Typhoid fever, caused by Salmonella enterica serovar Typhi (S. Typhi) is estimated to involve ~21.7 million illnesses and 216,000 deaths annually Crump and Mintz, 2010; Mogasale et al., 2014, with most of these occurring in lower and middle-income countries. In Africa, overall typhoid is now estimated to have an average annual pooled incidence rate of 112.1 (95% CI, 46.7–203.5) cases per 100,000 people Hopewell and Graham, 2014; Marchello et al., 2019 with a case fatality rate (CFR) of 5.4% (2.7–8.9) Marchello et al., 2020.

Control of typhoid is impeded by asymptomatic carriage, which historically was estimated to account for 2–5% of individuals infected Levine et al., 1982; Parry et al., 2002; Thanh Duy et al., 2020. However, there is a paucity of recent data on the frequency of carriers in different settings including sub-Saharan Africa (SSA) as well as the extent to which they contribute to disease transmission Gauld et al., 2018. A recent modelling study using data generated in Blantyre, Malawi, identified multidrug resistant (MDR) S. Typhi and/or the emergence of the lineage known as H58 (genotype 4.3.1) as a primary driver of an increasing number of typhoid fever cases. In this study, an estimated 45–95% of typhoid transmission was attributed to carriers Pitzer et al., 2015; Saad et al., 2017. S. Typhi H58 Wong et al., 2015 is a globally disseminated clade frequently associated with MDR (defined as resistance to chloramphenicol, ampicillin and co-trimoxazole) and an increasing frequency of reduced susceptibility to fluoroquinolones. H58 S. Typhi are rapidly displacing other lineages in many endemic areas Wong et al., 2015; Feasey et al., 2015; Kariuki et al., 2010; Park et al., 2018 and a new subclade that is extensively drug resistant (XDR), displaying resistance to ciprofloxacin and fluoroquinolones in addition to MDR, has been described in Pakistan Klemm et al., 2018.

Recent reports of epidemics of typhoid fever in SSA suggest that the disease may be becoming more widespread in the region Crump and Mintz, 2010; Park et al., 2018; Hendriksen et al., 2015; Lutterloh et al., 2012; Marks et al., 2017; Neil et al., 2012. In Kenya, the rapid growth of population has led to a huge rural-to-urban migration with people increasingly living in informal settlements where clean water and good sanitation are a major challenge Kyobutungi et al., 2008; Mberu et al., 2016. The incidence of typhoid in one such informal settlement, Kibera in Nairobi, was estimated at 247 cases per 100,000 with the highest rates in children 5–9 years old (596 per 100,000) Breiman et al., 2012. For the last two decades, the majority of cases of typhoid in Kenya have been MDR, with reduced susceptibility to fluoroquinolones rising in frequency Kariuki et al., 2010; Park et al., 2018; Mutai et al., 2018. Previously, we showed that S. Typhi H58 gained a foothold in Kenya in the 1990s, constituting >75% of the circulating S. Typhi we have characterised since 2001 Kariuki et al., 2010. Two H58 lineages were detected; lineage I being isolated between 1988 and 2008 and lineage II from 2004 onwards. We have previously observed carriage rates of 6% in households where typhoid cases were detected Kariuki et al., 2010, however these S. Typhi isolates were not characterised genetically and the role of asymptomatic carriers in transmission dynamics of typhoid in the community is still poorly understood. Over the past 7 years, we have been intensively studying typhoid and other invasive bacterial diseases in Mukuru, an informal settlement 15 km east of the city of Nairobi, Kenya. The prevalence of S. Typhi infections among 16,236 children was 1.4% (CI: 1.2–1.6%), and higher amongst males (1.8% vs 1.2% for females), with a high proportion of infections noted among older children 5–8 years in age Mbae et al., 2020. Risk factors predictive of S. Typhi infection in Mukuru were multiple but were predominantly associated with contaminated water sources and sanitation issues Mbae et al., 2020. Here, we analysed typhoid cases in Mukuru clinically and microbiologically, and identified frequent asymptomatic carriage among children below 16 years of age. By exploiting whole genome sequencing (WGS) and geospatial mapping we characterised the population structure and transmission dynamics of S. Typhi in this location.

In this case-control typhoid surveillance study, we observed an asymptomatic S. Typhi carriage rate of 1.1% among children aged 16 years and under from an informal settlement with endemic Water, Sanitation, and Hygiene (WaSH)-related enteric diseases Mbae et al., 2020; Kariuki et al., 2020; Kariuki et al., 2019. To our knowledge, there has not been systematic surveillance for typhoid carriage in communities in Africa, but globally carriage and shedding of S. Typhi has mostly been associated with older age groups Levine et al., 1982; Senthilkumar et al., 2012; Gunn et al., 2014. Our data highlights a role for paediatric carriage, revealing a lower percentage of carriers amongst infants ≤ 1 year of age (0.62%), increasing to 1.2% in children between 7 and 16 years (Supplementary file 6). Thus, carriage and shedding, especially among school age children, is likely an important factor in the onward transmission of typhoid in this setting Mbae et al., 2020. Symptomatic typhoid fever is common in school age children, with a case culture positive rate of 4.3% among febrile children 7–16 years of age, though our data shows that there is also a substantial burden among younger children 1–7 years of age, and infants up to 1 year of age, with culture-positive rates among febrile participants of 3.1% and 2.2%, respectively (Supplementary file 6). Our WGS data shows that serotyping frequently leads to mis-classification of S. Typhi vs non-typhoidal Salmonella, highlighting the importance of molecular diagnostics in future studies and surveillance programs. WGS has been widely adopted by Salmonella reference labs in high-income countries Ashton et al., 2016; Köser et al., 2012; Arnold et al., 2018 but remains rare in low-income countries, although KEMRI now sequences all Salmonella isolates on-site immediately after serotyping.

