The purpose of time series analysis via mechanistic models is to reconcile the known or hypothesized structure of a dynamical system with observations collected over time. We develop a framework for constructing nonlinear mechanistic models and carrying out inference. Our framework permits the consideration of implicit dynamic models, meaning statistical models for stochastic dynamical systems which are specified by a simulation algorithm to generate sample paths. Inference procedures that operate on implicit models are said to have the plug-and-play property. Our work builds on recently developed plug-and-play inference methodology for partially observed Markov models. We introduce a class of implicitly specified Markov chains with stochastic transition rates, and we demonstrate its applicability to open problems in statistical inference for biological systems. As one example, these models are shown to give a fresh perspective on measles transmission dynamics. As a second example, we present a mechanistic analysis of cholera incidence data, involving interaction between two competing strains of the pathogen Vibrio cholerae. 1. Introduction. A

The success of an infectious disease to invade a population is strongly controlled by the population’s specific connectivity structure. Here a network model is presented as an aid in understanding the role of social behavior and heterogeneous connectivity in determining the spatio-temporal patterns of disease dynamics. We explore the controversial origins of longterm recurrent oscillations believed to be characteristic to diseases that have a period of temporary immunity after infection. In particular, we focus on sexually transmitted diseases such as syphilis where this controversy is currently under review. Although temporary immunity plays a key role, it is found that in realistic small-world networks, the social and sexual behavior of individuals also has great influence in generating longterm cycles. The model generates circular waves of infection with unusual spatial dynamics that depend on focal areas that act as pacemakers in the population. Eradication of the disease can be efficiently achieved by

An expanded model describing viral evolution within organism was proposed as an extension of the SEIRS model with illness symptoms- SEI[Is]RS. It has been implemented in a self-organizing, multi-agent system based on social networks and social time model. This article presents the results of simulation of simultaneous spreading within the population of two viral strains which provide total cross immunity to each other. The effect of displacing one viral strain by another, supported by empirical observations, is also present in obtained simulation results. It might have important consequences for genetic and antigenic diversity of viruses in populations. Epidemic, SEIRS, virus spreading, social network, I.

A model for emergence of high pathogenicity

Abstract—This study investigated climatic factors associated with influenza cases in Southern Thailand. The main aim for use regression analysis to investigate possible causual relationship of climatic factors and variability between the border of the Andaman Sea and the Gulf of Thailand. Southern Thailand had the highest Influenza incidences among four regions (i.e. north, northeast, central and southern Thailand). In this study, there were 14 climatic factors: mean relative humidity, maximum relative humidity, minimum relative humidity, rainfall, rainy days, daily maximum rainfall, pressure, maximum wind speed, mean wind speed, sunshine duration, mean temperature, maximum temperature, minimum temperature, and temperature difference (i.e. maximum – minimum temperature). Multiple stepwise regression technique was used to fit the statistical model. The results indicated that the mean wind speed and the minimum relative humidity were positively associated with the number of influenza cases on the Andaman Sea side. The maximum wind speed was positively associated with the number of influenza cases on the Gulf of Thailand side.

Abstract—This study investigated the pattern and seasonal index of influenza cases in Thailand. Our results showed that southern Thailand had the highest influenza incidence among the four regions of Thailand (i.e. north, northeast, central and southern Thailand). The influenza pattern in southern Thailand was similar to that of northeastern Thailand. Seasonal index values of influenza cases in Thailand were higher in the hot season than in the wet season. Influenza cases started to increase at the beginning of the hot season (April), reached a maximum in August, rapidly declined in the middle of the wet season and reached the lowest value in December. Seasonal index values for northern Thailand differed from other regions of Thailand. Keywords—Influenza, Disease Index, Seasonal index, Thailand. I.

www.elsevier.com/locate/tpb On the role of cross-immunity and vaccines on the survival of less fit flu-strains

Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host–pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite–host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.

Optimization over a class of tree shape statistics

Improving the realism of deterministic multi-strain models: implications for modelling influenza A