Zoonoses and Public Health最新文献

Aims

Haemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease transmitted by rodents. The distribution of HFRS in the European part of Russia has been studied quite well; however, much less is known about the endemic area in the Russian Far East. The mutual influence of the epidemic situation in the border regions and the possibility of cross-border transmission of infection remain poorly understood. This study aims to identify the spatiotemporal hot spots of the incidence and the impact of environmental drivers on the HFRS distribution in the Russian Far East.

Methods and Results

A two-scale study design was performed. Kulldorf's spatial scan statistic was used to conduct spatiotemporal analysis at a regional scale from 2000 to 2020. In addition, an ecological niche model based on maximum entropy was applied to analyse the contribution of various factors and identify spatial favourability at the local scale. One spatiotemporal cluster that existed from 2002 to 2011 and located in the border area and one pure temporal cluster from 2004 to 2007 were revealed. The best suitability for orthohantavirus persistence was found along rivers, including those at the Chinese–Russian border, and was mainly explained by land cover, NDVI (as an indicator of vegetation density and greenness) and elevation.

Conclusions

Despite the stable incidence in recent years in, targeted prevention strategies are still needed due to the high potential for HRFS distribution in the southeast of the Russian Far East.

Aims

Outbreaks of avian influenza in poultry farms are currently increasing in frequency, with devastating consequences for animal welfare, farmers and supply chains. Some studies have documented the direct spread of the avian influenza virus between farms. Prevention of spread between farms relies on biosecurity surveillance and control measures. However, the evolution of an outbreak on a farm might vary depending on the virus strain and poultry species involved; this would have important implications for surveillance systems, epidemiological investigations and control measures.

Methods and Results

In this study, we utilized existing parameter estimates from the literature to evaluate the predicted course of an epidemic in a standard poultry flock with 10,000 birds. We used a stochastic SEIR simulation model to simulate outbreaks in different species and with different virus subtypes. The simulations predicted large differences in the duration and severity of outbreaks, depending on the virus subtypes. For both turkeys and chickens, outbreaks with HPAI were of shorter duration than outbreaks with LPAI. In outbreaks involving the infection of chickens with different virus subtypes, the shortest epidemic involved H7N7 and HPAIV H5N1 (median duration of 9 and 17 days, respectively) and the longest involved H5N2 (median duration of 68 days). The most severe outbreaks (number of chickens infected) were predicted for H5N1, H7N1 and H7N3 virus subtypes, and the least severe for H5N2 and H7N7, in which outbreaks for the latter subtype were predicted to develop most slowly.

Conclusions

These simulation results suggest that surveillance of certain subtypes of avian influenza virus, in chicken flocks in particular, needs to be sensitive and timely if infection is to be detected with sufficient time to implement control measures. The variability in the predictions highlights that avian influenza outbreaks are different in severity, speed and duration, so surveillance and disease response need to be nuanced and fit the specific context of poultry species and virus subtypes.

Introduction

Public health preparedness is based on timely and accurate information. Time series forecasting using disease surveillance data is an important aspect of preparedness. This study compared two approaches of time series forecasting: seasonal auto-regressive integrated moving average (SARIMA) modelling and the artificial neural network (ANN) algorithm. The goal was to model weekly seasonal influenza activity in Canada using SARIMA and compares its predictive accuracy, based on root mean square prediction error (RMSE) and mean absolute prediction error (MAE), to that of an ANN.

Methods

An initial SARIMA model was fit using automated model selection by minimizing the Akaike information criterion (AIC). Further inspection of the autocorrelation function and partial autocorrelation function led to ‘manual’ model improvements. ANNs were trained iteratively, using an automated process to minimize the RMSE and MAE.

Results

A total of 378, 462 cases of influenza was reported in Canada from the 2010–2011 influenza season to the end of the 2019–2020 influenza season, with an average yearly incidence risk of 20.02 per 100,000 population. Automated SARIMA modelling was the better method in terms of forecasting accuracy (per RMSE and MAE). However, the ANN correctly predicted the peak week of disease incidence while the other models did not.

Conclusion

Both the ANN and SARIMA models have shown to be capable tools in forecasting seasonal influenza activity in Canada. It was shown that applying both in tandem is beneficial, SARIMA better forecasted overall incidence while ANN correctly predicted the peak week.

