Count Data Regression Modelling: An Application to Monkeypox Confirmed Cases
,
Abstract
Introduction: With the presence of COVID 19, some countries also faced an increase in number of cases due to Monkeypox virus.
The main aim of this research was to investigate whether it is possible to fit count data regression models to predict the daily incidence of Monkeypox confirmed cases.
Methods: In this study we have used two types of traditional count regression models like Poisson regression model and Negative binomial regression model using identity and logarithmic link function. Since our data was overdispersed, Negative binomial regression model with logarithmic link function fitted well as compared to other models. The parameters were estimated using SPSS, version 23.0.
Results: The Negative Binomial Regression model with logarithm function fits well to the data related to Monkeypox cases. Therefore, the model shows that majority of the countries like Brazil, Canada, France, Germany, Peru, Spain, United Kingdom and United States of America shows significant decrease in number of cases with respect to time. The prediction line was plotted using this model where the line predicts well about the daily Monkeypox cases reported by different countries.
Conclusion: From our study, we concluded that the count data regression model can be used widely to predict the incidence of any disease. The countries like Canada and Brazil have largest and smallest slope coefficient which shows maximum and minimum decrease in expected number of cases confirmed each day respectively.
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25. Aljabali AAa, Obeid MA, Nusair MB, Hmedat A, Tambuwala MM. Monkeypox virus: An emerging epidemic. Microb Pathog. 2022 Dec;173:105794.
26. Sidumo B, Sonono E, Takaidza I. Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data. Ann Data Sci [Internet]. 2023 Apr 13 [cited 2023 May 4]; Available from: https://link. springer.com/10.1007/s40745-023-00464-6
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29. Bekalo DB, Kebede DT. Zero-Inflated Models for Count Data: An Application to Number of Antenatal Care Service Visits. Ann Data Sci. 2021 Dec;8(4):683–708.
30. Mehdizadeh A, Cai M, Hu Q, Alamdar Yazdi MA, Mohabbati-Kalejahi N, Vinel A, et al. A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling. Sensors. 2020 Feb 18;20(4):1107.
31. Dwivedi AK, Dwivedi SN, Deo S, Shukla R. Statistical models for predicting number of involved nodes in breast cancer patients. Health (N Y). 2010;02(07):641–51.
32. Salinas-Rodríguez A, Manrique- Espinoza B, Sosa-Rubí SG. Análisis estadístico para datos de conteo: aplicaciones para el uso de los servicios de salud. Salud Pública México [Internet]. 2009 Oct [cited 2023 May 4];51(5). Available from: http://www.scielosp.org/ scielo.php?script=sci_arttext&pid=S0036- 36342009000500007&lng=es&nrm=iso&tlng =es
33. Long JS, Freese J. Regression models for categorical dependent variables using Stata. College Station, Tex: Stata Press; 2001. 341 p.
34. Yu L. Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant. Int J Inf Technol Comput Sci. 2012 Jul 16;4(8):63–70.
35. Roques L, Klein E, Papaıx J, Soubeyrand S. Mechanistic-statistical SIR modelling for early estimation of the actual number of cases and mortality rate from COVID-19.
36. Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M, et al. Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int. 2023 May 1;103(5):936–48.
37. Becker JU, Mayerich D, Padmanabhan M, Barratt J, Ernst A, Boor P, et al. Artificial intelligence and machine learning in nephropathology. Kidney Int. 2020 Jul 1;98(1):65–75.
38. Ozseven T. Infant cry classification by using different deep neural network models and hand-crafted features. Biomed Signal Process Control. 2023 May 1;83:104648.
39. Churyumov AY, Kazakova AA. Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling. Materials. 2023 Jan;16(3):1083.
40. Churyumov AY, Pozdniakov AV, Mondoloni B, Prosviryakov AS. Effect of boron concentration on hot deformation behavior of stainless steel. Results Phys. 2019 Jun 1;13:102340.
41. Arun Babu K, Prithiv TS, Gupta A, Mandal S. Modeling and simulation of dynamic recrystallization in super austenitic stainless steel employing combined cellular automaton, artificial neural network and finite element method. Comput Mater Sci. 2021 Jul 1;195:110482.
2. Wei F, Peng Z, Jin Z, Wang J, Xu X, Zhang X, et al. Study and prediction of the 2022 global monkeypox epidemic. J Biosaf Biosecurity. 2022 Dec;4(2):158–62.
