Back

Leveraging an Online Dashboard to Inform on Infectious Disease Surveillance: A case Study of COVID-19 in Kenya.

Mwanga, M. J.; Guzman-Rincon, L. M.; Kingwara, L.; Odhiambo, D. B.; Gathuri, H.; Lambisia, A. W.; Morobe, J. M.; Moraa, E.; Kutima, B.; Gitonga, J.; Mugo, D.; Agoti, C. N.; Nyagwange, J.; Warimwe, G. M.; Oyier, I.; Nokes, D. J.; Agweyu, A.; Kagucia, E. W.; Etyang, A. O.; Kiiru, J. N.; Githinji, G.

2024-09-17 epidemiology
10.1101/2024.09.14.24313681 medRxiv
Show abstract

A multi-pronged approach to combating the COVID-19 pandemic in Kenya resulted in the formation of multidisciplinary research initiatives including genomic sequencing, syndromic surveillance, sero-surveillance, vaccination, and mathematical modelling. These initiatives generated an overwhelming amount of data that posed a challenge to researchers and public health officials, to effectively manage, analyse and promptly interpret for immediate pandemic response. As a result, there was demand for a platform to collate and integrate these datasets with interpretable findings to aid in pandemic management. In response, we developed a web-based dashboard, and integrated multidisciplinary datasets collected by the Ministry of Health-Kenya (MoH-K) and other research organizations, to support surveillance and monitoring of COVID-19 in Kenya. The developed dashboard combines genomics, epidemiological, seroprevalence, modelling, vaccination, syndromic and phylogenetic data and provides real-time updates to the public and health sector experts. The dashboard provides temporal trends of reported COVID-19 cases, fatalities, variants, and vaccination, in addition to summary reports from multiple cross-sectional seroprevalence studies and ongoing facility-based inpatient syndromic surveillance from 15 health facilities across Kenya. This is the first detailed interactive dashboard in Kenya that combines multiple datasets from a disease outbreak to provide valuable insights to researchers, health policy makers, the media and public not only during pandemic but also during routine surveillance. This resource is a model for digital platform for infectious disease surveillance and for informing public health planning and intervention. Dashboard Linkhttps://kcd.kemri-wellcome.org/

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.