The Bacterial Resistance Analysis Group (BRAG)


Ben Cooper, MRC Senior Research Fellow & Associate Professor

Maliwan Hongsuwan, PhD Student

Cherry Lim, DPhil Student and Wellcome Trust Training Fellow (Microbiology)

Rene Niehus, Research Fellow

Mathupanee Oonsivilai, Research Assistant

Sreymom Pol, Research Assistant (COMRU)

Tamalee Roberts, Research Fellow (LOMRU)

Jiraboon Tosanguan, Research Assistant

Key Publications

van Kleef E, Luangasanatip N, Bonten MJ, Cooper BS (2016) Why sensitive bacteria are resistant to hospital infection control. Wellcome Open Res 2017, 2:16.

Turner P, Pol S, Soeng S, Sar P, Neou L, Chea P, Day N, Cooper B, Turner C (2016). High prevalence of disease-associated antimicrobial resistant Gram negative colonisation in hospitalized Cambodian young infants. Paed Infect Dis Journal 35(8):856-61

Worby CJ, O’Neill PD, Kyrpaios T, Robotham JV, De Angelis D, Cartwright JP, Peacock SJ, Cooper BS (2016). Reconstructing transmission trees for communicable diseases using densely sampled genomic data. Annals of Applied Statistics 10(1):395-417.

Luangasanatip N, Hongsuwan M, Limmathurotsakul D, Lubell Y, Lee AS, Harbarth S, Day NP, Graves N, Cooper BS (2015). Comparative efficacy of interventions to promote hand hygiene in hospital: systematic review and network meta-analysis. BMJ 351:h3728.

Tong SYC, Holden MTG, Nickerson EK, Cooper BS, Köser CU, Cori A, Jombart T, Cauchemez S, Fraser C, Wuthiekanun V, Thaipadungpanit J, Hongsuwan M, Day NP, Limmathurotsakul D, Parkhill J, Peacock SJ (2015). Genome sequencing defines phylogeny and spread of methicillin-resistant Staphylococcus aureus in a high transmission setting. Genome Research 25:111-118.

Hongsuwan M, Srisamang P, Kanoksil M, Luangasanatip N, Jatapai A, Day NP, Peacock SJ, Cooper B, Limmathurotsakul D (2014). Increasing incidence of hospital-acquired and healthcare-associated bacteremia in northeast Thailand: a multicenter surveillance study. PLoS ONE 9(10):e109324.

Cherry Lim1†, Emi Takahashi1†, Maliwan Hongsuwan1, Vanaporn Wuthiekanun1, Visanu Thamlikitkul2, Soawapak Hinjoy3, Nicholas PJ Day1,4, Sharon J Peacock1,5,6, Direk Limmathurotsakul1,4,7* Epidemiology and burden of multidrug- resistant bacterial infection in a developing country eLife 2016;5:e18082 doi: 10.7554/eLife.18082.



About: The Bacterial Resistance Analysis Group (BRAG)

The Bacterial Resistance Analysis Group (BRAG) is a cross-cutting research group with headquarters in MAEMOD but with members based throughout the MORU network, including Cambodia (COMRU) and Laos PDR (LOMRU).

The group’s aims are to

  • develop and evaluate innovate solutions to the problem of drug-resistant bacterial infections, with a focus on resource limited settings in SE Asia (but with considerable relevance beyond this region);
  • better quantify the burden of disease and socio-economic impact of drug-resistant infections in SE Asia and globally;
  • improve our basic understanding of the epidemiology and population biology of drug-resistant infections.

To achieve these aims members of the group use a diverse array of methodologies including novel statistical approaches, mechanistic mathematical models, qualitative research methods, analyses of whole genome sequencing and meta-genomic data, health economics, meta-analyses and prospective intervention studies.

The group is also actively involved in developing and applying new analytical approaches in many of these areas. 

Animation 1 (Above): Highly localized SIRS infection spread simulations

This animation shows the spread of infection (red) and resistance to infection (black) in a 100x100 grid based spatial susceptible (green) population. We can see a blooming pattern as each block passes infection outwards from their originating point because spread of infection is only limited to the immediate blocks surrounding an infectious block (extremely localized contacts). As the infection ring grows out, some infectious blocks make it through the black resistant band back into the center due to low resistance duration, causing reseeding inside creating smaller rings. Simulations like these can be used to compare the effects of localized contacts and spatial effects of infection in contrast to homogeneous mixing models.

Figure 1. Studying the impact of antibiotic treatment on resistance genes in individual patients

BRAG fig 1

This figure shows the abundance of the CTX-m resistance gene during the hospital stay of a single patient (black line). This gene abundance was measured at European hospitals from stool samples using quantitative PCR and normalised by the conserved 16s bacterial gene abundance. In addition, we know the duration of treatment with different antibiotics (coloured bars). We use this information available from 120 patients to draw conclusions about how antibiotic treatment affects the abundance of resistance genes in the gastrointestinal tract of individual patients. 

Figure 2. Time from admission to the emergence of multi-drug resistant bloodstream infections (MDR BSI) in a 1,200-bed general hospital


The figures show an increasing trend of MDR BSI cases with time from admission to infections for four important species (Acinetobacter spp., K. pneumoniae, S. aureus and E. coli) in Sappasitthiprasong Hospital in Ubon Ratchathani, Thailand. The size of red circle for each data point represents the number of BSI on that day. However, the characteristics of these trends were different for each species. Through the development of a mechanistic transmission model and model fitting with Maximum Likelihood Estimation (black line), key model parameters, force of infection (beta), and rate of selection of pre-existing resistant strains as a result of antimicrobial therapy (sigma) were quantified. It was observed that the Gram-negative bacteria (Acinetobacter spp., K. pneumoniae and E. coli) generally had higher values of sigma and lower values of beta compared to the Gram-positive S. aureus. This seems to indicate the importance of the selection process in the emergence of MDR BSI caused by Gram-negative bacteria.