Forecasting tuberculosis incidence: a review of time series and machine learning models for prediction and eradication strategies

References

• Ali MH, Khan DM, Jamal K, Ahmad Z, Manzoor S, Khan Z. 2021. Prediction of multidrug-resistant tuberculosis using machine learning algorithms in SWAT, Pakistan. J Healthc Eng. 20:1–11. doi: 10.1155/2021/2567080.
   Google Scholar
• Aryee G. 2018. Estimating the incidence of tuberculosis cases reported at a tertiary hospital in Ghana: a time series model approach 11 medical and health sciences 1117 public health and health services. BMC Public Health. 18(1). doi: 10.1186/s12889-018-6221-z.
   PubMedGoogle Scholar
• Asad M, Mahmood A, Usman M. 2020. A machine learning-based framework for predicting treatment failure in tuberculosis: a case study of six countries. Tuberc (Edinb). 123:101944. doi: 10.1016/j.tube.2020.101944.
   PubMed Web of Science ®Google Scholar
• Azeez A, Obaromi D, Odeyemi A, Ndege J, Muntabayi R. 2016. Seasonality and trend forecasting of tuberculosis prevalence data in eastern cape, South Africa, using a hybrid model. Int J Environ Res And Public Health. 13(8):757. doi: 10.3390/ijerph13080757.
   PubMed Web of Science ®Google Scholar
• Bonaccorso G. 2017. Machine learning algorithms. Packt Publishing Ltd. https://balasahebtarle.files.wordpress.com/2020/01/machine-learning-algorithms_text-book.pdf.
   Google Scholar
• Cao S. 2013. A hybrid seasonal prediction model for tuberculosis incidence in China. http://www.biomedcentral.com/1472-6947/13/56.
   Google Scholar
• Chen J, Ho E, Jiang Y, Whittaker R, Yang T, Bullen C. 2020. Mobile social network–based smoking cessation intervention for Chinese male smokers: pilot randomized controlled trial. JMIR Mhealth Uhealth. 8(10):10. doi: 10.2196/17522.
   Web of Science ®Google Scholar
• de Andrade HLP. 2021. Tuberculosis forecasting and temporal trends by sex and age in a high endemic city in northeastern Brazil: where were we before the Covid-19 pandemic. BMC Infect Dis. 21(1). doi: 10.1186/s12879-021-06978-9.
   PubMed Web of Science ®Google Scholar
• Dheda K. 2022. The intersecting pandemics of tuberculosis and COVID-19: population-level and patient-level impact, clinical presentation, and corrective interventions. Lancet Respir Med. 10(6):603–622. doi: 10.1016/S2213-2600(22)00092-3.
   PubMed Web of Science ®Google Scholar
• Dong S, Shen X, Xia Z, Li X, Pan Q, Zhao Q. 2019. Changes in the epidemic of pulmonary tuberculosis in Shanghai from 1992 to 2016. Trop Med Int Health. 24(2):220–228. doi: 10.1111/tmi.13187.
   PubMed Web of Science ®Google Scholar
• Frauenfeld L, Nann D, Sulyok Z, Feng YS, Sulyok M. 2020. Forecasting tuberculosis using diabetes-related google trends data. Pathog Glob Health. 114(5):236–241. doi: 10.1080/20477724.2020.1767854.
   PubMed Web of Science ®Google Scholar
• Guo X, Shen H, Liu S, Xie N, Yang Y, Jin J. 2021. Predicting the trend of infectious diseases using grey self-memory system model: a case study of the incidence of tuberculosis. Public Health. 201:108–114. doi: 10.1016/j.puhe.2021.09.025.
   PubMed Web of Science ®Google Scholar
• Heireman L, Bruynseels P, Camps K, Geysels D, Huyghe E, André E, Van Gasse N. 2022. Comparison of the QuantiFERON-TB® gold plus on LIAISON® XL and T-SPOT.TB for the diagnosis of latent mycobacterium tuberculosis infection in a low tuberculosis incidence population. Diagn Microbiol Infect Dis. 102(3):3. doi: 10.1016/j.diagmicrobio.2021.115613.
   Web of Science ®Google Scholar
• Li J. 2021. Forecasting the tuberculosis incidence using a novel ensemble empirical mode decomposition-based data-driven hybrid model in Tibet, China. Infect Drug Resist. 14:1941–1955. doi: 10.2147/IDR.S299704.
