The African swine fever modelling challenge: Model comparison and lessons learnt.

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers.

A combination of probabilistic and mechanistic approaches for predicting the spread of African swine fever on Merry Island.

Over the last decade African swine fever virus, one of the most virulent pathogens known to affect pigs, has devastated pork industries and wild pig populations throughout the world. Despite a growing literature on specific aspects of African swine fever transmission dynamics, it remains unclear which methods and approaches are most effective for controlling the disease during a crisis. As a consequence, an international modelling challenge was organized in which teams analyzed and responded to a stream of data from an in silico outbreak in the fictive country of Merry Island.

Correction: Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape.

[This corrects the article DOI: 10.1371/journal.pcbi.

Characterization of a Sex-Determining Region and Its Genomic Context via Statistical Estimates of Haplotype Frequencies in Daughters and Sons Sequenced in Pools.

Sex chromosomes are generally derived from a pair of autosomes that have acquired a locus controlling sex. Sex chromosomes may evolve reduced recombination around this locus and undergo a long process of molecular divergence. At that point, the original loci controlling sex may be difficult to pinpoint.

Biopesticides improve efficiency of the sterile insect technique for controlling mosquito-driven dengue epidemics.

Various mosquito control methods use factory raised males to suppress vector densities. But the efficiency of these methods is currently insufficient to prevent epidemics of arbovirus diseases such as dengue, chikungunya or Zika. Suggestions that the sterile insect technique (SIT) could be "boosted" by applying biopesticides to sterile males remain unquantified.

A framework for estimating the effects of sequential reproductive barriers: Implementation using Bayesian models with field data from cryptic species.

Determining how reproductive barriers modulate gene flow between populations represents a major step toward understanding the factors shaping the course of speciation. Although many indices quantifying reproductive isolation (RI) have been proposed, they do not permit the quantification of cross-direction-specific RI under varying species frequencies and over arbitrary sequences of barriers. Furthermore, techniques quantifying associated uncertainties are lacking, and statistical methods unrelated to biological process are still preferred for obtaining confidence intervals and P-values.

Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape.

Characterising the spatio-temporal dynamics of pathogens in natura is key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control.

Using sensitivity analysis to identify key factors for the propagation of a plant epidemic.

Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by , in a real landscape.

A random forest approach for predicting the presence of intermediate host . presence in relation to landscape characteristics in western China.

Understanding distribution patterns of hosts implicated in the transmission of zoonotic disease remains a key goal of parasitology. Here, random forests are employed to model spatial patterns of the presence of the plateau pika (.) small mammal intermediate host for the parasitic tapeworm which is responsible for a significant burden of human zoonoses in western China.

Mixtures of GAMs for habitat suitability analysis with overdispersed presence / absence data.

A new approach to species distribution modelling based on unsupervised classification via a finite mixture of GAMs incorporating habitat suitability curves is proposed. A tailored EM algorithm is outlined for computing maximum likelihood estimates. Several submodels incorporating various parameter constraints are explored.

Landscape composition and spatial prediction of alveolar echinococcosis in southern Ningxia, China.

Background: Alveolar echinococcosis (AE) presents a serious public health challenge within China. Mass screening ultrasound surveys can detect pre-symptomatic AE, but targeting areas identified from hospital records is inefficient regarding AE. Prediction of undetected or emerging hotspots would increase detection rates.