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641 results on '"Medley, Graham F."'

1. Factors Associated with Usage of Oral-PrEP among Female Sex Workers in Nairobi, Kenya, Assessed by Self-Report and a Point-of-Care Urine Tenofovir Immunoassay

Author

Shah, Pooja, Spinelli, Matthew, Irungu, Erastus, Kabuti, Rhoda, Ngurukiri, Pauline, Babu, Hellen, Kungu, Mary, Champions, The Maisha Fiti Study, Nyabuto, Chrispo, Mahero, Anne, Devries, Karen, Kyegombe, Nambusi, Medley, Graham F., Gafos, Mitzy, Seeley, Janet, Weiss, Helen A., Kaul, Rupert, Gandhi, Monica, Beattie, Tara S., and Kimani, Joshua

Published
2024
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3. Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets

Author

Fyles, Martyn, Vihta, Karina-Doris, Sudre, Carole H, Long, Harry, Das, Rajenki, Jay, Caroline, Wingfield, Tom, Cumming, Fergus, Green, William, Hadjipantelis, Pantelis, Kirk, Joni, Steves, Claire J, Ourselin, Sebastien, Medley, Graham F, Fearon, Elizabeth, and House, Thomas

Subjects
Statistics - Applications, Quantitative Biology - Populations and Evolution, 62P10
Abstract

Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes., Comment: 60 pages; 29 figures

Published
2021

7. Predicted Impact of COVID-19 on Neglected Tropical Disease Programs and the Opportunity for Innovation

Author

Toor, Jaspreet, Adams, Emily R, Aliee, Maryam, Amoah, Benjamin, Anderson, Roy M, Ayabina, Diepreye, Bailey, Robin, Basáñez, Maria-Gloria, Blok, David J, Blumberg, Seth, Borlase, Anna, Rivera, Rocio Caja, Castaño, María Soledad, Chitnis, Nakul, Coffeng, Luc E, Crump, Ronald E, Das, Aatreyee, Davis, Christopher N, Davis, Emma L, Deiner, Michael S, Diggle, Peter J, Fronterre, Claudio, Giardina, Federica, Giorgi, Emanuele, Graham, Matthew, Hamley, Jonathan ID, Huang, Ching-I, Kura, Klodeta, Lietman, Thomas M, Lucas, Tim CD, Malizia, Veronica, Medley, Graham F, Meeyai, Aronrag, Michael, Edwin, Porco, Travis C, Prada, Joaquin M, Rock, Kat S, Le Rutte, Epke A, Smith, Morgan E, Spencer, Simon EF, Stolk, Wilma A, Touloupou, Panayiota, Vasconcelos, Andreia, Vegvari, Carolin, de Vlas, Sake J, Walker, Martin, and Hollingsworth, T Déirdre

Subjects
Rare Diseases, Infectious Diseases, Vector-Borne Diseases, Orphan Drug, Infection, Good Health and Well Being, COVID-19, Humans, Neglected Diseases, Pandemics, SARS-CoV-2, Tropical Medicine, neglected tropical diseases, coronavirus, modeling, Biological Sciences, Medical and Health Sciences, Microbiology
Abstract

Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals.

Published
2021

10. Erratum to: Trachoma Prevalence After Discontinuation of Mass Azithromycin Distribution

Author

Godwin, William, Prada, Joaquin M, Emerson, Paul, Hooper, PJ, Bakhtiari, Ana, Deiner, Michael, Porco, Travis C, Mahmud, Hamidah, Landskroner, Emma, Hollingsworth, T Deirdre, Medley, Graham F, Pinsent, Amy, Bailey, Robin, Lietman, Thomas M, and Oldenburg, Catherine E

Subjects
Biological Sciences, Medical and Health Sciences, Microbiology
Published
2020

13. Evaluating and mitigating the potential indirect effect of COVID-19 on control programmes for seven neglected tropical diseases: a modelling study