We previously noted a dominance of MDR H58 S. Typhi over the last decade, essentially replacing the antimicrobial susceptible genotypes that dominated in the 1980–1990s Kariuki et al., 2015. The S. Typhi circulating in the informal settlement in the present study are largely comprised of descendants of the previously observed H58 sublineages 4.3.1.EA1 (36%) and 4.3.1.2.EA2 (48%) Wong et al., 2015; Park et al., 2018. Both EA1 and EA2 appear to be long established genotypes, with the mrca of EA1 existing circa 1990 (95% HPD, 1981–1999) (see Figure 4a ), consistent with the earliest recorded detection of H58 Lineage I in Kenya in 1988 Kariuki et al., 2010. Similarly we predict that the mrca of EA2 existed circa 1988–1990 (95% HPD, 1978–1997), earlier than the first recorded H58 Lineage II isolation in Kenya in 2004 Kariuki et al., 2010. Our data thus support contemporaneous imports of EA1 and EA2 in the late 1980s or early 1990s, shortly after the emergence of H58 in South Asia Wong et al., 2015 (circa 1982, 95% HPD 1974–1990), and show that both lineages have persisted and diversified locally alongside one another in the intervening decades. H58 sublineage EA3 was introduced later (we estimate that the Kenyan mrca existed circa ~2012 [95% HPD 2009 to 2014]), and consistent with this the lineage displays less diversity and accounts for a smaller fraction of cases and controls (11%). The topology of our global phylogeny (Figure 2) suggests that South Asia is the most likely origin of EA3 (as it is for EA1 and EA2), and that EA3 appears to have spread between Uganda and Kenya. This in line with multiple reports Feasey et al., 2015; Park et al., 2018 of H58 strains spreading through East Africa, mainly arising from intracontinental and transcontinental travel and concomitant risk factors associated with WASH conditions.

The sublineages of S. Typhi H58 in Kenya exhibit different antibiotic resistance profiles (Figure 2 and Table 2). Notably, EA1 has a large Kenyan sublineage of MDR strains with IncHI1 plasmids Wong et al., 2015; Park et al., 2018 (which are commonly associated with outbreaks in East Africa and Asia Wong et al., 2015; Park et al., 2018; Holt et al., 2011) but also a Kenyan sublineage with chromosomally integrated MDR. Chromosomal integration of MDR has not previously been reported in S. Typhi from Kenya (see supplementary data), but has been reported in Malawi and Tanzania Wong et al., 2015 and our new data suggests that the variant may have been transferred to these locations from Kenya (see Figure 2). MDR H58 isolates are now widespread across East Africa, having been detected in Malawi, Uganda, Rwanda, Tanzania, and Mozambique Wong et al., 2015; Feasey et al., 2015; Park et al., 2018; Wong et al., 2016; Ingle et al., 2019.

The three East African lineages differed markedly in their patterns of mutations conferring Decreased Ciprofloxacin Susceptibility (DCS). GyrB-S464F was conserved among all EA2, whereas all EA3 isolates carried the GyrA-S83Y mutation. The GyrA-S464F mutation was also detected at low frequency in EA1 (Table 2). This data indicates that ciprofloxacin resistance has been selected independently multiple times and is ongoing. Increasing rates of ciprofloxacin resistance have also been observed following similar introductions of H58 elsewhere in East Africa Park et al., 2018, and likely reflect a change in treatment practise following widespread dissemination of MDR S. Typhi elsewhere including South and Southeast Asia Britto et al., 2018; Britto et al., 2020; Rahman et al., 2020.

The different S. Typhi lineages appeared to be fairly evenly distributed between both acute cases and carriers, with the most common subgroup (EA2) accounting for 46% of acute cases and 50% of carriers. Similarly, all S. Typhi genotypes were identified throughout the study period and spatially across the study site, with most case/carrier monthly counts and geographic regions containing a diversity of genotypes (Figure 3, Figure 3—figure supplement 1). Our data therefore provides no evidence for major point-source single-genotype outbreaks, but is consistent with persistent contamination of water supplies with multiple S. Typhi genotypes. Higher temperatures were associated with lower S. Typhi case and carrier counts, however, no association with high rainfall was observed among our culture data. These findings are in line with previous studies focusing on seasonal trends in nearby Kibera Breiman et al., 2012. However, they contrast with trends previously observed in other settings including Malawi, where higher temperatures and rainfall were associated with increased risk of disease albeit with a time lag of multiple months Thindwa et al., 2019, and South Asia Baker et al., 2011; Dewan et al., 2013; Kanungo et al., 2008.

