a lockdown) is followed by a period of partial release. We found that both forms of superspreading have very different effects. On the one hand, increasing the level of infectiousness-related heterogeneity led to less outbreaks being observed following the introduction of one infected individual in the population. Furthermore, final outbreak sizes decreased, and outbreaks became slower, with lower and later peaks, and a lower herd immunity threshold. Finally, the risk for resurgence of an outbreak following a period of lockdown also decreased. On the other hand, when contact-related heterogeneity was high, this also led to smaller final sizes, but caused outbreaks to be more explosive regarding other aspects (such as higher peaks that occurred earlier). The herd immunity threshold also increased, as did the risk of resurgence of outbreaks.

Observed serial intervals of SARS-CoV-2 for the Omicron and Delta varaints in Belgium based on contact tracing data, 19 November to 31 December 2021 (preprint)

The SARS-CoV-2 Omicron BA.1 variant is rapidly spreading worldwide, possibly outcompeting the Delta strain. We investigated the empirical serial interval for both variants using contact tracing data. Overall, we observed a shorter serial interval for Omicron compared to Delta, suggesting faster transmission. Furthermore, results indicate a relation between the empirical serial interval and the vaccination status for both the Omicron and the Delta variant. Consequently, with the progression of the vaccination campaign, the reasons for and extent of dominance of Omicron over Delta may need further assessment.

COVID-19 surveillance in the Flemish school system: development of systematic data collection within the Public Health School System and descriptive analysis of cases reported between October 2020 and June 2021 (preprint)

Background: The age specific distribution of SARS-CoV-2 cases in schools is not well described. The numbers recorded reflect the intensity of community transmission while being shaped by biases from age-dependent testing regimes and effective age-specific interventions. A case-surveillance system was introduced within the Flemish school and health-prevention network during the 2020-2021 school year. We present epidemiological data of in-school reported cases in pre-, primary and secondary schools based on the surveillance system, in conjunction with test data and community cases from October 2020 to June 2021. Methods We describe the development of the surveillance system and provide the number of reported cases and standardized rates per grade over time. We calculate absolute and relative differences between incidence cases by grade of primary (grades 1-6) and secondary-school (grades 7-12) and compare these to grades 7-8, relating them to non-pharmaceutical infection prevention interventions. Cumulative population incidences (IP) stratified by age, province and social-economic status (SES) of the school population are presented with their 95% confidence intervals (CI). Results A total of 59,996 COVID-19 cases were reported in the school surveillance system, with the highest population adjusted IP in grade 11-12 of 7.39% (95%CI 7.24-7.53) and ranging from 2.23–6.25% from pre-school through grade 10. Age-specific reduction in in-person teaching and introduction of masks, are temporally associated with decreases in incident cases by grades. Lower pupil SES is associated with increased cumulative cases (excess 2,739/100,000 pupils compared to highest SES tertile). Community testing volumes varied more for children compared to adults, with overall higher child test-positivity. Holidays influence capturing of cases by the system, however efficiency increased to above 75% after further automation and integration in existing structures. Conclusion Integration of case surveillance within an electronic school health system is feasible, provides data to follow up the epidemic evolution in schoolchildren and should be part of public health surveillance and pandemic preparedness. The relationship towards community transmission needs careful evaluation because of age-different testing regimens. In the Flemish region, case incidence within schools follows an age gradient that is mitigated through grade specific interventions, while differences by SES remain.

EpiLPS: a fast and flexible Bayesian tool for near real-time estimation of the time-varying reproduction number (preprint)

In infectious disease epidemiology, the instantaneous reproduction number R ( t ) is a timevarying metric defined as the average number of secondary infections generated by individuals who are infectious at time t . It is therefore a crucial epidemiological parameter that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible envelopes of R ( t ) by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of R ( t ) in only a few seconds; and (2) an approach based on a MCMC scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a Negative Binomial distribution to account for potential excess variability in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a “plug-in” estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of R ( t ) as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and current SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France. Author summary The instantaneous reproduction number R ( t ) is a key metric that provides important insights into an epidemic outbreak. We present a flexible Bayesian approach called EpiLPS (Epidemiological modeling with Laplacian-P-splines) for smooth estimation of the epidemic curve and R ( t ). Computational speed and absence of arbitrary assumptions on smoothing makes EpiLPS an interesting tool for near real-time estimation of the reproduction number. An R software package is available ( https://github.com/oswaldogressani ).