arXiv subjects

Daniel A. M. Villela

Publications and source records attributed to Daniel A. M. Villela.

5 recordsLinked to original sources

arXiv2024

Predicting high dengue incidence in municipalities of Brazil using path signatures

Predicting whether to expect a high incidence of infectious diseases is critical for health surveillance. In the epidemiology of dengue, environmental conditions can significantly impact the transmission of the virus. Utilizing epidemiological indicators alongside environmental variables can enhance predictions of dengue incidence risk. This study analyzed a dataset of weekly case numbers, temperature, and humidity across Brazilian municipalities to forecast the risk of high dengue incidence using data from 2014 to 2023. The framework involved constructing path signatures and applying lasso regression for binary outcomes. Sensitivity reached 75%, while specificity was extremely high, ranging from 75% to 100%. The best performance was observed with information gathered after 35 weeks of observations using data augmentation via embedding techniques. The use of path signatures effectively captures the stream of information given by epidemiological and climate variables that influence dengue transmission. This framework could be instrumental in optimizing resources to predict high dengue risk in municipalities in Brazil and other countries after learning these country patterns.

q-bio.QM↗

arXiv2024

A Coupled Two-Tier Mathematical Transmission Model to Explore Virulence Evolution in Vector-Borne Diseases

The emergence or adaptation of pathogens may lead to epidemics, highlighting the need for a thorough understanding of pathogen evolution. The tradeoff hypothesis suggests that virulence evolves to reach an optimal transmission intensity relative to the mortality caused by the disease. This study introduces a mathematical model that incorporates key factors such as recovery times and mortality rates, focusing on the diminishing effects of parasite growth on transmission, with a focus on vector-borne diseases. The analysis reveals conditions under which heightened virulence occurs in hosts, indicating that these factors can support vector-host transmission of a pathogen, even if the host-only component is insufficient for sustainable transmission. This insight helps explain the significant presence of pathogens with high fatality rates, such as those in vector-borne diseases. The findings underscore an elevated risk for future outbreaks involving such diseases. Enhanced surveillance of mortality rates and techniques to monitor pathogen evolution are vital to effectively control future epidemics. This study provides essential insights for epidemic preparedness and highlights the need for ongoing research into pathogen evolution.

q-bio.PE↗

arXiv2020

Combining probability distributions: Extending the logarithmic pooling approach

Combining distributions is an important issue in decision theory and Bayesian inference. Logarithmic pooling is a popular method to aggregate expert opinions by using a set of weights that reflect the reliability of each information source. However, the resulting pooled distribution depends heavily on set of weights given to each opinion/prior and thus careful consideration must be given to the choice of weights. In this paper we review and extend the statistical theory of logarithmic pooling, focusing on the assignment of the weights using a hierarchical prior distribution. We explore several statistical applications, such as the estimation of survival probabilities, meta-analysis and Bayesian melding of deterministic models of population growth and epidemics. We show that it is possible learn the weights from data, although identifiability issues may arise for some configurations of priors and data. Furthermore, we show how the hierarchical approach leads to posterior distributions that are able to accommodate prior-data conflict in complex models.

stat.ME↗

arXiv2015

Imperfect Testing of Individuals for Infectious Diseases: Mathematical Model and Analysis

Testing symptomatic individuals for a disease can deliver treatment resources, if tests' results turn positive, which speeds up their treatment and might also decrease individuals' contacts to other ones. An imperfect test, however, might incorrectly consider susceptible individuals to be infected (false positives). In this case, testing reduces the epidemic in the expense of potentially misclassifying individuals. We present a mathematical model that describes the dynamics of an infectious disease and its testing. Susceptible individuals turn to "susceptible but deemed infected" at rate $θ$. Infected individuals go to a state "infected and tested positive" at rate $α$. Both of these rates are functions of test's sensitivity and specificity. Analysis of the model permits us to derive an expression for $R_0$ and to find the conditions for reaching $R_0<1$, i.e., when the disease--free equilibrium is stable. We find that under certain conditions it is possible to get $R_0<1$, when originally, i.e., without testing, we would have $R_0>1$. We also present numerical results to cover interesting scenarios such as using different tests and to compare these results.

q-bio.PE↗

arXiv2015

An analysis of the vectorial capacity using moment-generating functions

This paper describes a technique for analyzing the stochastic structure of the vectorial capacity using moment--generating functions. In such formulation, for an infectious disease transmitted by a vector, we obtain the generating function for the distribution of the number of infectious contacts (e.g., infectious bites from mosquitoes) between vectors and humans after a contact to a single infected individual. This approach permits us to generally derive the moments of the distribution and, under some conditions, derive the distribution function of the vectorial capacity. A stochastic modeling framework is helpful for analyzing the dynamics of disease spreading, such as performing sensitivity analysis.

q-bio.PE↗