Assessing the impact of aggregating disease stage data in model predictions of human African trypanosomiasis transmission and control activities in Bandundu province (DRC)

Castaño, María Soledad; Ndeffo-Mbah, Martial L.; Rock, Kat S.; Palmer, Cody; Knock, Edward; Miaka, Erick Mwamba; Ndung’u, Joseph Mathu; Torr, Stephen J.; Verlé, Paul; Spencer, Simon E. F.; Galvani, Alison P.; Bever, Caitlin A.; Keeling, Matt J.; Chitnis, Nakul

doi:10.1371/journal.pntd.0007976

Cited by 24 publications

(42 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As explored in a previous modelling study focusing on former Bandundu province [ 16 ], there is strong evidence of improvements to the passive surveillance system during 2000–2012 from examining the changes to the proportion of passive cases identified in stage 1 compared to stage 2. The health-zone level data utilised here for the main model fitting do not contain staging information before 2015 (and generally there are relatively few cases in 2015 and 2016).…”

Section: Methodsmentioning

confidence: 99%

Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo

et al. 2021

Self Cite

View full text Add to dashboard Cite

Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones (∼100,000 population size), which allows for calibration of a mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.14, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly—on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu—Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country.

show abstract

Section: Methodsmentioning

confidence: 99%

Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is noted that only the stage 1 detection rate was changed in Model W in [14] as this provided a good fit to the data at the province level. Here we alter both stage 1…”

Section: Improvements To Passive Case Detectionmentioning

confidence: 99%

“…The gHAT model equations are given below (1) and correspond with Fig 3. The model is a variant "Model 4" of that presented elsewhere [6,7,[11][12][13][14].…”

Section: Model Ghat Infection Modelmentioning

confidence: 99%

Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo

Crump

Huang

Knock

et al. 2020

Preprint

View full text Add to dashboard Cite

Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones (˜100,000 population size), which allows for calibration of mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.19, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly - on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu - Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country.

show abstract

“…An analysis including animal reservoirs could yield different results for YEOT predictions presented here, although our previous work suggests that we would probably not expect large qualitative differences. Another concernis asymptomatic humans maintaining transmission23,24 ; although a few modelling studies have utilised frameworks explicitly incorporating asymptomatic human infections21,22,24 , there is limited observational data to parameterise them with high certainty, and it is unclear how their inclusion in this study would impact projections.A new oral drug to treat gHAT, fexinidazole has now been approved for use in DRC, and is being utilised in the country. Despite the obvious advantages for patients, ease of transport and administration, it is not deemed suitable for use in individuals without parasitological case confirmation25 , and hence is unlikely to greatly impact on transmission as part of a strategy.…”

mentioning

confidence: 99%

Identifying regions for enhanced control of gambiense sleeping sickness in the Democratic Republic of Congo

Huang

Crump

Brown

et al. 2020

Preprint

View full text Add to dashboard Cite

Gambiense human African trypanosomiasis (gHAT) is a disease targeted for elimination of transmission (EOT) by 2030, however the likelihood of achieving it is unknown. We utilised modelling to study the impact of currently-available intervention methods on transmission across the Democratic Republic of Congo (DRC) - which accounts for ∼ 70% of global burden - and highlight regions requiring intensified interventions. A model previously fitted to case data in DRC was used to predict cases and new infections under four future strategies in 168 health zones. The strategies comprise of medical interventions - active and passive screening (AS and PS) - and some include large-scale vector control (VC). In each health zone, we estimate the median year of EOT and the probability of EOT by 2030 under each and compute the least ambitious strategy predicted to achieve EOT by 2030. The model predicts 42 health zones are very likely to achieve EOT (> 90% probability) using medical-only strategies continued at mean coverage levels; this increases to 52 when AS coverage is increased to maximum previous coverage. In all VC strategies, health zones are predicted to meet EOT by 2030, although there are several where increasing low AS coverage could achieve this. This analysis provides a priority list for consideration for supplementary VC implementation (Bagata, Bandundu, Bolobo, Kikongo, Kwamouth and Masi Manimba in former Bandundu province) in conjunction with the recent AS coverage.

show abstract

Assessing the impact of aggregating disease stage data in model predictions of human African trypanosomiasis transmission and control activities in Bandundu province (DRC)

Cited by 24 publications

References 25 publications

Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo

Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo

Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo

Identifying regions for enhanced control of gambiense sleeping sickness in the Democratic Republic of Congo

Contact Info

Product

Resources

About