Competitive evolution of H1N1 and H3N2 influenza viruses in the United States: A mathematical modeling study.

Seasonal influenza causes vast public health and economic impact globally. The prevention and control of the annual epidemics remain a challenge due to the antigenic evolution of the viruses. Here, we presented a novel modeling framework based on changes in amino acid sequences and relevant epidemiological data to retrospectively investigate the competitive evolution and transmission of H1N1 and H3N2 influenza viruses in the United States during October 2002 and April 2019. To do so, we estimated the time-varying disease transmission rate from the reported influenza cases and the time-varying antigenic change rate of the viruses from the changes in amino acid sequences. By incorporating the time-varying antigenic change rate into the transmission models, we found that the models could capture the evolutionary transmission dynamics of influenza viruses in the United States. Our modeling results also showed that the antigenic change of the virus plays an essential role in seasonal influenza dynamics.

Emerging infectious diseases may spread across pig trade networks in Thailand once introduced: a network analysis approach.

In Thailand, pork is one of the most consumed meats nationwide. Pig farming is hence an important business in the country. However, 95% of the farms were considered smallholders raising only 50 pigs or less. With limited budgets and resources, the biosecurity level in these farms is relatively low. Pig movements have been previously identified as a risk factor in the spread of infectious diseases. Therefore, the present study aimed to explicitly analyze the pig movement network structure and assess its vulnerability to the spread of emerging diseases in Thailand. We used official electronic records of nationwide pig movements throughout the year 2021 to construct a directed weighted one-mode network. Degree centrality, degree distribution, connected components, network community, and modularity were measured to explore the network architectures and properties. In this network, 484,483 pig movements were captured. In which, 379,948 (78.42%) were moved toward slaughterhouses and hence excluded from further analyses. From the remaining links, we suggested that the pig movement network in Thailand was vulnerable to the spread of emerging infectious diseases. Within the network, we found a strongly connected component (SCC) connecting 1044 subdistricts (38.6% of the nodes), a giant weakly connected component (GWCC) covering 98.2% of the nodes (2654/2704), and inter-regional communities with overall network modularity of 0.68. The disease may rapidly spread throughout the country. A better understanding of the nationwide pig movement networks is helpful in tailoring control interventions to cope with the newly emerged diseases once introduced.

A 'what-if' scenario: Nipah virus attacks pig trade chains in Thailand.

BACKGROUND: Nipah virus (NiV) is a fatal zoonotic agent that was first identified amongst pig farmers in Malaysia in 1998, in an outbreak that resulted in 105 fatal human cases. That epidemic arose from a chain of infection, initiating from bats to pigs, and which then spilled over from pigs to humans. In Thailand, bat-pig-human communities can be observed across the country, particularly in the central plain. The present study therefore aimed to identify high-risk areas for potential NiV outbreaks and to model how the virus is likely to spread. Multi-criteria decision analysis (MCDA) and weighted linear combination (WLC) were employed to produce the NiV risk map. The map was then overlaid with the nationwide pig movement network to identify the index subdistricts in which NiV may emerge. Subsequently, susceptible-exposed-infectious-removed (SEIR) modeling was used to simulate NiV spread within each subdistrict, and network modeling was used to illustrate how the virus disperses across subdistricts. RESULTS: Based on the MCDA and pig movement data, 14 index subdistricts with a high-risk of NiV emergence were identified. We found in our infectious network modeling that the infected subdistricts clustered in, or close to the central plain, within a range of 171 km from the source subdistricts. However, the virus may travel as far as 528.5 km (R0 = 5). CONCLUSIONS: In conclusion, the risk of NiV dissemination through pig movement networks in Thailand is low but not negligible. The risk areas identified in our study can help the veterinary authority to allocate financial and human resources to where preventive strategies, such as pig farm regionalization, are required and to contain outbreaks in a timely fashion once they occur.

Estimating the number of farms experienced foot and mouth disease outbreaks using capture-recapture methods.

