Conference Material > Slide Presentation
Wardley T, West KP, Tesfay B, Robinson N, Parry L, et al.
MSF Scientific Day International 2024. 16 May 2024; DOI:10.57740/EQ5OG2MuMi
Conference Material > Abstract
Wardley T, West KP, Tesfay B, Robinson N, Parry L, et al.
MSF Scientific Day International 2024. 16 May 2024; DOI:10.57740/a4TlzIISm
INTRODUCTION
Climate and environmental conditions are critical factors in malaria transmission. Médecins Sans Frontières (MSF) teams in South Sudan have seen changes in the timing and intensity of malaria seasonal peaks over the past decade. The Malaria Anticipation Project (MAP) aims to develop predictive early warning systems to better predict and act upon any expected rise in malaria cases, through routine surveillance.
METHODS
Predictive models were developed using environmental data collected from climate and space agencies and weekly outpatient department (OPD) malaria case count in Lankien hospital (Jonglei State, South Sudan) as the epidemiological input and output. An ensemble modelling approach was developed using linear regression and extreme gradient boosting (XGBoost) models in a recursive modelling framework. The models were developed using data from 2012–2020, verified with data from 2020–2022, and then monitored in real time in the 2022/23 season. To assess model performance, observed OPD malaria cases were compared with the monthly average cases and classified into categories to assess how often the model prediction was in the same category as the observed number of cases. We
also conducted a qualitative survey to explore community understanding of malaria and its relationship to climate.
RESULTS
During model development, the predictive performance was very high at 2 weeks’ lead time (75% classification accuracy). Model performance remained satisfactory at up to 8 weeks’ lead time (70% classification accuracy), while beyond this, it became increasingly susceptible to large prediction errors. In the 2020/21 and 2021/22 malaria seasons, the predictive performance at 2 weeks’ lead time was good, but it overpredicted for both seasons at 4–8 weeks. The 2022–23 season saw the lowest number of malaria cases of any year in the data used to train the model. The models predicted that the number of cases would be below the long-term average for Lankien hospital, but overpredicted the burden. Across all models, the shorter the lead time of the models, the greater their predictive performance.
CONCLUSION
This modelling approach has the potential to inform anticipatory action within an operationally useful timeframe. Given the models are trained on historical data and cannot include all factors affecting malaria transmission, if relationships between malaria and other conditions change over time, this will impact model performance, demonstrating the limits of forecasting approaches. The next stage of the MAP project will focus on replicability in other settings and pilot implementation to understand operational feasibility and improve performance.
Climate and environmental conditions are critical factors in malaria transmission. Médecins Sans Frontières (MSF) teams in South Sudan have seen changes in the timing and intensity of malaria seasonal peaks over the past decade. The Malaria Anticipation Project (MAP) aims to develop predictive early warning systems to better predict and act upon any expected rise in malaria cases, through routine surveillance.
METHODS
Predictive models were developed using environmental data collected from climate and space agencies and weekly outpatient department (OPD) malaria case count in Lankien hospital (Jonglei State, South Sudan) as the epidemiological input and output. An ensemble modelling approach was developed using linear regression and extreme gradient boosting (XGBoost) models in a recursive modelling framework. The models were developed using data from 2012–2020, verified with data from 2020–2022, and then monitored in real time in the 2022/23 season. To assess model performance, observed OPD malaria cases were compared with the monthly average cases and classified into categories to assess how often the model prediction was in the same category as the observed number of cases. We
also conducted a qualitative survey to explore community understanding of malaria and its relationship to climate.
RESULTS
During model development, the predictive performance was very high at 2 weeks’ lead time (75% classification accuracy). Model performance remained satisfactory at up to 8 weeks’ lead time (70% classification accuracy), while beyond this, it became increasingly susceptible to large prediction errors. In the 2020/21 and 2021/22 malaria seasons, the predictive performance at 2 weeks’ lead time was good, but it overpredicted for both seasons at 4–8 weeks. The 2022–23 season saw the lowest number of malaria cases of any year in the data used to train the model. The models predicted that the number of cases would be below the long-term average for Lankien hospital, but overpredicted the burden. Across all models, the shorter the lead time of the models, the greater their predictive performance.
CONCLUSION
This modelling approach has the potential to inform anticipatory action within an operationally useful timeframe. Given the models are trained on historical data and cannot include all factors affecting malaria transmission, if relationships between malaria and other conditions change over time, this will impact model performance, demonstrating the limits of forecasting approaches. The next stage of the MAP project will focus on replicability in other settings and pilot implementation to understand operational feasibility and improve performance.
Journal Article > ReviewFull Text
PLOS Glob Public Health. 16 April 2024; Volume 4 (Issue 4); e0003077.; DOI:10.1371/journal.pgph.0003077
Monk EJM, Jones TPW, Bongomin F, Kibone W, Nsubuga Y, et al.
