Flávio Codeço Coelho

Higher incidence of Zika in adult women than adult men in Rio de Janeiro suggests a significant contribution of sexual transmission from men to women

OBJECTIVES The recent emergence of Zika in Brazil and its association with an increased rate of congenital malformations has raised concerns over its impact on the birth rate in the country. Using data on the incidence of Zika in 2015-2016 and dengue in 2013 and 2015-2016 for the city of Rio de Janeiro (population 6.4 million), a massive increase of Zika in women compared to men was documented. METHODS The age-adjusted incidence was compared between men and women. A negative binomial Poisson generalized linear model was fitted to the Zika incidence data to determine the significance of sexual transmission statistically. RESULTS Even after correcting for the bias due to the systematic testing of pregnant women for Zika, there were found to be 90% more registered cases per 100000 women than men in the sexually active age group (15-65 years); this was not the case for age groups <15 years and >65 years. Assuming that infected men transmit the disease to women in their semen, but that the converse is not true, some extra incidence in women is to be expected. An alternate hypothesis would be that women visit doctors more often than men. To test this, the incidence of dengue fever was compared in men and women in 2015 and in 2013 (before Zika reached Rio de Janeiro): in both years, women were 30% more likely to be reported with dengue. CONCLUSION Women in the sexually active age group are far more likely to get Zika than men (+90% increase); sexual transmission is the most probable cause. Women in the 15-65 years age group are also 30% more likely to be reported with dengue than men, which is probably due to women being more careful with their health.

A Bayesian Framework for Parameter Estimation in Dynamical Models

Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.

Dynamic modeling of vaccinating behavior as a function of individual beliefs.

Individual perception of vaccine safety is an important factor in determining a persons adherence to a vaccination program and its consequences for disease control. This perception, or belief, about the safety of a given vaccine is not a static parameter but a variable subject to environmental influence. To complicate matters, perception of risk (or safety) does not correspond to actual risk. In this paper we propose a way to include the dynamics of such beliefs into a realistic epidemiological model, yielding a more complete depiction of the mechanisms underlying the unraveling of vaccination campaigns. The methodology proposed is based on Bayesian inference and can be extended to model more complex belief systems associated with decision models. We found the method is able to produce behaviors which approximate what has been observed in real vaccine and disease scare situations. The framework presented comprises a set of useful tools for an adequate quantitative representation of a common yet complex public-health issue. These tools include representation of beliefs as Bayesian probabilities, usage of logarithmic pooling to combine probability distributions representing opinions, and usage of natural conjugate priors to efficiently compute the Bayesian posterior. This approach allowed a comprehensive treatment of the uncertainty regarding vaccination behavior in a realistic epidemiological model.

Vaccinating in disease-free regions: a vaccine model with application to yellow fever

Concerns regarding natural or induced emergence of infectious diseases have raised a debate on the pros and cons of pre-emptive vaccination of populations under uncertain risk. In the absence of immediate risk, ethical issues arise because even smaller risks associated with the vaccine are greater than the immediate disease risk (which is zero). The model proposed here seeks to formalize the vaccination decision process looking from the perspective of the susceptible individual, and results are shown in the context of the emergence of urban yellow fever in Brazil. The model decomposes the individuals choice about vaccinating or not into uncertain components. The choice is modelled as a function of (i) the risk of a vaccine adverse event, (ii) the risk of an outbreak and (iii) the probability of receiving the vaccine or escaping serious disease given an outbreak. Additionally, we explore how this decision varies as a function of mass vaccination strategies of varying efficiency. If disease is considered possible but unlikely (risk of outbreak less than 0.1), delay vaccination is a good strategy if a reasonably efficient campaign is expected. The advantage of waiting increases as the rate of transmission is reduced (low R0) suggesting that vector control programmes and emergency vaccination preparedness work together to favour this strategy. The opposing strategy, vaccinating pre-emptively, is favoured if the probability of yellow fever urbanization is high or if expected R0 is high and emergency action is expected to be slow. In summary, our model highlights the nonlinear dependence of an individuals best strategy on the preparedness of a response to a yellow fever outbreak or other emergent infectious disease.

Complete treatment of uncertainties in a model for dengue R0 estimation

In real epidemic processes, the basic reproduction number R0 is the combined outcome of multiple probabilistic events. Nevertheless, it is frequently modeled as a deterministic function of epidemiological variables. This paper discusses the importance of adequate treatment of uncertainties in such models. This is done by comparing two methods of uncertainty analysis: Monte Carlo uncertainty analysis (MCUA) and the Bayesian melding (BM) method. These methods are applied to a model for the determination of R0 of dengue fever based on entomological parameters. The BM was shown to provide a complete treatment of the uncertainties associated with model parameters. In contrast to MCUA, the incorporation of uncertainties led to realistic posterior distributions for parameter and variables. The incorporation, by the BM, of all the available information, from observational data to expert opinions, allows for the constructive use of uncertainties generating informative posterior distributions for all of the models components that are coherent as a set.