Foot and mouth disease (FMD) is recognized as an endemic disease in Thailand and throughout other countries in Southeast Asia. The underreporting of FMD outbreaks has affected the true status of the disease. This study aimed to determine the number of dairy farms in Chiang Mai that had experienced FMD outbreaks (FMD outbreak farm) during 2015-2016 using capture-recapture (CR) methods. Two independent FMD outbreak data sources including data from the livestock authorities and survey questionnaires were analyzed using Chapman estimator and Chao estimator. Results showed that the estimated number of FMD outbreak farms was 264 (95% CI = 250, 277) and 273 (95% CI = 259, 292) farms based on the Chapman estimator and Chao estimator, respectively. The estimated prevalence of FMD corresponding to the Chapman estimator was lower than the Chao estimator. The active approach of the survey method offered a higher degree of sensitivity compared to the passive method used by the livestock authorities. Estimations from the CR method provided an upper bound for the true number of outbreak farms. This study demonstrated the use of the CR method to estimate the true status of FMD outbreaks. Our proposed approach can potentially be used as a tool to enhance the accuracy and sensitivity of established monitoring and surveillance systems.

A spatial assessment of Nipah virus transmission in Thailand pig farms using multi-criteria decision analysis.

BACKGROUND: Thailand's Central Plain is identified as a contact zone between pigs and flying foxes, representing a potential zoonotic risk. Nipah virus (NiV) has been reported in flying foxes in Thailand, but it has never been found in pigs or humans. An assessment of the suitability of NiV transmission at the spatial and farm level would be useful for disease surveillance and prevention. Multi-criteria decision analysis (MCDA), a knowledge-driven model, was used to map contact zones between local epizootic risk factors as well as to quantify the suitability of NiV transmission at the pixel and farm level. RESULTS: Spatial risk factors of NiV transmission in pigs were identified by experts as being of three types, including i) natural host factors (bat preferred areas and distance to the nearest bat colony), ii) intermediate host factors (pig population density), and iii) environmental factors (distance to the nearest forest, distance to the nearest orchard, distance to the nearest water body, and human population density). The resulting high suitable areas were concentrated around the bat colonies in three provinces in the East of Thailand, including Chacheongsao, Chonburi, and Nakhonnayok. The suitability of NiV transmission in pig farms in the study area was quantified as ranging from very low to medium suitability. CONCLUSIONS: We believe that risk-based surveillance in the identified priority areas may increase the chances of finding out NiV and other bat-borne pathogens and thereby optimize the allocation of financial resources for disease surveillance. In the long run, improvements of biosecurity in those priority areas may also contribute to preventing the spread of potential emergence of NiV and other bat-borne pathogens.

Simulation modeling of influenza transmission through backyard pig trade networks in a wildlife/livestock interface area.

Influenza constitutes a challenge to animal and human health. It is a highly contagious disease with wildlife reservoirs and considered as endemic among swine populations. Pigs are crucial in the disease dynamics due to their capacity to generate new reassortant viruses. The risk of informal animal trade in the spread of zoonotic diseases is well recognized worldwide. Nevertheless, the contribution of the backyard pig trade network in the transmission of influenza in a wildlife/livestock interface area is unknown. This study provides the first simulation of influenza transmission based on backyard farm connections in Mexico. A susceptible-infectious-recovered (SIR) model was implemented using the Epimodel software package in R, and 260 backyard farms were considered as nodes. Three different scenarios of connectivity (low, medium, and high) mediated by trade were generated and compared. Our results suggest that half of the pig population were infected within 5 days in the high connectivity scenario and the number of infected farms was approximately 65-fold higher compared to the low connected one. The consequence of connectivity variations directly influenced both time and duration of influenza virus transmission. Therefore, high connectivity driven by informal trade constitutes a significant risk to animal health. Trade patterns of animal movements are complex. This approach emphasizes the importance of pig movements and spatial dynamics among backyard production, live animal markets, and wildlife.

A mathematical model for Zika virus transmission dynamics with a time-dependent mosquito biting rate.

