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Avian Influenza Daily Digest
November 25, 2008 16:00 GMT
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60-Day HPAI H5N1 Outbreak Map
2008 WHO Confirmed Human Cases HPAI H5N1
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Quid Novi
China: Suspected AI in poultry in Jiangsu Province
Regional Reporting and Surveillance
Fiji: Pacific Avian and Pandemic Influenza Taskforce meeting underway
11/24/08 New Zealand Radio--The Pacific Avian and Pandemic Influenza Taskforce is meeting in Nadi in Fiji this week to assess the region?s preparedness for possible outbreaks of infectious diseases.
Regional Reporting and Surveillance
Indonesia: South Sulawesi Farmers to be Compensated for Bird Flu Losses
11/24/08 Asia Pulse The central government has allocated Rp1 billion in funds to compensate farmers in South Sulawesi whose poultry die of bird flu in 2009, a local animal husbandry official said.
Regional Reporting and Surveillance
Indonesia: Jakarta Post Photos
11/25/08 Jakarta Post--http://www.thejakartapost.com/files/images/1herjo2.jpg Photo caption: BIG, BAD, BIRD FLU SPRAYING: Sukardi, a bird trader at Karimata bird market in Semarang, Central Java, sprays his birds with disinfectant on Tuesday. Semarang Health Agency officials have asked traders at the market to increase the frequency of such spraying following a recent bird-flu-suspected death in the area. JP/Suherdjoko 11/25/08 Jakarta Post http://www.thejakartapost.com/files/images/p02-a_7.jpg Photo caption: City officials attempt to catch chickens in a residential area in Makassar subdistrict, Jakarta, on Monday, as part of an operation to prevent the spread of avian flu in the capital. (JP/J. Adiguna)
Regional Reporting and Surveillance
Science and Technology
Detecting influenza epidemics using search engine query data
11/24/08 Nature--Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3, 4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.
AI Research
Pandemic Preparedness
NIH: Study of Ancient and Modern Plagues Finds Common Features
11/25/08 NIH--In 430 B.C., a new and deadly disease ? its cause remains a mystery ? swept into Athens. The walled Greek city-state was teeming with citizens, soldiers and refugees of the war then raging between Athens and Sparta. As streets filled with corpses, social order broke down. Over the next three years, the illness returned twice and Athens lost a third of its population. It lost the war too. The Plague of Athens marked the beginning of the end of the Golden Age of Greece.
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Poultry monoculture?
11/25/08 Effect Measure
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Another Supect H5N1 Case in Semarang Indonesia
11/24/08 Recombinomics
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Quid Novi
China: Suspected AI in poultry in Jiangsu Province
11/24/08 ARGUS--An industry forum included a posting dated November 24 by a Xuzhou farmer who posted a claim that in Hai?an and Taidong cities, a major outbreak of H5N1 Re-4 avian influenza (AI) has killed up to 10 million poultry birds. The farmer asked other forum members to verify the claim.
Another farmer from an unspecified region of Jiangsu Province said that there are indeed deaths due to AI but that they are neither massive nor uncommon. That farmer claimed that the fatality rate was between 10-20% and that egg production rates have fallen drastically. He also wrote that county-level authorities do not dare probe the matter and thus have not taken any action.
According to the posting, AI has reportedly circulated widely across various parts of the province, and has not been dealt with since February this year. One forum member from Taidong implied that he was not aware of the outbreak. No further information was provided.
Article URL(s)
http://bbs.jbzyw.com/read.php?tid-31992.html
AI Research
Detecting influenza epidemics using search engine query data
11/24/08 Nature--
Jeremy Ginsberg1, Matthew H. Mohebbi1, Rajan S. Patel1, Lynnette Brammer2, Mark S. Smolinski1 & Larry Brilliant1
1. Google Inc., 1600 Amphitheatre Parkway, Mountain View, California 94043, USA
2. Centers for Disease Control and Prevention, 1600 Clifton Road, NE, Atlanta, Georgia 30333, USA
Correspondence to: Jeremy Ginsberg1Matthew H. Mohebbi1 Correspondence and requests for materials should be addressed to J.G. or M.H.M. (Email: flutrends-support@google.com).
Related Podcast by Jeremy Ginsberg: [download mp3]
Abstract
Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3, 4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.
Traditional surveillance systems, including those used by the US Centers for Disease Control and Prevention (CDC) and the European Influenza Surveillance Scheme (EISS), rely on both virological and clinical data, including influenza-like illness (ILI) physician visits. The CDC publishes national and regional data from these surveillance systems on a weekly basis, typically with a 1?2-week reporting lag.
In an attempt to provide faster detection, innovative surveillance systems have been created to monitor indirect signals of influenza activity, such as call volume to telephone triage advice lines5 and over-the-counter drug sales6. About 90 million American adults are believed to search online for information about specific diseases or medical problems each year7, making web search queries a uniquely valuable source of information about health trends. Previous attempts at using online activity for influenza surveillance have counted search queries submitted to a Swedish medical website (A. Hulth, G. Rydevik and A. Linde, manuscript in preparation), visitors to certain pages on a US health website8, and user clicks on a search keyword advertisement in Canada9. A set of Yahoo search queries containing the words 'flu' or 'influenza' were found to correlate with virological and mortality surveillance data over multiple years10.