Aims

Lyssavirus rabies (RABV) is responsible for a major zoonotic infection that is almost always lethal once clinical signs appear. Rabies can be (re)introduced into rabies-free areas through transboundary dog movements, thus compromising animal and human health. A number of measures have been implemented to prevent this happening, one of which is the waiting period (WP) after anti-rabies vaccination and serological testing. This WP ensures that antibodies assessed through the serological test are due to the vaccine, not to infection. Indeed, if antibodies are due to RABV infection, the dog should display clinical signs within this WP and would not therefore be imported.

Methods and Results

Within a framework of quantitative risk assessment, we used modelling approaches to evaluate the impact of this WP and its duration on the risk of introducing rabies via the importation of dogs into the European Union. Two types of models were used, a classical stochastic scenario tree model and an individual-based model, both parameterised using scientific literature or data specifically applicable to the EU. Results showed that, assuming perfect compliance, the current 3-month waiting period was associated with a median annual number of 0.04 infected dogs imported into the EU. When the WP was reduced, the risk increased. For example, for a 1-month WP, the median annual number of infected dogs imported was 0.17 or 0.15 depending on the model, which corresponds to a four-fold increase.

Conclusion

This in silico study, particularly suitable for evaluating rare events such as rabies infections in rabies-free areas, provided results that can directly inform policymakers in order to adapt regulations linked to rabies and animal movements.

Aims

Anthrax is reported with frequency but poorly understood in Southeast Asian countries including Vietnam. In Vietnam, anthrax surveillance is national. However, case detection, prevention, and control are implemented locally at the provincial level. Here, we describe the epidemiological characteristics, identify spatial clusters of human anthrax, and compare the variation in livestock anthrax vaccine coverage to disease incidence in humans and livestock using historical data in Son La province, Vietnam (2003–2020).

Methods and Results

Most human cases occurred between April and September. Most of the patients were male, aged 15–54 years old. The human cases were mainly reported by public district hospitals. There was a delay between disease onset and hospitalization of ~5 days. We identified spatial clusters of high–high incidence communes in the northern communes of the province using the local Moran's I statistic. The vaccine coverage sharply decreased across the study period. The province reported sporadic human anthrax outbreaks, while animal cases were only reported in 2005 and 2022.

Conclusions

These results suggest underreporting for human and livestock anthrax in the province. Intersectoral information sharing is needed to aid livestock vaccination planning, which currently relies on reported livestock cases. The spatial clusters identify areas for targeted surveillance and livestock vaccination, while the seasonal case data suggest prioritizing vaccination campaigns for February or early March ahead of the April peak. A regional approach for studying the role of livestock trading between Son La and neighbouring provinces in anthrax occurrence is recommended.

Aims

This study aimed to identify exposure to human, swine, and avian influenza A virus subtypes in rural companion and hunting dogs, backyard pigs, and feral pigs.

Methods and Results

The study took place in a region of southeastern Mexico where the sampled individuals were part of backyard production systems in which different domestic and wild species coexist and interact with humans. We collected blood samples from pigs and dogs at each of the sites. We used a nucleoprotein enzyme-linked immunosorbent assay to determine the exposure of individuals to influenza A virus. Haemagglutination inhibition was performed on the positive samples to determine the subtypes to which they were exposed. For data analysis, a binomial logistic regression model was generated to determine the predictor variables for the seropositivity of the individuals in the study. We identified 11 positive individuals: three backyard pigs, four companion dogs, and four hunting dogs. The pigs tested positive for H1N1 and H1N2. The dogs were positive for H1N1, H1N2, and H3N2. The model showed that dogs in contact with backyard chickens are more likely to be seropositive for influenza A viruses.

Conclusions

We demonstrated the essential role hunting dogs could play as intermediate hosts and potential mixing vessel hosts when exposed to human and swine-origin viral subtypes. These results are relevant because these dogs interact with domestic hosts and humans in backyard systems, which are risk scenarios in the transmission of influenza A viruses. Therefore, it is of utmost importance to implement epidemiological surveillance of influenza A viruses in backyard animals, particularly in key animals in the transmission of these viruses, such as dogs and pigs.

Aims

Enteric pathogens with a livestock reservoir pose a unique risk to people in occupations with regular contact with animals. However, public health surveillance of occupational exposures is inadequate, with surveillance for occupation typically focusing on the risk of transmission and the need for worker exclusion, rather than workplace exposures. To improve surveillance for occupational zoonoses, the Colorado Integrated Food Safety Center of Excellence convened a group of subject matter experts who developed a set of variables on occupation, industry, and exposures, which were integrated into Colorado's surveillance system in 2017. We evaluated the quality and completeness of these new occupational fields for interviewed cases with laboratory-confirmed zoonotic infections and compared occupations to cases with a non-zoonotic infection (Shigella) and to employment data from the Bureau of Labor Statistics.