   PubMed Web of Science ®Google Scholar
• Li Z. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population,” Infect Drug Resist, 2019; Volume 12, pp. 1011–1020. 10.2147/IDR.S190418.
   PubMed Web of Science ®Google Scholar
• Liang X, Xu Q, Guan R, Zhao Y. 2020. Forecasting tuberculosis incidence in China using Baidu index: a comparative study. ACM International Conference Proceeding Series p. 22–29. 10.1145/3418094.3418129.
   Google Scholar
• Liao Z, Zhang X, Zhang Y, Peng D. 2019. Seasonality and trend forecasting of tuberculosis incidence in Chongqing, China. Interdiscip Sci. 11(1):77–85. doi: 10.1007/s12539-019-00318-x.
   PubMed Web of Science ®Google Scholar
• Li C, Nie S, Cao Y, Yu Y, Hu Z. 2020. Trace-based dynamic gas estimation of loops in smart contracts. IEEE Open J Comput Soc. 1:295–306. doi: 10.1109/OJCS.2020.3039991.
   Google Scholar
• Li ZQ, Pan HQ, Liu Q, Song H, Wang JM. 2020. Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China. Infect Dis Poverty. 9(1). doi: 10.1186/s40249-020-00771-7.
   Web of Science ®Google Scholar
• Liu Q. Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses,” Infect Drug Resist, 2019; Volume 12, pp. 2311–2322. 10.2147/IDR.S207809.
   PubMed Web of Science ®Google Scholar
• Mao Q, Zhang K, Yan W, Cheng C. 2018. Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. J Infect Public Health. 11(5):707–712. doi: 10.1016/j.jiph.2018.04.009.
   PubMed Web of Science ®Google Scholar
• Mohammed SH, Ahmed MM, Al-Mousawi AM, Azeez A. 2018. Seasonal behavior and forecasting trends of tuberculosis incidence in Holy Kerbala, Iraq. Int J Mycobacteriol. 7(4):361–367. doi: 10.4103/ijmy.ijmy_109_18.
   PubMedGoogle Scholar
• Moosazadeh M, Khanjani N, Nasehi M, Bahrampour A. 2015. Predicting the incidence of smear positive tuberculosis cases in Iran using time series analysis. http://ijph.tums.ac.ir/.
   Google Scholar
• Moosazadeh M, Nasehi M, Bahrampour A, Khanjani N, Sharaf S, Ahmadi S. 2014. Forecasting tuberculosis incidence in Iran using box-Jenkins models. Iran Red Crescent Med J. 16(5). doi: 10.5812/ircmj.11779.
   Web of Science ®Google Scholar
• Orjuela-Cañón AD, Jutinico AL, González MED, García CEA, Vergara E, Palencia MA. 2022. Time series forecasting for tuberculosis incidence employing neural network models. Heliyon. 8(7):e09897. doi: 10.1016/j.heliyon.2022.e09897.
   PubMed Web of Science ®Google Scholar
• Page MJ. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 372:n71. doi: 10.1136/bmj.n71.
   PubMed Web of Science ®Google Scholar
• Pai M, Kasaeva T, Swaminathan S. 2022. Covid-19’s devastating effect on tuberculosis care — a path to recovery. N Engl J Med. 386(16):1490–1493. doi: 10.1056/NEJMp2118145.
   PubMed Web of Science ®Google Scholar
• Pereira FH. 2018. Nonlinear autoregressive neural network models for prediction of transformer oil-dissolved gas concentrations. Energies (Basel). 11(7):1691. doi: 10.3390/en11071691.
   Web of Science ®Google Scholar
• Petersen E. 2022. World TB day 2022: revamping and reshaping global tb control programs by advancing lessons learned from the COVID-19 pandemic. Int J Infect Dis. 124:S1–S3. doi: 10.1016/j.ijid.2022.02.057.
   PubMed Web of Science ®Google Scholar
• Starshinova A, Belyaeva E, Doktorova N, Korotkevich I, Kudlay D. 2023. Tuberculosis in the Russian federation: prognosis and epidemiological models in a situation after the COVID-19 pandemic. J Epidemiol Glob Health. 13(1):1–12. doi: 10.1007/s44197-023-00085-5.