BACKGROUND: Mathematical modeling has become a tool used to address many emerging diseases. One of the most basic and popular modeling frameworks is the compartmental model. Unfortunately, most of the available compartmental models developed for Zika virus (ZIKV) transmission were designed to describe and reconstruct only past, short-time ZIKV outbreaks in which the effects of seasonal change to entomological parameters can be ignored. To make an accurate long-term prediction of ZIKV transmission, the inclusion of seasonal effects into an epidemic model is unavoidable. METHODS: We developed a vector-borne compartmental model to analyze the spread of the ZIKV during the 2015-2016 outbreaks in Bahia, Brazil and to investigate the impact of two vector control strategies, namely, reducing mosquito biting rates and reducing mosquito population size. The model considered the influences of seasonal change on the ZIKV transmission dynamics via the time-varying mosquito biting rate. The model was also validated by comparing the model prediction with reported data that were not used to calibrate the model. RESULTS: We found that the model can give a very good fit between the simulation results and the reported Zika cases in Bahia (R-square = 0.9989). At the end of 2016, the total number of ZIKV infected people was predicted to be 1.2087 million. The model also predicted that there would not be a large outbreak from May 2016 to December 2016 due to the decrease of the susceptible pool. Implementing disease mitigation by reducing the mosquito biting rates was found to be more effective than reducing the mosquito population size. Finally, the correlation between the time series of estimated mosquito biting rates and the average temperature was also suggested. CONCLUSIONS: The proposed ZIKV transmission model together with the estimated weekly biting rates can reconstruct the past long-time multi-peak ZIKV outbreaks in Bahia.

A remotely sensed flooding indicator associated with cattle and buffalo leptospirosis cases in Thailand 2011-2013.

BACKGROUND: Leptospirosis is an important zoonotic disease worldwide, caused by spirochetes bacteria of the genus Leptospira. In Thailand, cattle and buffalo used in agriculture are in close contact with human beings. During flooding, bacteria can quickly spread throughout an environment, increasing the risk of leptospirosis infection. The aim of this study was to investigate the association of several environmental factors with cattle and buffalo leptospirosis cases in Thailand, with a focus on flooding. METHOD: A total of 3571 urine samples were collected from cattle and buffalo in 107 districts by field veterinarians from January 2011 to February 2013. All samples were examined for the presence of leptospirosis infection by loop-mediated isothermal amplification (LAMP). Environmental data, including rainfall, percentage of flooded area (estimated by remote sensing), average elevation, and human and livestock population density were used to build a generalized linear mixed model. RESULTS: A total of 311 out of 3571 (8.43%) urine samples tested positive by the LAMP technique. Positive samples were recorded in 51 out of 107 districts (47.66%). Results showed a significant association between the percentage of the area flooded at district level and leptospirosis infection in cattle and buffalo (p = 0.023). Using this data, a map with a predicted risk of leptospirosis can be developed to help forecast leptospirosis cases in the field. CONCLUSIONS: Our model allows the identification of areas and periods when the risk of leptospirosis infection is higher in cattle and buffalo, mainly due to a seasonal flooding. The increased risk of leptospirosis infection can also be higher in humans too. These areas and periods should be targeted for leptospirosis surveillance and control in both humans and animals.

Satellite telemetry tracks flyways of Asian Openbill storks in relation to H5N1 avian influenza spread and ecological change.

BACKGROUND: Asian Openbills, Anastomus oscitans, have long been known to migrate from South to Southeast Asia for breeding and nesting. In Thailand, the first outbreak of H5N1 highly pathogenic avian influenza (HPAI) infection in the Openbills coincided with the outbreak in the poultry. Therefore, the flyways of Asian Openbills was determined to study their role in the spread of H5N1 HPAI virus to poultry and wild birds, and also within their flocks. RESULTS: Flyways of 5 Openbills from 3 colonies were monitored using Argos satellite transmitters with positioning by Google Earth Programme between 2007 and 2013. None of the Openbills tagged with satellite telemeters moved outside of Thailand. Their home ranges or movement areas varied from 1.6 to 23,608 km2 per month (95% utility distribution). There was no positive result of the viral infection from oral and cloacal swabs of the Openbills and wild birds living in the vicinity by viral isolation and genome detection during 2007 to 2010 whereas the specific antibody was not detected on both Openbills and wild birds by using microneutralization assay after 2008. The movement of these Openbills did not correlate with H5N1 HPAI outbreaks in domestic poultry but correlated with rice crop rotation and populations of the apple snails which are their preferred food. Viral spread within the flocks of Openbills was not detected. CONCLUSIONS: This study showed that Openbills played no role in the spread of H5N1 HPAI virus, which was probably due to the very low prevalence of the virus during the monitoring period. This study revealed the ecological factors that control the life cycle of Asian Openbills.

Modeling the dynamics of backyard chicken flows in traditional trade networks in Thailand: implications for surveillance and control of avian influenza.

