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In a multi-host system, understanding host-species contribution to transmission is key to appropriately targeting control and preventive measures. Outbreak reconstruction methods aiming to identify who-infected-whom by combining epidemiological and genetic data could contribute to achieving this goal. However, the majority of these methods remain untested on realistic simulated multi-host data. Mycobacterium bovis is a slowly evolving multi-host pathogen and previous studies on outbreaks involving both cattle and wildlife have identified observation biases. Indeed, contrary to cattle, sampling wildlife is difficult. The aim of our study was to evaluate and compare the performances of three existing outbreak reconstruction methods (seqTrack, outbreaker2 and TransPhylo) on M. bovis multi-host data simulated with and without biases. Extending an existing transmission model, we simulated 30 bTB outbreaks involving cattle, badgers and wild boars and defined six sampling schemes mimicking observation biases. We estimated general and specific to multi-host systems epidemiological indicators. We tested four alternative transmission scenarios changing the mutation rate or the composition of the epidemiological system. The reconstruction of who-infected-whom was sensitive to the mutation rate and seqTrack reconstructed prolific super-spreaders. TransPhylo and outbreaker2 poorly estimated the contribution of each host-species and could not reconstruct the presence of a dead-end epidemiological host. However, the host-species of cattle (but not badger) index cases was correctly reconstructed by seqTrack and outbreaker2. These two specific indicators improved when considering an observation bias. We found an overall poor performance for the three methods on simulated biased and unbiased bTB data. This seemed partly attributable to the low evolutionary rate characteristic of M. bovis leading to insufficient genetic information, but also to the complexity of the simulated multi-host system. This study highlights the importance of an integrated approach and the need to develop new outbreak reconstruction methods adapted to complex epidemiological systems and tested on realistic multi-host data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Ticks are important vectors of disease, particularly in the context of One Health, where tick-borne diseases (TBDs) are increasingly prevalent worldwide. TBDs often involve co-infections, where multiple pathogens co-exist in a single host. Patients with chronic Lyme disease often have co-infections with other bacteria or parasites. This study aimed to create a co-infection model with Borrelia afzelii and tick-borne encephalitis virus (TBEV) in C3H mice and to evaluate symptoms, mortality, and pathogen level compared to single infections. Successful co-infection of C3H mice with B. afzelii and TBEV was achieved. Outcomes varied, depending on the timing of infection. When TBEV infection followed B. afzelii infection by 9 days, TBEV symptoms worsened and virus levels increased. Conversely, mice infected 21 days apart with TBEV showed milder symptoms and lower mortality. Simultaneous infection resulted in mild symptoms and no deaths. However, our model did not effectively infect ticks with TBEV, possibly due to suboptimal dosing, highlighting the challenges of replicating natural conditions. Understanding the consequences of co-infection is crucial, given the increasing prevalence of TBD. Co-infected individuals may experience exacerbated symptoms, highlighting the need for a comprehensive understanding through refined animal models. This study advances knowledge of TBD and highlights the importance of exploring co-infection dynamics in host-pathogen interactions., Competing Interests: The authors declare no conflict of interest.

Objectives: This review aimed to examine the available evidence about non-pharmacological interventions (NPIs) aimed at improving sleep quality in older adults without insomnia or dementia. Methods: Studies on NPIs targeting older adults' sleep were searched in the PsycInfo, PubMed and Scopus databases, with no restriction on publication year up to September 2021. Studies on NPIs for older adults with no diagnosed sleep disorders were included, while those on pharmacological therapies and/or concerning pathological samples were excluded. The risk of bias was assessed using tools based on Joanna Briggs' criteria. The data extracted were meta-analyzed using random effects models for subgroups of NPIs. Results: Of the 1,893 records identified, 31 studies on NPIs ( N  = 2,224; range of mean ages: 60-78 years) were analyzed. All NPIs improved self-reported sleep quality, albeit to a different extent (physical activity: d =.97 - 95% CI=.62, 1.32-; psychological/psychoeducational, or NPIs that combined more than one sleep-targeting activity: d range: .21 to .97). Only the NPIs based on physical activity improved objectively-measured sleep, d =.31 (.04, .57). The methodological quality of most studies was limited. Conclusion: The most often used NPIs targeting sleep rely on physical activity and sensory stimulation with promising results on sleep quality for the former. More data are needed on psychological/psychoeducational NPIs and combined interventions in order to test their effectiveness. The methodological weaknesses of the available studies suggest they their findings should be interpreted with caution., Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2022.2056879.

