1. World Health Organization. Leprosy update Geneva: World Health Organization; 2021 [updated 10 May 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/leprosy.
2. Somar PMW, Waltz MM, van Brakel WH. The impact of leprosy on the mental wellbeing of leprosy-affected persons and their family members–a systematic review. Global Mental Health. 2020;7.
3. World Health Organization. Weekly Epidemiological Record, 2020, vol. 95, 36 [full issue]. World Health Organization; 2020. p. 417–40.
4. World Health Organization. Global leprosy (Hansen’s Disease) strategy 2021–2030. World Health Organization; 2021.
5. Hambridge T, Nanjan Chandran SL, Geluk A, Saunderson P, Richardus JH. Mycobacterium leprae transmission characteristics during the declining stages of leprosy incidence: A systematic review. PLoS Neglected Tropical Diseases. 2021;15(5):e0009436.
6. Hollingsworth TD. Counting down the 2020 goals for 9 neglected tropical diseases: what have we learned from quantitative analysis and transmission modeling? Clinical Infectious Diseases. 2018;66:S237-S44.
7. Minter A, Pellis L, Medley GF, Hollingsworth TD. What can modelling tell us about sustainable end points for neglected tropical disease? Clinical Infectious Diseases. 2021;72.
8. World Health Organization. Global consultation of National Leprosy Programme managers, partners and affected persons on Global Leprosy Strategy 2021–2030: Report of the virtual meeting 26-30 October 2020. New Delhi: World Health Organization, Regional Office for South-East Asia; 2021.
9. de Oliveira GL, Oliveira JF, Andrade RF, Nery JS, Pescarini JM, Ichihara MY, et al. Estimating Under Reporting of Leprosy in Brazil using a Bayesian Approach. medRxiv. 2020.
10. Blok DJ, Crump RE, Sundaresh R, Ndeffo-Mbah M, Galvani AP, Porco TC, et al. Forecasting the new case detection rate of leprosy in four states of Brazil: A comparison of modelling approaches. Epidemics. 2017;18:92-100.
11. de Matos HJ, Blok DJ, de Vlas SJ, Richardus JH. Leprosy New Case Detection Trends and the Future Effect of Preventive Interventions in Pará State, Brazil: A Modelling Study. PLoS Negl Trop Dis. 2016;10(3):e0004507.
12. Odriozola EPD, Quintana AM, González V, Pasetto RA, Utgés ME, Bruzzone AO, et al. Towards leprosy elimination by 2020: forecasts of epidemiological indicators of leprosy in Corrientes, a province of northeastern Argentina that is a pioneer in leprosy elimination. Memórias do Instituto Oswaldo Cruz. 2017;112(6):419-27.
13. Souza CDFD, Medronho RDA, Santos FGB, Magalhães MDAFM, Luna CF. Modelagem espacial da hanseníase no estado da Bahia, Brasil,(2001-2015) e determinantes sociais da saúde. Ciência & Saúde Coletiva. 2020;25:2915-26.
14. Arksey H, O'Malley LS. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32.
15. Peters MD, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis. 2020;18:2119-26.
16. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic reviews. 2015;5(5).
17. Rethlefsen ML, Kirtley S, Waffenschmidt S, Ayala AP, Moher D, Page MJ, et al. PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic reviews. 2021;10.1:1-19.
18. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan — a web and mobile app for systematic reviews. Systematic Reviews. 2016;5:210.
19. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.
20. Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11.10:e1001744.
21. Wolff RF, Moons KG, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Annals of internal medicine. 2019;170(1):51-8.