In Southeast Asia, traditional poultry marketing chains have been threatened by epidemics caused by the highly pathogenic avian influenza H5N1 (HPAI H5N1) virus. In Thailand, the trade of live backyard chickens is based on the activities of traders buying chickens from villages and supplying urban markets with chicken meat. This study aims to quantify the flows of chickens traded during a 1-year period in a province of Thailand. A compartmental stochastic dynamic model was constructed to illustrate trade flows of live chickens from villages to slaughterhouses. Live poultry movements present important temporal variations with increased activities during the 15 days preceding the Chinese New Year and, to a lesser extent, other festivals (Qingming Festival, Thai New Year, Hungry Ghost Festival, and International New Year). The average distance of poultry movements ranges from 4 to 25 km, defining a spatial scale for the risk of avian influenza that spread through traditional poultry marketing chains. Some characteristics of traditional poultry networks in Thailand, such as overlapping chicken supply zones, may facilitate disease diffusion over longer distances through combined expansion and relocation processes. This information may be of use in tailoring avian influenza and other emerging infectious poultry disease surveillance and control programs provided that the cost-effectiveness of such scenarios is also evaluated in further studies.

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.

Collective activities of the Thai Coordinating Unit for One Health (CUOH): Past activities and future directions.

In Thailand, One Health concepts have been implemented among government agencies, academic institutions, intergovernment, and civil society organizations. The Thai Coordinating Unit for One Health (CUOH) was established as a collaborating body for One Health-related activities in the country in 2014. To better understand what activities CUOH has completed thus far and to assess future activities, we conducted a network analysis to identify and visualize linkages between organizations and activities from 2015 to 2021. Activities were divided into four categories: organizing meetings, developing products, providing funds, and managing resources. Most of the 114 CUOH-managed meeting participants were representatives from 72 government and 20 academic institutions. The Thai Ministry of Public Health's Department of Disease Control participated in 148 meetings, the highest attendance among all organizations working with CUOH. The first CUOH guideline or manual was published in 2020, and 11 were published in 2021. In funding management, the CUOH worked with 25 organizations to carry out 71 projects from 2015 to 2021. Additionally, the CUOH played an important role in allocating COVID-19 vaccines during the COVID-19 pandemic. The CUOH has connected organizations working in different health sectors to collaborate jointly through meetings and projects that use a One Health approach, which can holistically improve health management in Thailand. Diverse funding sources are needed to ensure the sustainability of the unit in the future.

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.

Stakeholder attitudes and perspectives on wildlife disease surveillance as a component of a One Health approach in Thailand.

Coordinated wildlife disease surveillance (WDS) can help professionals across disciplines effectively safeguard human, animal, and environmental health. The aims of this study were to understand how WDS in Thailand is utilized, valued, and can be improved within a One Health framework. An online questionnaire was distributed to 183 professionals (55.7% response rate) across Thailand working in wildlife, marine animal, livestock, domestic animal, zoo animal, environmental, and public health sectors. Twelve semi-structured interviews with key professionals were then performed. Three-quarters of survey respondents reported using WDS data and information. Sectors agreed upon ranking disease control (76.5% of respondents) as the most beneficial outcome of WDS, while fostering new ideas through collaboration was valued by few participants (2.0%). Accessing data collected by one's own sector was identified as the most challenging (50%) yet least difficult to improve (88.3%). Having legal authority to conduct WDS was the second most frequently identified challenge. Interviewees explained that legal documentation required for cross-institutional collaborations posed a barrier to efficient communication and use of human resources. Survey respondents identified allocation of human resources (75.5%), adequate budget (71.6%), and having a clear communication system between sectors (71.6%) as highest priority areas for improvement to WDS in Thailand. Authorization from administrative officials and support from local community members were identified as challenges during in-person interviews. Future outreach may be directed toward these groups. As 42.9% of marine health professionals had difficulty knowing whom to contact in other sectors and 28.4% of survey respondents indicated that communication with marine health professionals was not applicable to their work, connecting the marine sector with other sectors may be prioritized. This study identifies priorities for addressing current challenges in the establishment of a general WDS system and information management system in Thailand while presenting a model for such evaluation in other regions.

Prevalence and characterization of antimicrobial-resistant Escherichia coli isolated from veterinary staff, pets, and pet owners in Thailand.