In our phylogenetic trees, branch lengths and SNP counts can be interpreted as measures of evolutionary time, and thus terminal branches and isolate-specific SNP counts as a measure of time since that isolate shared a mrca with another sampled isolate. This total time includes (i) time from mrca to the acquisition of infection in the sampled host, plus (ii) time from initial acquisition of the infection to time of sampling (ie time within the sampled host). Variation in the latter is more likely to be explained by the symptom status of the sampled host (rather than the former which occurs prior to the infection of the sampled host). Hence the higher mean branch lengths and SNP counts in asymptomatic controls is consistent with the expectation that carriers have had, on average, a longer duration from acquisition of the infection to sampling in the clinic (which is triggered by routine visits and unrelated to symptoms or colonisation status), compared to acute cases who are presenting due to febrile illness triggered by a recently acquired S. Typhi infection. This supports the interpretation that S. Typhi-positive controls identified in this study represent genuine medium- to long-term typhoid carriers, rather than simply reflecting transient presence in the gut. The greater diversity observed here amongst controls (Table 2) further supports this interpretation. Longer branch lengths among carrier samples were also observed in a recent study Thanh Duy et al., 2020 of S. Typhi isolated from bile samples from the gallbladders of cholecystectomy patients in Nepal. The differences in terminal branch lengths were non-significant in our study, possibly reflecting low statistical power or, perhaps less likely, that our control data constitute a mix of multiple carriage types including convalescent (3 weeks to 3 months), temporary (3e to 12 months), and chronic (more than 1 year) carriers. Also in line with previous findings Thanh Duy et al., 2020, our analyses provide evidence of positive selection among carriage isolates, with a higher proportion of non-synonymous mutations detected among carriers in specific biological pathways including surface polysaccharides and antigens, transport/binding proteins, and anaerobic respiration (Figure 5, Supplementary file 13). This is exemplified in the genes encoding surface antigens, notably those responsible for biosynthesis of Vi capsule and O-antigen lipopolysaccharide.

Our study is not without limitations, firstly, our data are from a single informal settlement community in Nairobi, and thus may not be representative of the overall population structure and AMR patterns of typhoid in Kenya more broadly, or in older age groups. Similarly, our sample size yielded a relatively small number of isolates for WGS, and we thus lack statistical power for some genetic analyses.

All data generated or analysed during this study are included in the manuscript and supporting files. Raw Illumina sequence reads have been submitted to the European Nucleotide Archive (ENA) under accession PRJEB19289. Individual sequence accession numbers are listed in Table S1.

1. 1. Kariuki S
   2. Dyson Z

   (2020) NCBI BioProject

   ID PRJEB19289. Typhi_Kenya European Nucleotide Archive (ENA).

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB19289

1. 1. Parkhill J
   2. Dougan G
   3. James KD

   (2001) Nature

   ID 35101607. Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18.

   https://www.nature.com/articles/35101607
2. 1. Argimon S
   2. Abudahab K
   3. Goater RJE

   (2016) Microb Genom

   ID PMC5320705. Visualizing and sharing data for genomic epidemiology and phylogeography.

   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5320705/
3. 1. Wick RR
   2. Schultz MB
   3. Zobel J
   4. Holt KE

   (2015) Bioinformatics

   ID 26099265. Bandage: interactive visualization of de novo genome assemblies.

   https://pubmed.ncbi.nlm.nih.gov/26099265/
4. 1. Hawkey J
   2. Hamidian M
   3. Wick RR

   (2015) BMC Genomics

   ID 26336060. ISMapper: identifying transposase insertion sites in bacterial genomes from short read sequence data.

   https://pubmed.ncbi.nlm.nih.gov/26336060/

1. 1. Accou-Demartin M
   2. Gaborieau V
   3. Song Y

   (2011) Salmonella enterica Serotype Typhi with nonclassical quinolone resistance phenotype

   Emerg Infect Dis 17:1091–1094.

   https://doi.org/10.3201/eid/1706.101242

   • PubMed
   • Google Scholar
2. 1. Argimon S
   2. Abudahab K
   3. Goater RJE

   (2016) Microreact: visualizing and sharing data for genomic epidemiology and phylogeography

   Microbial Genomics 2:e000093.

   https://doi.org/10.1099/mgen.0.000093

   • PubMed
   • Google Scholar
3. 1. Argimón S
   2. Yeats CA
   3. Goater RJ
   4. Abudahab K
   5. Taylor B
   6. Underwood A
   7. Sánchez-Busó L
   8. Wong VK
   9. Dyson ZA
   10. Nair S
   11. Park SE
   12. Marks F
   13. Page AJ
   14. Keane JA
   15. Baker S
   16. Holt KE
   17. Dougan G
   18. Aanensen DM

   (2021) A global resource for genomic predictions of antimicrobial resistance and surveillance of salmonella typhi at pathogenwatch

   Nature Communications 12:2879.

   https://doi.org/10.1038/s41467-021-23091-2

   • PubMed
   • Google Scholar
4. 1. Arnold C
   2. Edwards K
   3. Desai M
   4. Platt S
   5. Green J
   6. Setup CD

   (2018) Validation, and Quality Control of a Centralized Whole-Genome-Sequencing Laboratory: Lessons Learned

   Journal of Clinical Microbiology 56:00261-18.

   https://doi.org/10.1128/JCM.00261-18

   • Google Scholar
5. 1. Ashton PM
   2. Nair S
   3. Peters TM

   (2016) Identification of Salmonella for public health surveillance using whole genome sequencing

   PeerJ 4:e1752.

   https://doi.org/10.7717/peerj.1752

   • PubMed
   • Google Scholar
6. 1. Baker S
   2. Holt KE
   3. Clements ACA

   (2011) Combined high-resolution genotyping and geospatial analysis reveals modes of endemic urban typhoid fever transmission

