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

Background: Brazil 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...

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Main Authors: David J. Blok, Ronald E. Crump, Ram Sundaresh, Martial Ndeffo-Mbah, Alison P. Galvani, Travis C. Porco, Sake J. de Vlas, Graham F. Medley, Jan Hendrik Richardus
Format: Article
Language:English
Published: Elsevier 2017-03-01
Series:Epidemics
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S175543651630072X
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spelling doaj-9a5cfef690c54015b997462fc503e27b2020-11-25T00:59:00ZengElsevierEpidemics1755-43651878-00672017-03-0118C9210010.1016/j.epidem.2017.01.005Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approachesDavid J. Blok0Ronald E. Crump1Ram Sundaresh2Martial Ndeffo-Mbah3Alison P. Galvani4Travis C. Porco5Sake J. de Vlas6Graham F. Medley7Jan Hendrik Richardus8Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The NetherlandsWarwick Infectious Disease Epidemiology Research, School of Life Sciences, Gibbet Hill Campus, The University of Warwick, Coventry CV4 7AL, UKYale University, Department of Public Health, USAYale University, Department of Public Health, USAYale University, Department of Public Health, USAFI Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, CA 94143-0412 USADepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, The NetherlandsDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UKDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, The NetherlandsBackground: Brazil 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. Methods: A 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. Results: Each 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. Conclusions: All 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.http://www.sciencedirect.com/science/article/pii/S175543651630072XLeprosyBrazilModel comparisonLMERBack-calculationCompartmentalIndividual-based modelForecast
collection DOAJ
language English
format Article
sources DOAJ
author David J. Blok
Ronald E. Crump
Ram Sundaresh
Martial Ndeffo-Mbah
Alison P. Galvani
Travis C. Porco
Sake J. de Vlas
Graham F. Medley
Jan Hendrik Richardus
spellingShingle David J. Blok
Ronald E. Crump
Ram Sundaresh
Martial Ndeffo-Mbah
Alison P. Galvani
Travis C. Porco
Sake J. de Vlas
Graham F. Medley
Jan Hendrik Richardus
Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches
Epidemics
Leprosy
Brazil
Model comparison
LMER
Back-calculation
Compartmental
Individual-based model
Forecast
author_facet David J. Blok
Ronald E. Crump
Ram Sundaresh
Martial Ndeffo-Mbah
Alison P. Galvani
Travis C. Porco
Sake J. de Vlas
Graham F. Medley
Jan Hendrik Richardus
author_sort David J. Blok
title Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches
title_short Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches
title_full Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches
title_fullStr Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches
title_full_unstemmed Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches
title_sort forecasting the new case detection rate of leprosy in four states of brazil: a comparison of modelling approaches
publisher Elsevier
series Epidemics
issn 1755-4365
1878-0067
publishDate 2017-03-01
description Background: Brazil 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. Methods: A 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. Results: Each 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. Conclusions: All 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.
topic Leprosy
Brazil
Model comparison
LMER
Back-calculation
Compartmental
Individual-based model
Forecast
url http://www.sciencedirect.com/science/article/pii/S175543651630072X
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