tetano
Editor, Senior Moderator
Math Biosci Eng
. 2022 Jan;19(2):2043-2055.
doi: 10.3934/mbe.2022096. Epub 2021 Dec 27.
Phenomenological and mechanistic models for predicting early transmission data of COVID-19
Takeshi Miyama[SUP] 1 2 [/SUP], Sung-Mok Jung[SUP] 2 3 [/SUP], Katsuma Hayashi[SUP] 2 [/SUP], Asami Anzai[SUP] 2 [/SUP], Ryo Kinoshita[SUP] 2 [/SUP], Tetsuro Kobayashi[SUP] 2 [/SUP], Natalie M Linton[SUP] 2 3 [/SUP], Ayako Suzuki[SUP] 2 [/SUP], Yichi Yang[SUP] 3 [/SUP], Baoyin Yuan[SUP] 3 [/SUP], Taishi Kayano[SUP] 2 [/SUP], Andrei R Akhmetzhanov[SUP] 3 [/SUP], Hiroshi Nishiura[SUP] 2 [/SUP]
Affiliations
Abstract
Forecasting future epidemics helps inform policy decisions regarding interventions. During the early coronavirus disease 2019 epidemic period in January-February 2020, limited information was available, and it was too challenging to build detailed mechanistic models reflecting population behavior. This study compared the performance of phenomenological and mechanistic models for forecasting epidemics. For the former, we employed the Richards model and the approximate solution of the susceptible-infected-recovered (SIR) model. For the latter, we examined the exponential growth (with lockdown) model and SIR model with lockdown. The phenomenological models yielded higher root mean square error (RMSE) values than the mechanistic models. When using the numbers from reported data for February 1 and 5, the Richards model had the highest RMSE, whereas when using the February 9 data, the SIR approximation model was the highest. The exponential model with a lockdown effect had the lowest RMSE, except when using the February 9 data. Once interventions or other factors that influence transmission patterns are identified, they should be additionally taken into account to improve forecasting.
Keywords: coronavirus disease 2019; epidemiology; forecasting; lockdown; mathematical model; non-pharmaceutical intervention; projection.
. 2022 Jan;19(2):2043-2055.
doi: 10.3934/mbe.2022096. Epub 2021 Dec 27.
Phenomenological and mechanistic models for predicting early transmission data of COVID-19
Takeshi Miyama[SUP] 1 2 [/SUP], Sung-Mok Jung[SUP] 2 3 [/SUP], Katsuma Hayashi[SUP] 2 [/SUP], Asami Anzai[SUP] 2 [/SUP], Ryo Kinoshita[SUP] 2 [/SUP], Tetsuro Kobayashi[SUP] 2 [/SUP], Natalie M Linton[SUP] 2 3 [/SUP], Ayako Suzuki[SUP] 2 [/SUP], Yichi Yang[SUP] 3 [/SUP], Baoyin Yuan[SUP] 3 [/SUP], Taishi Kayano[SUP] 2 [/SUP], Andrei R Akhmetzhanov[SUP] 3 [/SUP], Hiroshi Nishiura[SUP] 2 [/SUP]
Affiliations
- PMID: 35135241
- DOI: 10.3934/mbe.2022096
Abstract
Forecasting future epidemics helps inform policy decisions regarding interventions. During the early coronavirus disease 2019 epidemic period in January-February 2020, limited information was available, and it was too challenging to build detailed mechanistic models reflecting population behavior. This study compared the performance of phenomenological and mechanistic models for forecasting epidemics. For the former, we employed the Richards model and the approximate solution of the susceptible-infected-recovered (SIR) model. For the latter, we examined the exponential growth (with lockdown) model and SIR model with lockdown. The phenomenological models yielded higher root mean square error (RMSE) values than the mechanistic models. When using the numbers from reported data for February 1 and 5, the Richards model had the highest RMSE, whereas when using the February 9 data, the SIR approximation model was the highest. The exponential model with a lockdown effect had the lowest RMSE, except when using the February 9 data. Once interventions or other factors that influence transmission patterns are identified, they should be additionally taken into account to improve forecasting.
Keywords: coronavirus disease 2019; epidemiology; forecasting; lockdown; mathematical model; non-pharmaceutical intervention; projection.