   PubMed Web of Science ®Google Scholar
• Wang Y. 2019. Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. BMJ Open. 9(7):e024409. doi: 10.1136/bmjopen-2018-024409.
   PubMed Web of Science ®Google Scholar
• Wang Y. An advanced data-driven hybrid model of SARIMA-NNNAR for tuberculosis incidence time series forecasting in Qinghai province, China,” Infect Drug Resist, 2020a; Volume 13, pp. 867–880. 10.2147/IDR.S232854.
   PubMed Web of Science ®Google Scholar
• Wang Y. Secular seasonality and trend forecasting of tuberculosis incidence rate in China using the advanced error-trend-seasonal framework,” Infect Drug Resist, 2020b; Volume 13, pp. 733–747. 10.2147/IDR.S238225.
   PubMed Web of Science ®Google Scholar
• Wang Y, Chunjie X, Ren J, Weidong W, Zhao X, Chao L, Liang W, Yao S 2020. Secular seasonality and trend forecasting of tuberculosis incidence rate in China using the advanced error-trend-seasonal framework. Infect Drug Resist. 13:733–747. doi: 10.2147/IDR.S238225.
   PubMed Web of Science ®Google Scholar
• Wang KW, Deng C, Li JP, Zhang YY, Li XY, Wu MC. 2017. Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiol Infect. 145(6):1118–1129. doi: 10.1017/S0950268816003216.
   PubMed Web of Science ®Google Scholar
• Wang H, Tian CW, Wang WM, Luo XM. 2018. Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiol Infect. 146(8):935–939. doi: 10.1017/S0950268818001115.
   PubMed Web of Science ®Google Scholar
• Wang Y, Xu C, Yao S, Zhao Y. 2020. Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator. Epidemiol Infect. 148. doi: 10.1017/S095026882000237X.
   Web of Science ®Google Scholar
• Wei W. 2017. A new hybrid model using an autoregressive integrated moving average and a generalized regression neural network for the incidence of tuberculosis in Heng County, China. Am J Trop Med Hyg. 97(3):799–805. doi: 10.4269/ajtmh.16-0648.
   PubMed Web of Science ®Google Scholar
• Wei W, Xia L, Wu J, Zhou Z, Zhang W, Luan R. 2023. The environmental and socioeconomic effects and prediction of patients with tuberculosis in different age groups in Southwest China: a population-based study. JMIR Public Health Surveill. 9:e40659. doi: 10.2196/40659.
   PubMedGoogle Scholar
• WHO. 2015. Digital health for the end tb strategy: an agenda for action. Who; https://apps.who.int/iris/handle/10665/205222.
   Google Scholar
• WHO. 2019. WHO report on the global tobacco epidemic, 2019: offer help to quit tobacco use: executive summary. World Health Organization.
   Google Scholar
• WHO. 2021. Global tuberculosis report 2021. https://reliefweb.int/report/world/global-tuberculosis-report-2021.
   Google Scholar
• Wu Z, Chen Z, Long S, Wu A, Wang H. 2022. Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China. BMC Infect Dis. 22(1):1–10. doi: 10.1186/s12879-022-07620-y.
   PubMed Web of Science ®Google Scholar
• Zhang G. 2013. Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China. PLoS One. 8(11):e80969. doi: 10.1371/journal.pone.0080969.
   PubMed Web of Science ®Google Scholar
• Zheng Y, Zhang L, Wang L, Rifhat R. 2020. Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China. BMC Infect Dis. 20(1). doi: 10.1186/s12879-020-05033-3.
   Web of Science ®Google Scholar
• Zheng Y, Zhang X, Wang X, Wang K, Cui Y. 2021. Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China. BMJ Open. 11(1):e041040. doi: 10.1136/bmjopen-2020-041040.
   PubMed Web of Science ®Google Scholar
• Zimmer AJ. 2022. Tuberculosis in times of COVID-19. J Epidemiol Community Health. 76(3):310–316. doi: 10.1136/jech-2021-217529.
   PubMed Web of Science ®Google Scholar
• Zuo Z. 2020. Spatiotemporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 by the nationwide surveillance system. BMC Public Health. 20(1). doi: 10.1186/s12889-020-09331-y.
   Web of Science ®Google Scholar