Martins-Melo FR, Ramos AN, Alencar CH, Heukelbach J. Trends and spatial patterns of mortality related to neglected tropical diseases in Brazil. Parasite Epidemiol Control. 2016;1:56–65. https://doi.org/10.1016/j.parepi.2016.03.002.
Article PubMed PubMed Central Google Scholar
Martins-Melo FR, Carneiro M, Ramos AN, Heukelbach J, Ribeiro ALP, Werneck GL. The burden of neglected tropical diseases in Brazil, 1990–2016: a subnational analysis from the Global Burden of Disease Study 2016. PLoS Negl Trop Dis. 2018;12:e0006559. https://doi.org/10.1371/journal.pntd.0006559.
Article PubMed PubMed Central Google Scholar
Hotez PJ, Aksoy S, Brindley PJ, Kamhawi S. What constitutes a neglected tropical disease? PLoS Negl Trop Dis. 2020;14:e0008001. https://doi.org/10.1371/journal.pntd.0008001.
Article PubMed PubMed Central Google Scholar
Mitra AK, Mawson AR. Neglected tropical diseases: epidemiology and global burden. Trop Med Infect Dis. 2017;2:36. https://doi.org/10.3390/tropicalmed2030036.
Article PubMed PubMed Central Google Scholar
Uniting to Combat NTDs. Briefing document on neglected tropical diseases. 2019. Available: https://unitingtocombatntds.org/wp-content/uploads/2019/05/UNTC_briefing-doc_2019_3.pdf. Accessed 27 Sept 2023.
Sibuyi IN, De la Harpe R, Nyasulu P. A stakeholder-centered mHealth implementation inquiry within the digital health innovation ecosystem in South Africa: MomConnect as a demonstration case. JMIR Mhealth Uhealth. 2022;10:e18188. https://doi.org/10.2196/18188.
Article PubMed PubMed Central Google Scholar
World Health Organization. Global strategy on digital health 2020–2025. 2021 [cited 21 Jan 2023]. Available: http://apps.who.int/bookorders.
World Health Organization. Ending the neglect to attain the Sustainable Development Goals: a road map for neglected tropical diseases 2021–2030. Geneva; 2020. Available: https://www.who.int/neglected_diseases/Revised-Draft-NTD-Roadmap-23Apr2020.pdf. Accessed 27 Sept 2023.
Hoque E, Hope V, Scragg R, Baker M, Shrestha R. A descriptive epidemiology of giardiasis in New Zealand and gaps in surveillance data. N Z Med J. 2004;117(1205):U1149.
Li JPO, Liu H, Ting DSJ, Jeon S, Chan RVP, Kim JE, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res. 2021;82:100900. https://doi.org/10.1016/J.PRETEYERES.2020.100900.
Article CAS PubMed Google Scholar
Labrique AB, Wadhwani C, Williams KA, Lamptey P, Hesp C, Luk R, et al. Best practices in scaling digital health in low and middle income countries. 2018 [cited 6 Jul 2023]. https://doi.org/10.1186/s12992-018-0424-z
Holst C, Stelzle D, Diep LM, Sukums F, Ngowi B, Noll J, et al. Improving health knowledge through provision of free digital health education to rural communities in Iringa, Tanzania: nonrandomized intervention study. J Med Internet Res. 2022;24:e37666. https://doi.org/10.2196/37666.
Article PubMed PubMed Central Google Scholar
Holst C, Sukums F, Ngowi B, My Diep L, Kebede TA, Noll J, et al. Digital health intervention to increase health knowledge related to diseases of high public health concern in Iringa, Tanzania: protocol for a mixed methods study. JMIR Res Protoc. 2021;10:e25128. https://doi.org/10.2196/25128.
Article PubMed PubMed Central Google Scholar
Kraus S, Schiavone F, Pluzhnikova A, Invernizzi AC. Digital transformation in healthcare: analyzing the current state-of-research. J Bus Res. 2021;123:557–67. https://doi.org/10.1016/J.JBUSRES.2020.10.030.
