Uncertainty in On-The-Fly Epidemic Fitting

Abstract. The modern world features a plethora of social, technolog-ical and biological epidemic phenomena. These epidemics now spread at unprecedented rates thanks to advances in industrialisation, trans-port and telecommunications. Effective real-time decision making and management of modern epidemic outbreaks depends on the two factors: the ability to determine epidemic parameters as the epidemic unfolds, and the ability to characterise rigorously the uncertainties inherent in these parameters. This paper presents a generic maximum-likelihood-based methodology for online epidemic fitting of SIR models from a single trace which yields confidence intervals on parameter values. The method is fully automated and avoids the laborious manual efforts tra-ditionally deployed in the modelling of biological epidemics. We present case studies based on both synthetic and real data

Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

IntroductionCutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.Methodology/principal findingsWe fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation.SignificanceThese outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets

Inverse problems in demography and biodemography

Inverse problems play important role in science and engineering. Estimation of boundary conditions on the temperature distribution inside a metallurgical furnace, reconstruction of tissue density inside body on plane projections obtained with x-rays are examples. The similar problems exist in demography in the form of projection and estimation of population age distributions and age-specific mortality rates. The problem of residual demography is estimation of demographic process in wild nature on its manifestation in marked subjects with unobserved age, which again is inverse problem. The article presents examples and the ways of solution the inverse problems in demography and biodemography, discusses the ways of improving results by combination of demographic and genetic data.

A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts

Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation-based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS-CoV-2 dynamics, and apply our method to national surveillance counts from the 2013--2015 West Africa Ebola outbreak

Evaluation of approaches to control of Maedi-Visna disease of sheep using a Markov chain simulation model for a range of typical British Flocks

An epidemiological model is described that closely mimicked results of a published serological study of natural transmission of Maedi-Visna virus in a low ground flock of sheep. We adjusted parameters in the model from this baseline to explore the possible implications for the control of Maedi-Visna virus in typical British flocks. On closed hill farms, low probability of effective contact was most critical for control. In open low ground flocks, purchasing accredited replacements eliminated disease spread, otherwise flock size was the most important factor governing flock prevalence. Results highlighted the need for more epidemiological information about Maedi-Visna, particularly whether hill farms act as a hidden reservoir of virus or reduce the impact of this disease on the industry by providing a source of clean replacementsLivestock Production/Industries, Maedi-Visna, Model, Markov Chain, Sheep, Control,