BACKGROUND: Companion animals may act as antimicrobial resistance (AMR) reservoirs. This study investigated the prevalence and AMR patterns of Escherichia coli in pets and people in close contact with pets. METHODS: A total of 955 samples were collected from veterinary clinics across Thailand by rectal and skin or ear swabs from dogs and cats and fecal swabs from veterinarians, veterinary assistants, and pet owners. The minimum inhibitory concentrations (MICs) of the obtained isolates were investigated using Sensititre™ MIC plates against 21 different antimicrobial drugs. RESULTS: Escherichia coli from pets was frequently resistant to ampicillin (100%) and amoxicillin-clavulanic acid (100%), whereas E. coli from pet owners, veterinarians, and veterinary assistants was mostly resistant to tetracycline. The multiple antibiotic resistance index revealed that multidrug-resistant E. coli isolates were frequently found in dogs (34.92%), cats (62.12%), veterinarians (61.11%), veterinarian assistants (36.36%), and pet owners (47.62%). The most common AMR genes identified in this study were blaCTX-M, blaTEM, tetA, and tetB, which were associated with the antimicrobial susceptibility results. Additionally, extended-spectrum beta-lactamase (ESBL)-associated genes (i.e., blaCTX-M, blaTEM, and blaSHV) were found in 21.69%, 71.97%, 27.78%, and 21.43% of E. coli isolated from dogs, cats, veterinarians, and pet owners, respectively. CONCLUSIONS: Our findings demonstrated the presence of AMR genes, particularly ESBL-associated genes, in E. coli isolated from healthy pets and veterinarians. This implies that these sources of E. coli could potentially be reservoirs for antibiotic resistance, thereby increasing the risk of harm to both humans and animals. These findings highlight the importance of implementing effective AMR control measures in veterinary practices, as bacteria resistant to commonly used antimicrobials are present in humans and animals.

Detection and phylogenetic characterization of hepatitis E virus genotype 3 in a captive wild boar in Thailand.

Hepatitis E virus (HEV) was studied in different types of wild boar captive settings in Thailand, including a wildlife breeding research station, zoo, and commercial wild boar farm, which were located in different locations of Thailand. Fifty-one fecal samples were collected and screened for HEV RNA and then analyzed. One sample obtained from a wildlife breeding research station in Ratchaburi province was HEV positive. Phylogenetic characterization revealed that the virus was HEV genotype 3 and belongs to subgroup 3e, which is closely related to HEV recently isolated from domestic pigs and humans in the country. It was hypothesized that HEV is shared among wild boars, domestic pigs, and humans in Thailand.

The effects of geographical distributions of buildings and roads on the spatiotemporal spread of canine rabies: An individual-based modeling study.

Rabies is a fatal disease that has been a serious health concern, especially in developing countries. Although rabies is preventable by vaccination, the spread still occurs sporadically in many countries, including Thailand. Geographical structures, habitats, and behaviors of host populations are essential factors that may result in an enormous impact on the mechanism of propagation and persistence of the disease. To investigate the role of geographical structures on the transmission dynamics of canine rabies, we developed a stochastic individual-based model that integrates the exact configuration of buildings and roads. In our model, the spatial distribution of dogs was estimated based on the distribution of buildings, with roads considered to facilitate dog movement. Two contrasting areas with high- and low-risk of rabies transmission in Thailand, namely, Hatyai and Tepha districts, were chosen as study sites. Our modeling results indicated that the distinct geographical structures of buildings and roads in Hatyai and Tepha could contribute to the difference in the rabies transmission dynamics in these two areas. The high density of buildings and roads in Hatyai could facilitate more rabies transmission. We also investigated the impacts of rabies intervention, including reducing the dog population, restricting owned dog movement, and dog vaccination on the spread of canine rabies in these two areas. We found that reducing the dog population alone might not be sufficient for preventing rabies transmission in the high-risk area. Owned dog confinement could reduce more the likelihood of rabies transmission. Finally, a higher vaccination coverage may be required for controlling rabies transmission in the high-risk area compared to the low-risk area.

A Cutoff Determination of Real-Time Loop-Mediated Isothermal Amplification (LAMP) for End-Point Detection of Campylobacter jejuni in Chicken Meat.

