Determining the dynamics of influenza transmission by age

Spatiotemporal reproduction number with Bayesian model selection for evaluation of emerging infectious disease transmissibility: an application to COVID-19 national surveillance data

BACKGROUND

To control emerging diseases, governments often have to make decisions based on limited evidence. The effective or temporal reproductive number is used to estimate the expected number of new cases caused by an infectious person in a partially susceptible population. While the temporal dynamic is captured in the temporal reproduction number, the dominant approach is currently based on modeling that implicitly treats people within a population as geographically well mixed.

METHODS

In this study we aimed to develop a generic and robust methodology for estimating spatiotemporal dynamic measures that can be instantaneously computed for each location and time within a Bayesian model selection and averaging framework. A simulation study was conducted to demonstrate robustness of the method. A case study was provided of a real-world application to COVID-19 national surveillance data in Thailand.

RESULTS

Overall, the proposed method allowed for estimation of different scenarios of reproduction numbers in the simulation study. The model selection chose the true serial interval when included in our study whereas model averaging yielded the weighted outcome which could be less accurate than model selection. In the case study of COVID-19 in Thailand, the best model based on model selection and averaging criteria had a similar trend to real data and was consistent with previously published findings in the country.

CONCLUSIONS

The method yielded robust estimation in several simulated scenarios of force of transmission with computing flexibility and practical benefits. Thus, this development can be suitable and practically useful for surveillance applications especially for newly emerging diseases. As new outbreak waves continue to develop and the risk changes on both local and global scales, our work can facilitate policymaking for timely disease control.

Statistical Estimation of the Reproductive Number From Case Notification Data

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.

Estimating the relative probability of direct transmission between infectious disease patients

BACKGROUND

Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases.

METHODS

Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates.

RESULTS

In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany.

CONCLUSIONS

Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Practical considerations for measuring the effective reproductive number, Rt

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.

Practical considerations for measuring the effective reproductive number, R t

Estimation of the effective reproductive number, R t , is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using R t to assess the effectiveness of interventions and to inform policy. However, estimation of R t from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of R t , we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to spread. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting R t estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in R t estimation.

Monitoring the age-specificity of measles transmissions during 2009-2016 in Southern China

Background

Despite several immunization efforts, China saw a resurgence of measles in 2012. Monitoring of transmissions of individuals from different age groups could offer information that would be valuable for planning adequate disease control strategies. We compared the age-specific effective reproductive numbers (R) of measles during 2009–2016 in Guangdong, China.

Methods

We estimated the age-specific R values for 7 age groups: 0–8 months, 9–18 months, 19 months to 6 years, 7–15 years, 16–25 years, 26–45 years, and ≥46 years adapting the contact matrix of China. The daily numbers of laboratory and clinically confirmed cases reported to the Center for Disease Control and Prevention of Guangdong were used.

Results

The peak R values of the entire population were above unity from 2012 to 2016, indicating the persistence of measles in the population. In general, children aged 0–6 years and adults aged 26–45 years had larger values of R when comparing with other age groups after 2012. While the peaks of R values for children aged 0–6 years dropped steadily after 2013, the peaks of R values for adults aged 26–45 years kept at a high range every year.

Conclusions

Although the provincial supplementary immunization activities (SIAs) conducted in 2009 and 2010 were able to reduce the transmissions from 2009 to 2011, larger values of R for children aged 0–6 years were observed after 2012, indicating that the benefits of the SIAs were short-lived. In addition, the transmissions from adults aged between 26 and 45 years increased over time. Disease control strategies should target children and adult groups that carry high potential for measles transmission.

Linking influenza epidemic onsets to covariates at different scales using a dynamical model

Background

Evaluating the factors favoring the onset of influenza epidemics is a critical public health issue for surveillance, prevention and control. While past outbreaks provide important insights for understanding epidemic onsets, their statistical analysis is challenging since the impact of a factor can be viewed at different scales. Indeed, the same factor can explain why epidemics are more likely to begin (i) during particular weeks of the year (global scale); (ii) earlier in particular regions (spatial scale) or years (annual scale) than others and (iii) earlier in some years than others within a region (spatiotemporal scale).

Methods

Here, we present a statistical approach based on dynamical modeling of infectious diseases to study epidemic onsets. We propose a method to disentangle the role of covariates at different scales and use a permutation procedure to assess their significance. Epidemic data gathered from 18 French regions over six epidemic years were provided by the Regional Influenza Surveillance Group (GROG) sentinel network.

