Inferring ASF transmission in domestic pigs and wild boars using a paired model iterative approach.

The rapid spread of African swine fever (ASF) in recent years has once again raised awareness of the need to improve our preparedness in preventing and managing outbreaks, for which modelling-based forecasts can play an important role. This is even more important in the case of a disease such as ASF, involving several types of hosts, characterised by a high case-fatality rate and for which there is currently no treatment or vaccine. Within the framework of the ASF challenge, we proposed a modelling approach based on a stochastic mechanistic model and an inference procedure to estimate key transmission parameters from provided data (incomplete and noisy) and generate forecasts for unobserved time horizons. The model is partly data driven and composed of two modules, corresponding to epidemic and demographic dynamics in domestic pig and wild boar (WB) populations, interconnected through the networks of animal trade and/or spatial proximity. The inference consists in an iterative procedure, alternating between the two models and based on a criterion optimisation. Estimates of transmission and detection parameters appeared to be of similar magnitude for each of the three periods of the challenge, except for the transmission rates in WB population through contact with infectious individuals and carcasses, higher during the first period. The predicted number of infected domestic pig farms was in overall agreement with the data. The proportion of positive tested WB was overestimated, but with a trend close to that observed in the data. Comparison of the spatial simulated and observed distributions of detected cases also showed an overestimation of the spread of the pathogen within WB metapopulation. Beyond the quantitative results and the inherent difficulties of real-time forecasting, we built a modelling framework that is flexible enough to accommodate changes in transmission processes and control measures that may occur during an epidemic emergency.

Selecting sorting centres to avoid long distance transport of weaned beef calves.

The transport of weaned calves from cow-calf producers to fatteners is a general concern for the young bull industry due to its documented negative impact on the welfare, health and performance of the animals. These transfers are often managed by intermediaries who transport weaned calves to sorting centres, where they are grouped into batches before being sent to fattening units. In this study, we present an algorithm to limiting these transfer distances by appropriately selecting the sorting centre through which they must go. We tested the effectiveness of this algorithm on historical data from a French beef producer organization managing 136,892 transfers using 13 sorting centres. The results show a decrease in the transfer distances compared to the historical record, especially for the calves travelling over long distances (- 76 km, i.e. 18% on average for the 33% longest transfers). Moreover, the distribution of calves between the sorting centres proposed by the algorithm reveals differences in their efficiency in minimizing transfer distances. In addition to its usefulness as a management tool for the daily transport of cattle, this algorithm provides prospects for improving the management of the sorting centres themselves.

How mechanistic modelling supports decision making for the control of enzootic infectious diseases.

Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.

Investigating transmission in a two-wave epidemic of Chikungunya fever, Réunion Island.

An epidemic of Chikungunya fever, a mosquito-borne viral disease, spectacularly swept through Réunion Island (population 780,000) in 2005-2006. There were 3,000 cases in a first wave (March-June 2005) and more than 250,000 cases in a second (December 2005-April 2006). Adapting newly developed epidemiological tools to vector-borne diseases, we show that despite this massive difference in magnitude, the transmission potential as measured by the number of secondary cases per index case (or reproduction number), remained similar during the two consecutive waves. The best estimate for the initial reproduction number R(0) was 3.7, with a possible range from 2 to 11 depending on incubation duration and lifespan of the mosquito. We conclude that an increase in virulence between the two seasons was not necessary to explain the change in magnitude of the epidemics, and that the attack rate may be well over 50% in Chikungunya fever epidemics in the absence of intervention.