Author

Aliee, Maryam, Anderson, Roy M, Ayabina, Diepreye, Basáñez, Maria-Gloria, Blumberg, Seth, Caja Rivera, Rocio M, Chitnis, Nakul, Coffeng, Luc E, Davis, Christopher N, Deiner, Michael, Diggle, Peter J, Fronterrè, Claudio, Giorgi, Emanuele, Graham, Matthew, Hamley, Jonathan ID, Hollingsworth, T Deirdre, Keeling, Matt J, Kura, Klodeta, Lietman, Thomas M, Malizia, Veronica, Medley, Graham F, Michael, Edwin, Thumbi, S Mwangi, Mutono, Nyamai, Porco, Travis, Prada, Joaquín M, Rock, Kat S, Sharma, Swarnali, Spencer, Simon, Stolk, Wilma A, Touloupou, Panayiota, Vasconcelos, Andreia, Vegvari, Carolin, de Vlas, Sake J, Borlase, Anna, Le Rutte, Epke A, Castaño, Soledad, Blok, David J, Toor, Jaspreet, Giardina, Federica, and Davis, Emma L

Published
2022
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17. Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection

Author

Centre for Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Jombart, Thibaut, Ghozzi, Stéphane, Schumacher, Dirk, Taylor, Timothy J., Leclerc, Quentin J., Jit, Mark, Flasche, Stefan, Greaves, Felix, Ward, Tom, Eggo, Rosalind M., Nightingale, Emily, Meakin, Sophie, Brady, Oliver J., Medley, Graham F., Höhle, Michael, and Edmunds, W. John

Published
2021

21. Policy Lessons From Quantitative Modeling of Leprosy

Author

Medley, Graham F, Blok, David J, Crump, Ronald E, Hollingsworth, T Déirdre, Galvani, Alison P, Ndeffo-Mbah, Martial L, Porco, Travis C, and Richardus, Jan Hendrik

Subjects
Prevention, Infectious Diseases, Good Health and Well Being, Disease Eradication, Humans, Incidence, Leprosy, Models, Statistical, Models, Theoretical, Policy, leprosy, policy, mathematical modeling, elimination, diagnosis, Biological Sciences, Medical and Health Sciences, Microbiology
Abstract

Recent mathematical and statistical modeling of leprosy incidence data provides estimates of the current undiagnosed population and projections of diagnosed cases, as well as ongoing transmission. Furthermore, modeling studies have been used to evaluate the effectiveness of proposed intervention strategies, such as postleprosy exposure prophylaxis and novel diagnostics, relative to current approaches. Such modeling studies have revealed both a slow decline of new cases and a substantial pool of undiagnosed infections. These findings highlight the need for active case detection, particularly targeting leprosy foci, as well as for continued research into innovative accurate, rapid, and cost-effective diagnostics. As leprosy incidence continues to decline, targeted active case detection primarily in foci and connected areas will likely become increasingly important.

Published
2018

28. Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches

Author

Blok, David J, Crump, Ronald E, Sundaresh, Ram, Ndeffo-Mbah, Martial, Galvani, Alison P, Porco, Travis C, de Vlas, Sake J, Medley, Graham F, and Richardus, Jan Hendrik

Subjects
Epidemiology, Health Sciences, Infectious Diseases, Good Health and Well Being, Brazil, Forecasting, Humans, Incidence, Leprosy, Models, Statistical, Model comparison, LMER, Back-calculation, Compartmental, Individual-based model, Forecast, Clinical Sciences, Public Health and Health Services
Abstract

