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Development of a computational model to inform environmental surveillance sampling plans for Salmonella enterica serovar Typhi in wastewater

Burnor, E.; Morin, C.; Shirai, J.; Zhou, N.; Meschke, J. S.

2023-06-23 microbiology
10.1101/2023.06.22.545856 bioRxiv
Show abstract

Typhoid fever - an acute febrile disease caused by infection with the bacterium Salmonella enterica serotype Typhi (S. Typhi) - continues to be a leading cause of global morbidity and mortality, especially in developing countries with limited access to safe drinking water and adequate sanitation. Environmental surveillance, the process of detecting and enumerating disease-causing agents in wastewater, enables estimating disease prevalence and trends within a community and is a useful tool to monitor the circulation of typhoid fever in endemic regions. This study presents a computational model, which combines dynamic and probabilistic modeling techniques, to predict - on a spatial and temporal scale - the probability of detecting S. Typhi in a wastewater system. This model may be utilized in communities to inform environmental surveillance sampling plans and may provide useful insight into selecting appropriate sampling locations and times.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 2%
40.0%
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2
Science of The Total Environment
○ 186 papers in training set
Top 0.3%
9.9%
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3
Water Research
○ 79 papers in training set
Top 0.3%
4.9%
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50% of probability mass above
4
PLOS Water
● 13 papers in training set
Top 0.1%
4.1%
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5
Scientific Reports
○ 3612 papers in training set
Top 28%
3.5%
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6
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.3%
7
Malaria Journal
○ 58 papers in training set
Top 0.4%
2.4%
8
ACS ES&T Water
● 20 papers in training set
Top 0.2%
1.9%
9
Applied and Environmental Microbiology
● 339 papers in training set
Top 3%
1.9%
10
Epidemics
○ 116 papers in training set
Top 1%
1.5%
11
Journal of Microbiological Methods
○ 13 papers in training set
Top 0.2%
1.4%
12
mSystems
● 394 papers in training set
Top 5%
1.1%
13
Viruses
○ 332 papers in training set
Top 3%
1.1%
14
PeerJ
◐ 308 papers in training set
Top 8%
1.1%
15
One Health
○ 29 papers in training set
Top 0.4%
1.1%
16
PLOS Neglected Tropical Diseases
● 466 papers in training set
Top 5%
1.0%
17
Environmental Pollution
○ 37 papers in training set
Top 1%
0.9%
18
Bioinformatics
◐ 1204 papers in training set
Top 9%
0.9%
19
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.9%
0.6%
20
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 8%
0.6%
21
Journal of Applied Microbiology
◐ 20 papers in training set
Top 0.9%
0.6%

"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.