Inference and prediction of malaria transmission dynamics using time series data

Benyun Shi,Shang Xia,Xiao-Nong Zhou,Jiming Liu,Jie Cao,Qi Tan,Shan Lin,Xiaohong Zhou

doi:10.1186/s40249-020-00696-1

Abstract

BackgroundDisease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. To evaluate the severity of an epidemic, statistical methods are widely used to forecast the trend, seasonality, and the possible number of infections of a disease. However, most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. In this study, we focus on investigating malaria transmission dynamics based on time series data.MethodsA data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data. Specifically, the dynamics of malaria transmission is modeled based on the notion of vectorial capacity (VCAP) and entomological inoculation rate (EIR). A particle Markov chain Monte Carlo (PMCMC) method is employed to estimate the model parameters. Accordingly, a one-step-ahead prediction method is proposed to project the number of future malaria infections. Finally, two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling, Yunnan province, China.ResultsThe results show that the trained data-driven stochastic model can well fit the historical time series of P. vivax prevalence data in both counties from 2007 to 2010. Moreover, with well-trained model parameters, the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections.ConclusionsBy involving dynamically changing impact factors, the proposed data-driven model together with the PMCMC method can successfully (i) depict the dynamics of malaria transmission, and (ii) achieve accurate one-step-ahead prediction about malaria infections. Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.

Highlights

Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series
Based on the time series of disease prevalence, various statistical methods have been proposed to predict the number of disease cases, such as autoregressive integrated moving average (ARIMA) method [2] and exponential smoothing method [3]
In this paper, taking the Plasmodium vivax situations in two counties, Tengchong and Longling, in Yunnan province, China, as case studies, we focus on modeling the dynamics of P. vivax transmission based on time series of historical malaria prevalence data

Summary

Introduction

Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. Most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. Based on the time series of disease prevalence, various statistical methods have been proposed to predict the number of disease cases, such as autoregressive integrated moving average (ARIMA) method [2] and exponential smoothing method [3]. Such methods rely heavily on the statistical patterns of historical surveillance data, which are limited in understanding the underlying dynamics of disease transmission. To combat infectious diseases, it would be necessary and helpful for public health authorities to model the dynamics of disease transmission by involving various impact factors and make policies from a perspective of systems thinking [17, 18]

Methods

Results

Conclusion