Back

Digitized smart surveillance in malaria elimination programme in Mangaluru city, Karnataka, India - a detailed account of operationalization in post-digitization years

Baliga, B. S.; Baliga, S.; Jain, A.; Kulal, N.; Kumar, M.; Koduvattat, N.; Kumar, B. G. P.; Kumar, A.; GHOSH, S. K.

2020-07-07 public and global health
10.1101/2020.07.06.20147405 medRxiv
Show abstract

BackgroundAn indigenously developed digital handheld Android-based geographical information system (GIS)-tagged tablets (TABs) device has been deployed in Mangaluru city, Karnataka, India for smart surveillance in malaria elimination programme from October 2014. Here a detailed account is enumerated in the post-digitization years. The study was aimed to assess the effectiveness of the digitized surveillance system under the ongoing health system in Mangaluru city. MethodsA software developed for digitization of malaria surveillance was continued in the post-digitization years (PDY). The same digitization year (DY) protocol was followed in the post-digitization periods also. Secondary data from the malaria control software, total nunber of cases, active surveillance, malaria indices, and feedback from stakeholders were looked at and analyzed. ResultsDigital surveillance was sustained and the performance improved in the 5th year with participation of all stakeholders. Malaria indices significantly reduced to about 65% in the digitization years compared with digitization year (p<0.001). Slide positivity rate (SPR) decreased from 10.36 (DY) to 4.3 (PDY4). Annual parasite incidence (API) decreased from 16.17 (DY) to 5.4 (PDY4). There was a tempo-spatial correlation between closure of cases on 14th day and incidence of malaria. There was a negative correlation between contact smears and incidence of malaria (r = -0.907). Good impact was recorded in the pre-monsoon months ([~]85%) and low impact in July and August months ([~]40%). ConclusionSoftware helped to improve incidence-centric active surveillance, complete treatment with documentation of elimination of parasite, targeted vector control measures. The learnings and analytical output from the data helped to modify strategies for local control of both disease and the vector.

Matching journals

The top 3 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.