PLOS Glob Public Health. 16 April 2024; Volume 4 (Issue 4); e0003077.; DOI:10.1371/journal.pgph.0003077
Antimicrobial resistance (AMR) is a major global threat and AMR-attributable mortality is particularly high in Central, Eastern, Southern and Western Africa. The burden of clinically infected wounds, skin and soft tissue infections (SSTI) and surgical site infections (SSI) in these regions is substantial. This systematic review reports the extent of AMR from sampling of these infections in Africa, to guide treatment. It also highlights gaps in microbiological diagnostic capacity. PubMed, MEDLINE and Embase were searched for studies reporting the prevalence of Staphylococcus aureus, Eschericheria coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii in clinically infected wounds, SSTI and SSI in Central, Eastern, Southern or Western Africa, and studies reporting AMR from such clinical isolates. Estimates for proportions were pooled in meta-analyses, to estimate the isolation prevalence of each bacterial species and the proportion of resistance observed to each antibiotic class. The search (15th August 2022) identified 601 articles: 59 studies met our inclusion criteria. S. aureus was isolated in 29% (95% confidence interval [CI] 25% to 34%) of samples, E. coli in 14% (CI 11% to 18%), K. pneumoniae in 11% (CI 8% to 13%), P. aeruginosa in 14% (CI 11% to 18%) and A. baumannii in 8% (CI 5% to 12%). AMR was high across all five species. S. aureus was resistant to methicillin (MRSA) in >40% of isolates. E. coli and K. pneumoniae were both resistant to amoxicillin-clavulanic acid in ≥80% of isolates and resistant to aminoglycosides in 51% and 38% of isolates respectively. P. aeruginosa and A. baumannii were both resistant to anti-pseudomonal carbapenems (imipenem or meropenem) in ≥20% of isolates. This systematic review found that a large proportion of the organisms isolated from infected wounds, SSTI and SSI in Africa displayed resistance patterns of World Health Organisation (WHO) priority pathogens for critical or urgent antimicrobial development.
MSF Ethics Review Board > Templates & procedures
Gerstl S, Grandesso F, Siddiqui R, Greig J, du Cros PAK, et al.
25 October 2022
MSF Ethics Review Board > Templates & procedures
Grandesso F, Gerstl S, Siddiqui R, Greig J, du Cros PAK, et al.
25 October 2022
Mortality Survey Protocol - standardised, MSF ERB approved, intersectional
This collection of files includes an overview of the whole process of conducting a mortality survey and templates for concept papers, the protocol, questionnaires and consent and other related forms. Surveys that use this standardised intersectional protocol do not require MSF Ethics Review Board (ERB) review if the Medical Director of the relevant section takes responsibility for addressing the ethics issues. The exemption criteria of the MSF ERB for standardised intersectional survey protocols must be followed.
This collection of files includes an overview of the whole process of conducting a mortality survey and templates for concept papers, the protocol, questionnaires and consent and other related forms. Surveys that use this standardised intersectional protocol do not require MSF Ethics Review Board (ERB) review if the Medical Director of the relevant section takes responsibility for addressing the ethics issues. The exemption criteria of the MSF ERB for standardised intersectional survey protocols must be followed.
Journal Article > ResearchFull Text
Lancet Infect Dis. 5 May 2015; Volume 15 (Issue 7); DOI:10.1016/S1473-3099(15)00006-7
Rao VB, Johari N, du Cros PAK, Messina J, Ford NP, et al.
Lancet Infect Dis. 5 May 2015; Volume 15 (Issue 7); DOI:10.1016/S1473-3099(15)00006-7
An estimated 150 million people worldwide are infected with hepatitis C virus (HCV). HIV co-infection accelerates the progression of HCV and represents a major public health challenge. We aimed to determine the epidemiology of HCV and the prevalence of HIV co-infection in sub-Saharan Africa.
Journal Article > ResearchFull Text
PLOS One. 25 July 2019 (Issue 7)
Roddy P, Dalrymple U, Jensen TO, Dittrich S, Rao VB, et al.
