Two-stage algorithms for visually exploring spatio-temporal clustering of avian influenza virus outbreaks in poultry farms
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ABSTRACT:
SUBMITTER: Wu H
PROVIDER: S-EPMC8604947 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Low pathogenic avian influenza (LPAI) of subtype H9 outbreaks have been frequently occurring in major commercial hubs of Nepal including Chitwan, a central lowland area, causing substantial economic losses to the farmers. However, the risk factors associated with these outbreaks have been poorly understood, and hence, this case-control study was conducted in Chitwan, Nawalpur, and Makawanpur districts of Nepal from October 2019 to March 2020. A total of 102 farms were selected in which 51 were case farms, and 51 were controls. Case farms were avian influenza (AI)-subtype-H9-confirmed farms through polymerase chain reaction (PCR) assays on poultry samples. Control farms included farms that were AI-negative in the antigen test brought to the National Avian Disease Investigation Laboratory, Chitwan, for diagnosis during the study period. Each farm was visited to collect information using a semi-structured questionnaire. A total of 25 variables representing farm characteristics and biosecurity measures were considered as potential risk factors. The final multivariable model showed that distance of less than 0.5 km from the main road (OR = 4.04, 95% CI = 1.20-13.56, p = 0.023), distance of less than 1 km from a nearest infected farm (OR = 76.42, 95% CI = 7.17-814.06, p = 0.0003), and wild birds coming around the farm (OR = 6.12, 95% CI = 1.99-18.79, p = 0.0015) were risk factors for avian influenza type H9, whereas using apron or separate cloth inside the shed (OR = 0.109, 95% CI = 0.020-0.577, p = 0.0092) was shown to reduce the risk of farms being positive for AI subtype H9. These findings suggest that due consideration should be given to site selection while establishing the farms and the importance of implementing appropriate biosecurity measures, such as using separate cloth inside the shed and preventing the entry of wild birds inside the farm to reduce the potential risk of introduction of avian influenza type H9 to their poultry farms.
| S-EPMC9326661 | biostudies-literature
Project description:The poultry sector contributes four percent to the national GDP of Nepal. However, this sector is under threat with periodic outbreaks of Avian Influenza (AI) subtypes H5 and H9 since 2009. This has been both a public health threat and an economic issue. Since the past few years, outbreaks of AI subtype H9 have caused huge economic losses in major poultry producing areas of Nepal. However, the risk factors associated with these outbreaks have not been assessed. A retrospective case-control study was conducted from April 2018 to May 2019 to understand the risk factors associated with AI subtype H9 outbreaks in Kathmandu valley. Out of 100 farms selected, 50 were "case" farms, confirmed positive to H9 at Central Veterinary Laboratory, Kathmandu, and another 50 farms were "control" farms, matched for farm size and locality within a radius of three km from the case farm. Each farm was visited to collect information using a semi-structured questionnaire. Twelve potential risk factors were included in the questionnaire under the broad categories: birds and farm characteristics, and management and biosecurity status of the farms. Univariable and multivariable logistic regression analysis was conducted and corresponding odds ratios were calculated. Risk factors, associated with AI subtype H9 outbreaks in Kathmandu valley, identified in the final multivariable model were: "farms that have flock size greater than median flock size of study farms (>1500)" (OR = 4.41, 95% CI: 1.53-12.71, p = 0.006), "farms that did not apply rules to wear boots for visitors inside the farms" (OR = 4.32, 95% CI: 1.52-12.29, p = 0.006) and "other commercial farms located within one km periphery" (OR = 10, 95% CI: 1.8-50, p = 0.007). This study showed that outbreaks of AI subtype H9 in Kathmandu valley were associated with a higher population of birds in the farm, poor management practices, and weak biosecurity measures in poultry farms. We suggest improving management practices and increase biosecurity in the farms to reduce incidences of AI subtype H9 outbreaks in Kathmandu valley.
| S-EPMC7117692 | biostudies-literature
Project description:BackgroundThe number of outbreaks of HPAI-H5N1 reported by Bangladesh from 2007 through 2011 placed the country among the highest reported numbers worldwide. However, so far, the understanding of the epidemic progression, direction, intensity, persistence and risk variation of HPAI-H5N1 outbreaks over space and time in Bangladesh remains limited.Methodology/principal findingsTo determine the magnitude and spatial pattern of the highly pathogenic avian influenza A subtype H5N1 virus outbreaks over space and time in poultry from 2007 to 2009 in Bangladesh, we applied descriptive and analytical spatial statistics. Temporal distribution of the outbreaks revealed three independent waves of outbreaks that were clustered during winter and spring. The descriptive analyses revealed that the magnitude of the second wave was the highest as compared to the first and third waves. Exploratory mapping of the infected flocks revealed that the highest intensity and magnitude of the outbreaks was systematic and persistent in an oblique line that connects south-east to north-west through the central part of the country. The line follows the Brahmaputra-Meghna river system, the junction between Central Asian and East Asian flyways, and the major poultry trading route in Bangladesh. Moreover, several important migratory bird areas were identified along the line. Geostatistical analysis revealed significant latitudinal directions of outbreak progressions that have similarity to the detected line of intensity and magnitude.Conclusion/significanceThe line of magnitude and direction indicate the necessity of mobilizing maximum resources on this line to strengthen the existing surveillance.
