Simon Cauchemez

(Abstracted from Lancet 2016;387:2125–2132) An anthropod-borne virus, Zika (genus Flavivirus), since its identification in 1947, has spread throughout the Americas, with 28 countries having reported cases of the disease. The Zika virus has been associated with Guillain-Barré syndrome (an autoimmune disorder that causes acute or subacute flaccid paralysis) and the birth of babies with neurological complications, such as congenital microcephaly (a neurological birth defect).

Detection of human cases of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection internationally is a global public health concern. Rigorous risk assessment is particularly challenging in a context where surveillance may be subject to under-ascertainment and a selection bias towards more severe cases. We would like to assess whether the virus is capable of causing widespread human epidemics, and whether self-sustaining transmission is already under way. Here we review possible transmission scenarios for MERS-CoV and their implications for risk assessment and control. We discuss how existing data, future investigations and analyses may help in reducing uncertainty and refining the public health risk assessment and present analytical approaches that allow robust assessment of epidemiological characteristics, even from partial and biased surveillance data. Finally, we urge that adequate data be collected on future cases to permit rigorous assessment of the transmission characteristics and severity of MERS-CoV, and the public health threat it may pose. Going beyond minimal case reporting, open international collaboration, under the guidance of the World Health Organization and the International Health Regulations, will impact on how this potential epidemic unfolds and prospects for control.

Prior to emergence in human populations, zoonoses such as SARS cause occasional infections in human populations exposed to reservoir species. The risk of widespread epidemics in humans can be assessed by monitoring the reproduction number R (average number of persons infected by a human case). However, until now, estimating R required detailed outbreak investigations of human clusters, for which resources and expertise are not always available. Additionally, existing methods do not correct for important selection and under-ascertainment biases. Here, we present simple estimation methods that overcome many of these limitations. Our approach is based on a parsimonious mathematical model of disease transmission and only requires data collected through routine surveillance and standard case investigations. We apply it to assess the transmissibility of swine-origin influenza A H3N2v-M virus in the US, Nipah virus in Malaysia and Bangladesh, and also present a non-zoonotic example (cholera in the Dominican Republic). Estimation is based on two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F). If detection of a case does not affect detection of other cases from the same cluster, we find that R can be estimated by 1−G; otherwise R can be estimated by 1−F when the case detection rate is low. In more general cases, bounds on R can still be derived. We have developed a simple approach with limited data requirements that enables robust assessment of the risks posed by emerging zoonoses. We illustrate this by deriving transmissibility estimates for the H3N2v-M virus, an important step in evaluating the possible pandemic threat posed by this virus. Please see later in the article for the Editors' Summary When a virus emerges in the human population, such viruses can cause global epidemics potentially harming large numbers of people. Zoonotic viruses are viruses that are transmissible from animals to humans; the global health threat of zoonotic viruses was recently demonstrated by the 2009 H1N1 influenza pandemic and the SARS epidemic in 2003. Many zoonotic viruses are transmitted by means of an infected vector, while others can be transmitted by inhalation, contact with infected excretions, or by direct contact with an infected animal. Zoonotic viruses primarily cause occasional infections in human populations exposed to reservoir species (the animal species harboring the virus) because the pathogens are usually poorly adapted for sustained human-to-human transmission. However, zoonotic viruses are under strong selective pressure to acquire the ability for human-to-human transmission. The highly pathogenic H5N1 avian influenza epidemic was alarming to many because of the high mortality rate in humans and its rapid spread in avian populations. Public health response to outbreaks such as those of H5N1 avian influenza and SARS required reliable estimates of transmissibility (how easily it spreads between people) and severity (the proportion of infected people who needed hospital treatment). For efficient prevention and control of the emerging epidemic, quantitative and rigorous assessment of the associated risks is needed. Specifically, health officials and researchers need fast, reliable methods for estimating the extent to which a virus has acquired the ability to transmit from person to person. In this study, the authors developed a novel method to estimate a standard measure of transmissibility, the human-to-human reproduction number R (average number of persons infected by a human case) of a zoonotic virus, which overcomes many of the limitations of existing methods. The authors developed a simple method to estimate the reproduction number of emerging zoonoses from routine surveillance data. By using two simple summary statistics, the proportion infected by the natural reservoir among detected cases (G) and among the subset of the first detected cases in each cluster (F), the authors estimated R, the reproduction number of zoonoses in humans. The authors then applied their new approach to assess the human-to-human transmissibility of swine-origin influenza A variant (H1N1v, H1N2v, and H3N2v) virus, in particular that of the H3N2v-M virus, from US surveillance data for the period December 2005–December 2011, Nipah virus in Malaysia and Bangladesh, as well as to a non-zoonotic pathogen Vibrio Cholerae in the Dominican Republic. This study demonstrates the applicability of this novel approach to estimating R during zoonotic and certain non-zoonotic outbreaks. Cauchemez and colleagues show that their new approach will be useful in assessing human-to-human transmissions during zoonotic outbreaks. The authors show that their new method does not require as much of an investigation effort as existing methods, the statistical treatment of the data is extremely simple, and the robustness of the method is demonstrated even if larger clusters are more likely to be detected and if the ability to detect all cases in a cluster once a cluster is identified is low. This method of estimating R is designed for the context of subcritical outbreaks, i.e., RR≥1, other estimation methods will be needed. Please access these Web sites via the online version of this summary at http://dx.doi.org/ 10.1371/journal.pmed.1001399.

