Modeling influenza seasonality in the tropics and subtropics.
Climate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist....
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009050 |
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| author | Haokun Yuan Sarah C Kramer Eric H Y Lau Benjamin J Cowling Wan Yang |
| author_facet | Haokun Yuan Sarah C Kramer Eric H Y Lau Benjamin J Cowling Wan Yang |
| author_sort | Haokun Yuan |
| collection | DOAJ |
| description | Climate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist. In addition, multiple influenza viruses could cocirculate and shape epidemic dynamics. Here we construct seven mechanistic epidemic models to test the effect of two major climate drivers (humidity and temperature) and multi-strain co-circulation on influenza transmission in Hong Kong, an influenza epidemic center located in the subtropics. Based on model fit to long-term influenza surveillance data from 1998 to 2018, we found that a simple model incorporating the effect of both humidity and temperature best recreated the influenza epidemic patterns observed in Hong Kong. The model quantifies a bimodal effect of absolute humidity on influenza transmission where both low and very high humidity levels facilitate transmission quadratically; the model also quantifies the monotonic but nonlinear relationship with temperature. In addition, model results suggest that, at the population level, a shorter immunity period can approximate the co-circulation of influenza virus (sub)types. The basic reproductive number R0 estimated by the best-fit model is also consistent with laboratory influenza survival and transmission studies under various combinations of humidity and temperature levels. Overall, our study has developed a simple mechanistic model capable of quantifying the impact of climate drivers on influenza transmission in (sub)tropical regions. This model can be applied to improve influenza forecasting in the (sub)tropics in the future. |
| format | Article |
| id | doaj-art-27afc8501ea34fda8c23f418b60f9315 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-27afc8501ea34fda8c23f418b60f93152025-08-20T02:55:32ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-06-01176e100905010.1371/journal.pcbi.1009050Modeling influenza seasonality in the tropics and subtropics.Haokun YuanSarah C KramerEric H Y LauBenjamin J CowlingWan YangClimate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist. In addition, multiple influenza viruses could cocirculate and shape epidemic dynamics. Here we construct seven mechanistic epidemic models to test the effect of two major climate drivers (humidity and temperature) and multi-strain co-circulation on influenza transmission in Hong Kong, an influenza epidemic center located in the subtropics. Based on model fit to long-term influenza surveillance data from 1998 to 2018, we found that a simple model incorporating the effect of both humidity and temperature best recreated the influenza epidemic patterns observed in Hong Kong. The model quantifies a bimodal effect of absolute humidity on influenza transmission where both low and very high humidity levels facilitate transmission quadratically; the model also quantifies the monotonic but nonlinear relationship with temperature. In addition, model results suggest that, at the population level, a shorter immunity period can approximate the co-circulation of influenza virus (sub)types. The basic reproductive number R0 estimated by the best-fit model is also consistent with laboratory influenza survival and transmission studies under various combinations of humidity and temperature levels. Overall, our study has developed a simple mechanistic model capable of quantifying the impact of climate drivers on influenza transmission in (sub)tropical regions. This model can be applied to improve influenza forecasting in the (sub)tropics in the future.https://doi.org/10.1371/journal.pcbi.1009050 |
| spellingShingle | Haokun Yuan Sarah C Kramer Eric H Y Lau Benjamin J Cowling Wan Yang Modeling influenza seasonality in the tropics and subtropics. PLoS Computational Biology |
| title | Modeling influenza seasonality in the tropics and subtropics. |
| title_full | Modeling influenza seasonality in the tropics and subtropics. |
| title_fullStr | Modeling influenza seasonality in the tropics and subtropics. |
| title_full_unstemmed | Modeling influenza seasonality in the tropics and subtropics. |
| title_short | Modeling influenza seasonality in the tropics and subtropics. |
| title_sort | modeling influenza seasonality in the tropics and subtropics |
| url | https://doi.org/10.1371/journal.pcbi.1009050 |
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