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

Abstract

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.

Policy Lessons From Quantitative Modeling of Leprosy

“…Blok et al showed that, given current control, the NCDR is predicted to remain far above the target of 10 per 100000 by 2020 in several states or districts in India, Brazil, and Indonesia [ 18 ]. By way of validation, similar results were shown in the model comparison article focusing on 4 states in Brazil [ 17 ]. Specifically, long-term predictions of the NCDR trend in Brazil indicated that the target of 10 per 100000 is unlikely to be achieved before 2040 in high- and hyperendemic states.…”

“…To validate the results of the different models, we have compared the consistency of predictions generated by the respective models [ 17 ]. The range of model frameworks employed covers the gamut of quantitative methodologies: multilevel statistical trend models, statistical back-calculation, compartmental transmission models, and fully individualized simulations.…”

Short-term leprosy forecasting from an expert opinion survey

“…Unfortunately, expert opinion can also suffer from well-known cognitive biases [ 38 ] or from real or perceived economic or political interests. Statistical or mathematical methods for forecasting leprosy [ 39 ] may, within limits, provide greater openness and transparency, helping us understand what data and what structural features determine the value of a forecast. We anticipate that an ensemble-based forecasting algorithm that combines each method may yield better performance than each individual forecasting method (e.g.…”

Counting Down the 2020 Goals for 9 Neglected Tropical Diseases: What Have We Learned From Quantitative Analysis and Transmission Modeling?

“…However, in comparison with other infectious diseases, we still have limited epidemiological data on NTDs; thus, although we have performed formal model comparisons [10][11][12][13][14][15][16][17][18][19], there remain large uncertainties in processes and parameters that could have an impact on the dynamics, as highlighted below. This means that we need to be cautious about overstating our results, even when the policy need is acute.…”

“…The authors of this issue are aiming to increase the repeatability of our science. The code for the models used in this special issue were previously published alongside more technical articles [10][11][12][13][14][15][16][17][18][19] as supplementary information or on our website (www.ntdmodelling.org). The release of raw code is not a complete answer to accessibility and reproducibility, but it is a step in the right direction [20].…”