A network analysis of the local pig supply chain in a repeated outbreak area of human streptococcosis in Thailand

AIMS

The present study employed a network analysis approach to scrutinize a pig supply chain in a repeated outbreak province for human streptococcosis in Thailand and identified important actors that should be focused on for tailoring appropriate interventions.

METHODS AND RESULTS

Nakhon Sawan province was chosen as the study site as the cases of human streptococcosis have been consecutively reported since 2014, with the number of cases ranging from 21 to 63. A questionnaire survey was used to collect data from actors along the pig supply chain, including pig farms, slaughterhouses, pork sellers, restaurants and customers. A one-mode-directed network was then constructed. Degree and betweenness centrality values were measured. We found that the supply chain of pork products comprised 314 nodes and 296 directed ties. A retailer got the highest overall degree, out-degree and betweenness centrality values at 35, 34, and 65.3, respectively. For in-degree centrality, the highest was identified in a customer at 9. Interestingly, this customer bought pork products from nine different mobile groceries.

CONCLUSIONS

Both public health and veterinary authorities should extend their surveillance activities to cover all actors in the supply chain to strengthen overall disease prevention and control for streptococcosis.

An overlooked poultry trade network of the smallholder farms in the border provinces of Thailand, 2021: implications for avian influenza surveillance

Introduction

In Thailand, community-level poultry trade is conducted on a small-scale involving farmers and traders with many trade networks. Understanding the poultry movements may help identify different activities that farmers and traders might contribute to the spread of avian influenza.

Methods

This study aimed to describe the characteristics of players involved in the poultry trade network at the northeastern border of Thailand using network analysis approaches. Mukdahan and Nakhon Phanom provinces, which border Laos, and Ubon Ratchathani province, which borders both Laos and Cambodia, were selected as survey sites.

Results

Local veterinary officers identified and interviewed 338 poultry farmers and eight poultry traders in 2021. A weighted directed network identified incoming and outgoing movements of where the subdistricts traded chickens. Ninety-nine subdistricts and 181 trade links were captured. A self-looping (trader and consumer in the same subdistrict) feedback was found in 56 of 99 subdistricts. The median distance of the movements was 14.02 km (interquartile range (IQR): 6.04-102.74 km), with a maximum of 823.08 km. Most subdistricts in the network had few poultry trade connections, with a median of 1. They typically connected to 1-5 other subdistricts, most often receiving poultry from 1 to 2.5 subdistricts, and sending to 1-2 subdistricts. The subdistricts with the highest overall and in-degree centrality were located in Mukdahan province, whereas one with the highest out-degree centrality was found in Nakhon Phanom province.

Discussion

The poultry movement pattern observed in this network helps explain how avian influenza could spread over the networks once introduced.

Quantitative Risk Assessment for the Introduction of Bovine Leukemia Virus-Infected Cattle Using a Cattle Movement Network Analysis

The cattle industry is suffering economic losses caused by bovine leukemia virus (BLV) and enzootic bovine leukosis (EBL), the clinical condition associated with BLV infection. This pathogen spreads easily without detection by farmers and veterinarians due to the lack of obvious clinical signs. Cattle movement strongly contributes to the inter-farm transmission of BLV. This study quantified the farm-level risk of BLV introduction using a cattle movement analysis. A generalized linear mixed model predicting the proportion of BLV-infected cattle was constructed based on weighted in-degree centrality. Our results suggest a positive association between weighted in-degree centrality and the estimated number of introduced BLV-infected cattle. Remarkably, the introduction of approximately six cattle allowed at least one BLV-infected animal to be added to the farm in the worst-case scenario. These data suggest a high risk of BLV infection on farms with a high number of cattle being introduced. Our findings indicate the need to strengthen BLV control strategies, especially along the chain of cattle movement.

Cattle Manure Trade Network Analysis and the Relevant Spatial Pathways in an Endemic Area of Foot and Mouth Disease in Northern Thailand

Animal movement is one of the most important risk factors for outbreaks of foot and mouth disease (FMD) in cattle. Likewise, FMD can spread to cattle farms via vehicles contaminated with the FMD virus. In Northern Thailand, the movement of manure transport vehicles and the circulation of manure bags among cattle farms are considered as potential risk factors for FMD outbreaks among cattle farms. This study aimed to determine the characteristics and movement patterns of manure tradesman using social network analysis. A structured questionnaire was used to identify sequences of farms routinely visited by each tradesman. A total of 611 participants were interviewed, including 154 beef farmers, 407 dairy farmers, 36 tradesmen, and 14 final purchasers. A static weighted directed one-mode network was constructed, and the network metrics were measured. For the manure tradesman–cattle farmer network, the tradesman possessed the highest value of in- and out-degree centralities (71 and 4), betweenness centralities (114.5), and k-core values (2). These results indicated that the tradesman had a high frequency of farm visits and had a remarkable influence on other persons (nodes) in the network. The movement of vehicles ranged from within local districts, among districts, or even across provinces. Unclean manure plastic bags were circulated among cattle farms. Therefore, both vehicles and the bags may act as a disease fomite. Interestingly, no recording system was implemented for the movement of manure transport vehicles. This study suggested that the relevant authority and stakeholders should be aware of the risk of FMD spreading within this manure trading network. The findings from this study can be used as supporting data that can be used for enhancing FMD control measures, especially for FMD endemic areas.