   Open Biology 1:110008.

   https://doi.org/10.1098/rsob.110008

   • PubMed
   • Google Scholar
7. 1. Breiman RF
   2. Cosmas L
   3. Njuguna H

   (2012) Population-based incidence of typhoid fever in an urban informal settlement and a rural area in Kenya: implications for typhoid vaccine use in Africa

   PLOS ONE 7:e29119.

   https://doi.org/10.1371/journal.pone.0029119

   • Google Scholar
8. Software

   1. Briatte F

   (2021) Geometries to Plot Networks with ‘ggplot2, version 0.5.9

   R Package Ggnetwork.

   https://CRAN.R-project.org/package=ggnetwork
9. 1. Britto CD
   2. Dyson ZA
   3. Duchene S

   (2018) Laboratory and molecular surveillance of paediatric typhoidal Salmonella in Nepal: Antimicrobial resistance and implications for vaccine policy

   PLOS Neglected Tropical Diseases 12:e0006408.

   https://doi.org/10.1371/journal.pntd.0006408

   • PubMed
   • Google Scholar
10. 1. Britto CD
    2. Dyson ZA
    3. Mathias S

    (2020) Persistent circulation of a fluoroquinolone-resistant Salmonella enterica Typhi clone in the Indian subcontinent

    The Journal of Antimicrobial Chemotherapy 75:337–341.

    https://doi.org/10.1093/jac/dkz435

    • PubMed
    • Google Scholar
11. 1. Carattoli A
    2. Zankari E
    3. García-Fernández A

    (2014) In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing

    Antimicrobial Agents and Chemotherapy 58:3895–3903.

    https://doi.org/10.1128/AAC.02412-14

    • PubMed
    • Google Scholar
12. 1. Croucher NJ
    2. Page AJ
    3. Connor TR
    4. Delaney AJ
    5. Keane JA
    6. Bentley SD
    7. Parkhill J
    8. Harris SR

    (2015) Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins

    Nucleic Acids Research 43:e15.

    https://doi.org/10.1093/nar/gku1196

    • PubMed
    • Google Scholar
13. 1. Crump JA
    2. Mintz ED

    (2010) Global trends in typhoid and paratyphoid Fever

    Clinical Infectious Diseases 50:241–246.

    https://doi.org/10.1086/649541

    • PubMed
    • Google Scholar
14. 1. Dewan AM
    2. Corner R
    3. Hashizume M
    4. Ongee ET

    (2013) Typhoid Fever and its association with environmental factors in the Dhaka Metropolitan Area of Bangladesh: a spatial and time-series approach

    PLOS Neglected Tropical Diseases 7:e1998.

    https://doi.org/10.1371/journal.pntd.0001998

    • PubMed
    • Google Scholar
15. 1. Dixon P

    (2003) VEGAN, a package of R functions for community ecology

    Journal of Vegetation Science 14:927–930.

    https://doi.org/10.1111/j.1654-1103.2003.tb02228.x

    • Google Scholar
16. 1. Duchene S
    2. Holt KE
    3. Weill FX

    (2016) Genome-scale rates of evolutionary change in bacteria

    Microbial Genomics 2:e000094.

    https://doi.org/10.1099/mgen.0.000094

    • Google Scholar
17. Preprint

    1. Edwards DJ
    2. Duchêne S
    3. Pope B
    4. Holt KE

    (2020) SNPPAR: identifying convergent evolution and other homoplasies from microbial whole-genome alignments

    bioRxiv.

    https://doi.org/10.1101/2020.07.08.194480

    • Google Scholar
18. 1. Feasey NA
    2. Gaskell K
    3. Wong V
    4. Msefula C
    5. Selemani G
    6. Kumwenda S
    7. Allain TJ
    8. Mallewa J
    9. Kennedy N
    10. Bennett A
    11. Nyirongo JO
    12. Nyondo PA
    13. Zulu MD
    14. Parkhill J
    15. Dougan G
    16. Gordon MA
    17. Heyderman RS

    (2015) Rapid emergence of multidrug resistant, H58-lineage Salmonella typhi in Blantyre, Malawi

    PLOS Neglected Tropical Diseases 9:e0003748.

    https://doi.org/10.1371/journal.pntd.0003748

    • PubMed
    • Google Scholar
19. 1. Firth C
    2. Kitchen A
    3. Shapiro B
    4. Suchard MA
    5. Holmes EC
    6. Rambaut A

    (2010) Using time-structured data to estimate evolutionary rates of double-stranded DNA viruses

    Molecular Biology and Evolution 27:2038–2051.

    https://doi.org/10.1093/molbev/msq088

    • PubMed
    • Google Scholar
20. 1. Gauld JS
    2. Hu H
    3. Klein DJ
    4. Levine MM

    (2018) Typhoid fever in Santiago, Chile: Insights from a mathematical model utilizing venerable archived data from a successful disease control program

    PLOS Neglected Tropical Diseases 12:e0006759.

    https://doi.org/10.1371/journal.pntd.0006759

    • PubMed
    • Google Scholar
21. 1. Gunn JS
    2. Marshall JM
    3. Baker S
    4. Dongol S
    5. Charles RC
    6. Ryan ET

    (2014) Salmonella chronic carriage: epidemiology, diagnosis, and gallbladder persistence

    Trends in Microbiology 22:648–655.

