Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany

Background: Animal trade plays an important role for the spread of infectious diseases in livestock populations. As a case study, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. The central question is how infectious diseases can potentially spread within the system of trade contacts. We address this question by analyzing the underlying network of animal movements. Methodology/Findings: The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings: Our approach provides a general framework for a topological-temporal characterization of livestock trade networks. We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume does barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.Comment: main text 33 pages, 17 figures, supporting information 7 pages, 7 figure

Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark

Understanding animal movements is an important factor for the development of meaningful surveillance and control programs, but also for the development of disease spread models. We analysed the Danish pig movement network using static and temporal network analysis tools to provide deeper insight in the connection between holdings dealing with pigs, such as breeding and multiplier herds, production herds, slaughterhouses or traders. Pig movements, which occurred between 1st January 2006 and 31st December 2015 in Denmark, were summarized to investigate temporal trends such as the number of active holdings, the number of registered movements and the number of pigs moved. To identify holdings and holding types with potentially higher risk for introduction or spread of diseases via pig movements, we determined loyalty patterns, annual network components and contact chains for the 24 registered holding types. The total number of active holdings as well as the number of pig movements decreased during the study period while the holding sizes increased. Around 60-90% of connections between two pig holdings were present in two consecutive years and around one third of the connections persisted within the considered time period. Weaner herds showed the highest level of in-loyalty, whereas we observed an intermediate level of in-loyalty for all breeding sites and for production herds. Boar stations, production herds and trade herds showed a high level of out-loyalty. Production herds constituted the highest proportion of holdings in the largest strongly connected component. All production sites showed low levels of in-going contact chains and we observed a high level of out-going contact chain for breeding and multiplier herds. Except for livestock auctions, all transit sites also showed low levels of out-going contact chains. Our results reflect the pyramidal structure of the underlying network. Based on the considered disease, the time frame for the calculation of network measurements needs to be adapted. Using these adapted values for loyalty and contact chains might help to identify holdings with high potential of spreading diseases and thus limit the outbreak size or support control or eradication of the considered pathogen

Early warning of infectious disease outbreaks on cattle-transport networks.

Surveillance of infectious diseases in livestock is traditionally carried out at the farms, which are the typical units of epidemiological investigations and interventions. In Central and Western Europe, high-quality, long-term time series of animal transports have become available and this opens the possibility to new approaches like sentinel surveillance. By comparing a sentinel surveillance scheme based on markets to one based on farms, the primary aim of this paper is to identify the smallest set of sentinel holdings that would reliably and timely detect emergent disease outbreaks in Swiss cattle. Using a data-driven approach, we simulate the spread of infectious diseases according to the reported or available daily cattle transport data in Switzerland over a four year period. Investigating the efficiency of surveillance at either market or farm level, we find that the most efficient early warning surveillance system [the smallest set of sentinels that timely and reliably detect outbreaks (small outbreaks at detection, short detection delays)] would be based on the former, rather than the latter. We show that a detection probability of 86% can be achieved by monitoring all 137 markets in the network. Additional 250 farm sentinels-selected according to their risk-need to be placed under surveillance so that the probability of first hitting one of these farm sentinels is at least as high as the probability of first hitting a market. Combining all markets and 1000 farms with highest risk of infection, these two levels together will lead to a detection probability of 99%. We conclude that the design of animal surveillance systems greatly benefits from the use of the existing abundant and detailed animal transport data especially in the case of highly dynamic cattle transport networks. Sentinel surveillance approaches can be tailored to complement existing farm risk-based and syndromic surveillance approaches

Modelling control strategies against Classical Swine Fever: influence of traders and markets using static and temporal networks in Ecuador

Classical swine fever (CSF) in Ecuador is prevalent since 1940, pig farming represents an important economic and cultural sector. Recently, the National Veterinary Service (NVS) has implemented individual identification of pigs, movement control and mandatory vaccination against CSF, looking for a future eradication. Our aim was to characterise the pig premises according to risk criteria, analyse the effect of random and targeted strategies to control CSF and consider the temporal development of the network. We used social network analysis (SNA), SIRS (susceptible, infected, recovered, susceptible) network modelling and temporal network analysis. The data set contained 751,003 shipments and 6 million pigs from 2017 to 2019. 165,593 premises were involved: 144,118 farms, 138 industrials, 21,337 traders, and 51 markets. On annual average, 124,976 premises (75%) received or sent one movement with 1.5 pigs, in contrast, 166 (0.01%) with 1,372 movements and 11,607 pigs. Simulations resulted in CSF mean prevalence of 29.93%; Targeted selection strategy reduced the prevalence to 3.3%, while 24% with random selection. Selection of high-risk premises in every province was the best strategy using available surveillance infrastructure. Notably, selecting 10 traders/markets reduced the CSF prevalence to 4%, evidencing their prime influence over the network. Temporal analysis showed an overestimation of 38% (causal fidelity) in the number of transmission paths; The steps to cross the network were 4.3 (average path length), but take approximately 233 days. In conclusion, surveillance strategies applied by the NVS could be more efficient to find cases, reduce the spread of diseases and enable the implementation of risk-based surveillance. To focus the efforts on target selection of high-risk premises, special attention should be given to markets/traders which proved similar disease spread potential

Linking a compartment model for West Nile virus with a flight simulator for vector mosquitoes

Compartmental SIR and SEIR models have become the state of the art tools to study infection cycles of arthropod-borne viruses such as West Nile virus in specific areas. In 2018, the virus was detected for the first time in Germany, and incidents have been reported in humans, birds, and horses. The aim of the work presented here was to provide a tool for estimating West Nile virus infection scenarios, local hotspots and dispersal routes following its introduction into new locations through the movements of mosquitoes. For this purpose, we adapted a SEIR model for West Nile virus to the conditions in Germany (temperatures, geographical latitude, bird and mosquito species densities) and the characteristic transmission and life trait parameter of a possible host bird and vector mosquito species. We further extended it by a spatial component: an agent-based flight simulator for vector mosquitoes. It demonstrates how the female mosquitoes move within the landscape due to habitat structures and wind conditions and about how many of them leave the region in the different cardinal directions. We applied the space–time coupled model with a daily temporal and spatial resolution of 100 m x 100 m to the Eurasian magpie (Pica pica) and the Asian bush mosquito (Aedes japonicus japonicus). Both species are widely distributed in Germany and discussed as important hosts and vectors, respectively. We also applied the model to three study regions in Germany, each representing slightly different climatic conditions and containing significantly different pattern of suitable habitats for the mosquito species

The diffusion metrics of African swine fever in wild boar

Abstract To control African swine fever (ASF) efficiently, easily interpretable metrics of the outbreak dynamics are needed to plan and adapt the required measures. We found that the spread pattern of African Swine Fever cases in wild boar follows the mechanics of a diffusion process, at least in the early phase, for the cases that occurred in Germany. Following incursion into a previously unaffected area, infection disseminates locally within a naive and abundant wild boar population. Using real case data for Germany, we derive statistics about the time differences and distances between consecutive case reports. With the use of these statistics, we generate an ensemble of random walkers (continuous time random walks, CTRW) that resemble the properties of the observed outbreak pattern as one possible realization of all possible disease dissemination patterns. The trained random walker ensemble yields the diffusion constant, the affected area, and the outbreak velocity of early ASF spread in wild boar. These methods are easy to interpret, robust, and may be adapted for different regions. Therefore, diffusion metrics can be useful descriptors of early disease dynamics and help facilitate efficient control of African Swine Fever