Methods and Results

From March 2017 through December 2019, 3668 domestically acquired, laboratory-confirmed sporadic infections of Campylobacter, Cryptosporidium, Shiga toxin-producing Escherichia coli, and non-typhoidal Salmonella among individuals ≥14 years of age were interviewed by public health. We found asking explicitly about occupational exposure risks and focusing on animal exposures, improved data quality and accuracy. Of the cases who stated that they were employed, 262 (13%) reported working in an occupation with regular animal exposure, and 254 (14%) reported an industry with regular animal exposure. Cases with an animal exposure occupation were more likely to be male and live in a rural or frontier county compared to other occupations. All occupations with regular animal contact were reported at a higher frequency than among Shigella cases or the general population.

Conclusions

Public health efforts, both in occupational health and communicable disease sectors, should be made to improve surveillance for enteric zoonoses and identify opportunities for prevention strategies.

Aims

Lyme disease (LD) cases in the United States are estimated to be approaching 500,000 annually. Protective measures, such as repellent use and wearing protective clothing are recommended by public health officials. However, no protective measure has been proven to be consistently effective, partly because they require consistent and persistent behaviour change. While safe and effective vaccines are in development, it is unclear what factors influence the intention to vaccinate against LD.

This study uses the Health Belief Model (HBM) framework to determine key drivers associated with vaccine intention. The HBM is widely applied in public health research and uses the following constructs: perceived susceptibility and severity of disease, perceived benefits and barriers to disease prevention, and cues to action for disease prevention to predict health behaviours. To date, the HBM framework has not been applied to vaccination intention for LD.

Methods and Results

Data were collected from 874 adults and 834 caregivers of children residing in US states endemic to LD. Sampling adults and caregivers allows us to explore how the intention to vaccinate differs among those at-risk. Estimates from structural equation modelling (SEM) show that the HBM constructs explain much of the variation in intention to vaccinate against LD. Both adult and caregiver intentions to vaccinate are positively influenced by cues to action, perceived susceptibility of LD, and perceived benefits to vaccination. However, there is variation in the influence of constructs across the samples. Caregiver's intention to vaccinate is positively influenced by the perceived severity of LD and negatively influenced by safety concerns about the vaccine, whereas adult intention is negatively influenced by perceived barriers to vaccination.

Conclusion

A strong relationship of cues to action on vaccine intention in samples of adults and caregivers suggests the importance of a recommendation from a healthcare provider or the Centers for Disease Control and Prevention (CDC).

Background

Consumption of pork and pork products is a major source of human infection with Salmonella. Salmonella is typically subclinical in pigs, making it difficult to identify infected pigs. Therefore, effective surveillance of Salmonella in pigs critically relies on good knowledge on how well the diagnostic tests used perform. A test that has been used in several countries for Salmonella monitoring is serological testing of meat juice using an ELISA (MJ ELISA) to detect antibodies against Salmonella. This MJ ELISA data could be used to estimate infection prevalence and trends. However, as the MJ ELISA output is a sample-to-positive (S/P) ratio, which is a continuous outcome rather than a binary (positive/negative) result, the interpretation of this data depends upon a chosen cut-off.

Aim

To apply Bayesian latent class models (BLCMs) to estimate diagnostic accuracy of the MJ ELISA test values in the absence of a gold standard without needing to apply a cut-off.

Methods and Results

BLCMs were fitted to data from a UK abattoir survey carried out in 2006 in order to estimate the diagnostic accuracy of MJ ELISA with respect to the prevalence of active Salmonella infection. This survey consisted of a MJ ELISA applied in parallel with the bacteriological testing of caecal contents, carcass swabs and lymph nodes (n = 625). A BLCM was also fitted to the same data but with dichotomisation of the MJ ELISA results, in order to compare with the model using continuous outcomes. Estimates were obtained for sensitivity and specificity of the ELISA over a range of S/P values and for the bacteriological tests and were found to be similar between the models using continuous and dichotomous ELISA outcomes.

Conclusion

The Bayesian method without specifying a cut-off does allow prevalence to be inferred without specifying a cut-off for the ELISA. The study results will be useful for estimating infection prevalence from serological surveillance data.