    https://doi.org/10.1016/j.tim.2014.06.007

    • PubMed
    • Google Scholar
22. 1. Gupta SK
    2. Padmanabhan BR
    3. Diene SM

    (2014) ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes

    Antimicrobial Agents and Chemotherapy 58:212–220.

    https://doi.org/10.1128/AAC.01310-13

    • PubMed
    • Google Scholar
23. 1. Hawkey J
    2. Hamidian M
    3. Wick RR

    (2015) ISMapper: identifying transposase insertion sites in bacterial genomes from short read sequence data

    BMC Genomics 16:667.

    https://doi.org/10.1186/s12864-015-1860-2

    • PubMed
    • Google Scholar
24. 1. Hendriksen RS
    2. Leekitcharoenphon P
    3. Mikoleit M

    (2015) Genomic dissection of travel-associated extended-spectrum-beta-lactamase-producing Salmonella enterica serovar typhi isolates originating from the Philippines: a one-off occurrence or a threat to effective treatment of typhoid fever?

    Journal of Clinical Microbiology 53:677–680.

    https://doi.org/10.1128/JCM.03104-14

    • PubMed
    • Google Scholar
25. 1. Holt KE
    2. Parkhill J
    3. Mazzoni CJ

    (2008) High-throughput sequencing provides insights into genome variation and evolution in Salmonella Typhi

    Nature Genetics 40:987–993.

    https://doi.org/10.1038/ng.195

    • PubMed
    • Google Scholar
26. 1. Holt KE
    2. Thomson NR
    3. Wain J

    (2009) Pseudogene accumulation in the evolutionary histories of Salmonella enterica serovars Paratyphi A and Typhi

    BMC Genomics 10:36.

    https://doi.org/10.1186/1471-2164-10-36

    • PubMed
    • Google Scholar
27. 1. Holt KE
    2. Phan MD
    3. Baker S
    4. Duy PT
    5. Nga TVT
    6. Nair S
    7. Turner AK
    8. Walsh C
    9. Fanning S
    10. Farrell-Ward S
    11. Dutta S
    12. Kariuki S
    13. Weill F-X
    14. Parkhill J
    15. Dougan G
    16. Wain J

    (2011) Emergence of a globally dominant inchi1 plasmid type associated with multiple drug resistant typhoid

    PLOS Neglected Tropical Diseases 5:e1245.

    https://doi.org/10.1371/journal.pntd.0001245

    • PubMed
    • Google Scholar
28. Software

    1. Holt K

    (2021a) Reddog, version swh:1:rev:90707ace56d6189997526273cf97013d84b84d90

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:ca328c2159ec565754d77e7cfac2122aa34f41ac;origin=https://github.com/katholt/RedDog;visit=swh:1:snp:02487700a686fbf3a5f15ce7ea4c9bede43894d6;anchor=swh:1:rev:90707ace56d6189997526273cf97013d84b84d90
29. Software

    1. Holt K

    (2021b) Genotyphi, version swh:1:rev:f489d0e004e30bdca683c76c25ac7f4d79162791

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:f7c2ff7de21dc14cb011dda278424a1ef94fa7f3;origin=https://github.com/katholt/genotyphi;visit=swh:1:snp:724e03c18bc27bba59067574dbde6fbe83130e70;anchor=swh:1:rev:f489d0e004e30bdca683c76c25ac7f4d79162791
30. 1. Hopewell MR
    2. Graham JP

    (2014) Trends in access to water supply and sanitation in 31 major sub-Saharan African cities: an analysis of DHS data from 2000 to 2012

    BMC Public Health 14:208–212.

    https://doi.org/10.1186/1471-2458-14-208

    • PubMed
    • Google Scholar
31. 1. Ingle DJ
    2. Nair S
    3. Hartman H

    (2019) Informal genomic surveillance of regional distribution of Salmonella Typhi genotypes and antimicrobial resistance via returning travellers

    PLOS Neglected Tropical Diseases 13:e0007620.

    https://doi.org/10.1371/journal.pntd.0007620

    • PubMed
    • Google Scholar
32. 1. Inouye M
    2. Dashnow H
    3. Raven LA

    (2014) SRST2: Rapid genomic surveillance for public health and hospital microbiology labs

    Genome Medicine 6:90.

    https://doi.org/10.1186/s13073-014-0090-6

    • PubMed
    • Google Scholar
33. 1. Jaillard M
    2. Lima L
    3. Tournoud M

    (2018) A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events

    PLOS Genetics 14:e1007758.

    https://doi.org/10.1371/journal.pgen.1007758

    • PubMed
    • Google Scholar
34. 1. Jolley KA
    2. Maiden MCJ

    (2010) BIGSdb: Scalable analysis of bacterial genome variation at the population level

    BMC Bioinformatics 11:595.

    https://doi.org/10.1186/1471-2105-11-595

    • PubMed
    • Google Scholar
35. 1. Kanungo S
    2. Dutta S
    3. Sur D

    (2008) Epidemiology of typhoid and paratyphoid fever in India

    Journal of Infection in Developing Countries 2:454–460.

    https://doi.org/10.3855/jidc.161

    • PubMed
    • Google Scholar
36. 1. Kariuki S
    2. Revathi G
    3. Kiiru J

    (2010) Typhoid in Kenya is associated with a dominant multidrug-resistant Salmonella enterica serovar Typhi haplotype that is also widespread in Southeast Asia

    Journal of Clinical Microbiology 48:2171–2176.

