Talks

Quantitative methods are unquestionably valuable tools for disease propagation models, and have trackedattention of mathematicians, physicists, and statisticians for decades. Mathematical models for diseasepropagation have become more and more sophisticated, incorporating different sets of heterogeneities inthe hope to be as accurate as possible while still being mathematically and computationally tractable. Sociodemographic characteristics such as age distribution, study and work activities, and local and long-range mobility, have been key to study historical infection data and devise scenario assessment. Recent outbreaks of global concern such as the influenza H1N1pdm09, SARS, MERS-CoV, Ebola, Zika, and thecurrent COVID-19 have tested our ability to produce information that is fundamental for preparedness:reproductive number estimation, importation case probability, short-term predictions of disease evolution,and so on. On the other hand, endemic diseases ask for a complementary approach to disease propagationmodels, which is that of disease surveillance. How do we properly separate baseline activity,characterized by stochastic fluctuations and isolated case bursts, from epidemic activity? In other words,how many weekly cases we need to say that the current season of “disease A” have started? Is that levelthe same everywhere? Does influenza-like illnesses always start in the winter? As for real-timesurveillance, are the available data delivered in a timely manner or is there significant delay between caseoccurrence and official report? In this talk we will go over both approaches highlighting the nuances ofeach, their importance to provide relevant information to public health actions, and, hopefully, spark yourinterest in joining the field.

In this talk, we propose an approach based on machine learning algorithms to predict epidemic processes in complex networks. Specifically, we show that it is possible to estimate the outbreak size starting from a single node in a network and determine which networks properties are the most related to the spreading dynamics. Our approach is general and can be applied to any dynamical process running on top of complex networks. Likewise, our work constitutes an important step towards the application of machine learning methods to unravel dynamical patterns emerging in complex networked systems.

The overall malaria burden in the Americas has decreased dramatically over the past two decades, but residual transmission pockets persist across the Amazon Basin, where Plasmodium vivax is the predominant infecting species. Current elimination efforts require a better quantitative understanding of malaria transmission dynamics for planning, monitoring, and evaluating interventions at the community level. This can be achieved with mathematical models that properly account for risk heterogeneity in communities approaching elimination, where few individuals disproportionately contribute to overall malaria prevalence, morbidity, and onwards transmission. Here we analyse demographic information combined with routinely collected malaria morbidity data from the town of Mâncio Lima, the main urban transmission hotspot of Brazil. We estimate the proportion of high-risk subjects in the host population by fitting compartmental susceptible-infected-susceptible (SIS) transmission models simultaneously to age-stratified vivax malaria incidence densities and the frequency distribution of P. vivax malaria attacks experienced by each individual over 12 months. Simulations with the best-fitting SIS model indicate that 20% of the hosts contribute 86% of the overall vivax malaria burden. Despite the low overall force of infection typically found in the Amazon, about one order of magnitude lower than that in rural Africa, high-risk individuals gradually develop clinical immunity following repeated infections and eventually constitute a substantial infectious reservoir comprised of asymptomatic parasite carriers that is overlooked by routine surveillance but likely fuels onwards malaria transmission. High-risk individuals therefore represent a priority target for more intensive and effective interventions that may not be readily delivered to the entire community.

Globalization has created complex problems that can no longer be adequately understood and mitigated using traditional data analysis techniques and data sources. As such, there is a need for the integration of nontraditional data streams and approaches such as social media and machine learning to address these new challenges. In this talk, I will discuss how our team is applying approaches from the weather forecasting community including data collection, assimilating heterogeneous data streams into models, and quantifying uncertainty to forecast influenza and other infectious diseases. In addition, I will demonstrate that although epidemic forecasting is still in its infancy, it’s a growing field with great potential and mathematical modeling will play a key role in making this happen. 

In my presentation I will comment on the preliminary advances and results that we have in the Centro de Estudios de Variabilidad y Cambio Climático (CEVARCAM, FICH-UNL), where since last year a group of researchers is being formed in the line of assessing the impact of climate change on the health of the population. Particularly we study the incidence of infectious diseases and their relationship with different hydroclimatic and environmental indicators in Northeast Argentina. I work in deterministic modelling of infectious diseases while another partner works in stochastic modeling.