Behavioral modulation of the coexistence between Apis melifera and Varroa destructor: A defense against colony colapse disorder?

Colony Collapse Disorder has become a global problem for beekeepers and for the crops which depend on bee polination. Multiple factors are known to increase the risk of colony colapse, and the ectoparasitic mite Varroa destructor that parasitizes honey bees is among the main threats to colony health. Although this mite is unlikely to, by itself, cause the collapse of hives, it plays an important role as it is a vector for many viral diseases. Such diseases are among the likely causes for Colony Collapse Disorder. The effects of V. destructor infestation are disparate in different parts of the world. Greater morbidity - in the form of colony losses - has been reported in colonies of European honey bees (EHB) in Europe, Asia and North America. However, this mite has been present in Brasil for many years and yet there are no reports of Africanized honey bee (AHB) colonies losses. Studies carried out in Mexico showed that some resistance behaviors to the mite - especially grooming and hygienic behavior - appear to be different in each subspecies. Could those difference in behaviors explain why the AHB are less susceptible to Colony Collapse Disorder? In order to answer this question, we propose a mathematical model of the coexistence dynamics of these two species, the bee and the mite, to analyze the role of resistance behaviors in the overall health of the colony, and, as a consequence, its ability to face epidemiological challenges

Sensitivity Analysis in Zika Virus Dynamics and a Model Discrepancy Approach

International audienc

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

Epidemiological data accessibility in Brazil: Current challenges for an adequate response to emergencies

International audienceConcerns about data sharing and transparency during epidemiological emergencies are not new. Dye and colleagues have announced an initiative called Zika Open through which the manuscripts and respective data submitted to Bulletin of the World Health Organization would be publlished as open access from the date of submission onwards, under a Creative Commons License. This is an important initiative. Here we report challenges faced, particularly in Brazil, for timely, lawful access to governmental collected disease-notification data that are essential to understand the current Zika virus epidemic, and any future public health emergency

COVID-19 and hospitalizations for SARI in Brazil: a comparison up to the 12th epidemiological week of 2020.

Surveillance of the severe acute respiratory illness (SARI) in Brazil aims to characterize the circulation of the Influenza A and B viruses in hospitalized cases and deaths, having been expanded in 2012 to include other respiratory viruses. COVID-19 was detected in Brazil for the time in the 9th epidemiological week of 2020, and the test for the SARS-CoV-2 virus was included in the surveillance protocol starting in the 12th epidemiological week. This study's objective was to investigate the pattern of hospitalizations for SARI in Brazil since the entry of SARS-CoV-2, comparing the temporal and age profiles and laboratory results to the years 2010 through 2019. In 2020, hospitalizations for SARI, compiled from the date of the first confirmed case of COVID-19 up to the 12th week, exceeded the numbers observed during the same period in each of the previous 10 years. The age bracket over 60 years was the most heavily affected, at higher than historical levels. There was a considerable increase in negative laboratory tests, suggesting circulation of a different virus from those already present in the panel. We concluded that the increase in hospitalizations for SARI, the lack of specific information on the etiological agent, and the predominance of cases among the elderly during the same period in which there was an increase in the number of new cases of COVID-19 are all consistent with the hypothesis that severe cases of COVID-19 are already being detected by SARI surveillance, placing an overload on the health system. The inclusion of testing for SARS-CoV-2 in the SARI surveillance protocol and the test's effective nationwide deployment are extremely important for monitoring the evolution of severe COVID-19 cases in Brazil

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

Individual perception of vaccine safety is an important factor in determining a person's 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