Social network analysis for the assessment of pig, cattle and buffalo movement in Xayabouli, Lao PDR

The aim of this study is to understand the role that the movement patterns of pigs, cattle and buffalo play in the spread of foot-and-mouth disease (FMD). A cross-sectional survey consisting of a questionnaire was used in a hotspot area for FMD: Xayabouli Province, Lao People’s Democratic Republic. A total of 189 respondents were interviewed. We found that the key players in this network were people who were involved with more than one species of animal or occupation (multipurpose occupational node), which represents the highest number of activities of animals moved off the holding (shown with the highest out-degree centrality) and a high likelihood of being an intermediary between others (shown with the highest betweenness centrality). Moreover, the results show that the animals moved to and away from each node had few connections. Some nodes (such as traders) always received animals from the same group of cattle owners at different times. The subgroup connection within this network has many weak components, which means a connection in this network shows that some people can be reached by others, but most people were not. In this way, the number of connections present in the network was low when we defined the proportion of observed connections with all possible connections (density). These findings indicate that the network might not be busy; only one type of node is dominant which enables increased control of disease spread. We recommend that the relevant authorities implement control measures regarding the key players, which is the best way to effectively control the spread of infectious diseases.

The implementation of cattle market closure strategies to mitigate the foot-and-mouth disease epidemics: A contact modeling approach

Foot-and-mouth disease (FMD) is one of the most endemic diseases in livestock worldwide. The disease occurrence generally results in a huge economic impact. The virus may distribute across countries or even continents along the contact network of animal movements. The present study, therefore, aimed to explore a cattle movement network originated in Tak, a Thailand-Myanmar bordered province and to demonstrate how FMDV spread among the nodes of market, source and destination. Subsequently, we examined the effectiveness of market closure intervention. The market-market (M-M) network was constructed to highlight the inter-market connections and the FMDV was modeled to spread along the trade chain. Four market closure scenarios based on rapidness and duration of implementation were examined. Our results indicate that two of the three major markets located in the province were highly connected and a strongly connected component was identified. The intra-provincial animal movements, which were currently overlooked, should be moved into sights as most of the high-risk sources for FMD epidemics were recognized in a close proximity to the cattle markets. Simultaneously, remote destinations across the country were identified. The inter-provincial animal movement control must be strengthened once FMD outbreak is notified. Based on our simulations, closing markets with low inter-market connectivity may not prevent the spread of FMDV. A selective market closure strategy targeting highly connected markets together with cattle trader tracking system was an alternative approach. However, socio-economic consequences regarding this intervention must be considered.

Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches

BACKGROUND

The Zika virus was first discovered in 1947. It was neglected until a major outbreak occurred on Yap Island, Micronesia, in 2007. Teratogenic effects resulting in microcephaly in newborn infants is the greatest public health threat. In 2016, the Zika virus epidemic was declared as a Public Health Emergency of International Concern (PHEIC). Consequently, mathematical models were constructed to explicitly elucidate related transmission dynamics.

SURVEY METHODOLOGY

In this review article, two steps of journal article searching were performed. First, we attempted to identify mathematical models previously applied to the study of vector-borne diseases using the search terms "dynamics," "mathematical model," "modeling," and "vector-borne" together with the names of vector-borne diseases including chikungunya, dengue, malaria, West Nile, and Zika. Then the identified types of model were further investigated. Second, we narrowed down our survey to focus on only Zika virus research. The terms we searched for were "compartmental," "spatial," "metapopulation," "network," "individual-based," "agent-based" AND "Zika." All relevant studies were included regardless of the year of publication. We have collected research articles that were published before August 2017 based on our search criteria. In this publication survey, we explored the Google Scholar and PubMed databases.

RESULTS

We found five basic model architectures previously applied to vector-borne virus studies, particularly in Zika virus simulations. These include compartmental, spatial, metapopulation, network, and individual-based models. We found that Zika models carried out for early epidemics were mostly fit into compartmental structures and were less complicated compared to the more recent ones. Simple models are still commonly used for the timely assessment of epidemics. Nevertheless, due to the availability of large-scale real-world data and computational power, recently there has been growing interest in more complex modeling frameworks.

DISCUSSION

Mathematical models are employed to explore and predict how an infectious disease spreads in the real world, evaluate the disease importation risk, and assess the effectiveness of intervention strategies. As the trends in modeling of infectious diseases have been shifting towards data-driven approaches, simple and complex models should be exploited differently. Simple models can be produced in a timely fashion to provide an estimation of the possible impacts. In contrast, complex models integrating real-world data require more time to develop but are far more realistic. The preparation of complicated modeling frameworks prior to the outbreaks is recommended, including the case of future Zika epidemic preparation.