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1. Ebola virus disease mathematical models and epidemiological parameters: a systematic review

Author

Nash, Rebecca K, Bhatia, Sangeeta, Morgenstern, Christian, Doohan, Patrick, Jorgensen, David, McCain, Kelly, McCabe, Ruth, Nikitin, Dariya, Forna, Alpha, Cuomo-Dannenburg, Gina, Hicks, Joseph T, Sheppard, Richard J, Naidoo, Tristan, van Elsland, Sabine, Geismar, Cyril, Rawson, Thomas, Leuba, Sequoia Iris, Wardle, Jack, Routledge, Isobel, Fraser, Keith, Imai-Eaton, Natsuko, Cori, Anne, and Unwin, H Juliette T

Published
2024
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2. Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review

Author

Morris, Aaron, Forna, Alpha, Dighe, Amy, Cori, Anne, Hamlet, Arran, Lambert, Ben, Whittaker, Charlie, Morgenstern, Christian, Geismar, Cyril, Nikitin, Dariya, Jorgensen, David, Knock, Ed, Unwin, Ettie, Cuomo-Dannenburg, Gina, Thompson, Hayley, Routledge, Isobel, Skarp, Janetta, Hicks, Joseph, Fraser, Keith, Charniga, Kelly, McCain, Kelly, Geidelberg, Lily, Cattarino, Lorenzo, Kont, Mara, Baguelin, Marc, Imai, Natsuko, Moghaddas, Nima, Doohan, Patrick, Nash, Rebecca, McCabe, Ruth, van Elsland, Sabine, Bhatia, Sangeeta, Radhakrishnan, Sreejith, Cucunuba Perez, Zulma, Wardle, Jack, Unwin, H Juliette T, Nash, Rebecca K, and Hicks, Joseph T

Published
2024
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3. Subnational analysis and modelling of the Ebola epidemic in West Africa, 2013-2016 : application of machine learning algorithms for case fatality imputation

Author

Forna, Alpha, Donnelly, Christl, Nouvellet, Pierre, and Dorigatti, Ilaria

Abstract

Background The 2013-2016 West African Ebola epidemic has been the largest to date with more than 11,000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naïve CFR remain limited because 44% of survival outcomes were unreported. Methods Using a machine learning (MaLe) model, Boosted Regression Tree (BRT), I imputed survival outcomes (i.e. survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation and adjusted with imputation. I used semivariogram analysis and kriging to investigate subnational heterogeneities in CFR estimates. I used simulations to evaluate the performance of various MaLe inference methods for the estimation of CFR under different outbreak data scenarios. Results The adjusted CFR estimates were 82.8% (95% CI 45%.6-85.6%) overall and 89.1% (95% CI 40.8%-91.6%), 65.6% (95% CI 61.3%-69.6%) and 79.2% (95% CI 45.4%-84.1%) for Sierra Leone, Guinea and Liberia, respectively. BRT modelling accounted for most of the spatiotemporal variation and interactions in CFR, but moderate spatial autocorrelation remained. Combining district-level CFR estimates and kriged district-level residuals provided the best linear unbiased map of CFR. Temporal autocorrelation was not observed in the district-level residuals from the BRT estimates. Finally, I observed that the performance of MaLe inference methods for CFR imputation varies under different outbreak data scenarios. Conclusions Adjusted CFR estimates improved the naïve CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted CFR estimates and the unbiased CFR maps will inform future public health response to Ebola outbreaks. I confirm that, across the board, data imputation with adjustment for the sensitivity and specificity of MaLe inference methods reduces the bias in CFR estimates.

Published
2020
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4. Sequence-based detection of emerging antigenically novel influenza A viruses.

Author

Forna, Alpha, Weedop, K. Bodie, Damodaran, Lambodhar, Hassell, Norman, Kondor, Rebecca, Bahl, Justin, Drake, John M., and Rohani, Pejman

Subjects
*INFLUENZA viruses, *INFLUENZA vaccines, *VACCINE effectiveness, *SURFACE potential, *MACHINE learning
Abstract

The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5–98.0%) and 72.7% (95% CI, 43.4–90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model—33.3% (95% CI, 12.1–64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review

Author

Cuomo-Dannenburg, Gina, primary, McCain, Kelly, additional, McCabe, Ruth, additional, Unwin, H Juliette T, additional, Doohan, Patrick, additional, Nash, Rebecca K, additional, Hicks, Joseph T, additional, Charniga, Kelly, additional, Geismar, Cyril, additional, Lambert, Ben, additional, Nikitin, Dariya, additional, Skarp, Janetta, additional, Wardle, Jack, additional, Kont, Mara, additional, Bhatia, Sangeeta, additional, Imai, Natsuko, additional, van Elsland, Sabine, additional, Cori, Anne, additional, Morgenstern, Christian, additional, Morris, Aaron, additional, Forna, Alpha, additional, Dighe, Amy, additional, Hamlet, Arran, additional, Whittaker, Charlie, additional, Jorgensen, David, additional, Knock, Ed, additional, Unwin, Ettie, additional, Cuomo-Dannenburg, Gina, additional, Thompson, Hayley, additional, Routledge, Isobel, additional, Hicks, Joseph, additional, Fraser, Keith, additional, Geidelberg, Lily, additional, Cattarino, Lorenzo, additional, Baguelin, Marc, additional, Moghaddas, Nima, additional, Nash, Rebecca, additional, Radhakrishnan, Sreejith, additional, and Cucunuba Perez, Zulma, additional

Published
2023
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11. Case Fatality Ratio Estimates for the 2013–2016 West African Ebola Epidemic: Application of Boosted Regression Trees for Imputation.