BackgroundBrazil has the second highest annual number of new leprosy cases. The aim of this study is to formally compare predictions of future new case detection rate (NCDR) trends and the annual probability of NCDR falling below 10/100,000 of four different modelling approaches in four states of Brazil: Rio Grande do Norte, Amazonas, Ceará, Tocantins.MethodsA linear mixed model, a back-calculation approach, a deterministic compartmental model and an individual-based model were used. All models were fitted to leprosy data obtained from the Brazilian national database (SINAN). First, models were fitted to the data up to 2011, and predictions were made for NCDR for 2012-2014. Second, data up to 2014 were considered and forecasts of NCDR were generated for each year from 2015 to 2040. The resulting distributions of NCDR and the probability of NCDR being below 10/100,000 of the population for each year were then compared between approaches.ResultsEach model performed well in model fitting and the short-term forecasting of future NCDR. Long-term forecasting of NCDR and the probability of NCDR falling below 10/100,000 differed between models. All agree that the trend of NCDR will continue to decrease in all states until 2040. Reaching a NCDR of less than 10/100,000 by 2020 was only likely in Rio Grande do Norte. Prediction until 2040 showed that the target was also achieved in Amazonas, while in Ceará and Tocantins the NCDR most likely remain (far) above 10/100,000.ConclusionsAll models agree that, while incidence is likely to decline, achieving a NCDR below 10/100,000 by 2020 is unlikely in some states. Long-term prediction showed a downward trend with more variation between models, but highlights the need for further control measures to reduce the incidence of new infections if leprosy is to be eliminated.

Published
2017

32. Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study

Author

Jombart, Thibaut, O'Reilly, Kathleen, Endo, Akira, Hellewell, Joel, Nightingale, Emily S, Quilty, Billy J, Jarvis, Christopher I, Russell, Timothy W, Klepac, Petra, Bosse, Nikos I, Funk, Sebastian, Abbott, Sam, Medley, Graham F, Gibbs, Hamish, Pearson, Carl A B, Flasche, Stefan, Jit, Mark, Clifford, Samuel, Prem, Kiesha, Diamond, Charlie, Emery, Jon, Deol, Arminder K, Procter, Simon R, van Zandvoort, Kevin, Sun, Yueqian Fiona, Munday, James D, Rosello, Alicia, Auzenbergs, Megan, Knight, Gwen, Houben, Rein M G J, Liu, Yang, Davies, Nicholas G, Kucharski, Adam J, Eggo, Rosalind M, Gimma, Amy, and Edmunds, W John

Published
2020
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34. Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Author

Vasconcelos, Andreia, primary, King, Jonathan D, additional, Nunes-Alves, Cláudio, additional, Anderson, Roy, additional, Argaw, Daniel, additional, Basáñez, Maria-Gloria, additional, Bilal, Shakir, additional, Blok, David J, additional, Blumberg, Seth, additional, Borlase, Anna, additional, Brady, Oliver J, additional, Browning, Raiha, additional, Chitnis, Nakul, additional, Coffeng, Luc E, additional, Crowley, Emily H, additional, Cucunubá, Zulma M, additional, Cummings, Derek A T, additional, Davis, Christopher Neil, additional, Davis, Emma Louise, additional, Dixon, Matthew, additional, Dobson, Andrew, additional, Dyson, Louise, additional, French, Michael, additional, Fronterre, Claudio, additional, Giorgi, Emanuele, additional, Huang, Ching-I, additional, Jain, Saurabh, additional, James, Ananthu, additional, Kim, Sung Hye, additional, Kura, Klodeta, additional, Lucianez, Ana, additional, Marks, Michael, additional, Mbabazi, Pamela Sabina, additional, Medley, Graham F, additional, Michael, Edwin, additional, Montresor, Antonio, additional, Mutono, Nyamai, additional, Mwangi, Thumbi S, additional, Rock, Kat S, additional, Saboyá-Díaz, Martha-Idalí, additional, Sasanami, Misaki, additional, Schwehm, Markus, additional, Spencer, Simon E F, additional, Srivathsan, Ariktha, additional, Stawski, Robert S, additional, Stolk, Wilma A, additional, Sutherland, Samuel A, additional, Tchuenté, Louis-Albert Tchuem, additional, de Vlas, Sake J, additional, Walker, Martin, additional, Brooker, Simon J, additional, Hollingsworth, T Déirdre, additional, Solomon, Anthony W, additional, and Fall, Ibrahima Socé, additional

Published
2024
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37. Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets:How Can Quantitative Modeling Support Programmatic Decisions?

