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. 2020 Oct 18:2020:6397063.
doi: 10.1155/2020/6397063. eCollection 2020.

Phenomenological Modelling of COVID-19 Epidemics in Sri Lanka, Italy, the United States, and Hebei Province of China

A M C H Attanayake  1 , S S N Perera  2 , S Jayasinghe  3
Affiliations

Phenomenological Modelling of COVID-19 Epidemics in Sri Lanka, Italy, the United States, and Hebei Province of China

A M C H Attanayake et al. Comput Math Methods Med. .

Abstract

The COVID-19 pandemic has resulted in increasing number of infections and deaths every day. Lack of specialized treatments for the disease demands preventive measures based on statistical/mathematical models. The analysis of epidemiological curve fitting, on number of daily infections across affected countries, provides useful insights on the characteristics of the epidemic. A variety of phenomenological models are available to capture the dynamics of disease spread and growth. The number of daily new infections and cumulative number of infections in COVID-19 over four selected countries, namely, Sri Lanka, Italy, the United States, and Hebei province of China, from the first day of appearance of cases to 2nd July 2020 were used in the study. Gompertz, logistic, Weibull, and exponential growth curves were fitted on the cumulative number of infections across countries. AIC, BIC, RMSE, and R 2 were used to determine the best fitting curve for each country. Results revealed that the most appropriate growth curves for Sri Lanka, Italy, the United States, and China (Hebei) are the logistic, Gompertz, Weibull, and Gompertz curves, respectively. Country-wise, overall growth rate, final epidemic size, and short-term forecasts were evaluated using the selected model. Daily log incidences in each country were regressed before and after the identified peak time of the respective outbreak of epidemic. Hence, doubling time/halving time together with daily growth rates and predictions was estimated. Findings and relevant interpretations demonstrate that the outbreak seems to be extinct in Hebei, China, whereas further transmissions are possible in the United States. In Italy and Sri Lanka, current outbreaks transmit in a decreasing rate.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Daily incidences of coronavirus in Hebei.
Figure 2
Figure 2
Cumulative incidences of coronavirus in Hebei.
Figure 3
Figure 3
Daily incidences of coronavirus in Italy.
Figure 4
Figure 4
Cumulative incidences of coronavirus in Italy.
Figure 5
Figure 5
Daily incidences of coronavirus in Sri Lanka.
Figure 6
Figure 6
Cumulative incidences of coronavirus in Sri Lanka.
Figure 7
Figure 7
Daily incidences of coronavirus in the United States.
Figure 8
Figure 8
Cumulative incidences of coronavirus in the United States.
Figure 9
Figure 9
Epidemic curve of coronavirus disease 2019 in Hebei province, China.
Figure 10
Figure 10
Prediction models for infections in Hebei province, China.
Figure 11
Figure 11
Epidemic curve of coronavirus disease 2019 in Italy.
Figure 12
Figure 12
Prediction models for infections in Italy.
Figure 13
Figure 13
Epidemic curve of coronavirus disease 2019 in Sri Lanka.
Figure 14
Figure 14
Prediction models for infections in Sri Lanka.
Figure 15
Figure 15
Epidemic curve of coronavirus disease 2019 in the United States.
Figure 16
Figure 16
Epidemic curve for the first group of data.
Figure 17
Figure 17
Epidemic curve for the second group of data.
Figure 18
Figure 18
Prediction models for infections in the United States—first data set.
Figure 19
Figure 19
Prediction model for infections in the United States—second data set.
Figure 20
Figure 20
Growth models for Hebei province of China.
Figure 21
Figure 21
Growth models for Italy.
Figure 22
Figure 22
Growth models for Sri Lanka.
Figure 23
Figure 23
Growth models for the United States.
See this image and copyright information in PMC

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