Axis 2

Identifying the spatio-temporal patterns of infectious diseases and building accurate forecasting models.

Mosquito-borne diseases are thriving and expanding globally despite decades of large-scale control efforts. This axis aims to understand the patterns of infectious disease spread over time and space in order to help inform targeted intervention programs including education campaigns or vector elimination interventions. An accurate disease forecasting method would allow clinical and public health services to proactively plan and implement control and prevention measures. We often work with high resolution disease surveillance data in conjunction with other data sources such as national surveys, censuses, and environmental/climate data. Various statistical methods are used ranging from time series forecasting, machine learning algorithms such as random forests, to Bayesian spatio-temporal regression models.

Identifying disease determinants and forecasting potential of co-circulating arboviruses in Columbia.

Aedes mosquitoes are responsible for transmitting arboviruses such as chikungunya, dengue, and Zika. The geographic distribution of these viruses has dramatically expanded in the last few decades and Colombia is one of the countries in the Americas most affected by these arboviral epidemics.

The overall aim of this study is to use a multi-disease perspective to identify disease patterns, drivers, and accurate disease forecasting methods that allow clinical and public health services to proactively plan and implement control and prevention measures.

Various statistical methods are used such as Poisson-lognormal mixture models, Bayesian multivariate models, semi-mechanistic dynamic models and machine learning algorithms.

Malaria forecasting in Uganda

We have been involved with evaluating the effectiveness of a universal bednet campaign in Uganda as well as the estimating the impact of multiple rounds of indoor residual spraying (IRS) on malaria incidence in children.