Scaling properties and symmetrical patterns in the epidemiology of rotavirus infection

Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.

Logistic Wavelets and Their Application to Model the Spread of COVID-19 Pandemic

In the present paper, we model the cumulative number of persons, reported to be infected with COVID-19 virus, by a sum of several logistic functions (the so-called multilogistic function). We introduce logistic wavelets and describe their properties in terms of Eulerian numbers. Moreover, we implement the logistic wavelets into Matlab’s Wavelet Toolbox and then we use the continuous wavelet transform (CWT) to estimate the parameters of the approximating multilogistic function. Using the examples of several countries, we show that this method is effective as a method of fitting a curve to existing data. However, it also has a predictive value, and, in particular, allows for an early assessment of the size of the emerging new wave of the epidemic, thus it can be used as an early warning method.

Wavelet methods reveal big cat activity patterns and synchrony of activity with preys

Appropriate temporal and spatial scales are important prerequisites for obtaining reliable results in studies of wildlife activity patterns and interspecific interactions. The spread of camera-trap technology has increased interest in and feasibility of studying the activity patterns and interspecific interactions of wildlife. However, such studies are often conducted at arbitrary spatial and temporal scales, and the methods used impose scale on the study rather than determining how activity and species interactions change with spatial scale. In this study, we used a wavelet-based approach to determine the temporal and spatial scales for activity patterns and interspecific interactions on Amur leopard and their ungulate prey species that were recorded using camera traps in the main Amur leopard occurrence region in northeast China. Wavelets identified that Amur leopards were more active in spring and fall than summer, and fluctuated with periodicities of 9 and 17 days, respectively. Synchronous relationships between leopards and their prey commonly occurred in spring and fall, with a periodicity of about 20 days, indicating the appropriate seasons and temporal scales for interspecific interaction research. The influence of human activities on the activity patterns of Amur leopard or prey species often occurred over longer time periods (60-64 days). Two-dimensional wavelet analyses showed that interactions between leopard and prey were more significant at spatial scales of 1 km² . Overall, our study provides a feasible approach to studying the temporal and spatial scales for wildlife activity patterns and interspecific interaction research using camera trap data.

Malaria intensity in Colombia by regions and populations

Determining the distribution of disease prevalence among heterogeneous populations at the national scale is fundamental for epidemiology and public health. Here, we use a combination of methods (spatial scan statistic, topological data analysis and epidemic profile) to study measurable differences in malaria intensity by regions and populations of Colombia. This study explores three main questions: What are the regions of Colombia where malaria is epidemic? What are the regions and populations in Colombia where malaria is endemic? What associations exist between epidemic outbreaks between regions in Colombia? Plasmodium falciparum is most prevalent in the Pacific Coast, some regions of the Amazon Basin, and some regions of the Magdalena Basin. Plasmodium vivax is the most prevalent parasite in Colombia, particularly in the Northern Amazon Basin, the Caribbean, and municipalities of Sucre, Antioquia and Cordoba. We find an acute peak of malarial infection at 25 years of age. Indigenous and Afrocolombian populations experience endemic malaria (with household transmission). We find that Plasmodium vivax decreased in the most important hotspots, often with moderate urbanization rate, and was re-introduced to locations with moderate but sustained deforestation. Infection by Plasmodium falciparum, on the other hand, steadily increased in incidence in locations where it was introduced in the 2009-2010 generalized epidemic. Our findings suggest that Colombia is entering an unstable transmission state, where rapid decreases in one location of the country are interconnected with rapid increases in other parts of the country.

Temporal association of rotavirus vaccination and genotype circulation in South Africa: Observations from 2002 to 2014

BACKGROUND

Rotavirus vaccination has reduced diarrhoeal morbidity and mortality globally. The monovalent rotavirus vaccine was introduced into the public immunization program in South Africa (SA) in 2009 and led to approximately 50% reduction in rotavirus hospitalization in young children. The aim of this study was to investigate the rotavirus genotype distribution in SA before and after vaccine introduction.

MATERIALS AND METHODS

In addition to pre-vaccine era surveillance conducted from 2002 to 2008 at Dr George Mukhari Hospital (DGM), rotavirus surveillance among children <5 years hospitalized for acute diarrhoea was established at seven sentinel sites in SA from April 2009 to December 2014. Stool specimens were screened by enzyme immunoassay and rotavirus positive specimens genotyped using standardised methods.