3. Rosa RB, Ferreira de Castro E, Vieira da Silva M, Paiva Ferreira DC, Jardim ACG, Santos IA, et al. In vitro and in vivo models for monkeypox. iScience. 2023 Jan;26(1):105702.
4. Besombes C, Mbrenga F, Schaeffer L, Malaka C, Gonofio E, Landier J, et al. National Monkeypox Surveillance, Central African Republic, 2001–2021. Emerg Infect Dis. 2022 Dec;28(12):2435–45.
5. Miura F, Backer JA, van Rijckevorsel G, Bavalia R, Raven S, Petrignani M, et al. Time scales of human mpox transmission in the Netherlands [Internet]. Infectious Diseases (except HIV/AIDS); 2022 Dec [cited 2023 Feb 27]. Available from: http://medrxiv.org/ lookup/doi/10.1101/2022.12.03.22283056
6. Kozlov M. Monkeypox goes global: why scientists are on alert. Nature. 2022 Jun 2;606(7912):15–6.
7. Beaujean AA, Grant MB. Tutorial on Using Regression Models with Count Outcomes using R. [cited 2023 Feb 11]; Available from: https://scholarworks.umass. edu/pare/vol21/iss1/2/
8. Ogoina D. Sexual behaviours and clinical course of human monkeypox in Spain. The Lancet. 2022 Aug;400(10353):636–7.
9. 2022-23 Mpox (Monkeypox) Outbreak: Global Trends [Internet]. [cited 2023 May 2]. Available from: https:// worldhealthor g.shinyapps.io/mpx_ global/#35_Symptomatology
10. Chan S, Chu J, Zhang Y, Nadarajah S. Count regression models for COVID-19. Phys Stat Mech Its Appl. 2021 Feb;563:125460.
11. Du J, Park YT, Theera-Ampornpunt N, McCullough JS, Speedie SM. The use of count data models in biomedical informatics evaluation research. J Am Med Inform Assoc. 2012 Jan;19(1):39–44.
12. Coxe S, West SG, Aiken LS. The Analysis of Count Data: A Gentle Introduction to Poisson Regression and Its Alternatives. J Pers Assess. 2009 Feb 17;91(2):121–36.
13. Vetter TR, Schober P. Regression: The Apple Does Not Fall Far From the Tree. Anesth Analg. 2018 Jul;127(1):277–83.
14. Christou V, Fokianos K. Estimation and testing linearity for non-linear mixed poisson autoregressions. Electron J Stat [Internet]. 2015 Jan 1 [cited 2023 Feb 2];9(1). Available from: https://projecteuclid.org/ journals/electronic-journal-of-statistics/ volume-9/issue-1/Estimation-and-testing- linearity-for-non-linear-mixed-poisson- autoregressions/10.1214/15-EJS1044.full
15. Christou V, Fokianos K. QUASI- LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS: NEGATIVE BINOMIAL TIME SERIES. J Time Ser Anal. 2014 Jan;35(1):55– 78.
16. Fokianos K, Fried R. Interventions in log-linear Poisson autoregression. Stat Model. 2012 Aug;12(4):299–322.
17. Kitromilidou S, Fokianos K. Mallows’ quasi-likelihood estimation for log-linear Poisson autoregressions. Stat Inference Stoch Process. 2016 Oct;19(3):337–61.
18. Moksony F, Hegedűs R. The use of Poisson regression in the sociological study of suicide. Corvinus J Sociol Soc Policy. 2014 Dec 20;5(2):97–114.
19. Lindsey JK. On the Use of Corrections for Overdispersion. J R Stat Soc Ser C Appl Stat. 1999 Dec 1;48(4):553–61.
20. Dupont WD. Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data [Internet]. 2nd ed. Cambridge University Press; 2009 [cited 2023 May 10]. Available from: https://www.cambridge.org/core/product/ identifier/9780511575884/type/book
21. Mohapatra RK, Tuli HS, Sarangi AK, Chakraborty S, Chandran D, Chakraborty C, et al. Unexpected sudden rise of human monkeypox cases in multiple non-endemic countries amid COVID-19 pandemic and salient counteracting strategies: Another potential global threat? Int J Surg. 2022 Jul;103:106705.
22. Fifth Meeting of the International Health Regulations (2005) (IHR) Emergency Committee on the Multi-Country Outbreak of mpox (monkeypox) [Internet]. [cited 2023 May 13]. Available from: https://www.who. int/news/item/11-05-2023-fifth-meeting-of- the-international-health-regulations-(2005)- (ihr)-emergency-committee-on-the-multi- country-outbreak-of-monkeypox-(mpox)
23. Saavedra J, Faviero GF, Baruch R, Pinzon A. MPOX vaccines needed in Mexico. Lancet Reg Health - Am. 2023 Apr;20:100457.
