COVID-19 Incidence Prediction Model Based on Community Behavior With Neural Networks

Authors

  • Victor Hulu Department of Public Health, Faculty of Medicine, Dentistry and Health Sciences, Universitas Prima Indonesia, Medan, Indonesia https://orcid.org/0000-0003-0643-0338
  • RNS Fransiska Department of Midwifery, Sekolah Tinggi Ilmu Kesehatan Senior, Medan, Indonesia
  • Widya Yanti Sihotang Department of Public Health, Faculty of Medicine, Dentistry and Health Sciences, Universitas Prima Indonesia, Medan, Indonesia
  • Suharni Sinaga Department of Midwifery, Sekolah Tinggi Ilmu Kesehatan Senior, Medan, Indonesia
  • Frans Judea Samosir Department of Public Health, Faculty of Medicine, Dentistry and Health Sciences, Universitas Prima Indonesia, Medan, Indonesia
  • Astaria Br Ginting Midwifery Study Undergraduate Program, Sekolah Tinggi Ilmu Kesehatan Mitra Husada Medan, Medan, Indonesia
  • Riska Wani Eka Putri Department of Nursing, Akademik Keperawatan Kesdam I/BB, Pematangsiantar, Indonesia
  • Lam Murni Br Sagala Department of Nursing, Faculty of Nursing, Sekolah Tinggi Ilmu Kesehatan Murni Teguh, Medan, Indonesia
  • Yuni Vivi Santri P Department of Midwifery, Sekolah Tinggi Ilmu Kesehatan Senior, Medan, Indonesia
  • Nurhamida Fithri Department of Midwifery, Sekolah Tinggi Ilmu Kesehatan Senior, Medan, Indonesia
  • Faradita Wahyuni Department of Midwifery, Sekolah Tinggi Ilmu Kesehatan Senior, Medan, Indonesia
  • Putranto Manalu Department of Public Health, Faculty of Medicine, Dentistry and Health Sciences, Universitas Prima Indonesia, Medan, Indonesia

DOI:

https://doi.org/10.3889/oamjms.2022.9175

Keywords:

Behavior prediction model, COVID-19 incidence, Neural network

Abstract

Abstract

BACKGROUNDS : The COVID-19 pandemic has created a global health emergency that requires a public health response to prevent the spread of the virus.

AIM: The purpose of this study was to determine the prediction model for the incidence of COVID-19 based on community behavior.

METHODS: This study used a cross-sectional study design. The study population was all people aged >18 years in Medan City and obtained a sample of 395 people with stratified random sampling technique. The research instrument used a questionnaire in google form, then, using Microsoft Office Excel, we transferred the data from the survey to a computer program. Furthermore, the data was analyzed using the neural networks method. Then the features importance will be calculated using the Random Forest with Mean Decrease Impurity (RF-MDI) method.

RESULT: The results showed that based on the confusion matrix, the prediction value for those who did not suffer from COVID-19 was correct from negative data = 8, the correct prediction value for COVID-19 from positive data = 8. While the incorrect prediction value for machines that predicted negative results but the actual data was positive = 2, and predicts a positive result but the actual data is negative = 4. Thus, based on the neural net classification method, the accuracy value is 72%. The results of this study indicate that poor preventive behavior by the community greatly affects the spread of COVID-19 cases.

CONCLUSION: Poor community behavior, such as not limiting their interaction/contact with other people, not exercising frequently, leaving the house without keeping a safe distance, and not washing hands regularly, can all impact COVID-19 transmission in the community

Keywords: Behavior Prediction Model, COVID-19 Incidence, Neural Network

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Plum Analytics Artifact Widget Block

References

Ko CH, Yen JY. Impact of COVID-19 on gaming disorder: Monitoring and prevention. J Behav Addict. 2020;9(2):187-9. https://doi.org/10.1556/2006.2020.00040 PMid:32634111

Ren LL, Wang YM, Wu ZQ, Xiang ZC, Guo L, Xu T, et al. Identification of a novel COVID-19 causing severe pneumonia in human: A descriptive study. Chin Med J (Engl). 2020;133(9):1015-24. https://doi.org/10.1097/CM9.0000000000000722 PMid:32004165

WHO. Pneumonia of Unknown Cause – China. WHO; 2020.

World Health Organization. Report of the WHO-China Joint Mission on COVID-19 Disease 2019 (COVID-19) 16-24 February 2020; 2020.

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel COVID-19 in Wuhan, China. Lancet. 2020;395(10223):497-506. https://doi.org/10.1016/S0140-6736(20)30183-5 PMid:31986264

Roy D, Tripathy S, Kar SK, Sharma N, Verma SK, Kaushal V. Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during COVID-19 pandemic. Asian J Psychiatr. 2020;51:102083. https://doi.org/10.1016/j.ajp.2020.102083 PMid:32283510

Bachok N, Ghazali AK, Hami R. Knowledge, awareness, attitude and preventive behaviour on the transmission of the pandemic novel COVID-19 among Malaysians. Malays J Med Sci. 2021;28(2):106-18. https://doi.org/10.21315/mjms2021.28.2.10 PMid:33958965

Kemenkes RI, “Kasus Global Covid-19. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU),” satudata kemenkes, 2021. [Online]. Available: https://satudata.kemkes.go.id/infocovid19. [Last accessed on 2021May 17].

Kemenkes RI. “Monitoring of COVID-19 cases in Indonesia,” Indonesia; June 2021.

Medan, P. K. (2021). Data General Terkait Covid-19 Kota Medan. https://covid19.pemkomedan.go.id/index.php?page=stat_medan. [Last accessed on 2021 Jun 29].

Banik R, Rahman M, Sikder MT, Rahman QM, Pranta MU. Knowledge, attitudes, and practices related to the COVID-19 pandemic among Bangladeshi youth: A web-based cross-sectional analysis. J Public Heal. 2021;29(1):1–11. https://doi.org/10.1007/s10389-020-01432-7 PMid:33489718

Lee M, Kang BA, You M. Knowledge, attitudes, and practices (KAP) toward COVID-19: A cross-sectional study in South Korea. BMC Public Health. 2021;21(1):295–304. https://doi.org/10.1186/s12889-021-10285-y

Andarge E, Fikadu T, Temesgen R, Shegaze M, Feleke T, Haile F, et al. Intention and practice on personal preventive measures against the COVID-19 pandemic among adults with chronic conditions in Southern Ethiopia: A survey using the theory of planned behavior. J Multidiscip Healthc. 2020;13:1863-77. https://doi.org/10.2147/jmdh.s284707 PMid:33299323

Almeida A, Azkune G. Predicting human behaviour with recurrent neural networks. Appl Sci. 2018;8(2):305. https://doi.org/10.3390/app8020305

Melin P, Monica JC, Sanchez D, Castillo O. A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network. Soft Comput. 2020;19:1–10. https://doi.org/10.1007/s00500-020-05452-z PMid:33230389

Sánchez D, Melin P, Castillo O. Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng Appl Artif Intell. 2017;64:172-86. https://doi.org/10.1016/j.engappai.2017.06.007

Bruine de Bruin W. Age differences in COVID-19 risk perceptions and mental health: Evidence from a National U.S. Survey Conducted in March 2020. J Gerontol B Psychol Sci Soc Sci. 2021;76(2):e24-9. https://doi.org/10.1093/geronb/gbaa074 PMid:32470120

Cortis D. On determining the age distribution of COVID-19 pandemic. Front Public Health. 2020;8:202. https://doi.org/10.3389/fpubh.2020.00202 PMid:32574295

Kemenkes RI. Pusat Analisis Determinan Kesehatan; 2021. Avaialble from: http://www.padk.kemkes.go.id/article/read/2020/04/23/21/hindari-lansia-dari-COVID-19.html [Last accessed on 2021 Jun 28].

Nikolich-Zugich J, Knox KS, Rios CT, Natt B, Bhattacharya D, Fain MJ. SARS-CoV-2 and COVID-19 in older adults: What we may expect regarding pathogenesis, immune responses, and outcomes. Geroscience. 2020;42(2):505-14. https://doi.org/10.1007/s11357-020-00186-0 PMid:32274617

Hadjidemetriou GM, Sasidharan M, Kouyialis G, Parlikad AK. The impact of government measures and human mobility trend on COVID-19 related deaths in the UK. Transp Res Interdiscip Perspect. 2020;6:100167. https://doi.org/10.1016/j.trip.2020.100167

Shao W, Xie J, Zhu Y. Mediation by human mobility of the association between temperature and COVID-19 transmission rate. Environ Res. 2021;194:110608. https://doi.org/10.1016/j.envres.2020.110608 PMid:33338486

Rader B, Scarpino S V, Nande A, Hill AL, Adlam B, Reiner RC, et al. Crowding and the shape of COVID-19 epidemics. Nat Med . 2020;26(12):1829–34. Available from: https://doi.org/10.1038/ s41591-020-1104-0

Orgilés M, Morales A, Delvecchio E, Mazzeschi C, Espada JP. Immediate psychological effects of the COVID-19 quarantine in youth from Italy and Spain. Front Psychol. 2020;11:2986. https://doi.org/10.3389/fpsyg.2020.579038 PMid:33240167

Martarelli, C. S., Wolff, W., & Bieleke, M. Bored by bothering? A cost-value approach to pandemic boredom. Humanities and Social Sciences Communications. 2021, 8.1:1-10.https://doi.org/10.1057/s41599-021-00894-8

Boylan J, Seli P, Scholer AA, Danckert J. Boredom in the COVID-19 pandemic: Trait boredom proneness, the desire to act, and rule-breaking. Pers Individ Dif. 2021;171:110387. https://doi.org/10.1016/j.paid.2020.110387

Hacimusalar Y, Kahve AC, Yasar AB, Aydin MS. Anxiety and hopelessness levels in COVID-19 pandemic: A comparative study of healthcare professionals and other community sample in Turkey. J Psychiatr Res. 2020;129:181-8. https://doi.org/10.1016/j.jpsychires.2020.07.024 PMid:32758711

Bostan S, Akbolat M, Kaya A, Ozata M, Gunes D. Assessments of anxiety levels and working conditions of health employees working in COVİD-19 pandemic hospitals. Electron J Gen Med. 2020;17(5):1–5. https://doi.org/10.29333/ejgm/8228.

Roberts JA, David ME. Improving predictions of COVID-19 preventive behavior: Development of a sequential mediation model. J Med Internet Res. 2021;23(3):e23218. https://doi.org/10.2196/23218 PMid:33651707

Basch CH, Hillyer GC, Meleo-Erwin ZC, Jaime C, Mohlman J, Basch CE. Correction: Preventive behaviors conveyed on youtube to mitigate transmission of COVID-19: Cross-sectional study. JMIR Public Health Surveill. 2020;6(2):e19601. https://doi.org/10.2196/19601 PMid:32374718

Mao L. Evaluating the combined effectiveness of influenza control strategies and human preventive behavior. PLoS One. 2011;6(10):e24706. https://doi.org/10.1371/journal.pone.0024706 PMid:22043275

Downloads

Published

2022-04-15

How to Cite

1.
Hulu V, Fransiska R, Sihotang WY, Sinaga S, Samosir FJ, Ginting AB, Putri RWE, Sagala LMB, Santri P YV, Fithri N, Wahyuni F, Manalu P. COVID-19 Incidence Prediction Model Based on Community Behavior With Neural Networks. Open Access Maced J Med Sci [Internet]. 2022 Apr. 15 [cited 2022 Jul. 4];10(E):739-45. Available from: https://oamjms.eu/index.php/mjms/article/view/9175

Issue

Section

Public Health Epidemiology

Categories