Author

Forna, Alpha, Nouvellet, Pierre, Dorigatti, Ilaria, and Donnelly, Christl A

Subjects
*CONFIDENCE intervals, *DECISION trees, *EBOLA virus disease, *EPIDEMICS, *HOSPITAL care, *MACHINE learning, *SURVIVAL, *RECEIVER operating characteristic curves, *STATISTICAL models, *DESCRIPTIVE statistics
Abstract

Background The 2013–2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported. Methods Using a boosted regression tree model, we imputed survival outcomes (ie, survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR. Results The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5–75.6%); specificity, 69.8% (54.1–75.6%); percentage correctly classified, 69.9% (53.7–75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8–82.1%). The adjusted CFR estimates (95% CI) for the 2013–2016 West African epidemic were 82.8% (45.6–85.6%) overall and 89.1% (40.8–91.6%), 65.6% (61.3–69.6%), and 79.2% (45.4–84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR. Conclusions The adjusted CFR estimates improved the naive CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemics, and help better allocate resources and evaluate the effectiveness of future inventions. [ABSTRACT FROM AUTHOR]

Published
2020
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12. Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study

Author

Barry, Ahmadou, primary, Ahuka-Mundeke, Steve, additional, Ali Ahmed, Yahaya, additional, Allarangar, Yokouide, additional, Anoko, Julienne, additional, Archer, Brett Nicholas, additional, Aruna Abedi, Aaron, additional, Bagaria, Jayshree, additional, Belizaire, Marie Roseline Darnycka, additional, Bhatia, Sangeeta, additional, Bokenge, Théophile, additional, Bruni, Emanuele, additional, Cori, Anne, additional, Dabire, Ernest, additional, Diallo, Amadou Mouctar, additional, Diallo, Boubacar, additional, Donnelly, Christl Ann, additional, Dorigatti, Ilaria, additional, Dorji, Tshewang Choden, additional, Escobar Corado Waeber, Aura Rocio, additional, Fall, Ibrahima Socé, additional, Ferguson, Neil M, additional, FitzJohn, Richard Gareth, additional, Folefack Tengomo, Gervais Leon, additional, Formenty, Pierre Bernard Henri, additional, Forna, Alpha, additional, Fortin, Anne, additional, Garske, Tini, additional, Gaythorpe, Katy AM, additional, Gurry, Celine, additional, Hamblion, Esther, additional, Harouna Djingarey, Mamoudou, additional, Haskew, Christopher, additional, Hugonnet, Stéphane Alexandre Louis, additional, Imai, Natsuko, additional, Impouma, Benido, additional, Kabongo, Guylain, additional, Kalenga, Oly Ilunga, additional, Kibangou, Emerencienne, additional, Lee, Theresa Min-Hyung, additional, Lukoya, Charles Okot, additional, Ly, Ousmane, additional, Makiala-Mandanda, Sheila, additional, Mamba, Augustin, additional, Mbala-Kingebeni, Placide, additional, Mboussou, Franck Fortune Roland, additional, Mlanda, Tamayi, additional, Mondonge Makuma, Vital, additional, Morgan, Oliver, additional, Mujinga Mulumba, Anastasie, additional, Mukadi Kakoni, Patrick, additional, Mukadi-Bamuleka, Daniel, additional, Muyembe, Jean-Jaques, additional, Bathé, Ndjoloko Tambwe, additional, Ndumbi Ngamala, Patricia, additional, Ngom, Roland, additional, Ngoy, Guillaume, additional, Nouvellet, Pierre, additional, Nsio, Justus, additional, Ousman, Kevin Babila, additional, Peron, Emilie, additional, Polonsky, Jonathan Aaron, additional, Ryan, Michael J., additional, Touré, Alhassane, additional, Towner, Rodney, additional, Tshapenda, Gaston, additional, Van De Weerdt, Reinhilde, additional, Van Kerkhove, Maria, additional, Wendland, Annika, additional, Yao, N'Da Konan Michel, additional, Yoti, Zabulon, additional, Yuma, Etienne, additional, Kalambayi Kabamba, Guy, additional, Lukwesa Mwati, Jean de Dieu, additional, Mbuy, Gisele, additional, Lubula, Leopold, additional, Mutombo, Anny, additional, Mavila, Oscar, additional, Lay, Yyonne, additional, and Kitenge, Emma, additional

Published
2018
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