Nick Hengartner

Predicting scorpion sting incidence in an endemic region using climatological variables

Abstract Scorpionism is a public health problem in several regions of the world. The highest mortality, with over 1000 deaths per year, has been reported in Mexico. We analysed the significance of climatological variables to predict the incidence of scorpion stings in humans in the state of Colima (Mexico) for the years 2000–2001. The pluvial precipitation (mm), the evaporation (mm), and the mean, maximum, and minimum temperatures (°C) were obtained from local meteorological offices. There are approximately 3 stings/year per 1000 people in municipalities of Colima and Villa de Alvarez and about 18–30 stings/year per 1000 people in the rest of the municipalities. There is very little rain and there are few stings in the winter when the minimum temperature is below about 16°C. The number of scorpion stings is independent of the actual rainfall when this is above 30 mm/month. Using multiple linear regression, we used a backward model selection procedure to estimate that the minimum temperature is correlated with scorpion sting incidence with a statistically significance of 95%. We briefly discuss the application of predictive models of scorpion sting incidence in the appropriate allocation of antivenom serum in hospital clinics.

Donor-Recipient Identification in Para- and Poly-phyletic Trees Under Alternative HIV-1 Transmission Hypotheses Using Approximate Bayesian Computation

Diversity of the founding population of Human Immunodeficiency Virus Type 1 (HIV-1) transmissions raises many important biological, clinical, and epidemiological issues. In up to 40% of sexual infections, there is clear evidence for multiple founding variants, which can influence the efficacy of putative prevention methods, and the reconstruction of epidemiologic histories. To infer who-infected-whom, and to compute the probability of alternative transmission scenarios while explicitly taking phylogenetic uncertainty into account, we created an approximate Bayesian computation (ABC) method based on a set of statistics measuring phylogenetic topology, branch lengths, and genetic diversity. We applied our method to a suspected heterosexual transmission case involving three individuals, showing a complex monophyletic-paraphyletic-polyphyletic phylogenetic topology. We detected that seven phylogenetic lineages had been transmitted between two of the individuals based on the available samples, implying that many more unsampled lineages had also been transmitted. Testing whether the lineages had been transmitted at one time or over some length of time suggested that an ongoing superinfection process over several years was most likely. While one individual was found unlinked to the other two, surprisingly, when evaluating two competing epidemiological priors, the donor of the two that did infect each other was not identified by the host root-label, and was also not the primary suspect in that transmission. This highlights that it is important to take epidemiological information into account when analyzing support for one transmission hypothesis over another, as results may be nonintuitive and sensitive to details about sampling dates relative to possible infection dates. Our study provides a formal inference framework to include information on infection and sampling times, and to investigate ancestral node-label states, transmission direction, transmitted genetic diversity, and frequency of transmission.

Inferring the Distribution of Fitness Effects (DFE) of Newly-Arising Mutations Using Samples Taken from Evolving Populations in Real Time

The DFE characterizes the mutational “input” to evolution, while natural selection largely determines how this input gets sorted into an evolutionary “output”. The output cannot contain novel genetic material that is not present in the input and, as such, understanding the DFE and its dynamics is crucial to understanding evolution generally. Despite this centrality to evolution, however, the DFE has remained elusive primarily due to methodological difficulties. Here, we propose and assess a novel framework for estimating the DFE which removes the biasing effects of selection statistically. We propose a statistic for characterizing the difference between two inferred DFEs, taken from two different populations or from the same population at different time points. This allows us to study the evolution of the DFE and monitor for structural changes in the DFE.

Inference of direction, diversity, and frequency of HIV-1 transmission using approximate Bayesian computation

Diversity of the founding population of Human Immunodeficiency Virus Type 1 (HIV-1) transmissions raises many important biological, clinical, and epidemiological issues. In up to 40% of sexual infections there is clear evidence for multiple founding variants, which can influence the efficacy of putative prevention methods and the reconstruction of epidemiologic histories. To measure the diversity of the founding population and to compute the probability of alternative transmission scenarios, while explicitly taking phylogenetic uncertainty into account, we created an Approximate Bayesian Computation (ABC) method based on a set of statistics measuring phylogenetic topology, branch lengths, and genetic diversity. We applied our method to a heterosexual transmission pair showing a complex paraphyletic-polyphyletic donor-recipient phylogenetic topology. We found evidence identifying the donor that was consistent with the known facts of the case (Bayes factor >20). We also found that while the evidence for ongoing transmission between the pair was as good or better than the singular transmission event model, it was only viable when the rate of ongoing transmission was implausibly high (~1/day). We concluded that the singular transmission model, which was able to estimate the diversity of the founding population (mean 7% substitutions/site), was more biologically plausible. Our study provides a formal inference framework to investigate HIV-1 direction, diversity, and frequency of transmission. The ability to measure the diversity of founding populations in both simple and complex transmission situations is essential to understanding the relationship between the phylogeny and epidemiology of HIV-1 as well as in efforts developing new prevention technologies.

Fully Nonparametric Short Term Forecasting Electricity Consumption

Electricity Transmission System Operators (TSO) are responsible for operating, maintaining and developing the high and extra high voltage network. They guarantee the reliability and proper operation of the power network. Anticipating electricity demand helps to guarantee the balance between generation and consumption at all times, and directly influences the reliability of the power system. In this paper, we focus on predicting short term electricity consumption in France. Several competitors such as iterative bias reduction, functional nonparametric model or non-linear additive autoregressive approach are compared to the actual SARIMA method. Our results show that iterative bias reduction approach outperforms all competitors both on Mean Absolute Percentage Error and on the percentage of forecast errors higher than 2,000 MW.