Author

Vasconcelos, Andreia, King, Jonathan D., Nunes-Alves, Cláudio, Anderson, Roy, Argaw, Daniel, Basáñez, Maria Gloria, Bilal, Shakir, Blok, David J., Blumberg, Seth, Borlase, Anna, Brady, Oliver J., Browning, Raiha, Chitnis, Nakul, Coffeng, Luc E., Crowley, Emily H., Cucunubá, Zulma M., Cummings, Derek A.T., Davis, Christopher Neil, Davis, Emma Louise, Dixon, Matthew, Dobson, Andrew, Dyson, Louise, French, Michael, Fronterre, Claudio, Giorgi, Emanuele, Huang, Ching I., Jain, Saurabh, James, Ananthu, Kim, Sung Hye, Kura, Klodeta, Lucianez, Ana, Marks, Michael, Mbabazi, Pamela Sabina, Medley, Graham F., Michael, Edwin, Montresor, Antonio, Mutono, Nyamai, Mwangi, Thumbi S., Rock, Kat S., Saboyá-Díaz, Martha Idalí, Sasanami, Misaki, Schwehm, Markus, Spencer, Simon E.F., Srivathsan, Ariktha, Stawski, Robert S., Stolk, Wilma A., Sutherland, Samuel A., Tchuente, Louis Albert Tchuem, De Vlas, Sake J., Walker, Martin, Brooker, Simon J., Hollingsworth, T. Deirdre, Solomon, Anthony W., Fall, Ibrahima Soce, Vasconcelos, Andreia, King, Jonathan D., Nunes-Alves, Cláudio, Anderson, Roy, Argaw, Daniel, Basáñez, Maria Gloria, Bilal, Shakir, Blok, David J., Blumberg, Seth, Borlase, Anna, Brady, Oliver J., Browning, Raiha, Chitnis, Nakul, Coffeng, Luc E., Crowley, Emily H., Cucunubá, Zulma M., Cummings, Derek A.T., Davis, Christopher Neil, Davis, Emma Louise, Dixon, Matthew, Dobson, Andrew, Dyson, Louise, French, Michael, Fronterre, Claudio, Giorgi, Emanuele, Huang, Ching I., Jain, Saurabh, James, Ananthu, Kim, Sung Hye, Kura, Klodeta, Lucianez, Ana, Marks, Michael, Mbabazi, Pamela Sabina, Medley, Graham F., Michael, Edwin, Montresor, Antonio, Mutono, Nyamai, Mwangi, Thumbi S., Rock, Kat S., Saboyá-Díaz, Martha Idalí, Sasanami, Misaki, Schwehm, Markus, Spencer, Simon E.F., Srivathsan, Ariktha, Stawski, Robert S., Stolk, Wilma A., Sutherland, Samuel A., Tchuente, Louis Albert Tchuem, De Vlas, Sake J., Walker, Martin, Brooker, Simon J., Hollingsworth, T. Deirdre, Solomon, Anthony W., and Fall, Ibrahima Soce

Abstract

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.

Published
2024

38. Improving the Cost-efficiency of Preventive Chemotherapy:Impact of New Diagnostics on Stopping Decisions for Control of Schistosomiasis

Author

Coffeng, Luc E., Graham, Matthew, Browning, Raiha, Kura, Klodeta, Diggle, Peter J., Denwood, Matthew, Medley, Graham F., Anderson, Roy M., De Vlas, Sake J., Coffeng, Luc E., Graham, Matthew, Browning, Raiha, Kura, Klodeta, Diggle, Peter J., Denwood, Matthew, Medley, Graham F., Anderson, Roy M., and De Vlas, Sake J.