    https://doi.org/10.1128/JCM.01983-09

    • PubMed
    • Google Scholar
37. 1. Kariuki S
    2. Gordon MA
    3. Feasey N
    4. Parry CM

    (2015) Antimicrobial resistance and management of invasive Salmonella disease

    Vaccine 33:C21–C29.

    https://doi.org/10.1016/j.vaccine.2015.03.102

    • PubMed
    • Google Scholar
38. 1. Kariuki S
    2. Mbae C
    3. Onsare R

    (2019) Multidrug-resistant Nontyphoidal Salmonella Hotspots as Targets for Vaccine Use in Management of Infections in Endemic Settings

    Clinical Infectious Diseases 68:S10–S15.

    https://doi.org/10.1093/cid/ciy898

    • PubMed
    • Google Scholar
39. 1. Kariuki S
    2. Mbae C
    3. Van Puyvelde S

    (2020) High relatedness of invasive multi-drug resistant non-typhoidal Salmonella genotypes among patients and asymptomatic carriers in endemic informal settlements in Kenya

    PLOS Neglected Tropical Diseases 14:e0008440.

    https://doi.org/10.1371/journal.pntd.0008440

    • PubMed
    • Google Scholar
40. 1. Klemm EJ
    2. Shakoor S
    3. Page AJ
    4. Qamar FN
    5. Judge K
    6. Saeed DK
    7. Wong VK
    8. Dallman TJ
    9. Nair S
    10. Baker S
    11. Shaheen G
    12. Qureshi S
    13. Yousafzai MT
    14. Saleem MK
    15. Hasan Z
    16. Dougan G
    17. Hasan R

    (2018) Emergence of an Extensively Drug-Resistant Salmonella enterica Serovar Typhi Clone Harboring a Promiscuous Plasmid Encoding Resistance to Fluoroquinolones and Third-Generation Cephalosporins

    mBio 9:e00105-18.

    https://doi.org/10.1128/mBio.00105-18

    • PubMed
    • Google Scholar
41. Book

    1. KNBS KNBOS

    (2009)

    Kenya Population and Housing Census

    Kenya National Bureau of Statistics.

    • Google Scholar
42. 1. Köser CU
    2. Ellington MJ
    3. Cartwright EJP

    (2012) Routine use of microbial whole genome sequencing in diagnostic and public health microbiology

    PLOS Pathogens 8:e1002824.

    https://doi.org/10.1371/journal.ppat.1002824

    • PubMed
    • Google Scholar
43. 1. Kyobutungi C
    2. Ziraba AK
    3. Ezeh A
    4. Yé Y

    (2008) The burden of disease profile of residents of Nairobi’s slums: results from a demographic surveillance system

    Population Health Metrics 6:1.

    https://doi.org/10.1186/1478-7954-6-1

    • PubMed
    • Google Scholar
44. 1. Langmead B
    2. Salzberg SL

    (2012) Fast gapped-read alignment with Bowtie 2

    Nat Meth 9:357–359.

    https://doi.org/10.1038/nmeth.1923

    • Google Scholar
45. 1. Levine MM
    2. Black RE
    3. Lanata C

    (1982) Precise estimation of the numbers of chronic carriers of Salmonella typhi in Santiago, Chile, an endemic area

    The Journal of Infectious Diseases 146:724–726.

    https://doi.org/10.1093/infdis/146.6.724

    • PubMed
    • Google Scholar
46. 1. Li H
    2. Handsaker B
    3. Wysoker A

    (2009) The Sequence Alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
47. 1. Lutterloh E
    2. Likaka A
    3. Sejvar J

    (2012) Multidrug-resistant typhoid fever with neurologic findings on the Malawi-Mozambique border

    Clinical Infectious Diseases 54:1100–1106.

    https://doi.org/10.1093/cid/cis012

    • PubMed
    • Google Scholar
48. 1. Marchello CS
    2. Hong CY
    3. Crump JA

    (2019) Global Typhoid Fever Incidence: A Systematic Review and Meta-analysis

    Clinical Infectious Diseases 68:S105–S116.

    https://doi.org/10.1093/cid/ciy1094

    • PubMed
    • Google Scholar
49. 1. Marchello CS
    2. Birkhold M
    3. Crump JA

    (2020) Complications and mortality of typhoid fever: A global systematic review and meta-analysis

    The Journal of Infection 81:902–910.

    https://doi.org/10.1016/j.jinf.2020.10.030

    • PubMed
    • Google Scholar
50. 1. Marks F
    2. von K
    3. Aaby P

    (2017) Incidence of invasive salmonella disease in sub-Saharan Africa: a multicentre population-based surveillance study

    The Lancet Global Health 5:310–323.

    https://doi.org/10.1016/S2214-109X(17)30022-0

    • PubMed
    • Google Scholar
51. 1. Mbae C
    2. Mwangi M
    3. Gitau N

    (2020) Factors associated with occurrence of salmonellosis among children living in Mukuru slum, an urban informal settlement in Kenya

    BMC Infectious Diseases 20:422.

    https://doi.org/10.1186/s12879-020-05134-z

    • PubMed
    • Google Scholar
52. 1. Mberu BU
    2. Haregu TN
    3. Kyobutungi C
    4. Ezeh AC

    (2016) Health and health-related indicators in slum, rural, and urban communities: a comparative analysis

    Global Health Action 9:33163.

