Evaluation of the avian influenza surveillance system in Enugu State Nigeria, 2015-2017

INTRODUCTION: The resurgence of highly pathogenic avian influenza (HPAI) H5N1 was reported in Nigeria in 2014. The isolation of a reassortants strain of influenza A subtype H5N8 in 2017 has fueled speculation of a possible the emergence of a novel influenza strain with no prior human or animal immunity. About 3.4% of poultry in Enugu State were affected between 2015-17. Hence, the need for a comprehensive review of the avian influenza (AI) surveillance system in Enugu State, Nigeria. METHOD: we defined the case definition for suspected and confirmed cases of avian influenza. Described the functions of the avian influenza surveillance system and highlighted its challenges. A cross-sectional survey was conducted involving 27 poultry stakeholders (epidemiology officers, poultry farmers and surveillance point agents). Information was obtained using a pre-tested semi-structured questionnaire (SSQ), which accessed surveillance system attribute, (simplicity, flexibility, data quality, acceptability, sensitivity, predictive value positive, representativeness timelines and stability). Data obtained was transferred to Microsoft excel and analysis done. Mean, frequencies of responses were determined and variables displayed in tables. RESULT: in all, 10(37.1%) and 17(62.9%) of AI stakeholders had < 8 years and <10 years of work experience in AI surveillance respectively. Usefulness, between 2014-2015 about 1028 suspected AI sample were screened for avian influenza in Nigeria, 817(79.5%) was positive, Enugu State accounted for 3(0.37%) of these cases, 20(74%) of respondent reported that the system was simple and reporting platforms easy to populate, 23 (92.3%) posited that the system is flexible and can accommodate surveillance of other enzootic poultry diseases. Eighteen (66.7%) reported that AI surveillance was acceptable and wishes to continue to participate. Representatives of the surveillance system was Eight (47%), the stability of the system was 12(44%). The data quality was 101(47%) and the predictive positive value was 79.5%. Timeliness of reports was 51% in 2015, 48% in 2016, and 47%. CONCLUSION: the avian influenza surveillance system in Enugu state useful, simple, flexible, and acceptable. Poor data quality, stability, timeliness of reports were identified during the period of study. We recommend prompt compensation of affected farmers, this may engender trust between poultry farmers and epidemiology unit hence, early disease reporting, and improved data quality. The State Government may consider engaging more surveillance point officer to improve coverage of disease reporting from Local government areas.