Abstract

Background: Control of schistosomiasis (SCH) relies on the regular distribution of preventive chemotherapy (PC) over many years. For the sake of sustainable SCH control, a decision must be made at some stage to scale down or stop PC. These "stopping decisions"are based on population surveys that assess whether infection levels are sufficiently low. However, the limited sensitivity of the currently used diagnostic (Kato-Katz [KK]) to detect low-intensity infections is a concern. Therefore, the use of new, more sensitive, molecular diagnostics has been proposed. Methods: Through statistical analysis of Schistosoma mansoni egg counts collected from Burundi and a simulation study using an established transmission model for schistosomiasis, we investigated the extent to which more sensitive diagnostics can improve decision making regarding stopping or continuing PC for the control of S. mansoni. Results: We found that KK-based strategies perform reasonably well for determining when to stop PC at a local scale. Use of more sensitive diagnostics leads to a marginally improved health impact (person-years lived with heavy infection) and comes at a cost of continuing PC for longer (up to around 3 years), unless the decision threshold for stopping PC is adapted upward. However, if this threshold is set too high, PC may be stopped prematurely, resulting in a rebound of infection levels and disease burden (+45% person-years of heavy infection). Conclusions: We conclude that the potential value of more sensitive diagnostics lies more in the reduction of survey-related costs than in the direct health impact of improved parasite control.

Published
2024

39. Improving the Cost-efficiency of Preventive Chemotherapy : Impact of New Diagnostics on Stopping Decisions for Control of Schistosomiasis

Author

Coffeng, Luc E, Graham, Matthew, Browning, Raiha, Kura, Klodeta, Diggle, Peter J, Denwood, Matthew, Medley, Graham F, Anderson, Roy M, de Vlas, Sake J, Coffeng, Luc E, Graham, Matthew, Browning, Raiha, Kura, Klodeta, Diggle, Peter J, Denwood, Matthew, Medley, Graham F, Anderson, Roy M, and de Vlas, Sake J

Abstract

BackgroundControl of schistosomiasis (SCH) relies on the regular distribution of preventive chemotherapy (PC) over many years. For the sake of sustainable SCH control, a decision must be made at some stage to scale down or stop PC. These "stopping decisions" are based on population surveys that assess whether infection levels are sufficiently low. However, the limited sensitivity of the currently used diagnostic (Kato-Katz [KK]) to detect low-intensity infections is a concern. Therefore, the use of new, more sensitive, molecular diagnostics has been proposed.MethodsThrough statistical analysis of Schistosoma mansoni egg counts collected from Burundi and a simulation study using an established transmission model for schistosomiasis, we investigated the extent to which more sensitive diagnostics can improve decision making regarding stopping or continuing PC for the control of S. mansoni.ResultsWe found that KK-based strategies perform reasonably well for determining when to stop PC at a local scale. Use of more sensitive diagnostics leads to a marginally improved health impact (person-years lived with heavy infection) and comes at a cost of continuing PC for longer (up to around 3 years), unless the decision threshold for stopping PC is adapted upward. However, if this threshold is set too high, PC may be stopped prematurely, resulting in a rebound of infection levels and disease burden (+45% person-years of heavy infection).ConclusionsWe conclude that the potential value of more sensitive diagnostics lies more in the reduction of survey-related costs than in the direct health impact of improved parasite control.

Published
2024

42. Modelling spatiotemporal patterns of visceral leishmaniasis incidence in two endemic states in India using environment, bioclimatic and demographic data, 2013–2022

Author

Subramanian, Swaminathan, primary, Maheswari, Rajendran Uma, additional, Prabavathy, Gopalakrishnan, additional, Khan, Mashroor Ahmad, additional, Brindha, Balan, additional, Srividya, Adinarayanan, additional, Kumar, Ashwani, additional, Rahi, Manju, additional, Nightingale, Emily S., additional, Medley, Graham F., additional, Cameron, Mary M., additional, Roy, Nupur, additional, and Jambulingam, Purushothaman, additional

Published
2024
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48. Comparison of collection methods for Phlebotomus argentipes sand flies to use in a molecular xenomonitoring system for the surveillance of visceral leishmaniasis

Author

McIntyre-Nolan, Shannon, primary, Kumar, Vijay, additional, Mark-Carew, Miguella, additional, Kumar, Kundan, additional, Nightingale, Emily S., additional, Dalla Libera Marchiori, Giorgia, additional, Rogers, Matthew E., additional, Kristan, Mojca, additional, Campino, Susana, additional, Medley, Graham F., additional, Das, Pradeep, additional, and Cameron, Mary M., additional

Published
2023
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