Climate variability

Predicting Dengue Outbreaks Using Climate Variability and Markov Chain Monte Carlo Techniques in a Stochastic Susceptible-Infected-Eliminated Model

This article was originally published here

Sci Rep. 2022 Mar 31;12(1):5459. doi: 10.1038/s41598-022-09489-y.


The recent increase in the global incidence of dengue has resulted in over 2.7 million cases in Latin America and numerous cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic epidemic forecasting. EWS for dengue outbreaks are imperative; considering that dengue fever is linked to environmental factors due to its prevalence in the tropics. Forecasting is an integral part of SAP, which depends on several factors including climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in the development of new predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high incidence of dengue fever in 2019 and 2020, respectively. . A susceptible-infected-removed (SIR) model based on random sampling was used to obtain estimates of the susceptible fraction to model the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique which was used to fit the model. to data from the Singapore and Honduras case reports from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal pattern accounted for 98.5% and 92.8% of the variance in case counts in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance of the Singapore and Honduras outbreaks, respectively, in addition to accounting for 75.4% of the variance of the main 2013 Singapore outbreak, 71.5% of the variance of the 2019 Singapore outbreak and over 70% of the variance of the 2015 and 2016 outbreaks in Honduras. The seasonal pattern accounted for 14.2% and 83.1% of the variance of the 2013 and 2019 outbreaks in Singapore, respectively, in addition to 91% and 59.5% of the variance of the 2015 and 2016 outbreaks in Honduras, respectively. Autocorrelation lag tests showed that the climate model exhibited better predictive dynamics for outbreaks in Singapore during the dry season from May to August and during the rainy season from June to October in Honduras. After incorporating sensitive fractions, the seasonal model showed greater accuracy in predicting larger outbreaks, including those of the 2019-2020 dengue epidemic, compared to the climate model, which was more accurate in the smaller epidemics. Such modeling studies could be carried out further in various epidemics, such as the ongoing COVID-19 pandemic, to understand the dynamics of the epidemic and predict the occurrence of future epidemics.

PMID:35361845 | DOI:10.1038/s41598-022-09489-y