RESULTS

At DGM, there was a significant decrease in G1 strains from pre-vaccine introduction (34%; 479/1418; 2002-2009) compared to post-vaccine introduction (22%; 37/170; 2010-2014; p for trend <.001). Similarly, there was a significant increase in non-G1P[8] strains at this site (p for trend <.001). In expanded sentinel surveillance, when adjusted for age and site, the odds of rotavirus detection in hospitalized children with diarrhoea declined significantly from 2009 (46%; 423/917) to 2014 (22%; 205/939; p<.001). The odds of G1 detection declined significantly from 2009 (53%; 224/421) to 2010-2011 (26%; 183/703; aOR=0.5; p<.001) and 2012-2014 (9%; 80/905; aOR=0.1; p<.001). Non-G1P[8] strains showed a significant increase from 2009 (33%; 139/421) to 2012-2014 (52%; 473/905; aOR=2.5; p<.001).

CONCLUSIONS

Rotavirus vaccination of children was associated with temporal changes in circulating genotypes. Despite these temporal changes in circulating genotypes, the overall reduction in rotavirus disease in South Africa remains significant.

Spectral analysis based on fast Fourier transformation (FFT) of surveillance data: the case of scarlet fever in China

Many infectious diseases exhibit repetitive or regular behaviour over time. Time-domain approaches, such as the seasonal autoregressive integrated moving average model, are often utilized to examine the cyclical behaviour of such diseases. The limitations for time-domain approaches include over-differencing and over-fitting; furthermore, the use of these approaches is inappropriate when the assumption of linearity may not hold. In this study, we implemented a simple and efficient procedure based on the fast Fourier transformation (FFT) approach to evaluate the epidemic dynamic of scarlet fever incidence (2004-2010) in China. This method demonstrated good internal and external validities and overcame some shortcomings of time-domain approaches. The procedure also elucidated the cycling behaviour in terms of environmental factors. We concluded that, under appropriate circumstances of data structure, spectral analysis based on the FFT approach may be applicable for the study of oscillating diseases.

Time-series analysis of hepatitis A, B, C and E infections in a large Chinese city: application to prediction analysis

Viral hepatitis is recognized as one of the most frequently reported diseases, and especially in China, acute and chronic liver disease due to viral hepatitis has been a major public health problem. The present study aimed to analyse and predict surveillance data of infections of hepatitis A, B, C and E in Wuhan, China, by the method of time-series analysis (MemCalc, Suwa-Trast, Japan). On the basis of spectral analysis, fundamental modes explaining the underlying variation of the data for the years 2004-2008 were assigned. The model was calculated using the fundamental modes and the underlying variation of the data reproduced well. An extension of the model to the year 2009 could predict the data quantitatively. Our study suggests that the present method will allow us to model the temporal pattern of epidemics of viral hepatitis much more effectively than using the artificial neural network, which has been used previously.

Modeling rotavirus strain dynamics in developed countries to understand the potential impact of vaccination on genotype distributions

Understanding how immunity shapes the dynamics of multistrain pathogens is essential in determining the selective pressures imposed by vaccines. There is currently much interest in elucidating the strain dynamics of rotavirus to determine whether vaccination may lead to the replacement of vaccine-type strains. In developed countries, G1P[8] strains constitute the majority of rotavirus infections most years, but occasionally other genotypes dominate for reasons that are not well understood. We developed a mathematical model to examine the interaction of five common rotavirus genotypes. We explored a range of estimates for the relative strength of homotypic vs. heterotypic immunity and compared model predictions against observed genotype patterns from six countries. We then incorporated vaccination in the model to examine its impact on rotavirus incidence and the distribution of strains. Our model can explain the coexistence and cyclical pattern in the distribution of genotypes observed in most developed countries. The predicted frequency of cycling depends on the relative strength of homotypic vs. heterotypic immunity. Vaccination that provides strong protection against G1 and weaker protection against other strains will likely lead to an increase in the relative prevalence of non-G1 strains, whereas a vaccine that provides equally strong immunity against all strains may promote the continued predominance of G1. Overall, however, disease incidence is expected to be substantially reduced under both scenarios and remain below prevaccination levels despite the possible emergence of new strains. Better understanding of homotypic vs. heterotypic immunity, both natural and vaccine-induced, will be critical in predicting the impact of vaccination.