| S-EPMC3170297 | biostudies-literature
Project description:BackgroundSince 2003, the H5 highly pathogenic avian influenza (HPAI) subtype has caused massive economic losses in the poultry industry in South Korea. The role of inland water bodies in avian influenza (AI) outbreaks has not been investigated. Identifying water bodies that facilitate risk pathways leading to the incursion of the HPAI virus (HPAIV) into poultry farms is essential for implementing specific precautionary measures to prevent viral transmission.ObjectivesThis matched case-control study (1:4) examined whether inland waters were associated with a higher risk of AI outbreaks in the neighboring poultry farms.MethodsRivers, irrigation canals, lakes, and ponds were considered inland water bodies. The cases and controls were chosen based on the matching criteria. The nearest possible farms located within a radius of 3 km of the case farms were chosen as the control farms. The poultry farms were selected randomly, and two HPAI epidemics (H5N8 [2014-2016] and H5N6 [2016-2017]) were studied. Conditional logistic regression analysis was applied.ResultsStatistical analysis revealed that inland waters near poultry farms were significant risk factors for AI outbreaks. The study speculated that freely wandering wild waterfowl and small animals contaminate areas surrounding poultry farms.ConclusionsPet birds and animals raised alongside poultry birds on farm premises may wander easily to nearby waters, potentially increasing the risk of AI infection in poultry farms. Mechanical transmission of the AI virus occurs when poultry farm workers or visitors come into contact with infected water bodies or their surroundings. To prevent AI outbreaks in the future, poultry farms should adopt strict precautions to avoid contact with nearby water bodies and their surroundings.
| S-EPMC9149499 | biostudies-literature
Project description:BACKGROUND:Meningococcal meningitis is a major health problem in the "African Meningitis Belt" where recurrent epidemics occur during the hot, dry season. In Niger, a central country belonging to the Meningitis Belt, reported meningitis cases varied between 1,000 and 13,000 from 2003 to 2009, with a case-fatality rate of 5-15%. METHODOLOGY/PRINCIPAL FINDINGS:In order to gain insight in the epidemiology of meningococcal meningitis in Niger and to improve control strategies, the emergence of the epidemics and their diffusion patterns at a fine spatial scale have been investigated. A statistical analysis of the spatio-temporal distribution of confirmed meningococcal meningitis cases was performed between 2002 and 2009, based on health centre catchment areas (HCCAs) as spatial units. Anselin's local Moran's I test for spatial autocorrelation and Kulldorff's spatial scan statistic were used to identify spatial and spatio-temporal clusters of cases. Spatial clusters were detected every year and most frequently occurred within nine southern districts. Clusters most often encompassed few HCCAs within a district, without expanding to the entire district. Besides, strong intra-district heterogeneity and inter-annual variability in the spatio-temporal epidemic patterns were observed. To further investigate the benefit of using a finer spatial scale for surveillance and disease control, we compared timeliness of epidemic detection at the HCCA level versus district level and showed that a decision based on threshold estimated at the HCCA level may lead to earlier detection of outbreaks. CONCLUSIONS/SIGNIFICANCE:Our findings provide an evidence-based approach to improve control of meningitis in sub-Saharan Africa. First, they can assist public health authorities in Niger to better adjust allocation of resources (antibiotics, rapid diagnostic tests and medical staff). Then, this spatio-temporal analysis showed that surveillance at a finer spatial scale (HCCA) would be more efficient for public health response: outbreaks would be detected earlier and reactive vaccination would be better targeted.
| S-EPMC3308932 | biostudies-literature
Project description:To successfully control highly pathogenic avian influenza (HPAI), understanding the risk factors related to the incursion of the virus into poultry farms is essential. In this study, we focused on the presence of inland waters surrounding poultry farms as a potential risk factor of incursion of the virus. To evaluate the influence of inland waters surrounding poultry farms on HPAI outbreaks in Japan, a simple matched case-control study was conducted. The results of the conditional regression analyses indicated that the number of farms with neighbouring inland waters was significantly high among the affected farms during the 2016-2017 outbreak period. These results provide good grounds for strengthening biosecurity management at farms located near inland waters.
