SACIDS validates One Health Knowledge Repository

By Afyadata Administrator

17th September, 2018 17:14 Volume 3, Number 4&5
SACIDS validates One Health Knowledge Repository

To enhance early detection, reporting and feedback loops, the Southern African Centre for Infectious Disease Surveillance (SACIDS) developed One Health Knowledge Repository (OHKR). OHKR is a decision-making expert system that helps local communities, ministries responsible for human and animal health to make prompt and appropriate decision required to prevent and control diseases.

This database of expertly authored health contents includes guidelines, fact sheets, standard case definitions, frequently asked questions for different disease conditions, response protocols and recommendation and first aid advice from livestock and human health perspectives.

Initially, a priority list of key diseases was established based on their epidemic potential as well as being targeted for control and eradication by the Tanzanian government. Furthermore, a template was developed to guide content generation by the medical interns and veterinary graduates. The contents developed were proof read and endorsed by medical and veterinary experts working with the ministries responsible for health and livestock development before been released for testing in the field.

For human disease conditions, OHKR content was developed for the 15 priority diseases namely: Dengue, Ebola Virus Disease, Marburg virus disease, Crimean-Congo Hemorrhagic fever, Rift Valley fever, Cerebrospinal meningitis, Anthrax, Highly Pathogenic Avian Influenza, Rabies, Plague, Measles, Typhoid fever, Malaria, Cholera and Yellow fever.  Fourteen animal disease conditions whose OHKR content were developed are: Foot and Mouth Disease, Rift Valley fever, Peste des Petits Ruminants, Brucellosis, Trypanosomosis, Newcastle Disease, Contagious Bovine Pleuropneumonia, Contagious Caprine Pleuropneumonia, Lumpy Skin Disease, Anthrax, Rabies, Highly Pathogenic Avian Influenza, African Swine Fever and Malignant catarrhal fever.

In order to improve the functionality of OHKR, its validation was carried out by involving key stakeholders purposively selected. From veterinary side, these included farm/ranch veterinarian, wildlife veterinarian, those in small animal and mixed practice, poultry practice, and scientists from Tanzania Veterinary Laboratory Agency zonal centers. From human health side, professionals included experienced medical specialists from Muhimbili National Hospital, Muhimbili University of Health and Allied Sciences (MUHAS), University of Dodoma Faculty of Human Health and Allied Sciences, Dodoma Regional Referral Hospital and Morogoro Regional Referral Hospital. Other stakeholders included private practitioners, academicians from the College of Veterinary Medicine and Biomedical Sciences at Sokoine University of Agriculture (SUA) and MUHAS.

During the validation exercise that was carried out at SUA on May 18, 2018, the initially developed contents of OHKR were subjected to scrutiny process to enrich the built knowledge base. This was followed by development of scoring matrix of the clinical manifestations for diseases archived in the OHKR. The matrices were eventually used to create algorithms in the process of developing a decision-making system to support the prediction of most likely disease conditions based on the reported signs and symptoms from community level. The outputs of the scoring matrix have been integrated into the AfyaData platform.

A list of recommended actions created earlier for targeted users (community health workers/reporters, livestock extension officers, in-charge of health facilities, and district medical/veterinary officers) were improved. The contents are available in audio, video and text formats. The contents for OHKR, prepared in English and Kiswahili languages, have been archived in the multilingual web-based interface at SACIDS server. The OHKR archiving system has been programmed to receive data from community level, and automatically send messages to relevant user on artificial intelligence and alerts of possible disease conditions occurring in human and animal populations. This output format enable the user to quickly establish the trend in terms of host, space (location) and time (date) of disease occurrence, and take necessary actions to manage the disease.


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