Results

Our results failed to highlight a significant impact of mobility flows on epidemic onset dates. Absolute humidity had a significant impact, but only at the spatial scale. No link between demographic covariates and influenza epidemic onset dates could be established.

Discussion

Dynamical modeling presents an interesting basis to analyze spatiotemporal variations in the outcome of epidemic onsets and how they are related to various types of covariates. The use of these models is quite complex however, due to their mathematical complexity. Furthermore, because they attempt to integrate migration processes of the virus, such models have to be much more explicit than pure statistical approaches. We discuss the relation of this approach to survival analysis, which present significant differences but may constitute an interesting alternative for non-methodologists.

Synchronicity of influenza activity within Phoenix, AZ during the 2015-2016 seasonal epidemic

Background

Variability in the timing of influenza epidemics has been observed across global and regional scales, but this variability has not been studied extensively at finer spatial scales. As such, the aim of this study was to test whether influenza cases were synchronized across sites and/or age-groups within a major city.

Methods

We used influenza cases identified by rapid influenza tests from a network of clinics across Phoenix, AZ during the 2015–2016 influenza A season. We used a combination of KS tests and a bootstrapping approach to evaluate whether the temporal distribution of cases varied by site and/or age group.

Results

Our analysis indicates that the timing of influenza cases during the 2015–2016 seasonal influenza epidemic were generally synchronized across sites and age groups. That said, we did observe some statistically significant differences in the timing of cases across some sites, and by site and age group. We found no evidence that influenza activity consistently begins or peaks earlier in children than in adults.

Conclusions

To our knowledge, this is the first study to investigate differences in the intra-urban timing of influenza using influenza-specific case data. We were able to show evidence that influenza cases are not entirely synchronized across an urban area, but the differences we observed were relatively minor. It is important to understand the geographic scale at which influenza is synchronized in order to gain a better understanding of local transmission dynamics, and to determine the appropriate geographic scale that influenza surveillance data should be aggregated for prediction and warning systems.

Estimating age-specific reproductive numbers-A comparison of methods

Large outbreaks, such as those caused by influenza, put a strain on resources necessary for their control. In particular, children have been shown to play a key role in influenza transmission during recent outbreaks, and targeted interventions, such as school closures, could positively impact the course of emerging epidemics. As an outbreak is unfolding, it is important to be able to estimate reproductive numbers that incorporate this heterogeneity and to use surveillance data that is routinely collected to more effectively target interventions and obtain an accurate understanding of transmission dynamics. There are a growing number of methods that estimate age-group specific reproductive numbers with limited data that build on methods assuming a homogenously mixing population. In this article, we introduce a new approach that is flexible and improves on many aspects of existing methods. We apply this method to influenza data from two outbreaks, the 2009 H1N1 outbreaks in South Africa and Japan, to estimate age-group specific reproductive numbers and compare it to three other methods that also use existing data from social mixing surveys to quantify contact rates among different age groups. In this exercise, all estimates of the reproductive numbers for children exceeded the critical threshold of one and in most cases exceeded those of adults. We introduce a flexible new method to estimate reproductive numbers that describe heterogeneity in the population.

The role of different social contexts in shaping influenza transmission during the 2009 pandemic

Evaluating the relative importance of different social contexts in which infection transmission occurs is critical for identifying optimal intervention strategies. Nonetheless, an overall picture of influenza transmission in different social contexts has yet to emerge. Here we provide estimates of the fraction of infections generated in different social contexts during the 2009 H1N1 pandemic in Italy by making use of a highly detailed individual-based model accounting for time use data and parametrized on the basis of observed age-specific seroprevalence. We found that 41.6% (95%CI: 39-43.7%) of infections occurred in households, 26.7% (95%CI: 21-33.2) in schools, 3.3% (95%CI: 1.7-5%) in workplaces, and 28.4% (95%CI: 24.6-31.9%) in the general community. The above estimates strongly depend on the lower susceptibility to infection of individuals 19+ years old compared to younger ones, estimated to be 0.2 (95%CI 0.12-0.28). We also found that school closure over the weekends contributed to decrease the effective reproduction number of about 8% and significantly affected the pattern of transmission. These results highlight the pivotal role played by schools in the transmission of the 2009 H1N1 influenza. They may be relevant in the evaluation of intervention options and, hence, for informing policy decisions.