Campylobacter jejuni is one of the leading causes of foodborne illness worldwide. C. jejuni is commonly found in poultry. It is the most frequent cause of contamination and thus resulting in not only public health concerns but also economic impacts. To test for this bacterial contamination in food processing plants, this study attempted to employ a simple and rapid detection assay called loop-mediated isothermal amplification (LAMP). The best cutoff value for the positive determination of C. jejuni calculated using real-time LAMP quantification cycle (Cq) was derived from the receiver operating characteristic (ROC) curve modeling. The model showed an area under curve (AUC) of 0.936 (95% Wald CI: 0.903-0.970). Based on Youden's J statistic, the optimal cutoff value which had the highest sensitivity and specificity from the model was calculated as 18.07. The LAMP assay had 96.9% sensitivity, 95.8% specificity, and 93.9 and 97.9% positive and negative predictive values, respectively, compared to a standard culture approach for C. jejuni identification. Among all non-C. jejuni strains, the LAMP assay gave each of 12.5% false-positive results to C. coli and E. coli (1 out of 8 samples). The assay can detect C. jejuni at the lowest concentration of 103 CFU/mL. Our results suggest a preliminary indicator for the application of end-point LAMP assays, such as turbidity and UV fluorescence tests, to detect C. jejuni in field operations. The LAMP assay is an alternative screening test for C. jejuni contamination in food samples. The method provides a rapid detection, which requires only 9 min with a cutoff value of Cq. We performed the extraction of DNA from pure cultures and the detection of C. jejuni using the LAMP assay within 3 h. However, we were not able to reduce the time for the process of enrichment involved in our study. Therefore, we suggest that alternative enrichment media and rapid DNA extraction methods should be considered for further study. Compared to other traditional methods, our proposed assay requires less equipment and time, which is applicable at any processing steps in the food production chain.

Modeling of the combined dynamics of leptospirosis transmission and seroconversion in herds.

Leptospirosis is a zoonotic disease-causing illness in both humans and animals resulting in related economic impacts due to production loss as well as prevention and control efforts. Several mathematical models have been proposed to study the dynamics of infection but none of them has so far taken into account the dynamics of seroconversion. In this study, we have developed a general framework, based on the kinetic model for animal leptospirosis, that combines both the antibody (exposure marker) and infection dynamics to simultaneously follows both seroconversion and infection status of leptospirosis in a herd population. It is a stochastic compartmental model (for transition rates) with time delay (for seroconversion) which describes the progression of infection by a SEIRS (susceptible, exposed, infected, removed and susceptible) approach and seroconversion by four-state antibody kinetics (antibody negative and three antibody positive states of different antibody levels). The model shows that it is possible to assess and follow both seroconversion and infection status through the prism of diagnostic testing. Such an approach of combined kinetics could prove very useful to assist the competent authorities in their analyzes of epidemic situations and in the implementation of strategies for controlling and managing the associated risks.

Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression.

Despite ongoing control efforts, rabies remains an endemic zoonotic disease in many countries. Determining high-risk areas and the space-time patterns of rabies spread, as it relates to epidemiologically important factors, can support policymakers and program managers alike to develop evidence-based targeted surveillance and control programs. In this One Health approach which selected Thailand as the example site, the location-based risk of contracting dog-mediated rabies by both human and animal populations was quantified using a Bayesian spatial regression model. Specifically, a conditional autoregressive (CAR) Bayesian zero-inflated Poisson (ZIP) regression was fitted to the reported human and animal rabies case counts of each district, from the 2012-2017 period. The human population was used as an offset. The epidemiologically important factors hypothesized as risk modifiers and therefore tested as predictors included: number of dog bites/attacks, the population of dogs and cats, number of Buddhist temples, garbage dumps, animal vaccination, post-exposure prophylaxis, poverty, and shared administrative borders. Disparate sources of data were used to improve the estimated associations and predictions. Model performance was assessed using cross-validation. Results suggested that accounting for the association between human and animal rabies with number of dog bites/attacks, number of owned and un-owned dogs; shared country borders, number of Buddhist temples, poverty levels, and accounting for spatial dependence between districts, may help to predict the risk districts for dog-mediated rabies in Thailand. The fitted values of the spatial regression were mapped to illustrate the risk of dog-mediated rabies. The cross-validation indicated an adequate performance of the spatial regression model (AUC = 0.81), suggesting that had this spatial regression approach been used to identify districts at risk in 2015, the cases reported in 2016/17 would have been predicted with model sensitivity and specificity of 0.71 and 0.80, respectively. While active surveillance is ideal, this approach of using multiple data sources to improve risk estimation may inform current rabies surveillance and control efforts including determining rabies-free zones, and the roll-out of human post-exposure prophylaxis and anti-rabies vaccines for animals in determining high-risk areas.