24. Gordon NC, Rampling T. The 2022 monkeypox outbreak: the need for clinical curiosity. Lancet Infect Dis. 2023 Feb;23(2):133–4.
25. Aljabali AAa, Obeid MA, Nusair MB, Hmedat A, Tambuwala MM. Monkeypox virus: An emerging epidemic. Microb Pathog. 2022 Dec;173:105794.
26. Sidumo B, Sonono E, Takaidza I. Count Regression and Machine Learning Techniques for Zero-Inflated Overdispersed Count Data: Application to Ecological Data. Ann Data Sci [Internet]. 2023 Apr 13 [cited 2023 May 4]; Available from: https://link. springer.com/10.1007/s40745-023-00464-6
27. Desjardins CD. Modeling Zero- Inflated and Overdispersed Count Data: An Empirical Study of School Suspensions. J Exp Educ. 2016 Jul 2;84(3):449–72.
28. Aráujo EG, Vasconcelos JCS, Dos Santos DP, Ortega EMM, De Souza D, Zanetoni JPF. The Zero-Inflated Negative Binomial Semiparametric Regression Model: Application to Number of Failing Grades Data. Ann Data Sci [Internet]. 2021 Jun 30 [cited 2023 May 4]; Available from: https://link. springer.com/10.1007/s40745-021-00350-z
29. Bekalo DB, Kebede DT. Zero-Inflated Models for Count Data: An Application to Number of Antenatal Care Service Visits. Ann Data Sci. 2021 Dec;8(4):683–708.
30. Mehdizadeh A, Cai M, Hu Q, Alamdar Yazdi MA, Mohabbati-Kalejahi N, Vinel A, et al. A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling. Sensors. 2020 Feb 18;20(4):1107.
31. Dwivedi AK, Dwivedi SN, Deo S, Shukla R. Statistical models for predicting number of involved nodes in breast cancer patients. Health (N Y). 2010;02(07):641–51.
32. Salinas-Rodríguez A, Manrique- Espinoza B, Sosa-Rubí SG. Análisis estadístico para datos de conteo: aplicaciones para el uso de los servicios de salud. Salud Pública México [Internet]. 2009 Oct [cited 2023 May 4];51(5). Available from: http://www.scielosp.org/ scielo.php?script=sci_arttext&pid=S0036- 36342009000500007&lng=es&nrm=iso&tlng =es
33. Long JS, Freese J. Regression models for categorical dependent variables using Stata. College Station, Tex: Stata Press; 2001. 341 p.
34. Yu L. Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant. Int J Inf Technol Comput Sci. 2012 Jul 16;4(8):63–70.
35. Roques L, Klein E, Papaıx J, Soubeyrand S. Mechanistic-statistical SIR modelling for early estimation of the actual number of cases and mortality rate from COVID-19.
36. Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M, et al. Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int. 2023 May 1;103(5):936–48.
37. Becker JU, Mayerich D, Padmanabhan M, Barratt J, Ernst A, Boor P, et al. Artificial intelligence and machine learning in nephropathology. Kidney Int. 2020 Jul 1;98(1):65–75.
38. Ozseven T. Infant cry classification by using different deep neural network models and hand-crafted features. Biomed Signal Process Control. 2023 May 1;83:104648.
39. Churyumov AY, Kazakova AA. Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling. Materials. 2023 Jan;16(3):1083.
40. Churyumov AY, Pozdniakov AV, Mondoloni B, Prosviryakov AS. Effect of boron concentration on hot deformation behavior of stainless steel. Results Phys. 2019 Jun 1;13:102340.
41. Arun Babu K, Prithiv TS, Gupta A, Mandal S. Modeling and simulation of dynamic recrystallization in super austenitic stainless steel employing combined cellular automaton, artificial neural network and finite element method. Comput Mater Sci. 2021 Jul 1;195:110482.
| Files | ||
| Issue | Vol 9 No 2 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v9i2.14626 | |
| Keywords | ||
| Negative Binomial Regression model Poisson Regression model Monkeypox Akaike Information Criterion (AIC) Bayesian Information Criterion (BIC). | ||
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How to Cite
1.
Nair D, Hadaye R. Count Data Regression Modelling: An Application to Monkeypox Confirmed Cases. JBE. 2023;9(2):227-240.