    https://doi.org/10.3402/gha.v9.33163

    • PubMed
    • Google Scholar
53. 1. Mogasale V
    2. Maskery B
    3. Ochiai RL

    (2014) Burden of typhoid fever in low-income and middle-income countries: a systematic, literature-based update with risk-factor adjustment

    The Lancet Global Health 2:e570–e580.

    https://doi.org/10.1016/S2214-109X(14)70301-8

    • PubMed
    • Google Scholar
54. 1. Mutai WC
    2. Muigai AWT
    3. Waiyaki P
    4. Kariuki S

    (2018) Multi-drug resistant Salmonella enterica serovar typhi isolates with reduced susceptibility to ciprofloxacin in Kenya

    BMC Microbiology 18:187.

    https://doi.org/10.1186/s12866-018-1332-3

    • PubMed
    • Google Scholar
55. 1. Mutisya E
    2. Yarime M

    (2011)

    Understanding the grassroots dynamics of slums in Nairobi: The dilemma of Kibera informal settlements

    International Transaction Journal of Engineering, Management, and Applied Sciences and Technologies 2011:1–18.

    • Google Scholar
56. 1. Neil KP
    2. Sodha SV
    3. Lukwago L

    (2012) A large outbreak of typhoid fever associated with a high rate of intestinal perforation in Kasese District, Uganda, 2008-2009

    Clinical Infectious Diseases 54:1091–1099.

    https://doi.org/10.1093/cid/cis025

    • PubMed
    • Google Scholar
57. 1. Olack B
    2. Burke H
    3. Cosmas L

    (2011) Nutritional status of under-five children living in an informal urban settlement in Nairobi, Kenya

    Journal of Health, Population, and Nutrition 29:357–363.

    https://doi.org/10.3329/jhpn.v29i4.8451

    • PubMed
    • Google Scholar
58. 1. Paradis E
    2. Claude J
    3. Strimmer K

    (2004) APE: Analyses of Phylogenetics and Evolution in R language

    Bioinformatics 20:289–290.

    https://doi.org/10.1093/bioinformatics/btg412

    • PubMed
    • Google Scholar
59. 1. Park SE
    2. Pham DT
    3. Boinett C

    (2018) The phylogeography and incidence of multi-drug resistant typhoid fever in sub-Saharan Africa

    Nature Communications 9:5094.

    https://doi.org/10.1038/s41467-018-07370-z

    • PubMed
    • Google Scholar
60. 1. Parkhill J
    2. Dougan G
    3. James KD

    (2001) Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18

    Nature 413:848–852.

    https://doi.org/10.1038/35101607

    • PubMed
    • Google Scholar
61. 1. Parry CM
    2. Hien TT
    3. Dougan G
    4. White NJ
    5. Farrar JJ

    (2002) Typhoid fever

    The New England Journal of Medicine 347:1770–1782.

    https://doi.org/10.1056/NEJMra020201

    • PubMed
    • Google Scholar
62. Software

    1. Patel JB
    2. Cockerill FR
    3. Bradford PA

    (2015) Performance Standards for Antimicrobial Susceptibility Testing

    Clinical and Laboratory Standards Institute.

    https://clsi.org/standards/products/microbiology/documents/m100/
63. 1. Pham Thanh D
    2. Karkey A
    3. Dongol S

    (2016) A novel ciprofloxacin-resistant subclade of H58 Salmonella Typhi is associated with fluoroquinolone treatment failure

    eLife 5:e14003.

    https://doi.org/10.7554/eLife.14003

    • PubMed
    • Google Scholar
64. 1. Phan MD
    2. Kidgell C
    3. Nair S

    (2009) Variation in Salmonella enterica serovar typhi IncHI1 plasmids during the global spread of resistant typhoid fever

    Antimicrobial Agents and Chemotherapy 53:716–727.

    https://doi.org/10.1128/AAC.00645-08

    • PubMed
    • Google Scholar
65. 1. Pitzer VE
    2. Feasey NA
    3. Msefula C

    (2015) Mathematical modeling to assess the drivers of the recent emergence of Typhoid fever in blantyre, malawi

    Clinical Infectious Diseases 61:251–258.

    https://doi.org/10.1093/cid/civ710

    • PubMed
    • Google Scholar
66. 1. Rahman SIA
    2. Dyson ZA
    3. Klemm EJ

    (2020) Population structure and antimicrobial resistance patterns of Salmonella Typhi isolates in urban Dhaka, Bangladesh from 2004 to 2016

    PLOS Neglected Tropical Diseases 14:e0008036.

    https://doi.org/10.1371/journal.pntd.0008036

    • PubMed
    • Google Scholar
67. 1. Rambaut A
    2. Lam TT
    3. Max Carvalho L
    4. Pybus OG

    (2016) Exploring the temporal structure of heterochronous sequences using TEMPEST (formerly Path-o-gen)

    Virus Evolution 2:vew007.

    https://doi.org/10.1093/ve/vew007

    • PubMed
    • Google Scholar
68. 1. Saad NJ
    2. Bowles CC
    3. Grenfell BT

    (2017) The impact of migration and antimicrobial resistance on the transmission dynamics of typhoid fever in Kathmandu, Nepal: A mathematical modelling study

    PLOS Neglected Tropical Diseases e0005547.

    https://doi.org/10.1371/journal.pntd.0005547

    • PubMed
    • Google Scholar
69. 1. Senthilkumar B
    2. Senbagam D
    3. Rajasekarapandian M

    (2012)

    An epidemiological surveillance of asymptomatic typhoid carriers associated in respect to socioeconomic status in India

    J Public Health 22:297–301.