Beef exports represent a substantial part of Paraguay's agricultural sector. Cattle movements involve a high risk due to the possible spread of bovine diseases that can have a significant impact on the country's economy. We analyzed cattle movements from 2014 to 2018 using the networks analysis methodology at the holding and district levels at different temporal scales. We built two types of networks to identify network characteristics that may contribute to the spread of two diseases with different epidemiological characteristics: i) a network including all cattle movements to consider the transmission of a disease of rapid spread like foot and mouth disease, and ii) a network including only cow movements to account for bovine brucellosis, a disease of slow spread that occurs mainly in adult females. Network indicators did not vary substantially among the cattle and cow only networks. The holdings/districts included in the largest strongly connected components were distributed throughout the country. Percolation analysis performed at the holding level showed that a large number of holdings should be removed to make the largest strongly connected component disappear. Higher values of the centrality indicators were found for markets than for farms, indicating that they may play an important role in the spread of an infectious disease. At the holding level (but not at the district level), the networks exhibited characteristics of small-world networks. This property may facilitate the spread of foot and mouth disease in case of re-emergence, or of bovine brucellosis in the country through cattle movements. They should be taken into account when implementing surveillance or control measures for these diseases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Avalos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Global trade has been ranked as one of the top five drivers of infectious disease threat events. More specifically, livestock trade is known to increase the speed at which infectious diseases circulate and to facilitate their dissemination over large distances Therefore, predicting animal movements arising from trade is crucial for assessing epidemic risk and the impact of preventive measures. In this study, we developed a statistical framework for predicting trading events using predictors accessible from routinely collected data. We focused on veal calves, a category of animals with significant commercial value; the dataset considered the veal calf trade in France between January 2011 and June 2019. A subset of farms with consistent trade behaviour over time was built to be used throughout the study. To predict sale or purchase event occurrences, our predictive framework was based on random forests as a binary classification tool, an approach that allows a large number of potential predictors. We explored the robustness of model predictions with respect to the delay in data acquisition and prediction lag time. Overall, sales were more accurately predicted than purchasing events. Unsurprisingly, a delay in data acquisition led to a decrease in the performance of indicators, whereas prediction lag time had little impact. Sale-related predictors mostly reflected past trading events, whereas purchase-related predictors were associated with past trading events, farm management and general farm characteristics. The model outputs also suggested that the veal calf trading network is driven by sales rather than by purchases. Regardless of the length of the delay in data acquisition and prediction lag, the random forest approach fitted on data with municipality as trading unit and a 28-day trading period provided better performance scores (F1-score, positive predictive value and negative predictive value) than scenarios with finer temporal and spatial aggregation units. Predicted trade events can therefore be used to reconstruct the entire veal calf trading network and transfers between selling and purchasing units for each period. This predicted network could be further used to simulate the spread of pathogens via animal trade., Competing Interests: Conflict of interest The authors declare no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Foot-and-mouth disease (FMD) affects the livestock industry and socioeconomic sustainability of many African countries. The success of FMD control programs in Africa depends largely on understanding the dynamics of FMD virus (FMDV) spread. In light of the recent outbreaks of FMD that affected the North-Western African countries in 2018 and 2019, we investigated the evolutionary phylodynamics of the causative serotype O viral strains all belonging to the East-Africa 3 topotype (O/EA-3). We analyzed a total of 489 sequences encoding the FMDV VP1 genome region generated from samples collected from 25 African and Western Asian countries between 1974 and 2019. Using Bayesian evolutionary models on genomic and epidemiological data, we inferred the routes of introduction and migration of the FMDV O/EA-3 topotype at the inter-regional scale. We inferred a mean substitution rate of 6.64 × 10 -3  nt/site/year and we predicted that the most recent common ancestor for our panel of samples circulated between February 1967 and November 1973 in Yemen, likely reflecting the epidemiological situation in under sampled cattle-exporting East African countries. Our study also reinforces the role previously described of Sudan and South Sudan as a frequent source of FMDVs spread. In particular, we identified two transboundary routes of O/EA-3 diffusion: the first from Sudan to North-East Africa, and from the latter into Israel and Palestine AT; a second from Sudan to Nigeria, Cameroon, and from there to further into West and North-West Africa. This study highlights the necessity to reinforce surveillance at an inter-regional scale in Africa and Western Asia, in particular along the identified migration routes for the implementation of efficient control measures in the fight against FMD., (© 2022 The Authors. Transboundary and Emerging Diseases published by Wiley-VCH GmbH.)

In two "départements" in the South-West of France, bovine tuberculosis (bTB) outbreaks due to Mycobacterium bovis spoligotype SB0821 have been identified in cattle since 2002 and in wildlife since 2013. Using whole genome sequencing, the aim of our study was to clarify badger contribution to bTB transmission in this area. We used a Bayesian evolutionary model, to infer phylogenetic trees and migration rates between two pathogen populations defined by their host-species. In order to account for sampling bias, sub-population structure was inferred using the marginal approximation of the structured coalescent (Mascot) implemented in BEAST2. We included 167 SB0821 strains (21 isolated from badgers and 146 from cattle) and identified 171 single nucleotide polymorphisms. We selected a HKY model and a strict molecular clock. We estimated a badger-to-cattle transition rate (median: 2.2 transitions/lineage/year) 52 times superior to the cattle-to-badger rate (median: 0.042 transitions/lineage/year). Using the maximum clade credibility tree, we identified that over 75% of the lineages from 1989 to 2000 were present in badgers. In addition, we calculated a median of 64 transition events from badger-to-cattle (IQR: 10-91) and a median of zero transition event from cattle-to-badger (IQR: 0-3). Our model enabled us to infer inter-species transitions but not intra-population transmission as in previous epidemiological studies, where relevant units were farms and badger social groups. Thus, while we could not confirm badgers as possible intermediaries in farm-to-farm transmission, badger-to-cattle transition rate was high and we confirmed long-term presence of M. bovis in the badger population in the South-West of France., (© 2022. The Author(s).)

In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.