Massaro M. Digital transformation in the healthcare sector through blockchain technology Insights from academic research and business developments. Technovation. 2021;120:102386. https://doi.org/10.1016/J.TECHNOVATION.2021.102386.
Woulfe F, Fadahunsi KP, Smith S, Chirambo GB, Larsson E, Henn P, et al. Identification and evaluation of methodologies to assess the quality of mobile health apps in high-, low-, and middle-income countries: rapid review JMIR Mhealth Uhealth 2021;9(10):e28384. https://mhealth.jmir.org/2021/10/e28384. https://doi.org/10.2196/28384. Accessed 27 Sept 2023.
Ford G, Compton M, Millett G, Tzortzis A. The role of digital disruption in healthcare service innovation. Service Business Model Innovation in Healthcare and Hospital Management. 2017;57–70.https://doi.org/10.1007/978-3-319-46412-1_4
Agarwal R, Gao GG, DesRoches C, Jha AK. The digital transformation of healthcare: current status and the road ahead. Inf Syst Res. 2010;21:796–809. https://doi.org/10.1287/ISRE.1100.0327.
Ali F, El-Sappagh S, Islam SMR, Ali A, Attique M, Imran M, et al. An intelligent healthcare monitoring framework using wearable sensors and social networking data. Futur Gener Comput Syst. 2021;114:23–43. https://doi.org/10.1016/J.FUTURE.2020.07.047.
World Health Organization. Neglected tropical diseases. In: Seventy-third world health assembly. 2020 [cited 5 Jul 2023] pp. 1–8. Available: https://apps.who.int/gb/ebwha/pdf_files/WHA73/A73_8-en.pdf
van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84:523–38. https://doi.org/10.1007/s11192-009-0146-3.
Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62:107–15. https://doi.org/10.1111/J.1365-2648.2007.04569.X.
Vaismoradi M, Turunen H, Bondas T. Content analysis and thematic analysis: implications for conducting a qualitative descriptive study. Nurs Health Sci. 2013;15:398–405. https://doi.org/10.1111/NHS.12048.
Flodgren G, Conterno LO, Mayhew A, Omar O, Pereira CR, Shepperd S. Interventions to improve professional adherence to guidelines for prevention of device-related infections. Cochrane Database Syst Rev. 2013;2013. https://doi.org/10.1002/14651858.CD006559.PUB2/MEDIA/CDSR/CD006559/IMAGE_N/NCD006559-CMP-002-07.PNG
Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101. https://doi.org/10.1191/1478088706QP063OA.
Sundler AJ, Lindberg E, Nilsson C, Palmér L. Qualitative thematic analysis based on descriptive phenomenology. Nurs Open. 2019;6:733–9. https://doi.org/10.1002/NOP2.275.
Article PubMed PubMed Central Google Scholar
dos Santos RP, Lopes GR. Thematic series on social network analysis and mining. J Intern Serv Appl. 2019;10:14. https://doi.org/10.1186/s13174-019-0113-z.
Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. (2015) Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. Int J Evid Based Healthc. 2015;13:147–53. https://doi.org/10.1097/XEB.0000000000000054.
Ackley C, Elsheikh M, Zaman S. Scoping review of neglected tropical disease interventions and health promotion: a framework for successful NTD interventions as evidenced by the literature. PLoS Negl Trop Dis. 2021;15:e0009278. https://doi.org/10.1371/JOURNAL.PNTD.0009278.
Article CAS PubMed PubMed Central Google Scholar
Ackley C, Elsheikh M, Zaman S. Scoping review of neglected tropical disease interventions and health promotion: a framework for successful NTD interventions as evidenced by the literature. In: Yazdi-Feyzabadi V, editor. PLoS Negl Trop Dis. 2021;15:e0009278. https://doi.org/10.1371/journal.pntd.0009278
Guarino N. Understanding, building and using ontologies. Int J Human Comput Stud. 1997;46. https://doi.org/10.1006/ijhc.1996.0091
Ismail S, Fildes R, Ahmad R, Wan Mohamad Ali WN, Omar T. The practicality of Malaysia dengue outbreak forecasting model as an early warning system. Infect Dis Model. 2022;7:510–25. https://doi.org/10.1016/j.idm.2022.07.008.