    • Google Scholar
70. 1. Stamatakis A

    (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

    Bioinformatics 30:1312–1313.

    https://doi.org/10.1093/bioinformatics/btu033

    • PubMed
    • Google Scholar
71. 1. Suchard MA
    2. Lemey P
    3. Baele G
    4. Ayres DL
    5. Drummond AJ
    6. Rambaut A

    (2018) Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10

    Virus Evolution 4:vey016.

    https://doi.org/10.1093/ve/vey016

    • PubMed
    • Google Scholar
72. 1. Thanh Duy P
    2. Thieu NTV
    3. Nguyen Thi Nguyen T
    4. Ngoc Dan Thanh H
    5. Dongol S
    6. Karkey A
    7. Carey M
    8. Basnyat B
    9. Dougan G
    10. Rabaa MA
    11. Baker S

    (2020) Gallbladder carriage generates genetic variation and genome degradation in salmonella typhi

    PLOS Pathogens 16:e1008998.

    https://doi.org/10.1371/journal.ppat.1008998

    • PubMed
    • Google Scholar
73. 1. Thindwa D
    2. Chipeta MG
    3. Henrion MYR
    4. Gordon MA

    (2019) Distinct climate influences on the risk of typhoid compared to invasive non-typhoid Salmonella disease in Blantyre

    Malawi. Sci Rep 9:20310–20311.

    https://doi.org/10.1038/s41598-019-56688-1

    • PubMed
    • Google Scholar
74. 1. Tinline RR
    2. Gregory D

    (1988)

    The Universal Transverse Mercator Code: A location code for disease reporting

    The Canadian Veterinary Journal = La Revue Veterinaire Canadienne 29:825–829.

    • PubMed
    • Google Scholar
75. 1. Wick RR
    2. Schultz MB
    3. Zobel J
    4. Holt KE

    (2015) Bandage: interactive visualization of de novo genome assemblies

    Bioinformatics 31:3350–3352.

    https://doi.org/10.1093/bioinformatics/btv383

    • PubMed
    • Google Scholar
76. 1. Wick RR
    2. Judd LM
    3. Gorrie CL
    4. Holt KE

    (2017) Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads

    PLOS Computational Biology 13:e1005595.

    https://doi.org/10.1371/journal.pcbi.1005595

    • PubMed
    • Google Scholar
77. 1. Wong VK
    2. Baker S
    3. Pickard DJ

    (2015) Phylogeographical analysis of the dominant multidrug-resistant H58 clade of Salmonella Typhi identifies inter- and intracontinental transmission events

    Nature Genetics 47:632–639.

    https://doi.org/10.1038/ng.3281

    • PubMed
    • Google Scholar
78. 1. Wong VK
    2. Baker S
    3. Connor TR

    (2016) An extended genotyping framework for Salmonella enterica serovar Typhi, the cause of human typhoid

    Nature Communications 7:12827.

    https://doi.org/10.1038/ncomms12827

    • PubMed
    • Google Scholar
79. 1. Wood DE
    2. Salzberg SL

    (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments

    Genome Biology 15:46.

    https://doi.org/10.1186/gb-2014-15-3-r46

    • PubMed
    • Google Scholar
80. 1. Yu G
    2. Smith DK
    3. Zhu H
    4. Guan Y
    5. TT-Y L

    (2016)

    Ggtree: An rpackage for visualization and annotation of phylogenetic trees with their covariates and other associated data

    Methods in Ecology and Evolution 8:28–36.

    • Google Scholar

Author details

1. Samuel Kariuki

   1. Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya
   2. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision, Validation, Writing – original draft, Writing – review and editing

   Contributed equally with

   Zoe A Dyson

   For correspondence

   samkariuki2@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3209-9503

2. Zoe A Dyson

   1. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, United Kingdom
   2. Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Department of Medicine, University of Cambridge, Cambridge, United Kingdom
   3. London School of Hygiene & Tropical Medicine, London, United Kingdom
   4. Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia

   Contribution

   Writing – original draft, Writing – review and editing

   Contributed equally with

   Samuel Kariuki

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8887-3492

3. Cecilia Mbae

   Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

   Contribution

   Investigation, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

4. Ronald Ngetich

   Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

5. Susan M Kavai

   Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. Celestine Wairimu

   Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

7. Stephen Anyona

   Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

8. Naomi Gitau

   Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

9. Robert Sanaya Onsare

   Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

   Contribution

   Formal analysis, Project administration

   Competing interests

   No competing interests declared

10. Beatrice Ongandi

    Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

11. Sebastian Duchene

    Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

12. Mohamed Ali

    Department of International Health, John’s Hopkins University, Baltimore, United States

    Contribution

    Data curation, Formal analysis

    Competing interests

    No competing interests declared

13. John David Clemens

    International Diarrheal Diseases Research Centre, Dhaka, Bangladesh

    Contribution

    Conceptualization, Formal analysis, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

14. Kathryn E Holt

    1. London School of Hygiene & Tropical Medicine, London, United Kingdom
    2. Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia

    Contribution

    Validation, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

15. Gordon Dougan

    Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Contribution

    Conceptualization, Methodology, Validation, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

• 1,490

  views

• 195

  downloads

• 39

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.