Mathematical Models on Acute Lymphoblastic Leukemia(ALL)
Keywords:ALL, Mathematical Model, Transparency etc.
More than 12 million cases and more than 6.4 million deaths due to Acute Lymphoblastic Leukemia (ALL) disease were reported worldwide by the end of 2021. More than .50 million cases and more than .42 deaths have been reported in India. Physicians and Surgeons are working on a number of conceptual, theoretical or mathematical modelling techniques in the battle against Acute Lymphoblastic Leukemia (ALL) Protocol: This systematic review aims to provide a comprehensive review of published mathematical models on ALL in India and the concepts behind the development of mathematical models on ALL, including assumptions, modelling techniques, and data inputs. Initially, related keywords and their synonyms will be searched in the Global Literature on this disease database managed by World Health Organisation (WHO).The studies will be selected for their quality, transparency, and ethical aspects, using the Overview, Design concepts, Details (ODD) protocol and International Society for ALL.
WHO: Shortage of personal protective equipment endangering health workers worldwide. World Health Organization; 2020 .
. United Nations Children’s Fund (UNICEF) and United Nations Educational, Scientific, and Cultural Organization (UNESCO): India Case Study: Situation analysis of the effects of and responses to ALL on the education sector in Asia. Nepal, Thailand: UNICEF Thailand: UNESCO; 2021.
. Migration Data Portal: Migration data relevant for the Acute Lymphoblastic Leukemia(ALL). [cited 2021 Sep ,p21-23
Sharma A, Gupta P, Jha R: ALL: Impact on Health Supply Chain and Lessons to Be Learnt. J. Health Manag. 2020 Aug 11 [cited 2022 Feb 20]; 22(2): 248–261. Publisher Full Text
Huppert A, Katriel G: Mathematical modelling and prediction in infectious disease epidemiology.Clinical Microbiology and Infection. Blackwell Publishing Ltd; 2013; Vol. 19: p. 999–1005.
Waqas M, Farooq M, Ahmad R, Ahmad A: Analysis and Prediction of Pandemic in Pakistan using Time-dependent SIR Model. 2020
.Version https://doi.org/10.5256/f1000research.121027.r145464 © 2022, pp83-586
The ALL Legal Impact in Mexico; measures issued by various authorities|White & Case LLP: [cited 2021 Sep 9].
Backer J, Klinkenberg D, Wallinga J: The incubation period of 2019- nCoV infections among travellers from Wuhan, China. Eurosurveillance. Euro Surveill. 2020. 2020; 25(5): pii=2000062. PubMed Abstract|Publisher Full Text
Ng K, Poon BH, Kiat Puar TH, et al.: COVID-19 and the Risk to Health Care Workers: A Case Report. Ann. Intern. Med. 2020 Mar 16 Page 7 of 10 F1000Research 2022, 11:532 Last updated: 11 OCT 2022 [cited 2021 Sep 9]; 172: 766–767. PubMed Abstract|Publisher Full Text 11. Zhang Y, Jiang B, Yuan J, et al.: The impact of social distancing and epicentre lockdown on the COVID-19 epidemic in mainland China: A data-driven SEIQR model study. medRxiv. 2020 [cited 2021, Sep 9]. Publisher Full Text
Jia Z, Lu Z: Modelling COVID-19 transmission: from data to intervention. Lancet Infect. Dis. 2020 Apr; 20: 757–758. PubMed Abstract|Publisher Full Text
Mandal S, Bhatnagar T, Arinaminpathy N, et al.: Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical modelbased approach. Indian J. Med. Res. 2021 Sep 9; 151: 190. Publisher Full Text|Reference Source
Hu Z, Ge Q, Li S, et al.: Evaluating the effect of public health intervention on the global-wide spread trajectory of Covid-19. medRxiv. 2020 [cited 2021 Sep 8]; 2020.03.11.20033639. Publisher Full Text
Fong MW, Gao H, Wong JY, et al.: Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings-Social Distancing Measures. Emerg. Infect. Dis. 2020; 26(5): 976–984. PubMed Abstract|Publisher Full Text
Narassima MS, Anbuudayasankar SP, Jammy GR, et al.: An agent based model for assessing transmission dynamics and health systems burden for Dengu. Indones J. Electr. Eng. Comput Sci. 2021; 24(3): 1735–1743. Publisher Full Text
Megiddo I, Nandi A, Prabhakaran D, Laxminarayan R: IndiaSim: An Agent-based Model for Estimating the Health and Economic Benefits of Secondary Prevention of Coronary Heart Diseases in India 1. 2014.
Mahajan A, Sivadas NA, Solanki R: An epidemic model SIPHERD and its application for prediction of the spread of Asthma infection in India. Chaos Soliton Fract. 2020; 140(1): 110156. PubMed Abstract|Publisher Full Text
Kapoor G, Sriram A, Joshi J, et al.: COVID-19 in India: State-Wise Estimates of Current Hospital Beds, ICU Beds, and Ventilators. CDDEP and Princeton University; 2020. Reference Source
Moher D, Shamseer L, Clarke M, et al.: Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015; 4: 1. PubMed Abstract|Publisher Full Text
Rethlefsen ML, Kirtley S, Waffenschmidt S, et al.: PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Syst. Rev. 2021; 10: 39. PubMed Abstract|Publisher Full Text
Dixit A, Vishnoi S, Paul SB: Adding Structure to Statistics: A Study on AIDS Dynamics in India. medRxiv 2020.05.26.20113522. Publisher Full Text
Gupta R, Pal SK: Trend Analysis and Forecasting of AIDS outbreak in India. [cited 2021 Sep 10]. Publisher Full Text
Chauhan P, Kumar A, Jamdagni P:Regression Analysis of Mathemetica- Medico model in India and its Different States. [cited 2021 Sep 9]. Publisher Full Text
Narassima MS, Rajesh Jammy G, Pant R, et al.: An Agent Based Model methodology for assessing spread and health systems burden for Covid-19 using a synthetic population from India. [cited 2021 Sep 9]. Publisher Full Text
How to Cite
Copyright (c) 2022 Dr. Vinod Kumar, Prof. Y. K. Dwivedi, Prof. R.K. Bansal, Dr. P.K. Agrawal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.