| S-EPMC5818671 | biostudies-literature
Project description:In recent years, different subtypes of highly pathogenic avian influenza (HPAI) viruses caused outbreaks in several poultry types worldwide. Early detection of HPAI virus infection is crucial to reduce virus spread. Previously, the use of a mortality ratio threshold to expedite notification of suspicion in layer farms was proposed. The purpose of this study was to describe the clinical signs reported in the early stages of HPAI H5N8 and H5N6 outbreaks on chicken and Pekin duck farms between 2014 and 2018 in the Netherlands and compare them with the onset of an increased mortality ratio (MR). Data on daily mortality and clinical signs from nine egg-producing chicken farms and seven Pekin duck farms infected with HPAI H5N8 (2014 and 2016) and H5N6 (2017-2018) in the Netherlands were analysed. In 12 out of 15 outbreaks for which a MR was available, MR increase preceded or coincided with the first observation of clinical signs by the farmer. In one chicken and two Pekin duck outbreaks, clinical signs were observed prior to MR increase. On all farms, veterinarians observed clinical signs of general disease. Nervous or locomotor signs were reported in all Pekin duck outbreaks, but only in two chicken outbreaks. Other clinical signs were observed less frequently in both chickens and Pekin ducks. Compared to veterinarians, farmers observed and reported clinical signs, especially respiratory and gastrointestinal signs, less frequently. This case series suggests that a MR with a set threshold could be an objective parameter to detect HPAI infection on chicken and Pekin duck farms at an early stage. Observation of clinical signs may provide additional indication for farmers and veterinarians for notifying a clinical suspicion of HPAI infection. Further assessment and validation of a MR threshold in Pekin ducks are important as it could serve as an important tool in HPAI surveillance programs.
| S-EPMC8048556 | biostudies-literature
Project description:Highly pathogenic avian influenza (HPAI) is often controlled through culling of poultry. Compensating farmers for culled chickens or ducks facilitates effective culling and control of HPAI. However, ensuing price shifts can create incentives that alter the disease dynamics of HPAI. Farmers control certain aspects of the dynamics by setting a farm size, implementing infection control measures, and determining the age at which poultry are sent to market. Their decisions can be influenced by the market price of poultry which can, in turn, be set by policy makers during an HPAI outbreak. Here, we integrate these economic considerations into an epidemiological model in which epidemiological parameters are determined by an outside agent (the farmer) to maximize profit from poultry sales. Our model exhibits a diversity of behaviors which are sensitive to (i) the ability to identify infected poultry, (ii) the average price of infected poultry, (iii) the basic reproductive number of avian influenza, (iv) the effect of culling on the market price of poultry, (v) the effect of market price on farm size, and (vi) the effect of poultry density on disease transmission. We find that under certain market and epidemiological conditions, culling can increase farm size and the total number of HPAI infections. Our model helps to inform the optimization of public health outcomes that best weigh the balance between public health risk and beneficial economic outcomes for farmers.
| S-EPMC3851691 | biostudies-literature
Project description:PurposeIn 2012, Cameroon experienced a large measles outbreak of over 14,000 cases. To determine the spatio-temporal dynamics of measles transmission in Cameroon, we analyzed weekly case data collected by the Ministry of Health.MethodsWe compared several multivariate time-series models of population movement to characterize the spatial spread of measles in Cameroon. Using the best model, we evaluated the contribution of population mobility to disease transmission at increasing geographic resolutions: region, department, and health district.ResultsOur spatio-temporal analysis showed that the power law model, which accounts for long-distance population movement, best represents the spatial spread of measles in Cameroon. Population movement between health districts within departments contributed to 7.6% (range: 0.4%-13.4%) of cases at the district level, whereas movement between departments within regions contributed to 16.0% (range: 1.3%-23.2%) of cases. Long-distance movement between regions contributed to 16.7% (range: 0.1%-59.0%) of cases at the region level, 20.1% (range: 7.1%-30.0%) at the department level, and 29.7% (range: 15.3%-47.6%) at the health district level.ConclusionsPopulation long-distance mobility is an important driver of measles dynamics in Cameroon. These findings demonstrate the need to improve our understanding of the roles of population mobility and local heterogeneity of vaccination coverage in the spread and control of measles in Cameroon.
| S-EPMC7056523 | biostudies-literature
Project description:We present and analyse data collected during a severe epidemic of foot-and-mouth disease (FMD) that occurred between July and September 2000 in a region of northeastern Greece with strategic importance since it represents the southeastern border of Europe and Asia. We implement generic Bayesian methodology, which offers flexibility in the ability to fit several realistically complex models that simultaneously capture the presence of 'excess' zeros, the spatio-temporal dependence of the cases, assesses the impact of environmental noise and controls for multicollinearity issues. Our findings suggest that the epidemic was mostly driven by the size and the animal type of each farm as well as the distance between farms while environmental and other endemic factors were not important during this outbreak. Analyses of this kind may prove useful to informing decisions related to optimal control measures for potential future FMD outbreaks as well as other acute epidemics such as FMD.
| S-EPMC9150481 | biostudies-literature