Conference Material > Slide Presentation
Wardley T, West KP, Tesfay B, Robinson N, Parry L, et al.
MSF Scientific Days International 2024. 2024 May 16; DOI:10.57740/EQ5OG2MuMi
Journal Article > ResearchFull Text
Confl Health. 2024 January 30; Volume 18 (Issue 1); 13.; DOI:10.1186/s13031-024-00571-y
Baertlein L, Dubad BA, Sahelie B, Damulak IC, Osman M, et al.
Confl Health. 2024 January 30; Volume 18 (Issue 1); 13.; DOI:10.1186/s13031-024-00571-y
BACKGROUND
This study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019–2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components.
METHODS
We carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019–January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others.
RESULTS
1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community’s response expectations not being met.
CONCLUSIONS
Event-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.
This study evaluated an early warning, alert and response system for a crisis-affected population in Doolo zone, Somali Region, Ethiopia, in 2019–2021, with a history of epidemics of outbreak-prone diseases. To adequately cover an area populated by a semi-nomadic pastoralist, or livestock herding, population with sparse access to healthcare facilities, the surveillance system included four components: health facility indicator-based surveillance, community indicator- and event-based surveillance, and alerts from other actors in the area. This evaluation described the usefulness, acceptability, completeness, timeliness, positive predictive value, and representativeness of these components.
METHODS
We carried out a mixed-methods study retrospectively analysing data from the surveillance system February 2019–January 2021 along with key informant interviews with system implementers, and focus group discussions with local communities. Transcripts were analyzed using a mixed deductive and inductive approach. Surveillance quality indicators assessed included completeness, timeliness, and positive predictive value, among others.
RESULTS
1010 signals were analysed; these resulted in 168 verified events, 58 alerts, and 29 responses. Most of the alerts (46/58) and responses (22/29) were initiated through the community event-based branch of the surveillance system. In comparison, one alert and one response was initiated via the community indicator-based branch. Positive predictive value of signals received was about 6%. About 80% of signals were verified within 24 h of reports, and 40% were risk assessed within 48 h. System responses included new mobile clinic sites, measles vaccination catch-ups, and water and sanitation-related interventions. Focus group discussions emphasized that responses generated were an expected return by participant communities for their role in data collection and reporting. Participant communities found the system acceptable when it led to the responses they expected. Some event types, such as those around animal health, led to the community’s response expectations not being met.
CONCLUSIONS
Event-based surveillance can produce useful data for localized public health action for pastoralist populations. Improvements could include greater community involvement in the system design and potentially incorporating One Health approaches.
Conference Material > Abstract
Wardley T, West KP, Tesfay B, Robinson N, Parry L, et al.
MSF Scientific Days International 2024. 2024 May 16; 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.
Conference Material > Slide Presentation
Tremblay LL, Wardley T, Tesfay B, Galban-Horcajo F, West KP, et al.
MSF Scientific Days International 2023. 2023 June 7; DOI:10.57740/9wsa-v278
Journal Article > ResearchFull Text
Trans R Soc Trop Med Hyg. 2016 July 1; Volume 110 (Issue 7); DOI:10.1093/trstmh/trw046
Uadiale K, Bestman A, Kamau C, Caugant DA, Greig J
Trans R Soc Trop Med Hyg. 2016 July 1; Volume 110 (Issue 7); DOI:10.1093/trstmh/trw046
Neisseria meningitidis serogroup C (NmC) has caused outbreaks in Nigeria of increasing size in three consecutive years since 2013. Rapid diagnostic tests (RDTs) for meningitis can facilitate quick identification of the causative pathogen; Pastorex can detect N. meningitidis serogroups A, C (NmC), Y/W135, N. meningitidis serogroup B/Escherichia coli K1, Haemophilus influenzae type b (Hib), Streptococcus pneumoniae, and group B Streptococcus. There is no published field evaluation of Pastorex in the identification of NmC. We report our experience with Pastorex in detecting NmC in field conditions.