Article PubMed PubMed Central Google Scholar
Rahman MdS, Safa NT, Sultana S, Salam S, Karamehic-Muratovic A, Overgaard HJ. Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh. Parasite Epidemiol Control. 2022;18:e00266. https://doi.org/10.1016/j.parepi.2022.e00266.
Article PubMed PubMed Central Google Scholar
Pise R, Patil K, Pise N. Automatic classification of mosquito genera using transfer learning. J Theor Appl Inf Technol. 2022;100:1929–40.
Jin B, Cruz L, Goncalves N. Deep facial diagnosis: deep transfer learning from face recognition to facial diagnosis. IEEE Access. 2020;8:123649–61. https://doi.org/10.1109/ACCESS.2020.3005687.
Barde PV, Mishra N, Singh N. Timely diagnosis, use of information technology and mosquito control prevents dengue outbreaks: experience from central India. J Infect Public Health. 2018;11:739–41. https://doi.org/10.1016/j.jiph.2018.03.002.
da Silva Neto SR, Tabosa Oliveira T, Teixeira IV, Aguiar de Oliveira SB, Souza Sampaio V, Lynn T, et al. Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: a systematic review. Dinglasan RR, editor. PLoS Negl Trop Dis. 2022;16:e0010061. https://doi.org/10.1371/journal.pntd.0010061
Pataki BA, Garriga J, Eritja R, Palmer JRB, Bartumeus F, Csabai I. Deep learning identification for citizen science surveillance of tiger mosquitoes. Sci Rep. 2021;11. https://doi.org/10.1038/s41598-021-83657-4
Lopez DM, de Mello FL, Giordano Dias CM, Almeida P, Araújo M, Magalhães MA, et al. Evaluating the surveillance system for spotted fever in Brazil using machine-learning techniques. Front Public Health. 2017;5. https://doi.org/10.3389/fpubh.2017.00323
Guiyab RB. Development of prediction models for the dengue survivability prediction: an integration of data mining and decision support system. Intern J Innov Technol Exploring Eng. 2019;8:2199–205. https://doi.org/10.35940/ijitee.J9411.0881019.
Kumar NK, Sikamani KT. Prediction of chronic and infectious diseases using machine learning classifiers-a systematic approach. Intern J Intell Eng Sys. 2020;13:11–20. https://doi.org/10.22266/IJIES2020.0831.02.
Ekpo UF, Hürlimann E, Schur N, Oluwole AS, Abe EM, Mafe MA, et al. Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling. Geospat Health. 2013;7:355–66. https://doi.org/10.4081/gh.2013.92.
Kwofie SK, Anyimadu DT, Aryee S, Asare B, Kokroko N, Owusu JA, et al. BuDb: a curated drug discovery database for Buruli ulcer. J Comput Biophys Chem. 2023;22:31–41. https://doi.org/10.1142/S2737416523500011.
Zorn KM, Sun S, McConnon CL, Ma K, Chen EK, Foil DH, et al. A machine learning strategy for drug discovery identifies anti-schistosomal small molecules. ACS Infect Dis. 2021;7:406–20. https://doi.org/10.1021/acsinfecdis.0c00754.
Article CAS PubMed PubMed Central Google Scholar
Korotcov A, Tkachenko V, Russo DP, Ekins S. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm. 2017;14:4462–75. https://doi.org/10.1021/acs.molpharmaceut.7b00578.
Article CAS PubMed PubMed Central Google Scholar
Sood SK, Sood V, Mahajan I, Sahil. Fog–cloud assisted iot-based hierarchical approach for controlling dengue infection. Comput J. 2022;65:67–79. https://doi.org/10.1093/comjnl/bxaa005.
Comments (0)