Random distance dependent attachment as a model for neural network generation in the Caenorhabditis elegans

MOTIVATION

The topology of the network induced by the neurons connectivity's in the Caenorhabditis elegans differs from most common random networks. The neurons positions of the C.elegans have been previously explained as being optimal to induce the required network wiring. We here propose a complementary explanation that the network wiring is the direct result of a local stochastic synapse formation process.

RESULTS

We show that a model based on the physical distance between neurons can explain the C.elegans neural network structure, specifically, we demonstrate that a simple model based on a geometrical synapse formation probability and the inhibition of short coherent cycles can explain the properties of the C.elegans' neural network. We suggest this model as an initial framework to discuss neural network generation and as a first step toward the development of models for more advanced creatures. In order to measure the circle frequency in the network, a novel graph-theory circle length measurement algorithm is proposed.

Time-dependent spectral analysis of epidemiological time-series with wavelets

In the current context of global infectious disease risks, a better understanding of the dynamics of major epidemics is urgently needed. Time-series analysis has appeared as an interesting approach to explore the dynamics of numerous diseases. Classical time-series methods can only be used for stationary time-series (in which the statistical properties do not vary with time). However, epidemiological time-series are typically noisy, complex and strongly non-stationary. Given this specific nature, wavelet analysis appears particularly attractive because it is well suited to the analysis of non-stationary signals. Here, we review the basic properties of the wavelet approach as an appropriate and elegant method for time-series analysis in epidemiological studies. The wavelet decomposition offers several advantages that are discussed in this paper based on epidemiological examples. In particular, the wavelet approach permits analysis of transient relationships between two signals and is especially suitable for gradual change in force by exogenous variables.

A Method for Estimating the Order of Influenza Infection between Adults and Children

This paper puts forward a method for determining the infection order of influenza between adults and children from the daily variations in the amount of influenza anti-viral agents treated at a pharmacy. The time series of Tamiflu Capsule for adults and Tamiflu Dry Syrup for children are compared by means of the cross-correlation function. The results from pharmacies located in Tokyo and Kanagawa show that the influenza infection period of adults is earlier than that of children, indicating the infection order: first adults and second children. However, a pharmacy in Saitama yields no clear result.

Method for estimating infection route and speed of influenza

This paper puts forward a method for estimating the infection route and speed of influenza from the daily variations in the amount of influenza formulations supplied at distant city pharmacies. The cross-correlation function between the time variations at the pharmacies indicates as for the drug sales, how many days a pharmacy lags behind another pharmacy. The comparison of the time lags between the pharmacies can lead to the estimation of the infection route of influenza. Taking into account the distance between the locations of the pharmacies, we can calculate the infection speed of influenza. Three pharmacies located in Tokyo and its vicinity (Saitama and Kanagawa) are taken as an example. The thrust of this paper is to introduce the new strategy that can take full advantage of the information every pharmacy has in possession.

RNA viruses as complex adaptive systems

RNA viruses have high mutation rates and so their populations exist as dynamic and complex mutant distributions. It has been consistently observed that when challenged with a new environment, viral populations adapt following hyperbolic-like kinetics: adaptation is initially very rapid, but then slows down as fitness reaches an asymptotic value. These adaptive dynamics have been explained in terms of populations moving towards the top of peaks on rugged fitness landscapes. Fitness fluctuations of varying magnitude are observed during adaptation. Often the presence of fluctuations in the evolution of physical systems indicates some form of self-organization, or where many components of the system are simultaneously involved. Here we analyze data from several in vitro evolution experiments carried out with vesicular stomatitis virus (VSV) looking for the signature of criticality and scaling. Long-range fitness correlations have been detected during the adaptive process. We also found that the magnitude of fitness fluctuations, far from being trivial, conform to a Weibull probability distribution function, suggesting that viral adaptation belongs to a broad category of phenomena previously documented in other fields and related with emergence.