Required Number: One (1)    

Employment Type: Contractual for 6 months with possibility of extension

Purpose: Ethiopia is experiencing different public health emergencies. An effective and efficient emergency forecast, communication, preparedness and response will reduce mortality and morbidity of the affected population. Resolve to Save Lives, an initiative of Vital Strategies, is supporting the Ethiopian Public Health Institute (EPHI) to improve capacities to prevent, detect, respond to public health threats, including early warning capacity related to risk identification and preparedness. In line with this, the center for Public Health Emergency Management (PHEM) needs a competitive and experienced expert to anticipate/forecast public health emergencies by developing and utilizing public health emergencies occurrence forecasting model.

Job description: 

• Collect, analyze and interpret surveillance, meteorological, WASH, environmental and other health related data to identify/forecast any public health emergencies and related risks.
• Develop models that integrate public health surveillance, metrological, WASH, environmental and other health related data for identifying/forecasting public health emergencies and associated risks.
• Predict/forecast extent of damage, magnitude of impact and the likelihood occurrence of public health emergencies.
• Closely analyze and follow the trend of emergencies occurrence (occurred annually and at longer intervals)
• Work closely with other department, sectors and partners to develop our capacity in forecasting public health risks.
• Continuously revise the identified/forecasted/predicted public health emergencies/risks by monitoring public health surveillance, metrological, WASH and environmental data and based on vulnerability risk assessment finding.
• Modify the already developed public health emergency occurrence forecasting models as needed
• Collaborate in developing strategies and provide for responsible bodies to prevent, mitigate and manage identified/forecasted/predicted public health emergencies and associated risks.
• Collaborate with other directorates, partners and sectors to avoid or reduce the consequence of identified/forecasted public health emergencies and associated risks.
• Support and capacitate regions to develop and utilize public health emergency occurrence forecasting models
• Support and capacitate regions to identify/forecast public health emergencies and risks by integrating climate and other health related data with the public health surveillance data.
• Participate in planning and conducting supervisions and assessments conducted by the team
• Collaborate with the respective directorate in developing surveillance indicators to monitor surveillance performance.
• Develop outbreak thresholds for prioritized diseases and events under surveillance for those without a threshold.
• Develop guidelines, training manuals, training materials and provide capacity building trainings for national, regional, zonal, woreda and health facility level health workers
• Work with GIS expert to map public health emergency risky areas
• Prepare and submit cumulative and progress implementation report of assigned tasks
• Regularly produce supervision, assessment and other technical reports timely
• Carryout task assigned by his/her immediate supervisor

Qualification:

• PhD in statistics or similar field with at least one (1)) year working experience in health-related departments

Qualification and experience:

• Above two years professional experience in data analysis, modeling and interpretation
• Ability to develop, train and utilize public health emergency/outbreak occurrence prediction models
• Ability to meet task deadlines; work independently and cooperatively with teams
• Knowledge and understanding of disease surveillance, epidemiology and public health practice

Required Skills and Expertise:

•  Ability to write high quality reports, working ability in a team and emergency situations
• Training on PHEM and health related emergency field mission experience has added value