Our proposed system builds on this earlier work by using an automated method of discovering influenza-related search queries. By processing hundreds of billions of individual searches from 5 years of Google web search logs, our system generates more comprehensive models for use in influenza surveillance, with regional and state-level estimates of ILI activity in the United States. Widespread global usage of online search engines may eventually enable models to be developed in international settings.
By aggregating historical logs of online web search queries submitted between 2003 and 2008, we computed a time series of weekly counts for 50 million of the most common search queries in the United States. Separate aggregate weekly counts were kept for every query in each state. No information about the identity of any user was retained. Each time series was normalized by dividing the count for each query in a particular week by the total number of online search queries submitted in that location during the week, resulting in a query fraction (Supplementary Fig. 1).
We sought to develop a simple model that estimates the probability that a random physician visit in a particular region is related to an ILI; this is equivalent to the percentage of ILI-related physician visits. A single explanatory variable was used: the probability that a random search query submitted from the same region is ILI-related, as determined by an automated method described below. We fit a linear model using the log-odds of an ILI physician visit and the log-odds of an ILI-related search query: logit(I(t)) = alphalogit(Q(t)) + epsilon, where I(t) is the percentage of ILI physician visits, Q(t) is the ILI-related query fraction at time t, alpha is the multiplicative coefficient, and epsilon is the error term. logit(p) is simply ln(p/(1 - p)).
Publicly available historical data from the CDC's US Influenza Sentinel Provider Surveillance Network (http://www.cdc.gov/flu/weekly) was used to help build our models. For each of the nine surveillance regions of the United States, the CDC reported the average percentage of all outpatient visits to sentinel providers that were ILI-related on a weekly basis. No data were provided for weeks outside of the annual influenza season, and we excluded such dates from model fitting, although our model was used to generate unvalidated ILI estimates for these weeks.
We designed an automated method of selecting ILI-related search queries, requiring no previous knowledge about influenza. We measured how effectively our model would fit the CDC ILI data in each region if we used only a single query as the explanatory variable, Q(t). Each of the 50 million candidate queries in our database was separately tested in this manner, to identify the search queries which could most accurately model the CDC ILI visit percentage in each region. Our approach rewarded queries that showed regional variations similar to the regional variations in CDC ILI data: the chance that a random search query can fit the ILI percentage in all nine regions is considerably less than the chance that a random search query can fit a single location (Supplementary Fig. 2).
The automated query selection process produced a list of the highest scoring search queries, sorted by mean Z-transformed correlation across the nine regions. To decide which queries would be included in the ILI-related query fraction, Q(t), we considered different sets of n top-scoring queries. We measured the performance of these models based on the sum of the queries in each set, and picked n such that we obtained the best fit against out-of-sample ILI data across the nine regions (Fig. 1).
Figure 1: An evaluation of how many top-scoring queries to include in the ILI-related query fraction.
Figure 1 : An evaluation of how many top-scoring queries to include in the ILI-related query fraction. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.com
Maximal performance at estimating out-of-sample points during cross-validation was obtained by summing the top 45 search queries. A steep drop in model performance occurs after adding query 81, which is 'oscar nominations'.
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Combining the n = 45 highest-scoring queries was found to obtain the best fit. These 45 search queries, although selected automatically, appeared to be consistently related to ILIs. Other search queries in the top 100, not included in our model, included topics like 'high school basketball', which tend to coincide with influenza season in the United States (Table 1).
Table 1: Topics found in search queries which were found to be most correlated with CDC ILI data
Table 1 - Topics found in search queries which were found to be most correlated with CDC ILI data
Full table
Using this ILI-related query fraction as the explanatory variable, we fit a final linear model to weekly ILI percentages between 2003 and 2007 for all nine regions together, thus obtaining a single, region-independent coefficient. The model was able to obtain a good fit with CDC-reported ILI percentages, with a mean correlation of 0.90 (min = 0.80, max = 0.96, n = 9 regions; Fig. 2).
Figure 2: A comparison of model estimates for the mid-Atlantic region (black) against CDC-reported ILI percentages (red), including points over which the model was fit and validated.
Figure 2 : A comparison of model estimates for the mid-Atlantic region (black) against CDC-reported ILI percentages (red), including points over which the model was fit and validated. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.com
A correlation of 0.85 was obtained over 128 points from this region to which the model was fit, whereas a correlation of 0.96 was obtained over 42 validation points. Dotted lines indicate 95% prediction intervals. The region comprises New York, New Jersey and Pennsylvania.
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The final model was validated on 42 points per region of previously untested data from 2007 to 2008, which were excluded from all previous steps. Estimates generated for these 42 points obtained a mean correlation of 0.97 (min = 0.92, max = 0.99, n = 9 regions) with the CDC-observed ILI percentages.
Throughout the 2007?08 influenza season we used preliminary versions of our model to generate ILI estimates, and shared our results each week with the Epidemiology and Prevention Branch of Influenza Division at the CDC to evaluate timeliness and accuracy. Figure 3 illustrates data available at different points throughout the season. Across the nine regions, we were able to estimate consistently the current ILI percentage 1?2 weeks ahead of the publication of reports by the CDC's US Influenza Sentinel Provider Surveillance Network.
Figure 3: ILI percentages estimated by our model (black) and provided by the CDC (red) in the mid-Atlantic region, showing data available at four points in the 2007-2008 influenza season.
Figure 3 : ILI percentages estimated by our model (black) and provided by the CDC (red) in the mid-Atlantic region, showing data available at four points in the 2007-2008 influenza season. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.com
During week 5 we detected a sharply increasing ILI percentage in the mid-Atlantic region; similarly, on 3 March our model indicated that the peak ILI percentage had been reached during week 8, with sharp declines in weeks 9 and 10. Both results were later confirmed by CDC ILI data.
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Because localized influenza surveillance is particularly useful for public health planning, we sought to validate further our model against weekly ILI percentages for individual states. The CDC does not make state-level data publicly available, but we validated our model against state-reported ILI percentages provided by the state of Utah, and obtained a correlation of 0.90 across 42 validation points (Supplementary Fig. 3).
Google web search queries can be used to estimate ILI percentages accurately in each of the nine public health regions of the United States. Because search queries can be processed quickly, the resulting ILI estimates were consistently 1?2 weeks ahead of CDC ILI surveillance reports. The early detection provided by this approach may become an important line of defence against future influenza epidemics in the United States, and perhaps eventually in international settings.
Up-to-date influenza estimates may enable public health officials and health professionals to respond better to seasonal epidemics. If a region experiences an early, sharp increase in ILI physician visits, it may be possible to focus additional resources on that region to identify the aetiology of the outbreak, providing extra vaccine capacity or raising local media awareness as necessary.
This system is not designed to be a replacement for traditional surveillance networks or supplant the need for laboratory-based diagnoses and surveillance. Notable increases in ILI-related search activity may indicate a need for public health inquiry to identify the pathogen or pathogens involved. Demographic data, often provided by traditional surveillance, cannot be obtained using search queries.
In the event that a pandemic-causing strain of influenza emerges, accurate and early detection of ILI percentages may enable public health officials to mount a more effective early response. Although we cannot be certain how search engine users will behave in such a scenario, affected individuals may submit the same ILI-related search queries used in our model. Alternatively, panic and concern among healthy individuals may cause a surge in the ILI-related query fraction and exaggerated estimates of the ongoing ILI percentage.
The search queries in our model are not, of course, exclusively submitted by users who are experiencing influenza-like symptoms, and the correlations we observe are only meaningful across large populations. Despite strong historical correlations, our system remains susceptible to false alerts caused by a sudden increase in ILI-related queries. An unusual event, such as a drug recall for a popular cold or flu remedy, could cause such a false alert.
Harnessing the collective intelligence of millions of users, Google web search logs can provide one of the most timely, broad-reaching influenza monitoring systems available today. Whereas traditional systems require 1?2 weeks to gather and process surveillance data, our estimates are current each day. As with other syndromic surveillance systems, the data are most useful as a means to spur further investigation and collection of direct measures of disease activity. This system will be used to track the spread of ILI throughout the 2008?09 influenza season in the United States. Results are freely available online at http://www.google.org/flutrends.
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Methods Summary
Privacy
None of the queries in the Google database for this project can be associated with a particular individual. The database retains no information about the identity, internet protocol (IP) address, or specific physical location of any user. Furthermore, any original web search logs older than 9 months are being made anonymous in accordance with Google's privacy policy (http://www.google.com/privacypolicy.html).
Search query database
For the purposes of our database, a search query is a complete, exact sequence of terms issued by a Google search user; we don't combine linguistic variations, synonyms, cross-language translations, misspellings, or subsequences, although we hope to explore these options in future work. For example, we tallied the search query 'indications of flu' separately from the search queries 'flu indications' and 'indications of the flu'.
Our database of queries contains 50 million of the most common search queries on all possible topics, without pre-filtering. Billions of queries occurred infrequently and were excluded. Using the internet protocol address associated with each search query, the general physical location from which the query originated can often be identified, including the nearest major city if within the United States.
Model data
In the query selection process, we fit per-query models using all weeks between 28 September 2003 and 11 March 2007 (inclusive) for which the CDC reported a non-zero ILI percentage, yielding 128 training points for each region (each week is one data point). Forty-two additional weeks of data (18 March 2007 through to 11 May 2008) were reserved for final validation. Search query data before 2003 was not available for this project.
Full methods accompany this paper.
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References
1. World Health Organization. Influenza fact sheet. left fencehttp://www.who.int/mediacentre/factsheets/2003/fs211/en/right fence (2003)
2. World Health Organization. WHO consultation on priority public health interventions before and during an influenza pandemic. left fencehttp://www.who.int/csr/disease/avian_influenza/consultation/en/right fence (2004)
3. Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209?214 (2005) | Article | PubMed | ISI | ChemPort |
4. Longini, I. M. et al. Containing pandemic influenza at the source. Science 309, 1083?1087 (2005) | Article | PubMed | ISI | ChemPort |
5. Espino, J., Hogan, W. & Wagner, M. Telephone triage: A timely data source for surveillance of influenza-like diseases. AMIA Annu. Symp. Proc. 215?219 (2003)
6. Magruder, S. Evaluation of over-the-counter pharmaceutical sales as a possible early warning indicator of human disease. Johns Hopkins APL Tech. Digest 24, 349?353 (2003)
7. Fox, S. Online Health Search 2006. Pew Internet & American Life Project left fencehttp://www.pewinternet.org/pdfs/pip_online_health_2006.pdfright fence (2006)
8. Johnson, H. et al. Analysis of Web access logs for surveillance of influenza. Stud. Health Technol. Inform. 107, 1202?1206 (2004)
9. Eysenbach, G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMI Symp. Proc. 244?248 (2006)
10. Polgreen, P. M., Chen, Y., Pennock, D. M. & Forrest, N. D. Using internet searches for influenza surveillance. Clin. Infect. Dis. 47, 1443?1448 (2008) | Article | PubMed |
11. David, F. The moments of the z and F distributions. Biometrika 36, 394?403 (1949)
12. Dean, J. & Ghemawat, S. Mapreduce: Simplified data processing on large clusters. Sixth Symp. Oper. Syst. Des. Implement. (2004)
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Supplementary Information
Supplementary information accompanies this paper.
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Acknowledgements
We thank L. Finelli for providing background knowledge, helping us validate results and comments on this manuscript. We are grateful to R. Rolfs, L. Wyman and M. Patton for providing ILI data. We thank V. Sahai for his contributions to data collection and processing, and C. Nevill-Manning, A. Roetter and K. Sarvian for their comments on this manuscript.
Author Contributions J.G. and M.H.M. conceived, designed and implemented the system. J.G., M.H.M. and R.S.P. analysed the results and wrote the paper. L.B. contributed data. All authors edited and commented on the paper.
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Online Methods
Automated query selection process
Using linear regression with fourfold cross validation, we fit models to four 96-point subsets of the 128 points in each region. Each per-query model was validated by measuring the correlation between the model's estimates for the 32 held-out points and the CDC's reported regional ILI percentage at those points. Temporal lags were considered, but ultimately not used in our modelling process.
Each candidate search query was evaluated nine times, once per region, using the search data originating from a particular region to explain the ILI percentage in that region. With four cross-validation folds per region, we obtained 36 different correlations between the candidate model's estimates and the observed ILI percentages. To combine these into a single measure of the candidate query's performance, we applied the Fisher Z-transformation11 to each correlation, and took the mean of the 36 Z-transformed correlations.
Computation and pre-filtering
In total, we fit 450 million different models to test each of the candidate queries. We used a distributed computing framework12 to divide the work among hundreds of machines efficiently. The amount of computation required could have been reduced by making assumptions about which queries might be correlated with ILI. For example, we could have attempted to eliminate non-influenza-related queries before fitting any models. However, we were concerned that aggressive filtering might accidentally eliminate valuable data. Furthermore, if the highest-scoring queries seemed entirely unrelated to influenza, it would provide evidence that our query selection approach was invalid.
Constructing the ILI-related query fraction
We concluded the query selection process by choosing to keep the search queries whose models obtained the highest mean Z-transformed correlations across regions: these queries were deemed to be 'ILI-related'.
To combine the selected search queries into a single aggregate variable, we summed the query fractions on a regional basis, yielding our estimate of the ILI-related query fraction, Q(t), in each region. Note that the same set of queries was selected for each region.
Fitting and validating a final model
We fit one final univariate model, used for making estimates in any region or state based on the ILI-related query fraction from that region or state. We regressed over 1,152 points, combining all 128 training points used in the query selection process from each of the nine regions. We validated the accuracy of this final model by measuring its performance on 42 additional weeks of previously untested data in each region, from the most recently available time period (18 March 2007 through to 11 May 2008). These 42 points represent approximately 25% of the total data available for the project, the first 75% of which was used for query selection and model fitting.
State-level model validation
To evaluate the accuracy of state-level ILI estimates generated using our final model, we compared our estimates against weekly ILI percentages provided by the state of Utah. Because the model was fit using regional data through 11 March 2007, we validated our Utah ILI estimates using 42 weeks of previously untested data, from the most recently available time period (18 March 2007 through to 11 May 2008).
Regional Reporting and Surveillance
Fiji: Pacific Avian and Pandemic Influenza Taskforce meeting underway
11/24/08 New Zealand Radio--The Pacific Avian and Pandemic Influenza Taskforce is meeting in Nadi in Fiji this week to assess the region?s preparedness for possible outbreaks of infectious diseases.
The Epidemiologist at the Secretariat of the Pacific Community, or SPC, Dr Tom Kiedrzynski says preparedness will help Pacific countries control emerging scourges like severe acute respiratory syndrome and current epidemics such as dengue fever.
A workshop on dengue is being held on Thursday following the three-day regional forum on avian and pandemic influenza preparedness.
The SPC?s Animal Health and Production adviser, Dr Ken Cokanasiga, says Pacific Island countries are still vulnerable to the bird flu virus which is persisting in countries adjacent to the region, such as Indonesia.
Animal health specialists will also meet this week to discuss issues specific to improving regional response capacity for emergency animal diseases that may threaten not only food security but also human lives.
Conferences and Training
USAID Job Vacancies, Indonesia and Egypt
https://www.ghfp.net/recruitment/index.fsp?FUNC=1&PID=12790
Title: Avian Influenza Technical Advisor/Program Manager
Location: Cairo, Egypt
Number: GHFP-08-120
Status: Open
Close Date: 12/19/2008
Global Health Fellows Program
Technical Advisor III: Avian Influenza Technical Advisor/Program Manager
USAID/Egypt
Location: Cairo, Egypt
Assignment: Two year fellowship
GHFP-08-120
https://www.ghfp.net/recruitment/index.fsp?FUNC=1&PID=11346
Title: Senior Avian and Pandemic Influenza Advisor
Location: Jakarta, Indonesia
Number: GHFP-08-101
Status: Open
Close Date: 12/15/2008
Global Health Fellows Program
Technical Advisor III: Senior Avian and Pandemic Influenza Advisor
Office of Basic Human Services, United States Agency for International Development
Location: Jakarta, Indonesia
Assignment: Two year fellowship
GHFP-08-101
Conferences and Training
UNICEF Tajikistan Job Announcement
VACANCY ANNOUNCEMENT
Post Title: Avian Influenza (AI) Communication Officer, Temporary Fixed Term at NOA level
Duty Station: UNICEF, Dushanbe, Tajikistan
Duration: 01 Jan. -31 Dec. 2009
Closing date: 05 December 2008
Duties and responsibilities:
Under the general supervision of the Health Specialist, assist UNICEF Tajikistan in implementing communication activities planned under the communication component of the prevention of Avian Influenza Control and Human Pandemic Preparedness Project. This requires close collaboration with the Ministry of Health, Ministry of Agriculture, State Committee on Emergency and Civil Defence, State Committee on TV/Radio and various counterparts.
1. In close cooperation with WHO, WB, FAO and other agencies advocate for Avian Influenza/ Human Influenza planning, coordination and monitoring arrangements, operating within existing sector-wide coordination mechanisms under government leadership.
2. Act as liaison between UNICEF and Project Management Unit in coordinating all issues related to timely implementation of Project Action Plan, disbursement of instalments, submission of quarterly reports and financial statements.
3. Facilitate and coordinate activities of the local and international NGOs/ SCOs selected by UNICEF for the implementation of communication actions and community mobilization interventions addressing avian influenza prevention.
4. In coordination with JHU/CCP facilitate the preparation and conduction of four-day communication research training to UNICEF M&E staff, researchers of the State Strategic Research Centre, Department of Statistics, and other relevant partners.
5. Develop with the partners Media Plan for the Mass Media and Social Mobilization Campaign, including new communication materials and channels. Develop clear guidelines on the use of the new materials. Follow-up on distribution of materials, their use and efficiency. Undertake selective field visits to districts, jamoats, families and schools in order to check the adequacy of the local distribution processes and use. Undertake planned field visits to monitor project implementation
6. Assist in the development of thematic media programmes and interventions on avian influenza, involving main specialists in the area, and including a variety of formats in order to reach a wide range of audiences.
7. Assist the UNICEF AI focal point in organizing planned media events and field trips related to the Campaign, including development of background materials for media, constant communication with media and support of the logistics.
8. Assist the UNICEF in monitoring of the impact of the campaign and adjusting strategies and messages for a possible next phase;
9. In close cooperation with Communication Working Group and other partners facilitate development of the questionnaires for the KAP survey, coordinate conduction of the survey in selected areas;
10. In close consultation with the State Committee on statistics and JHU/CCP M&E focal point facilitate timely analysis of the survey results and its presentation to the project stakeholders;
11. Assist UNICEF in conduction of final evaluation of project activities, prepare regular inputs to progress reports; undertake regular field monitoring, control allotments and expenditures; ensure accurate implementation of UNICEF rules and procedures.
Qualification:
University degree in Education, Social Sciences, Behavioural Sciences and Communication ? two years professional experience in the area of communication, especially related to health, preferably with International organizations ? knowledge of and experience with communication for behavioural impact ? professional writing skills ? fluency in Tajik, Russian, and English ? familiarity with & knowledge of UNICEF context/priorities ? ability to learn quickly and apply procedures in an international organization
The Vacancy Announcement will be advertised through webs- www.unicef.org/tajikistan and www.untj.org/vacancies and through newspapers-"Asia Plus" & "Digest" .
Letter of application, along with detailed CV should be addressed to:
UNICEF, 37/1 Bokhtar street,
734001 Dushanbe, Tajikistan
Attention to: Operations Manager ? Confidential or e-mailed to recruitmentdushanbe@unicef.org
UN candidates should provide the latest performance evaluation.
-Only short listed candidates will be notified.
-UNICEF is a smoke-free environment.
-Qualified women are encouraged to apply.
National Avian Influenza (AI) Communication Officer, Temporary Fixed Term at NOA level
Duty Station: UNICEF, Dushanbe, Tajikistan
Duration: 01 Jan. -31 Dec. 2009
Closing date: 04 December 2008
BACKGROUND
Avian flu and the subsequent risk of a human pandemic poses one of the most challenging issues in our times. UNICEF as a knowledge based organization needs to be able to rapidly understand the context of this disease in order to develop appropriate response and advocacy strategies. At the same time it needs to establish baseline indicators and data against which to measure the impact of any strategies that it develops. Advocacy rests on the articulation and provision of good evidence
Since 2003, there have been multiple outbreaks of the Influenza A(H5N1) virus (avian influenza), and recently it has begun to spread from Asia to Europe. More than 150 million poultry have died, either from the disease itself or from culling. The H5N1 virus has also caused 142 laboratory-confirmed human infections, over half of them fatal. It already exhibits some of the same mutations as the virus which caused the 1918 pandemic; should it become transmissible between humans, it will almost certainly cause a pandemic. Thus, the immediate priority is to prevent human exposure to the virus. The recent outbreak of AI in Turkey and Azerbaijan underlines the need for urgency in this regard.
UNICEF Tajikistan will be the main leading agency in implementation of communication activities on AF within WB funded AI prevention project. The communication activities will include intensified communication and social mobilization efforts: interpersonal communication, community mobilization, outdoor communication and communication via a variety of media channels.
UNICEF works in global partnerships with various agencies and government stakeholders that give leadership to national governments to take a leading role in coordination of efforts and implementation of action plan. Support will be based on country comprehensive action plan on AI prevention. At country level, UNICEF will work with the WB, WHO, FAO and relevant ministries and agencies to advocate for Avian Influenza/ Human Influenza planning, coordination and monitoring arrangements, operating within existing sector-wide coordination mechanisms under government leadership. UNICEF will play a leading role in providing technical support in development of comprehensive communication strategic plan and its implementation at community and family levels in close cooperation with aforementioned agencies. The main focus will be placed on the interpersonal communication.
In this context, the implementation of specific communication activities outlined below, monitoring of the communication material distribution and use process and organization of a special action in schools and communities requires additional capacities in the area of communication.
To assist in their implementing, UNICEF Office in Tajikistan will recruit National Project Officer.
PURPOSE OF ASSIGNMENT
Under the general supervision of the Health Specialist, assist UNICEF Tajikistan in implementing communication activities planned under the communication component of the prevention of Avian Influenza Control and Human Pandemic Preparedness Project. This requires close collaboration with the Ministry of Health, Ministry of Agriculture, State Committee on Emergency and Civil Defence, State Committee on TV/Radio and various counterparts.
SPECIFIC ACTIVITIES
1. In close cooperation with WHO, WB, FAO and other agencies advocate for Avian Influenza/ Human Influenza planning, coordination and monitoring arrangements, operating within existing sector-wide coordination mechanisms under government leadership.
2. Act as liaison between UNICEF and Project Management Unit in coordinating all issues related to timely implementation of Project Action Plan, disbursement of instalments, submission of quarterly reports and financial statements.
3. Facilitate and coordinate activities of the local and international NGOs/ SCOs selected by UNICEF for the implementation of communication actions and community mobilization interventions addressing avian influenza prevention.
4. In coordination with JHU/CCP facilitate the preparation and conduction of four-day communication research training to UNICEF M&E staff, researchers of the State Strategic Research Centre, Department of Statistics, and other relevant partners. The overall objective of the workshop will be to educate participants in the development of an evaluation plan, including setting research objectives and indicators, selecting an appropriate methodology, and developing preliminary research instruments for their communication interventions.
5. Develop with the partners Media Plan for the Mass Media and Social Mobilization Campaign, including new communication materials and channels. Develop clear guidelines on the use of the new materials. Follow-up on distribution of materials, their use and efficiency. Undertake selective field visits to rayons, jamoats, families and schools in order to check the adequacy of the local distribution processes and use. Undertake planned field visits to monitor project implementation.
6. Assist in the development of thematic media programmes and interventions on avian flu, involving main specialists in the area, and including a variety of formats in order to reach a wide range of audiences.
7. Assist the UNICEF AI focal point in organizing planned media events and field trips related to the Campaign, including development of background materials for media, constant communication with media and support of the logistics.
8. Assist the UNICEF in monitoring of the impact of the campaign and adjusting strategies and messages for a possible next phase;
9. In close cooperation with Communication WG and other partners facilitate development of the questionnaires for the KAP survey, coordinate conduction of the survey in selected areas;
10. In close consultation with the State Committee on statistics and JHU/CCP M&E focal point facilitate timely analysis of the survey results and its presentation to the project stakeholders;
11. Provide clear guidance to the AI assistant on the monitoring and evaluation activities. Ensure timely and qualitative feedback to the counterparts on the results of field monitoring.
12. Assist UNICEF in conduction of final evaluation of project activities, prepare regular inputs to progress reports; undertake regular field monitoring, control allotments and expenditures; ensure accurate implementation of UNICEF rules and procedures.
MAIN OUTPUTS
1. Communication materials for families, guidelines for teachers, information sheets for students developed in two languages.
2. Information and education materials for health and veterinary workers and volunteers
3. Distribution plan for communication materials, including new audiences and channels.
4. Guidelines for the interpersonal communication on avian flu.
5. Completed media plan for the second phase of the campaign.
6. Report on the activities and results of the consultancy.
DELIVERABLES AND DELIVERY DATES
All the materials will be presented in hard copies and in electronic formats.
SUPERVISION ARRANGEMENTS
The consultant will work under the supervision of the Health Specialist and Deputy Representative.
QUALIFICATIONS/SPECIALIZED KNOWLEDGE/EXPERIENCE REQUIRED:
University degree in Education, Social Sciences, Behavioural Sciences and Communication ? two years professional experience in the area of communication, especially related to health, preferably with International organizations ? knowledge of and experience with communication for behavioural impact ? professional writing skills ? fluency in Tajik, Russian, and English ? familiarity with & knowledge of UNICEF context/priorities ? ability to learn quickly and apply procedures in an international organization
PERFORMANCE INDICATORS
Consultant?s performance will be evaluated against the following criteria: timeliness, responsibility, initiative, communication, accuracy, and quality of the products delivered.
ESTIMATED BUDGET AND PAYMENT ARRANGEMENTS
National Officer will be paid a monthly salary at NOA level taking into account the level of responsibilities and complexity of tasks and assignments.
HEALTH STATEMENT & CERTIFICATE
The Health Statement and certificate of Good Health will be received prior to signing the contract.
Public AI Blog Discussions
Poultry monoculture?
11/25/08 Effect Measure
Public AI Blog Discussions
Another Supect H5N1 Case in Semarang Indonesia
11/24/08 Recombinomics
A word on public AI blogs
Over the past few years I have received some complaints about the opinions expressed in the public blogs on Avian Influenza. Instead of publishing the content of the blog article, I will include occasional links in the AI Digest. Our subscriber list is well over 2,400 professionals from around the world working Avian and Pandemic Influenza, let us know what information you would like to read in the AI Digest.
Claudinne
Regional Reporting and Surveillance
Indonesia: South Sulawesi Farmers to be Compensated for Bird Flu Losses
11/24/08 Asia Pulse The central government has allocated Rp1 billion in funds to compensate farmers in South Sulawesi whose poultry die of bird flu in 2009, a local animal husbandry official said.
"The funds will be used to compensate poultry breeders whose chickens die of bird flu in several districts and municipalities in South Sulawesi," Arifin Daud, a spokesman of South Sulawesi's animal husbandry office, said here on Monday. Daud said poultry breeders who had lost animals by the disease would be paid Rp12,500 to Rp20,000 per chicken.
"We are going to survey the chicken mortality rates and conditions in every district and municipality to determine the amount of compensation each region will receive," Daud said.
Regional Reporting and Surveillance
Indonesia: Jakarta Post Photos
11/25/08 Jakarta Post--http://www.thejakartapost.com/files/images/1herjo2.jpg
Photo caption: BIG, BAD, BIRD FLU SPRAYING: Sukardi, a bird trader at Karimata bird market in Semarang, Central Java, sprays his birds with disinfectant on Tuesday. Semarang Health Agency officials have asked traders at the market to increase the frequency of such spraying following a recent bird-flu-suspected death in the area. JP/Suherdjoko
11/25/08 Jakarta Post http://www.thejakartapost.com/files/images/p02-a_7.jpg
Photo caption: City officials attempt to catch chickens in a residential area in Makassar subdistrict, Jakarta, on Monday, as part of an operation to prevent the spread of avian flu in the capital. (JP/J. Adiguna)
Pandemic Preparedness
NIH: Study of Ancient and Modern Plagues Finds Common Features
11/25/08 NIH--In 430 B.C., a new and deadly disease ? its cause remains a mystery ? swept into Athens. The walled Greek city-state was teeming with citizens, soldiers and refugees of the war then raging between Athens and Sparta. As streets filled with corpses, social order broke down. Over the next three years, the illness returned twice and Athens lost a third of its population. It lost the war too. The Plague of Athens marked the beginning of the end of the Golden Age of Greece.
The Plague of Athens is one of 10 historically notable outbreaks described in an article in The Lancet Infectious Diseases by authors from the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health. The phenomenon of widespread, socially disruptive disease outbreaks has a long history prior to HIV/AIDS, severe acute respiratory syndrome (SARS), H5N1 avian influenza and other emerging diseases of the modern era, note the authors.
"There appear to be common determinants of disease emergence that transcend time, place and human progress," says NIAID Director Anthony S. Fauci, M.D., one of the study authors. For example, international trade and troop movement during wartime played a role in both the emergence of the Plague of Athens as well as in the spread of influenza during the pandemic of 1918-19. Other factors underlying many instances of emergent diseases are poverty, lack of political will, and changes in climate, ecosystems and land use, the authors contend. "A better understanding of these determinants is essential for our preparedness for the next emerging or re-emerging disease that will inevitably confront us," says Dr. Fauci.
"The art of predicting disease emergence is not well developed," says David Morens, M.D., another NIAID author. "We know, however, that the mixture of determinants is becoming ever more complex, and out of this increased complexity comes increased opportunity for diseases to reach epidemic proportions quickly."
For example, more people travel more often over greater distances and in less time now than at any time in the past. One consequence of the increased mobility in the modern age can be seen in the 2003 outbreak of the novel illness SARS, which rapidly spread from Hong Kong to Toronto and elsewhere as infected passengers traveled by air.
To better understand and predict disease emergence, Dr. Morens and his coauthors stress the need for research aimed at broadly understanding infectious diseases as well as specifically understanding how disease-causing microorganisms make the jump from animals to humans.
In a narrow sense, epidemics are caused by particular microorganisms, and the study of infectious disease has historically been microbe-focused. For example, the Black Death (bubonic plague), which killed some 34 million Europeans in the middle of the 14th century, was caused by the bacterium Yersinia pestis. In a broader sense, however, epidemics are caused by complex and not fully predictable interactions between the disease-causing microbe, the human host and multiple environmental factors, the authors note. The Black Death, for instance, was borne westward along newly established land and sea trade routes from its probable origin, China, into multiple European countries. Similarly, patterns of human movement along trade routes, specifically truck routes throughout Africa, played a role in the spread of HIV throughout that continent. Greater consideration must be given, say the NIAID authors, to broader, interlinked factors such as climate, urbanization, increased international travel and the rise of drug-resistant microbes, and the ways in which these factors combine to spark new epidemics.
Aside from commerce and travel, the NIAID authors point to several other factors that underlie many notable emerging diseases: poverty, the breakdown of public hygiene practices, and susceptibility of human populations to microbes against which they have no pre-existing immunity. This last factor played a key role in the smallpox epidemic that afflicted the Aztecs of 16th century Mexico. Smallpox had ravaged European communities for centuries, but until the Spanish arrived on the Yucatan coast in 1519, the disease was unknown in the New World. Historians believe that some 3.5 million people in central Mexico died in the first year of the epidemic.
Epidemics also can spur advances in public health, note the authors. They point to the yellow fever epidemics of 1793-98, which began in the then-U.S. capital, Philadelphia. Though the entire federal government and most Philadelphians fled, those who remained formed an emergency government and mobilized such marginalized groups as African-Americans and immigrants to fight the outbreak. In 1798, Congress established the Marine Hospital System ? forerunner of the modern U.S. Public Health Service ? to provide, at public expense, medical care for sick and injured merchant seamen. Historians generally agree that a prime impetus for creating the Marine Hospital System was the yellow fever epidemics.
Modern epidemiology began in reaction to another epidemic, says Dr. Morens. In the early 1830s, as cholera made its way along waterways from Asia towards Europe, French officials attempted to prepare their country in advance of an outbreak. Teams of scientists were sent to Poland and Russia to observe the outbreaks there. Throughout France, coastal health agencies and new quarantine stations were established; in Paris, a network of health inspection offices was created to coordinate inspection of wells, cesspools and latrines of both public and private buildings. Despite these efforts, cholera arrived in Paris on March 29, 1832, with explosive effect ? within two weeks, there were 1,000 cases, 85 percent of them fatal. Daily newspapers published lists of cases allowing armchair epidemiologists to see trends in illness and deaths. "For the first time in history," write the NIAID authors, "a large-scale emerging epidemic was scientifically investigated in 'real time' using census data in a prospective population-based approach that featured analyses of morbidity and mortality stratified by age-group, sex, occupation, socioeconomic status and location."
NIAID conducts and supports research ? at NIH, throughout the United States, and worldwide ? to study the causes of infectious and immune-mediated diseases, and to develop better means of preventing, diagnosing and treating these illnesses. News releases, fact sheets and other NIAID-related materials are available on the NIAID Web site at http://www.niaid.nih.gov.
The National Institutes of Health (NIH) ? The Nation's Medical Research Agency ? includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. It is the primary federal agency for conducting and supporting basic, clinical and translational medical research, and it investigates the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.
Reference:
DM Morens, GK Folkers and AS Fauci. Emerging infections: A perpetual challenge. The Lancet Infectious Diseases DOI: 10.1016/S1473-3099(08)70256-1 (2008).
Note:
The complete paper is available at: http://www3.niaid.nih.gov/about/directors/pdf/EmergingInfectionsLancetID.pdf
Conferences and Training
Unhealthy Governance: Securitising Infectious Diseases in Asia
The Centre of Asian Studies (CAS), at the University of Hong Kong (HKU), in conjunction with the Department of Community Medicine (HKU) and the Southeast Asia Research Centre at the City University of Hong Kong is calling for paper proposals towards a workshop on the topic of Unhealthy Governance: Securitising Infectious Diseases in Asia, to be held in May 2009.
This workshop is being funded by the Ford Foundation (Beijing) and the Strategic Research Theme: Law, Policy & Development (HKU). It is the aim of this workshop to undertake a systemic review of threat-based responses to infectious diseases in Asia – both in terms of specific disease outbreaks as well as in terms of comparative responses between different disease outbreaks. These responses will be focused at the international, regional, state and sub-state levels. By analysing these responses through the combined lens of securitisation and governance it will be possible to understand the priority different actors accord the threats posed by infectious diseases as well as the interplay of different actors during the securitisation process. From this understanding, the workshop will be able to evaluate how committed Asian states and the related regional/international organisations are to countering infectious diseases outbreaks as well as to what extent other variables political, economic, social or legal alter the securitisation of infectious diseases.
The full call for papers can be found at < http://www.hku.hk/cas/Event/Call%20for%20Abstracts.pdf>.
Conferences and Training
University of Sydney National Centre for Biosecurity
2nd annual Biosecurity Symposium
?Integrating Knowledge, Implementing Change?
9 ? 10 February 2009
Footbridge Theatre, University of Sydney
At a time of increasing worldwide concern about the impact of infectious diseases on health and security, this symposium explores naturally-occurring threats (e.g. pandemics), threats arising from human agency (e.g. bioterrorism), the overlapping responses to both types of threat, and requirements for changes to policy and practice.
The symposium is an opportunity for the exchange of information and ideas between scientists, academics, health professionals, government officials, members of non-government organisations, and other interested individuals. Topics will include:
? Responding to infectious disease emergencies in Australia and the Asia-Pacific
? The impact of infectious disease outbreaks on health governance, state functioning and the economy
? The development and use of biological weapons by state and non-state actors
? National biodefence programs
? Dual use dilemmas and security risks of research on pathogenic micro-organisms
? International law and domestic regulation
? Relevance and applications of new technologies to biosecurity challenges
? Ethical, social and cultural dimensions of biosecurity
The National Centre for Biosecurity invites you to register for this event at:
https://www.eventsinteractive.com/usyd/getdemo.ei?id=101&s=_6EK0ND3H0
Fees (AUD): Full registration (2 days) $250
Single day $150
Student $100
Dinner (optional) $80
The deadline for registration is 30 January 2009
For additional information, go to http://www.biosecurity.edu.au/conferences_bl.php
Enquiries: Jon Herington
Project Officer (Biosecurity)
Centre for International Security Studies
University of Sydney
(+61) 02 9351 5739
j.herington@econ.usyd.edu.au
