Mathematics 2024, 12(23), 3669; https://doi.org/10.3390/math12233669 (registering DOI) - 23 Nov 2024
Abstract
The 2022–2023 mpox outbreak exhibited an uneven global distribution. While countries such as the UK, Brazil, and the USA were most heavily affected in 2022, many Asian countries, specifically China, Japan, South Korea, and Thailand, experienced the outbreak later, in 2023, with significantly
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The 2022–2023 mpox outbreak exhibited an uneven global distribution. While countries such as the UK, Brazil, and the USA were most heavily affected in 2022, many Asian countries, specifically China, Japan, South Korea, and Thailand, experienced the outbreak later, in 2023, with significantly fewer reported cases relative to their populations. This variation in timing and scale distinguishes the outbreaks in these Asian countries from those in the first wave. This study evaluates the predictability of mpox outbreaks with smaller case counts in Asian countries using popular epidemic forecasting methods, including the ARIMA, Prophet, GLM, GAM, n-Sub-epidemic, and Sub-epidemic Wave frameworks. Despite the fact that the ARIMA and GAM models performed well for certain countries and prediction windows, their results were generally inconsistent and highly dependent on the country, i.e., the dataset, as well as the prediction interval length. In contrast, n-Sub-epidemic Ensembles demonstrated more reliable and robust performance across different datasets and predictions, indicating the effectiveness of this model on small datasets and its utility in the early stages of future pandemics.
Full article
(This article belongs to the Special Issue Advances in Mathematical Biology and Applications)
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Figure 1
Figure 1
<p>Mpox weekly cases (weekly incidence) for China (<b>A</b>), Japan (<b>B</b>), South Korea (<b>C</b>), and Thailand (<b>D</b>) from 2 February 2023 to 28 December 2023. Weekly incidence predictions were issued for the dates that are in the highlighted area from 18 May 2023 to 30 November 2023.</p> Full article ">Figure 2
<p>MSE of predictions across China, Japan, South Korea, and Thailand and four prediction horizons (1, 2, 3, and 4 weeks forward). Teal bars are the best metric for the particular combination of location and horizon, and dark orange bars are the models with the worst metrics. SW stands for Sub-epidemic Wave and SE for n-Sub-epidemic frameworks. Ranked models are singular models used for the ensembles.</p> Full article ">Figure 3
<p>MAE of predictions across China, Japan, South Korea, and Thailand and four prediction horizons (1, 2, 3, and 4 weeks forward). Teal bars are the best metric for the particular combination of location and horizon, and dark orange bars are the models with the worst metrics. SW stands for Sub-epidemic Wave and SE for n-Sub-epidemic frameworks. Ranked models are singular models used for the ensembles.</p> Full article ">Figure 4
<p>Coverage 95% Prediction Interval metrics across China, Japan, South Korea, and Thailand and four prediction horizons (1, 2, 3, and 4 weeks forward). Teal bars are the best metric for the particular combination of location and horizon, and dark orange bars are the models with the worst metric. SW stands for Sub-epidemic Wave and SE for n-Sub-epidemic frameworks. Ranked models are singular models used for the ensembles.</p> Full article ">Figure 5
<p>WIS metrics for predictions across China, Japan, South Korea, and Thailand and four prediction horizons (1, 2, 3, and 4 weeks forward). The metric is on a logarithmic scale with a base of 10. Furthermore, all values were multiplied by 10, so no value would not go below 0. Teal bars are the best metric for the particular combination of location and horizon, and dark orange bars are the models with the worst metrics. SW stands for Sub-epidemic Wave and SE for n-Sub-epidemic frameworks. Ranked models are singular models used for the ensembles.</p> Full article ">
<p>Mpox weekly cases (weekly incidence) for China (<b>A</b>), Japan (<b>B</b>), South Korea (<b>C</b>), and Thailand (<b>D</b>) from 2 February 2023 to 28 December 2023. Weekly incidence predictions were issued for the dates that are in the highlighted area from 18 May 2023 to 30 November 2023.</p> Full article ">Figure 2
<p>MSE of predictions across China, Japan, South Korea, and Thailand and four prediction horizons (1, 2, 3, and 4 weeks forward). Teal bars are the best metric for the particular combination of location and horizon, and dark orange bars are the models with the worst metrics. SW stands for Sub-epidemic Wave and SE for n-Sub-epidemic frameworks. Ranked models are singular models used for the ensembles.</p> Full article ">Figure 3
<p>MAE of predictions across China, Japan, South Korea, and Thailand and four prediction horizons (1, 2, 3, and 4 weeks forward). Teal bars are the best metric for the particular combination of location and horizon, and dark orange bars are the models with the worst metrics. SW stands for Sub-epidemic Wave and SE for n-Sub-epidemic frameworks. Ranked models are singular models used for the ensembles.</p> Full article ">Figure 4
<p>Coverage 95% Prediction Interval metrics across China, Japan, South Korea, and Thailand and four prediction horizons (1, 2, 3, and 4 weeks forward). Teal bars are the best metric for the particular combination of location and horizon, and dark orange bars are the models with the worst metric. SW stands for Sub-epidemic Wave and SE for n-Sub-epidemic frameworks. Ranked models are singular models used for the ensembles.</p> Full article ">Figure 5
<p>WIS metrics for predictions across China, Japan, South Korea, and Thailand and four prediction horizons (1, 2, 3, and 4 weeks forward). The metric is on a logarithmic scale with a base of 10. Furthermore, all values were multiplied by 10, so no value would not go below 0. Teal bars are the best metric for the particular combination of location and horizon, and dark orange bars are the models with the worst metrics. SW stands for Sub-epidemic Wave and SE for n-Sub-epidemic frameworks. Ranked models are singular models used for the ensembles.</p> Full article ">
