Interaction Between Humans and Poultry, Rural Cambodia

Because avian influenza H5N1 infection risks are associated with exposure to infected poultry, we conducted a knowledge, attitudes, and practices survey of poultry-handling behavior among villagers in rural Cambodia. Despite widespread knowledge of avian influenza and personal protection measures, most rural Cambodians still have a high level of at-risk poultry handling

Hazard Analysis of Critical Control Points Assessment as a Tool to Respond to Emerging Infectious Disease Outbreaks

Highly pathogenic avian influenza virus (HPAI) strain H5N1 has had direct and indirect economic impacts arising from direct mortality and control programmes in over 50 countries reporting poultry outbreaks. HPAI H5N1 is now reported as the most widespread and expensive zoonotic disease recorded and continues to pose a global health threat. The aim of this research was to assess the potential of utilising Hazard Analysis of Critical Control Points (HACCP) assessments in providing a framework for a rapid response to emerging infectious disease outbreaks. This novel approach applies a scientific process, widely used in food production systems, to assess risks related to a specific emerging health threat within a known zoonotic disease hotspot. We conducted a HACCP assessment for HPAI viruses within Vietnam’s domestic poultry trade and relate our findings to the existing literature. Our HACCP assessment identified poultry flock isolation, transportation, slaughter, preparation and consumption as critical control points for Vietnam’s domestic poultry trade. Introduction of the preventative measures highlighted through this HACCP evaluation would reduce the risks posed by HPAI viruses and pressure on the national economy. We conclude that this HACCP assessment provides compelling evidence for the future potential that HACCP analyses could play in initiating a rapid response to emerging infectious diseases

Knowledge, attitudes and practices (KAP) relating to avian influenza in urban and rural areas of China

<p>Abstract</p> <p>Background</p> <p>Studies have revealed that visiting poultry markets and direct contact with sick or dead poultry are significant risk factors for H5N1 infection, the practices of which could possibly be influenced by people's knowledge, attitudes and practices (KAPs) associated with avian influenza (AI). To determine the KAPs associated with AI among the Chinese general population, a cross-sectional survey was conducted in China.</p> <p>Methods</p> <p>We used standardized, structured questionnaires distributed in both an urban area (Shenzhen, Guangdong Province; n = 1,826) and a rural area (Xiuning, Anhui Province; n = 2,572) using the probability proportional to size (PPS) sampling technique.</p> <p>Results</p> <p>Approximately three-quarters of participants in both groups requested more information about AI. The preferred source of information for both groups was television. Almost three-quarters of all participants were aware of AI as an infectious disease; the urban group was more aware that it could be transmitted through poultry, that it could be prevented, and was more familiar with the relationship between AI and human infection. The villagers in Xiuning were more concerned than Shenzhen residents about human AI viral infection. Regarding preventative measures, a higher percentage of the urban group used soap for hand washing whereas the rural group preferred water only. Almost half of the participants in both groups had continued to eat poultry after being informed about the disease.</p> <p>Conclusions</p> <p>Our study shows a high degree of awareness of human AI in both urban and rural populations, and could provide scientific support to assist the Chinese government in developing strategies and health-education campaigns to prevent AI infection among the general population.</p

Clinical Characteristics of 26 Human Cases of Highly Pathogenic Avian Influenza A (H5N1) Virus Infection in China

BACKGROUND: While human cases of highly pathogenic avian influenza A (H5N1) virus infection continue to increase globally, available clinical data on H5N1 cases are limited. We conducted a retrospective study of 26 confirmed human H5N1 cases identified through surveillance in China from October 2005 through April 2008. METHODOLOGY/PRINCIPAL FINDINGS: Data were collected from hospital medical records of H5N1 cases and analyzed. The median age was 29 years (range 6-62) and 58% were female. Many H5N1 cases reported fever (92%) and cough (58%) at illness onset, and had lower respiratory findings of tachypnea and dyspnea at admission. All cases progressed rapidly to bilateral pneumonia. Clinical complications included acute respiratory distress syndrome (ARDS, 81%), cardiac failure (50%), elevated aminotransaminases (43%), and renal dysfunction (17%). Fatal cases had a lower median nadir platelet count (64.5 x 10(9) cells/L vs 93.0 x 10(9) cells/L, p = 0.02), higher median peak lactic dehydrogenase (LDH) level (1982.5 U/L vs 1230.0 U/L, p = 0.001), higher percentage of ARDS (94% [n = 16] vs 56% [n = 5], p = 0.034) and more frequent cardiac failure (71% [n = 12] vs 11% [n = 1], p = 0.011) than nonfatal cases. A higher proportion of patients who received antiviral drugs survived compared to untreated (67% [8/12] vs 7% [1/14], p = 0.003). CONCLUSIONS/SIGNIFICANCE: The clinical course of Chinese H5N1 cases is characterized by fever and cough initially, with rapid progression to lower respiratory disease. Decreased platelet count, elevated LDH level, ARDS and cardiac failure were associated with fatal outcomes. Clinical management of H5N1 cases should be standardized in China to include early antiviral treatment for suspected H5N1 cases

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

Background: The ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time. Methods: Predicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010. Results: The overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data. Conclusions: A persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.</br

Incorporating human mobility data improves forecasts of Dengue fever in Thailand

Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems