This article explains an improved method to fit the evolution dynamics of the COVID-19 pandemics in China and Thailand during January – April 2020. It is done by using a conventional compartmental model and piecewise linear approximation of the model parameters. The reported COVID-19 data of China between 22nd January and 22nd March (60 days) and Thailand between 14th March and 16th April (34 days) are considered. According to the evolution trends, estimations on the total numbers of population involved in the spreading are made and they are about 83000 for China and 3000 for Thailand. By further analyzing the data along with the Susceptible-Infected-Recovered model, relevant epidemiological parameters, which indicate the degree of the outbreak, can be extracted. For the China data, a good fit is obtained when linear time-varying functions for the parameters are assumed. In case of Thailand data, a reasonable fit is obtained with constant parameter values and it can be improved by considering the time lag of 13 days before the triggering of the recovery rate. Based on the obtained model parameters, a forecast of epidemic situation in Thailand is made. The expected end point of critical pandemic period is at the mid of June 2020. The calculated basic reproduction number of 3.39 is reported for the epidemic spreading of COVID-19 in Thailand.
Keywords: COVID-19, China, Thailand, Compartmental Model, Piecewise Linear Approximation
Adam, D. (2020). Modelling the pandemic – The simulations driving the world’s response to COVID-19. Nature, 580, 316-318.
Castorina, P., Iorio, A., & Lanteri, D. (2020). Data analysis on Coronavirus spreading by macroscopic growth laws. Retrieved from https://arxiv.org/abs/2003.00507
Chen, Q., Allot, A., & Lu., Z. (2020). Keep up with the latest coronavirus research. Nature, 579, 193.
Elsevier. (2020). Coronavirus. Retrieved from https://www.elsevier.com/connect/coronavirus-information-center
Foreignpolicy.com. (2020). Mapping the Coronavirus Outbreak. Retrieved from https://foreignpolicy.com/
Hu, Z., Ge, Q., Li, S., Jin, L., & Xiong, M. (2020). Artificial intelligence forcasting of Covid-19 in China. Retrieved from https://arxiv.org/abs/2002.07112
Huang, R., Liu, M., & Ding, Y. (2020). Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis. The Journal of Infection in Developing Countries, 14(3), 256-253.
Huang, Y., Yang, L., Dai, H., Tian, F., & Chen, K. (2020). Epidemic situation and forecasting of COVID-19 in and outside China. [Submitted]. Bull World Health Organ. E-pub: 16 March 2020. Retrieved from http://dx.doi.org/10.2471/BLT.20.25515
Johansson, R. (2019). Numerical Python - Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib (2nd ed.). New York: Apress.
Johns Hopkins University. (2020). the Center for Systems Science and Engineering Data. Retrieved from https://github.com/CSSEGISandData/COVID-19
Keeling, M. J., & Rohani, P. (2008). Modeling Infectious Diseases in Humans and Animals. New Jersey: Princeton University Press.
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700–721.
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., … Feng, Z. (2020). Early transmission dynamics in Wuhan, China, of novel Coronavirus – infected pneumonia. The New England Journal of Medicine, 382, 1199-1207.
Linge, S., & Langtangen, H. P. (2020). Programming for Computations–Python. Berlin: Springer.
Pongkitivanichkul, C., Samart, D., Tangphati, T., Koomhin, P., Pimton P., Dam-O, P., … Channuie, P. (2020). Estimating the size of COVID-19 epidemic outbreak. Retrieved from https://www.medrxiv.
Sanche, S., Lin, Y. T., Xu, C., Romero-Severson, E., Hengartner, N., & Ke, R. (2020). The novel Coronavirus, 2019-nCoV, is highly contagious and more infectious than initially estimated. Retrieved from https://www.medrxiv.org/content/10.1101/2020.02.07.20021154v1
Singh, R., & Adhikari, R. (2020). Age-structured impact of social distancing on the COVID-19 epidemic in India. Retrieved from https://arxiv.org/abs/2003.12055
Vynnycky, E., & White, R. G. (2010). An Introduction to Infectious Disease Modelling. New York: Oxford.
Wang, C., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan – Big data analytics, new technology, and proactive testing. JAMA, 323(14), 1341-1342.
WHO. (2020).Coronavirus disease (COVID-2019) situation reports. Retrieved from https://www.who.int/
Worldometers. (2020). Coronavirus. Retrieved from https://www.worldometers.info/coronavirus/
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.