Serological studies are the gold standard method to estimate influenza infection attack rates (ARs) in human populations. In a common protocol, blood samples are collected before and after the epidemic in a cohort of individuals; and a rise in haemagglutination-inhibition (HI) antibody titers during the epidemic is considered as a marker of infection. Because of inherent measurement errors, a 2-fold rise is usually considered as insufficient evidence for infection and seroconversion is therefore typically defined as a 4-fold rise or more. Here, we revisit this widely accepted 70-year old criterion. We develop a Markov chain Monte Carlo data augmentation model to quantify measurement errors and reconstruct the distribution of latent true serological status in a Vietnamese 3-year serological cohort, in which replicate measurements were available. We estimate that the 1-sided probability of a 2-fold error is 9.3% (95% Credible Interval, CI: 3.3%, 17.6%) when antibody titer is below 10 but is 20.2% (95% CI: 15.9%, 24.0%) otherwise. After correction for measurement errors, we find that the proportion of individuals with 2-fold rises in antibody titers was too large to be explained by measurement errors alone. Estimates of ARs vary greatly depending on whether those individuals are included in the definition of the infected population. A simulation study shows that our method is unbiased. The 4-fold rise case definition is relevant when aiming at a specific diagnostic for individual cases, but the justification is less obvious when the objective is to estimate ARs. In particular, it may lead to large underestimates of ARs. Determining which biological phenomenon contributes most to 2-fold rises in antibody titers is essential to assess bias with the traditional case definition and offer improved estimates of influenza ARs. Each year, seasonal influenza is responsible for about three to five million severe illnesses and about 250,000 to 500,000 deaths worldwide. In order to assess the burden of disease and guide control policies, it is important to quantify the proportion of people infected by an influenza virus each year. Since infection usually leaves a “signature” in the blood of infected individuals (namely a rise in antibodies), a standard protocol consists in collecting blood samples in a cohort of subjects and determining the proportion of those who experienced such rise. However, because of inherent measurement errors, only large rises are accounted for in the standard 4-fold rise case definition. Here, we revisit this 70 year old and widely accepted and applied criterion. We present innovative statistical techniques to better capture the impact of measurement errors and improve our interpretation of the data. Our analysis suggests that the number of people infected by an influenza virus each year might be substantially larger than previously thought, with important implications for our understanding of the transmission and evolution of influenza – and the nature of infection.

The risk of influenza infection depends on biological characteristics, individual or collective behaviors and the environmental context. The Cohorts for Pandemic Influenza (CoPanFlu) France study was set up in 2009 after the identification of the novel swine-origin A/H1N1 pandemic influenza virus. This cohort of 601 households (1450 subjects) representative for the general population aims at using an integrative approach to study the risk and characteristics of influenza infection as a complex combination of data collected from questionnaires regarding sociodemographic, medical, behavioral characteristics of subjects and indoor environment, using biological samples or environmental databases.

Data collected during outbreaks are essential to better understand infectious disease transmission and design effective control strategies. But analysis of such data is challenging owing to the dependency between observations that is typically observed in an outbreak and to missing data. In this paper, we discuss strategies to tackle some of the ongoing challenges in the analysis of outbreak data. We present a relatively generic statistical model for the estimation of transmission risk factors, and discuss algorithms to estimate its parameters for different levels of missing data. We look at the problem of computational times for relatively large datasets and show how they can be reduced by appropriate use of discretization, sufficient statistics and some simple assumptions on the natural history of the disease. We also discuss approaches to integrate parametric model fitting and tree reconstruction methods in coherent statistical analyses. The methods are tested on both real and simulated datasets of large outbreaks in structured populations.

Evaluating the impact of different social networks on the spread of respiratory diseases has been limited by a lack of detailed data on transmission outside the household setting as well as appropriate statistical methods. Here, from data collected during a H1N1 pandemic (pdm) influenza outbreak that started in an elementary school and spread in a semirural community in Pennsylvania, we quantify how transmission of influenza is affected by social networks. We set up a transmission model for which parameters are estimated from the data via Markov chain Monte Carlo sampling. Sitting next to a case or being the playmate of a case did not significantly increase the risk of infection; but the structuring of the school into classes and grades strongly affected spread. There was evidence that boys were more likely to transmit influenza to other boys than to girls (and vice versa), which mimicked the observed assortative mixing among playmates. We also investigated the presence of abnormally high transmission occurring on specific days of the outbreak. Late closure of the school (i.e., when 27% of students already had symptoms) had no significant impact on spread. School-aged individuals (6–18 y) facilitated the introduction and spread of influenza in households, but only about one in five cases aged >18 y was infected by a school-aged household member. This analysis shows the extent to which clearly defined social networks affect influenza transmission, revealing strong between-place interactions with back-and-forth waves of transmission between the school, the community, and the household.

We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimicks the susceptible–infectious–removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.