PLOS One. 25 July 2019 (Issue 7)
Severe-febrile-illness (SFI) is a common cause of morbidity and mortality across sub-Saharan Africa (SSA). The burden of SFI in SSA is currently unknown and its estimation is fraught with challenges. This is due to a lack of diagnostic capacity for SFI in SSA, and thus a dearth of baseline data on the underlying etiology of SFI cases and scant SFI-specific causative-agent prevalence data. To highlight the public health significance of SFI in SSA, we developed a Bayesian model to quantify the incidence of SFI hospital admissions in SSA. Our estimates indicate a mean population-weighted SFI-inpatient-admission incidence rate of 18.4 (6.8-31.1, 68% CrI) per 1000 people for the year 2014, across all ages within areas of SSA with stable Plasmodium falciparum transmission. We further estimated a total of 16,200,337 (5,993,249-27,321,779, 68% CrI) SFI hospital admissions. This analysis reveals the significant burden of SFI in hospitals in SSA, but also highlights the paucity of pathogen-specific prevalence and incidence data for SFI in SSA. Future improvements in pathogen-specific diagnostics for causative agents of SFI will increase the abundance of SFI-specific prevalence and incidence data, aid future estimations of SFI burden, and enable clinicians to identify SFI-specific pathogens, administer appropriate treatment and management, and facilitate appropriate antibiotic use.
Journal Article > ResearchFull Text
medRxiv. 17 August 2019; DOI:10.1101/19003434
Funk S, Takahashi S, Hellewell J, Gadroen K, Carrion Martin AI, et al.
medRxiv. 17 August 2019; DOI:10.1101/19003434
The Katanga region in the Democratic Republic of Congo (DRC) has been struck by repeated epidemics of measles, with large outbreaks occurring in 2010–13 and 2015. In many of the affected health zones, reactive mass vaccination campaigns were conducted in response to the outbreaks. Here, we attempted to determine how effective the vaccination campaigns in 2015 were in curtailing the ongoing outbreak. We further sought to establish whether the risk of large measles outbreaks in different health zones could have been determined in advance to help prioritise areas for vaccination campaign and speed up the response. In doing so, we first attempted to identify factors that could have been used in 2015 to predict in which health zones the greatest outbreaks would occur. Administrative vaccination coverage was not a good predictor of the size of outbreaks in different health zones. Vaccination coverage derived from surveys, on the other hand, appeared to give more reliable estimates of health zones of low vaccination coverage and, consequently, large outbreaks. On a coarser geographical scale, the provinces most affected in 2015 could be predicted from the outbreak sizes in 2010–13. This, combined with the fact that the vast majority of reported cases were in under-5 year olds, would suggest that there are systematic issues of undervaccination. If this was to continue, outbreaks would be expected to continue to occur in the affected health zones at regular intervals, mostly concentrated in under-5 year olds. We further used a model of measles transmission to estimate the impact of the vaccination campaigns, by first fitting a model to the data including the campaigns and then re-running this without vaccination. We estimated the reactive campaigns to have reduced the size of the overall outbreak by approximately 21,000 (IQR: 16,000–27,000; 95% CI: 8300–38,000) cases. There was considerable heterogeneity in the impact of campaigns, with campaigns started earlier after the start of an outbreak being more impactful. Taken together, these findings suggest that while a strong routine vaccination regime remains the most effective means of measles control, it might be possible to improve the effectiveness of reactive campaigns by considering predictive factors to trigger a more targeted vaccination response.
Protocol > Research Protocol
Doyle K, Isidro Carrion Martin A, Piening T, Ramirez A, Fesselet JF, et al.
1 July 2018
These materials can be used, adapted and copied as long as citation of the source is given including the direct URL to the material. This work is licensed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/ https://i.creativecommons.org/l/by/4.0/88x31.png
Journal Article > CommentaryFull Text
Vaccine. 23 October 2019; Volume 37; DOI:10.1016/j.vaccine.2019.09.038
van Zandvoort K, Checchi F, Diggle E, Eggo RM, Gadroen K, et al.
Vaccine. 23 October 2019; Volume 37; DOI:10.1016/j.vaccine.2019.09.038
Streptococcus pneumoniae is a common human commensal that causes a sizeable part of the overall childhood mortality in low income settings. Populations affected by humanitarian crises are at especially high risk, because a multitude of risk factors that are enhanced during crises increase pneumococcal transmission and disease severity. Pneumococcal conjugate vaccines (PCVs) provide effective protection and have been introduced into the majority of routine childhood immunisation programmes globally, though several barriers have hitherto limited their uptake during humanitarian crises. When PCV coverage cannot be sustained during crises or when PCV has not been part of routine programmes, mass vaccination campaigns offer a quick acting and programmatically feasible bridging solution until services can be restored. However, we currently face a paucity of evidence on which to base the structure of such campaigns. We believe that, now that PCV can be procured at a substantially reduced price through the Humanitarian Mechanism, this lack of information is a remaining hurdle to PCV use in humanitarian crises. Considering the difficulties in conducting research in crises, we propose an evidence generation pathway consisting of primary data collection in combination with mathematical modelling followed by quasi-experimental evaluation of a PCV intervention, which can inform on optimal vaccination strategies that consider age targeting, dosing regimens and impact duration.