Back

Low cost and real-time surveillance of enteric infection and diarrhoeal disease using rapid diagnostic tests: A pilot study

Watson, S. I.; Ul Alam, M. A.; Rego, R. T.; Lilford, R. J.; Barman, K.; Alam, B.; Faruque, A. S. G.; Islam, M. S.

2022-03-09 epidemiology
10.1101/2022.03.07.22271752 medRxiv
Show abstract

BackgroundReal-time disease surveillance is an important component of infection control in at-risk populations. However, data on cases or from lab testing is often not available in many low-resource settings. Rapid diagnostic tests (RDT), including immunochromatographic assays, may provide a low cost, expedited source of infection data. MethodsWe conducted a pilot survey-based prevalence mapping study of enteric infection in Camp 24 of the camps for the forcibly displaced Rohingya population from Myanmar in Coxs Bazar, Bangladesh. We randomly sampled the population and collected and tested stool from under-fives for eight pathogens using RDTs in January-March 2021 and September-October 2021. A Bayesian geospatial statistical model allowing for imperfect sensitivity and specificity of the tests was adapted. ResultsWe collected and tested 396 and 181 stools in the two data collection rounds. Corrected prevalence estimates ranged from 0.5% (Norovirus) to 27.4% (Giardia). Prevalence of E.coli O157, Campylobacter, and Cryptosporidium were predicted to be higher in the high density area of the camp with relatively high probability (70-95%), while Adenovirus, Norovirus, and Rotavirus were lower in the areas with high water chlorination. Clustering of cases of Giardia and Shigella was also observed, although associated with relatively high uncertainty. ConclusionsWith an appropriate correction for diagnostic performance RDTs can be used to generate reliable prevalence estimates, maps, and well-calibrated uncertainty estimates at a significantly lower cost than lab-based studies, providing a useful approach for disease surveillance in these settings.

Matching journals

The top 6 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.