Zika in Rio de Janeiro: Assessment of basic reproductive number and its comparison with dengue

Zika virus infection was declared a public health emergency of international concern in February 2016 in response to the outbreak in Brazil and its suspected link with congenital anomalies. In this study we use notification data and disease natural history parameters to estimate the basic reproduction number (R0) of Zika in Rio de Janeiro, Brazil. We also obtain estimates of R0 of dengue from time series of dengue cases in the outbreaks registered in 2002 and 2012 in the city, when DENV-3 and DENV-4 serotypes respectively, had just emerged. Our estimates of the basic reproduction number for Zika in Rio de Janeiro based on surveillance notifications (R0 = 2.33, 95% CI: 1.97 − 2.97) were higher than those obtained for dengue in the city (year 2002: R0 = 1.70 [1.50 − 2.02]; year 2012: Ro = 1.25 [1.18 − 1.36]). Given the role of Aedes aegypti as vector of both the Zika and dengue viruses, we also derive Ro of Zika as a function of both dengue reproduction number and entomological and epidemiological parameters for dengue and Zika. Using the dengue outbreaks from previous years allowed us to estimate the potential R0 of Zika. Our estimates were closely in agreement with our first Zika’s R0 estimation from notification data. Hence, these results validate deriving the potential risk of Zika transmission in areas with recurring dengue outbreaks. Whether transmission routes other than vector-based can sustain a Zika epidemic still deserves attention, but our results suggest that the Zika outbreak in Rio de Janeiro emerged due to population susceptibility and ubiquitous presence of Ae. aegypti.

Zika in Rio de Janeiro: assessment of basic reproduction number and comparison with dengue outbreaks

Zika virus infection was declared a public health emergency of international concern in February 2016 in response to the outbreak in Brazil and its suspected link with congenital anomalies. In this study, we use notification data and disease natural history parameters to estimate the basic reproduction number (R 0) of Zika in Rio de Janeiro, Brazil. We also obtain estimates of R 0 of dengue from time series of dengue cases in the outbreaks registered in 2002 and 2012 in the city, when DENV-3 and DENV-4 serotypes, respectively, had just emerged. Our estimates of the basic reproduction number for Zika in Rio de Janeiro based on surveillance notifications (R 0 = 2·33, 95% CI: 1·97-2·97) were higher than those obtained for dengue in the city (year 2002: R 0 = 1·70 [1·50-2·02]; year 2012: R 0 = 1·25 [1·18-1·36]). Given the role of Aedes aegypti as vector of both the Zika and dengue viruses, we also derive R 0 of Zika as a function of both dengue reproduction number and entomological and epidemiological parameters for dengue and Zika. Using the dengue outbreaks from previous years allowed us to estimate the potential R 0 of Zika. Our estimates were closely in agreement with our first Zikas R 0 estimation from notification data. Hence, these results validate deriving the potential risk of Zika transmission in areas with recurring dengue outbreaks. Whether transmission routes other than vector-based can sustain a Zika epidemic still deserves attention, but our results suggest that the Zika outbreak in Rio de Janeiro emerged due to population susceptibility and ubiquitous presence of Ae. aegypti.

RESTING RESPIRATORY BEHAVIOR IN MINIMALLY INSTRUMENTED TOADS - EFFECTS OF VERY LONG APNEAS ON BLOOD GASES AND pH

Resting respiratory behavior of Bufo marinus in minimally instrumented toads is described for a period of 24 hours in which the animals are left undisturbed. Torpor-related long apneas are described and their implications for blood gas levels are investigated. Results show that the resting ventilation rate of Bufo marinus is much lower than that reported so far. Levels of arterial oxygen, carbon dioxide, and pH are monitored during artificial long apneas induced by anesthesia. The toads showed an unexpected ability to unload carbon dioxide by non-respiratory means, even while being kept on dry plastic box with no access to water. Oxygen arterial partial pressure dropped to very low levels after one hour of apnea. This suggests that these animals may endure very well severe hypoxia for long periods of time while in torpor.

Estimating the Attack Ratio of Dengue Epidemics under Time-varying Force of Infection using Aggregated Notification Data

Quantifying the attack ratio of disease is key to epidemiological inference and public health planning. For multi-serotype pathogens, however, different levels of serotype-specific immunity make it difficult to assess the population at risk. In this paper we propose a Bayesian method for estimation of the attack ratio of an epidemic and the initial fraction of susceptibles using aggregated incidence data. We derive the probability distribution of the effective reproductive number, Rt, and use MCMC to obtain posterior distributions of the parameters of a single-strain SIR transmission model with time-varying force of infection. Our method is showcased in a data set consisting of 18 years of dengue incidence in the city of Rio de Janeiro, Brazil. We demonstrate that it is possible to learn about the initial fraction of susceptibles and the attack ratio even in the absence of serotype specific data. On the other hand, the information provided by this approach is limited, stressing the need for detailed serological surveys to characterise the distribution of serotype-specific immunity in the population.

Measuring the heart rate of the spider, Aphonopelma hentzi: a non-invasive technique

In this work we describe a non‐invasive and precise technique to record the heartbeats of a spider. A linear output Hall effect transducer in conjunction with a small magnet was used to monitor the micromovements on the dorsal surface of the abdomen of the tarantula Aphonopelma hentzi (Girard) (Theraphosidae). The exoskeleton in this region is in direct contact with suspensory ligaments connected to the heart, and the dorsal cuticle of the opisthosoma moves with each heartbeat. The technique allowed the discrimination of the different stages of the spiders cardiac cycle. The method can be also adapted for a smaller spider or other arthropods. We believe that the method proposed in this paper allows investigators to gain insights into a spiders natural heart rate by gathering unbiased data with a non‐invasive and very precise technique. We have found the resting heart rate of A. hentzi to be 5.6 ± 1.47 beats/min, which is lower than previously reported values.